U.S. Department of the Interior

U.S. Geological Survey

Building Unified Geospatial Data for Land-Change 

Modeling—A Case Study in the Area of Richmond, 

Virginia

By David I. Donato and Jason L. Shapiro

Open-File Report 2016–1176
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment—visit

https://www.usgs.gov/ or call 1–888–ASK–USGS (1–888–275–8747). 

For an overview of USGS information products, including maps, imagery, and publications,visit

https://store.usgs.gov. 

Any use of trade, firm, or product names is for descriptive purposes only and does not imply

endorsement by the U.S. Government.

Although this information product, for the most part, is in the public domain, it also may

contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items

must be secured from the copyright owner.

Suggested citation:

Donato, D.I., and Shapiro, J.L., 2016, Building unified geospatial data for land-change modeling—A case 

study in the area of Richmond, Virginia: U.S. Geological Survey Open-File Report 2016‒1176, 84 p., 

https://doi.org/10.3133/ofr20161176. 

ISSN 2331-1258 (online)

Cover: Map showing the location of the 24 cities and counties in the Richmond, Va., area for which 

data were collected. Base from U.S. Geological Survey National Elevation Dataset. Shaded relief at 200-

meter resolution from the National Atlas of the United States, 2006. City and boundary names from the 

National Historical Geographic Information System. Albers Equal-Area Conic projection. North American 

Datum of 1983.
iii

Contents

Abstract ......................................................................................................................................................... 1

Introduction.................................................................................................................................................... 1

Background ................................................................................................................................................... 2

Why This Report is Necessary................................................................................................................... 2

Requirements for Land-Change Modeling ................................................................................................. 3

Geospatial-Data Portals............................................................................................................................. 4

Ontologies.................................................................................................................................................. 5

Metadata and Existing Standards .............................................................................................................. 6

Concepts and Terms.................................................................................................................................. 6

Desirable Qualities of a Unified GIS Dataset ............................................................................................. 7

Difficulties of Building a Unified GIS Dataset from Independently Developed Datasets............................. 9

Conventions for Naming, Coding, and Describing Files and Data Tables................................................ 10

The Richmond Area..................................................................................................................................... 10

The Process of Building the Dataset and Geodatabases ............................................................................ 12

Phase 1: Discovery, Acquisition, and Preliminary Compilation of the Data.............................................. 12

Discovery ............................................................................................................................................. 12

Acquisition............................................................................................................................................ 13

Preliminary Compilation ....................................................................................................................... 13

Phase 2: Combining and Unifying the Data ............................................................................................. 16

Phase 3: Reformatting and Consolidating Data into Geodatabases ........................................................ 19

Summary of Methods................................................................................................................................... 20

Method 1: Unifying Data Names and Attribute Tables ............................................................................. 20

Method 2: Eliminating Data Overlaps....................................................................................................... 20

Method 3: Merging Extents ...................................................................................................................... 21

Data Availability ........................................................................................................................................... 21

Summary and Recommendations................................................................................................................ 21

References Cited......................................................................................................................................... 23

Glossary ...................................................................................................................................................... 26

Appendix 1. Data Sources and Data Restrictions for the Richmond Area ................................................... 33

Appendix 2. Contents of the Richmond Unified Geographic Dataset........................................................... 38

Figures

1. Map showing the location of the 24 jurisdictions in the Richmond, Va., area for 

which data were collected..................................................................................................... 11

2. Map showing the 24 jurisdictions in the Richmond, Va., area for which data were 

collected................................................................................................................................ 15

3a. Screenshot image showing the top-level directory structure and file geodatabases 

of the unified geographic dataset for the Richmond, Va., Metropolitan Statistical Area ........ 18

3b. Screenshot image showing the top-level structure and file geodatabases of the unified 

geographic dataset for the Richmond, Va., Metropolitan Statistical Area (MSA) .................. 19

A2‒1. Screenshot image of the Python code used to create the list of the contents of the 

Richmond unified geographic dataset provided in table A2‒1 .............................................. 39
iv

Tables

1. List of jurisdictions in the Richmond, Va., Metropolitan Statistical Area (MSA)..................... 12

2. List of feature classes in the geographic dataset .................................................................. 14

A1–1. Table of data sources and data restrictions for the Richmond Metropolitan Statistical 

Area (MSA)........................................................................................................................... 33 

A2‒1. Table listing the file names and classification levels of the Richmond unified geographic 

dataset.................................................................................................................................. 39
v 

Conversion Factors

U.S. customary units to International System of Units

Multiply By To obtain

Length

mile (mi) 1.609 kilometer (km)

International System of Units to U.S. customary units

Multiply By To obtain

Length

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Datum

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83). 

Supplemental Information

Terms in the “Glossary” are shown in bold type upon first occurrence in the report.

Abbreviations

API application programming interface

DCR Virginia Department of Conservation and Recreation

EGSC Eastern Geographic Science Center

ETL extract-transform-load

FTP File Transfer Protocol

GDAL Geospatial Data Abstraction Library

GIS geographic information system 

GRASS Geographic Resources Analysis Support System

LCM land-change model or land-change modeling

LULC land use and land cover 

MSA Metropolitan Statistical Area

NAD 83 North American Datum of 1983 

NPS National Park Service

NTFS New Technology File System

U.S. United States

USDA U.S. Department of Agriculture

USFWS U.S. Fish and Wildlife Service

USGS U.S. Geological Survey

VDGIF Virginia Department of Game and Inland Fisheries

VDOF Virginia Department of Forestry

VGIN Virginia Geographic Information Network

VITA Virginia Information Technologies Agency

XML Extensible Markup Language

1 

Building Unified Geospatial Data for Land-Change 

Modeling—A Case Study in the Area of Richmond, 

Virginia

By David I. Donato and Jason L. Shapiro

Abstract

An effort to build a unified collection of geospatial data for use in land-change modeling 

(LCM) led to new insights into the requirements and challenges of building an LCM data 

infrastructure. A case study of data compilation and unification for the Richmond, Va., 

Metropolitan Statistical Area (MSA) delineated the problems of combining and unifying 

heterogeneous data from many independent localities such as counties and cities. The study also 

produced conclusions and recommendations for use by the national LCM community, 

emphasizing the critical need for simple, practical data standards and conventions for use by 

localities. This report contributes an uncopyrighted core glossary and a much needed operational 

definition of data unification. 

Introduction

Compiling comparable data from local-level cadastral, land use, survey, and other data sets is 

an important challenge for the land change modeling community.

—National Research Council

(2014, Advancing Land Change Modeling, p. 96)

The U.S. Geological Survey (USGS) Eastern Geographic Science Center (EGSC)

required a unified1 and complete set of spatially referenced2 land-use and land-cover (LULC) 

data for use in land-change modeling (LCM) projects for the area including and surrounding the 

City of Richmond, Va. Because an adequate collection of LULC data was not already available

for this area, the EGSC took on the effort to find, acquire, and compile a unified set of geospatial

LULC data for Richmond and its surrounding area. The EGSC collected and processed 

heterogeneous geospatial data for 30 categories of geographic features from more than 20 data 

producers. 

In retrospect, the work done by the EGSC on this task illustrates and exemplifies the 

challenges of preparing data for use with process-based land-change models (National Research 

Council, 2014, p. 3) as well as steps that can be taken to address these challenges. Further, it is 

 

1

The term “unified” as used in this report has a specific meaning explained in the “Concepts and Terms” section and in the 

“Glossary.”

2

The “Concepts and Terms” section compares the terms “spatially referenced,” “geospatial,” and “geographic information 

system” (GIS) when used as adjectives to describe data.
2 

now clear that the community of land-change modelers can benefit from this work’s incremental 

contributions toward standards and methods for creating, documenting, and organizing a data 

infrastructure for land-change modeling. This work acts on recommendations in a recent 

National Research Council report on advancing land-change modeling, which advised, “Future 

infrastructure developments need to further support compilation, curation, and comparison of the 

heterogeneous data sources for input to, and parameterization and validation of LCMs [landchange models]. This component of the infrastructure for land change modeling requires open 

access to, documentation of, and structured organization of heterogeneous data for land change 

science...” (National Research Council, 2014, p. 96–97). 

This report presents the data-unification effort for the Richmond area as a case study, 

showing how the general problems of unifying heterogeneous data from independent sources 

were addressed and either solved or mitigated for the specific case of LULC data in this area. 

This case study tracks months of effort to acquire and prepare a rich collection of geospatial data 

for use in process-based land-change modeling, and it presents the work in the form of concrete 

steps taken to build a unified, consistent, and coherent dataset3

. The case study and report 

generalize the discoveries and conclusions made from working with data for the Richmond area 

into specific recommendations for the land-change modeling community. These 

recommendations suggest how the modeling community might use and extend these 

contributions in order to advance the development of an LCM4 data infrastructure (National 

Research Council, 2014, p. 96–98). 

The innovations presented in this report include (1) a much needed operational definition 

of data unification, (2) the core of a working glossary, (3) a starting point for developing data 

standards and data conventions for localities, and (4) an outline of three generalizable methods of 

geographic information system (GIS) processing. 

Background

Why This Report is Necessary

The authors acknowledge that professionals who regularly work with geographic 

information system (GIS) data are already well aware of the frequent need to combine, merge, 

and otherwise aggregate heterogeneous spatial data into more readily usable datasets (Butenuth 

and others, 2007). These professionals may therefore reasonably ask why this report is necessary, 

and whether the work described in this report is really any different from what any number of 

GIS users and analysts do almost every day. 

The answer is that the work described in this report differs from everyday GIS activities 

with respect to problem scope and scale of effort. As long as the scope of the problem is as 

narrow as just finishing today’s task involving just a few GIS data files, and as long as the scale 

of the effort is just one or two day’s work, there is no need for a report on the methods applied. 

When the problem scope expands, however, to the breadth of creating a data infrastructure for a 

professional community of land-change modelers, a report like this one is necessary because the 

inefficiencies that are (but perhaps should not be) tolerated on a day-to-day basis, when 

aggregated over the efforts of an entire modeling community, become unacceptable and 

intolerable. By the same token, when the scale of effort is weeks or months of work with 

 

3

The term “dataset” is subject to more confusion than many other terms in geographic information science. Refer to the 

“Glossary” for a definition of its intended use in this report. 4

LCM may stand for either “land-change model” or “land-change modeling” depending on context.
3 

hundreds of GIS data files, attention must be paid to the causes and cures of the major 

inefficiencies.

Requirements for Land-Change Modeling

An important challenge to making the most of remotely sensed data for use within LCMs is to 

integrate them with a variety of heterogeneous data sets. Land change information at a variety 

of spatial and temporal resolutions can be integrated with socioeconomic and biogeophysical 

data for coupling of LCMs and other types of models such as models of climate change, 

ecosystem services and biodiversity, energy use, and urbanization.

—National Research Council

(2014, Advancing Land Change Modeling, p. 87)

The data requirements for a land-change model (LCM) or modeling project depend on 

the specifics of the model or project. Even so, it is possible to say in general that process-based 

land-change models require a richer collection of spatially referenced data about land use and 

land cover than many of the other types of LCMs (National Research Council, 2014, p. 3). For 

example, consider a cellular model (which is not process based) that projects alternative futures 

based on a small number of transition rules that govern changes on a cell-by-cell basis from one 

land-use or land-cover class to another. Such a model is less likely to require detailed socioeconomic data (such as data on population, housing density, and detailed location of amenities 

and dis-amenities) than a model that simulates the natural, social, and economic processes that 

result in changes in land cover and land use. 

The EGSC is developing a process-based land-change model that is designed to forecast 

plausible alternative futures and allow users of the model to investigate how both natural and 

human-induced changes may interact to influence the spatially-explicit structure of future urban 

and regional landscapes. The process-based model will enable testing, study, and understanding 

of the effects of specific changes in socio-economic behavior and public policy on environmental 

outcomes. For example, the model can simulate the effects of a trend towards multi-centric and 

increasingly walkable cities on the space required for parks, roads, and housing. As another 

example, the process-based model will allow county-based or city-based forecasting of future 

water requirements under various zoning and taxation-policy scenarios. Hence, understanding 

and forecasting changes with the process-based model requires detailed spatial data on 

population, employment, roads, streams, water bodies, topography, soils, business, industry, 

climate, urban and suburban buildings and infrastructure, and major structures and institutions. 

Like other classes of land-change models, process-based LCMs require both current and 

historical data. At the national extent, comparable satellite image data have become increasingly 

available over the course of decades. Unfortunately, at the local level the situation with respect to 

socio-economic and land-use data is more problematic (National Research Council, 2014, p. 87 

and 96–97). Obtaining detailed local historical data is difficult or impossible at this time because 

such detailed data are typically produced and provided by counties or other localities that serve 

their local businesses and citizens with up-to-date information and data, but typically they do not 

regard the provision of historical data as an important service for this constituency. Historical 

data will, no doubt, become more readily available over time because of the increasing 

digitization of records. The availability of historical data will be a natural result of providing 

data in digital form, since digital data are fairly easy to archive, and because serving data from a 
4 

digital archive is simpler and less expensive than providing information from paper records and 

other non-digital media. However, even if historical digital records from localities become more 

readily available in the coming decades, deficiencies in documentation and incompatibilities in 

data formats will continue to impede quick and easy comparison between current and historical 

data.

Geospatial-Data Portals 

It is natural for readers to question whether the datasets discussed in this report can be 

located and obtained through an online geospatial-data portal. Available geospatial-data portals 

that provide data at no cost to the user include the following: 

• ArcGIS Open Data—An Esri Web site with published data that anyone can use without 

charge at http://opendata.arcgis.com/. 

• DATA.GOV—A U.S. General Services Administration Web site that is home to the U.S. 

Government’s open data at https://www.data.gov/. 

• Geospatial Data Gateway—A U.S. Department of Agriculture (USDA) Web site that

provides access to a map library of over 100 high-resolution vector and raster layers in 

the Geospatial Data Warehouse at https://gdg.sc.egov.usda.gov/. 

• Geospatial Platform—A Federal Geographic Data Committee (FGDC) Web site that 

provides “shared and trusted geospatial data, services, and applications for use by the 

public and by government agencies and partners to meet their mission needs” at 

https://www.geoplatform.gov/. 

• Landsat Data Access—A USGS Web site that allows search and download of Landsat 

image data at http://landsat.usgs.gov/Landsat_Search_and_Download.php. 

• The National Map—A USGS Web site for “a collaborative effort among the USGS and 

other Federal, State, and local partners to improve and deliver topographic information 

for the Nation” at http://nationalmap.gov/. 

• Virginia Geographic Information Network Geospatial Services—A Virginia Information 

Technologies Agency (VITA) Web site that provides geospatial information and services 

related to areas within the Commonwealth of Virginia at 

http://www.vita.virginia.gov/isp/default.aspx?id=12096. 

As readers may confirm, there are a number of sources of freely available geospatial data 

that provide consistent and complete coverage for a few particular types of data over national, 

multi-State regional, and even State extents. Examples of features that are widely and 

consistently covered include major roads and other transportation features, streams and water 

bodies, land-cover classification, and topography. National coverage with satellite images is also 

readily available. None of these sources, however, provides consistent and complete coverage for 

the many other types of detailed local data that are typically provided by counties, cities, and 

other localities. The most detailed, complete, and current data for localities (such as counties, 

cities, and towns) are generally available only from these localities’ governments. Thus, at this 

time there is no one-stop geospatial data portal, so GIS analysts and data managers seeking 

detailed GIS data for small extents must expect to look for multiple data sources, particularly 

agencies of county and city governments. 
5 

Ontologies

An explanation about ontologies is in order because of growing awareness among GIS 

data producers and consumers of the need for a conceptual and terminological framework for 

geospatial data, and also because of the incomplete state of development of the ontologies that 

might be relevant to geospatial data (Buccella and others, 2009). The term “ontology” as used in 

reference to data management and information management, however, is not easily defined in a 

few words. This term puzzles and offends some readers because many of the definitions of the 

term, and much of the discussion of ontologies in the professional literature, are opaque to 

anyone who is not already familiar with ontologies. In lieu of a definition, a brief description of 

the purpose of ontologies is provided here. 

The purpose of an ontology is to provide an unambiguous and precise set of words, 

concepts, and relationships for a specific and circumscribed field or area of human concern (Lutz 

and others, 2009). An ontology is intended to remove the ambiguity and contradictions that are 

normally present in English and other natural5 languages, and an ontology is intended to present 

a single way of viewing, speaking about, and writing about a field or area of concern. For 

example, an ontology for governmental boundaries within the United States would provide 

words, concepts, and relationships for all of the different types of governmental entities and 

governmental boundaries found in the United States, including terms and concepts for States, 

Territories, counties, parishes, boroughs, towns, cities, townships, regions, and both disputed 

and undisputed boundaries. Such an ontology would also describe the spatial nature and 

geometry of these governmental entities and boundaries, such as the presumption that in most 

States the set of all counties makes up a mutually exclusive and complete cover of the entire 

surface area of their State. The specific vocabulary and view provided by an ontology is 

intended, among other goals, to allow independent data providers and data consumers to produce 

compatible data products and results. 

Two important points need to be made about ontologies: 

1. Although an ontology can provide a useful common basis for development of geospatial 

data, an ontology ordinarily would not include data standards, coding conventions, file 

formats, or other specifications for geospatial data. 

2. At this time no single, comprehensive ontology for geospatial data exists,

6

 nor do 

domain-level ontologies exist for all of the socio-economic and geographic features 

frequently represented by geospatial data (Podobnikar and Ceh, 2012, p. 4‒6). 

In view of these points, it should be clear that ontologies for the many types of LULC 

data used in process-based LCM might be useful (if they existed and were readily available) but 

would not, in and of themselves, be sufficient to promote the independent development of 

compatible GIS datasets by localities. For the purpose of developing an LCM data infrastructure, 

accessible and therefore widely used standards and conventions for geospatial data would be of 

much greater and more immediate practical value. Because the coherent view and precise 

language provided by ontologies would facilitate the development of common geospatial data 

 

5

The English language is natural as opposed to artificial. Artificial languages, such as computer programming languages, are 

mostly free of the ambiguity found in natural languages.

6

Although some specialists do refer to a “universal ontology” for geography, the reference is not intended to mean that there is an 

accessible document or document collection that constitutes a universal ontology and that provides a complete framework of 

words and ideas about geography and geospatial data and information. No such document or collection exists in any practical 

form. What does exist is a virtual collection of partially incompatible and incompletely realized ontologies and controlled 

vocabularies scattered among numerous publications and other sources.
6 

standards and conventions, ontologies and data standards could be complementary. A data 

infrastructure for LCM would benefit from parallel and coordinated development of data 

standards and ontologies. 

Metadata and Existing Standards

Many standards for geospatial data and metadata have been developed (Federal 

Geographic Data Committee, 2015a), yet the data produced by localities in the Richmond area 

were not standardized. The reason may be that the information required by resource-constrained 

localities (for producing standards-compliant geospatial data) is scattered, costly, and difficult to 

use. Information published by the Federal Geographic Data Committee on the internet (Federal 

Geographic Data Committee, 2016) about geospatial standards illustrates this problem. Not only 

are there numerous categories of standards and guidelines, but many are only available for 

purchase through the American National Standards Institute (American National Standards 

Institute, 2016) or the International Organization for Standardization (International Organization 

for Standardization, 2016). Because of the complexity and changing state of geospatial 

standards, even large, better-funded local governments may find it difficult to locate the 

standards, guidelines, and software tools needed for building standards-compliant geospatial 

datasets.

In obtaining geospatial data directly from localities, structured metadata are of limited 

use. Under current standards, metadata are structured in Extensible Markup Language (XML) in 

order to enable and facilitate machine indexing and searching of large collections of data. The 

offerings from localities tend to be limited, so the localities and their government staff provide 

more useful help in finding and understanding their geospatial data than do (or would) XML 

files. Metadata for geospatial data are more important for data producers or distributors than for 

data consumers since metadata files are better structured for machine consumption than for 

human reading, and especially because metadata files do not consistently include effective user 

documentation, such as lists of attribute codes and definitions for data fields. 

Concepts and Terms

Some of the terms that are defined in the “Glossary” require special explanation here 

because they are used inconsistently and confusingly in professional books, reports, and articles. 

• In this report the term “dataset” has essentially the same meaning as the term “data 

collection7

” (this term is also frequently rendered as “data set”). Some writers use 

“dataset” to refer to a single computer data file, but in this report “dataset” only 

occasionally refers to a single computer data file and more often refers to a collection of 

any number of related computer data files. With rare exceptions, the computer data files 

that make up a dataset are related; hence a dataset is in some sense a unified or coherent 

collection. This report’s use of “dataset” as equivalent to “data collection” is consistent 

with other terminology in the field of GIS, such as the use of the term “shapefile” to refer 

to a collection of computer files.

• In general, the term “attribute” is used in multiple senses in articles and documentation 

related to GIS. In this report, however, the term is used only to denote a specific, single 

value or characteristic associated with a specific geographic feature. For example, the 

 

7

The word “collection” here refers to a set of data files, not to the process of collecting them.
7 

name of a particular school building might be “Benjamin Franklin Elementary School;” 

in this case, the name “Benjamin Franklin Elementary School” is an attribute of a 

particular school building. In a feature-class file containing data on a number of schools, 

there may be a table of attribute data that includes a column of school names. In this 

report, the column of attributes is called a column or field, and the name of this column 

or field is called the field name or column name. This usage differs from the practice of 

some writers who use the term “attribute” to refer to an individual value in a table column 

and also to refer to the entire table column. To avoid this ambiguity, in this report the 

term “attribute” is not used to refer to a column or field of data. The set of all potential 

attributes within a column or field of data is referred to as its “range.” 

• Data that include coordinate pairs that each identify a precise point on the surface of the 

Earth are variously called geospatial data, spatially referenced data, and GIS data. 

Although these terms are used in the professional literature of geographic information 

science with meanings that only partially overlap, in this report they are used essentially 

interchangeably.

• Terms that are often used to describe operations to make heterogeneous data more 

compatible, coherent, consistent, or comparable include “integration,” “fusion,” and 

“reformatting.” The meanings of these terms vary according to context and source, so 

there is some advantage to avoiding them. This report applies the alternative term “data 

unification” consistently throughout. The next sub-section provides a new operational 

definition of this term.

• The term “feature class” can refer either to a geospatial data file or to a collection of 

actual, on-the-ground geographic features, depending on context. When the meaning is 

not clear from context, the qualified term “feature-class file” is used to specify a data file 

as opposed to a group of actual geographic features (such as canyons or roads or lakes). 

The terms “feature class” and “feature type” are not equivalent. A feature type specifies 

general geometric or spatial characteristics, such as whether the feature is a point, line, 

curve, polygon, or area. 

The “Glossary” provides a detailed listing and explanation of the concepts and terms used 

in this report that might otherwise be subject to misunderstanding.

Desirable Qualities of a Unified GIS Dataset

Because there seems to be no technical term in widespread use that denotes the qualities 

required in the collection of spatially referenced LULC data for the Richmond area, this report 

proposes and applies the term “unified” to describe a suitable collection of spatially referenced 

computer data files (Gotway and Young, 2002; Buccella and others, 2009). Such a collection of 

files is allowed to be redundant, but it must not be inconsistent. The collection may include, for 

example, the same kind of feature-class files separately for several counties as well as the same 

feature-class files merged together to provide a seamless feature cover for the entire multicounty area; in this case, the redundancy is regarded as beneficial because it provides geographic 

analysts with immediate access either to data for a single county or data for a larger area.

As the term is used in this report, a collection of GIS data files for an area is said to be 

“unified” if it has the following desirable qualities:
8 

• Availability of documentation—Self-contained documents that describe all data files in 

the collection should be provided. These documents should be formatted for ease of 

reference and human-eye perusal, and should include user notes and complete lists and 

definitions of attribute codes, data types, and data fields.

• Completeness of coverage of the area by data for all essential features—There should 

be no gaps in the important and required features, although partial coverage by optional 

features is acceptable. This means that the dataset can include optional data that are 

provided by some counties or cities but not others, as long as essential features are 

available for the entire area. The determination of which features are essential and which 

are not depends on the specifics of the modeling task for which data are being gathered 

and unified. 

• Consistency in the use of codes, descriptions, and other values within data tables—

As examples, the same codes for types of roads should be used throughout the data 

tables; the same classifications for streams and water bodies should be used; and the same 

land-cover types should be used throughout. For example, an interstate highway should 

not be coded as “80” in one file and as “ISHwy” in another file. 

• Consistency in use of field names—A data table for a particular kind of feature in any 

one of the files within a unified GIS dataset should use the same field (column) name as 

any other data table for the same kind of feature in any other file of this unified dataset. 

• Equivalence of level of detail—Raster image files should be based on the same overall 

grid for the entire area covered by the collection of data files, and should use the same 

cell size. The spacing of points for each vector feature should be approximately equal 

throughout all of the files. Data that have an equivalent level of detail can be accurately 

mapped at the same maximum map scale. 

• Seamlessness of merged data—There should be no cracks, seams, overlaps, gaps, 

slivers, or other artifacts in merged datasets. When raster data are merged by 

constructing a mosaic, each composite feature should be free of artificial jags or offsets. 

Merged vector line and vector area features should be joined smoothly and without 

visible or detectable artificial offsets.

• Uniformity of accuracy—A collection of GIS data files cannot be regarded as “unified” 

if any of the sets is substantially less accurate than any other of the sets. Accuracy is not 

the same as level of detail. Accuracy is relative to the level of detail.

• Uniformity of spatial reference—All coordinate data in the data files of the collection 

apply in the same system of geographic reference, such as the combination of a map 

projection, datum, and zone. 

• Uniqueness of data—Although data from the data files for sub-areas, such as counties, 

may be duplicated in the merged data files for the entire area, there should be only one 

version of data for any particular feature within a particular sub-area or within the files 

for the entire area.

The desirable qualities listed in this section, taken together, describe an ideal unified 

dataset. This ideal is presented here as a long-term goal and as an expression of the need for data 

standards and coordination among developers and distributors of spatially referenced datasets 

(Gotway and Young, 2002; Buccella and others, 2009). This ideal is difficult or impossible to 
9 

achieve when combining datasets that were independently developed without the application of 

common data conventions and standards. 

Difficulties of Building a Unified GIS Dataset from Independently Developed Datasets

The EGSC’s experience with data for the area of Richmond, Va., illustrates the 

uncoordinated current state of dataset development and distribution across localities in the 

United States. To say that these independently produced geospatial data are heterogeneous is an 

understatement. There is value, however, in studying the many differences among these 

independently produced datasets. Contrasting the desirable, ideal qualities of unified data with 

the contrary, actual qualities of real-world data is a useful exercise for achieving a clearer 

understanding of the need for widespread adoption of common standards and conventions for 

defining, naming, organizing, and coding spatially referenced data.

The effort to build a unified GIS dataset for the Richmond area was typical of projects to 

collect and consolidate GIS data for urban areas or similar-sized regions. This is because of the 

difficulties encountered in almost any attempt to unify and consolidate a collection of 

independently-developed spatial data files (Williams and Dreza, 2005). These challenges and 

hindrances include the following: 

• Variations in file formats and distribution media; 

• Differences in spatial reference; 

• Differences in spatial extent; 

• Overlaps of coverage;

• Contradictions within areas of overlapping coverage; 

• Inconsistencies in the availability of features among the sources;

• Restrictions on the use and redistribution of data; 

• Differences in the set of features included in feature classes;

• Incompatibilities among conventions for naming files and fields;

• Incompatibilities among definitions of geographic features and their attributes;

• Inconsistencies in the labeling of fields (columns) within data tables; and

• Inconsistencies in the coding or representation of feature attributes. 

An attempt to unify heterogeneous, independently-developed GIS datasets can be only 

partially successful. Until, or unless, independent jurisdictions follow common standards and 

ontologies for the description, selection, collection, formatting, and packaging of spatially 

referenced data, the ideal unified dataset described in the preceding sub-section will be only 

partially achievable (Butenuth and others, 2007). 

These observations about the difficulties of building unified GIS datasets are not new 

(Zhao and others, 2008; Federal Geographic Data Committee, 2015a,b). There is also an 

abundance of published information related to the compilation, integration, and unification of 

spatial data, much of it addressing these difficulties through the topics of semantic 

homogenization, shared vocabularies, ontologies, and extract-transform-load (ETL) tools and 

techniques (Ziegler and Dittrich, 2004; Program Manager, Information Sharing Environment, 

2015). Unfortunately, much of the information published on this topic is too abstract to be of 

immediate help to the GIS specialist who is building a dataset. The unification of spatial data 
10 

remains a challenge despite the abundance of published information about the problem (Buccella 

and others, 2009; Lutz and others, 2009; Vassiliadis, 2009). Any effort to unify heterogeneous 

and disparately sourced spatial data will benefit from the development of practical tools and 

methods, especially until widespread adoption of common standards and conventions leads to the 

general availability of compatible data.

Conventions for Naming, Coding, and Describing Files and Data Tables

Although there is an abundance of standards for spatially referenced data, it will take 

years for them to be adopted and implemented within the working procedures of those who 

collect and distribute data for cities, towns, counties, States, and even Federal agencies. 

Furthermore, finding, understanding, and applying data standards is a resource-consuming task 

in its own right, one that often must be subordinated to the primary goal of collecting and 

distributing useful data, even if those data are not fully compliant with available standards. The 

effort in compiling data for the Richmond, Va., area was itself resource-constrained, requiring 

the development of conventions and standards for naming and coding data files and data tables.

The new naming conventions developed for files from the Richmond area specify simple 

names that describe the essential characteristics of their content and therefore make it easy to 

identify files, to distinguish one file from another, and to organize files into sub-collections. 

Concatenating short codes for the key characteristics of data in file names created meaningful 

names that also facilitate the classification of files. Names include short codes for each type of 

spatial extent (such as a State or county), feature class (such as a road or railroad), data shape 

(such as points or polygons), data source, and the year of data currency. 

The Richmond Area

The EGSC compiled a dataset for numerous geographic features of the area surrounding 

and including the city of Richmond, Va. This area was chosen because it is well suited to a study 

of land development and change using a process-based land-change model. The resulting set of 

LULC data covers 24 distinct jurisdictions (counties and incorporated cities) within an 

approximately 50-mile8

 (80 kilometer) radius of the city of Richmond, Va. (fig. 1, table 1) in 

formats suitable for processing with a GIS. This dataset corresponds to the Richmond 

Metropolitan Statistical Area (MSA) as it is defined by the U.S. Census Bureau, except for the 

exclusion of Cumberland County9

 and the inclusion of a few additional jurisdictions (fig. 2, table 

1) (U.S. Census Bureau, 2009). 

This geographic dataset includes more than 30 feature classes (table 2) and covers the 

Richmond area with both raster and vector datasets representing a wide array of natural and 

anthropogenic features. The set includes data files for topography, waterways, water bodies, 

population, transportation, and other features of the built and natural environments. These 

geographic data are potentially useful especially for land-change and other environmental and 

policy-related studies.

 

8

A 50-mile radius includes the areas in which land development is most strongly influenced by the socio-economic activity 

centered on the city of Richmond. The selection of a 50-mile radius is partially arbitrary, but it meets the basic requirement of 

providing a large enough area for a preliminary land-change modeling study. Depending on the findings and goals of the study, 

the area may need to be expanded for subsequent phases of the study.

9 Cumberland County was omitted from the dataset because it lies outside of the 50-mile-radius circle around Richmond, 

Virginia.
11 

This report describes the decisions and procedures that went into building this set of 

LULC data for the Richmond, Va., area. It identifies the difficulties that were encountered and 

the methods used to overcome or work around some of these problems. 

Figure 1. Map showing the location of the 24 jurisdictions (cities and counties) in the Richmond, Va., 

area for which data were collected; shown in light grey. Virginia jurisdictions outside of the area of data 

collection are shown in green. The city of Richmond is shown in black.

Because of restrictions placed on much of the data by their sources, the final collection of 

data files and file geodatabases cannot be redistributed in its entirety. Some portions of the 

unified dataset, however, are available for redistribution as explained in the section on “Data 

availability.” Appendix 1 provides information on the data sources and lists restrictions on redistribution by source. 
12 

Table 1. List of jurisdictions in the Richmond, Va., Metropolitan Statistical Area (MSA). 

[The 5 jurisdictions included in the dataset described in this report that are not in the MSA as defined by the U.S. 

Census Bureau are marked with [+]; and the one jurisdiction that is in the MSA as defined by the U.S. Census 

Bureau that is not in the dataset described in this report is marked with [–] and shown in red type. Refer to appendix 

1 for contact information for these and other data sources]

Name of Jurisdiction Type of Jurisdiction

Amelia County

Caroline County

Charles City County

Chesterfield County

Colonial Heights City

Cumberland [–] County

Dinwiddie County

Goochland County

Hanover County

Henrico County

Hopewell City

James City [+] County

King and Queen County

King George [+] County

King William County

Louisa County

New Kent County

Nottoway [+] County

Petersburg City

Powhatan County

Prince George County

Richmond City

Spotsylvania [+] County

Surry [+] County

Sussex County

The Process of Building the Dataset and Geodatabases

Phase 1: Discovery, Acquisition, and Preliminary Compilation of the Data

Discovery

The first step was to seek out data from the various counties in the Richmond, Va., area 

because, in general, counties are the choice of first resort for up-to-date and detailed local 

information. At the same time, data were also requested from cities in the area because 

incorporated cities in Virginia commonly provide many of the same data and information 

services provided by counties. Web sites were used for finding information from these 

jurisdictions. E-mail and telephone contacts helped to locate additional information. Once the 

data were in hand, comparison and classification began. Data were classified by the following 

characteristics:

• Data type; 

• Feature class;

• Spatial extent;

• Dates of data and data sources;
13 

• Cost, if any, for the data; and 

• Data licenses and other restrictions on the use or redistribution of data. 

After the availability of data from local sources was assessed, the search turned to 

sources that might be able to provide coverage of the entire extent of the Richmond area. 

Notably, the Virginia Geographic Information Network (VGIN) (VGIN and VITA, 2016a) and 

the Virginia GIS Clearinghouse (VGIN and VITA, 2016b) provided statewide data that included 

coverage of the Richmond area. In addition, several Federal agencies provided data for the 

growing geographic dataset for the Richmond area. 

Acquisition

The specific method of obtaining data from each source varied. In some cases, data were 

simply downloaded from a Web site. In others, data were sent via e-mail. More than one 

jurisdiction placed data on an FTP site. In one case, data were written onto a CD-ROM disc and 

mailed.

In general, the ease of obtaining data from a jurisdiction was correlated with the 

population of the jurisdiction. More populous jurisdictions have larger GIS staffs in their local 

government and these jurisdictions are therefore more capable of making data readily available. 

By contrast, smaller jurisdictions have smaller GIS staffs or, in some cases, no staff; and the 

amount and variety of GIS data available from these smaller jurisdictions are noticeably more 

limited, and the data are more difficult to obtain. 

Ultimately, data were obtained from 24 different sources including the VGIN, three 

Virginia State agencies, and several Federal agencies. Most of the data acquired were in the form 

of shapefiles or raster image files. A complete list of the files as they are classified and were 

compiled is included in appendix 2. The most important classes of geographic features (or 

“feature classes”) for which data were acquired (features for which area-wide coverage was 

highly desirable) were as follows: 

• Road and transportation networks; 

• Water bodies and water courses;

• Parks (local, State, and Federal); 

• Protected areas;

• Land-use data; and

• Land-cover data.

Some of the jurisdictions of the Richmond MSA require a monetary fee for some of their 

data. For this project, however, the only data collected were those that were provided free of 

charge, leaving it to a potential future project to supplement this collection of data for the 

Richmond area by purchasing additional data.

Preliminary Compilation

Before attempting to combine and unify the data from various sources, it was necessary 

to examine and compare the files and folders provided by each source, to compare the systems of 

spatial reference used for the various datasets, and to compile lists of features and associated data 

(attributes) for the entire collection of datasets. This initial exploration resulted in a preliminary 
14 

plan for selecting the most important and uniformly available LULC data and organizing these 

data into a combined collection for the entire Richmond MSA. 

Table 2. List of feature classes in the geographic dataset. 

Feature Class Type

Address points

Boundaries

Buildings

Contours and elevation (topography)

Flood plains

Forests

Future land use

Golf courses

Land cover

Landmarks

Land use

Parcels

Parks

Population

Protected areas

Railroads

Rivers

Roads

Schools

Slope

Soils

Streams

Topography

Transit (public transportation)

Tree coverage

Utilities

Water bodies

Watersheds

Wetlands and swamps

ZIP codes

Zoning
15 

Figure 2. Map showing the 24 jurisdictions (cities and counties) in the Richmond, Va., area for which 

data were collected. 
16 

Phase 2: Combining and Unifying the Data

As mentioned previously, the datasets provided by the many sources varied from one 

another in important respects. Consequently, combining and unifying these datasets required 

several steps.

The first step was to create an empty, hierarchical structure for the computer directories 

(folders) used to hold the combined and unified datasets as they became available for storage. 

The hierarchical structure consists of 3 or 4 classification levels, which allows the data to be 

aggregated and organized logically (figs. 3a and 3b; appendix 2). The top-level directory 

structure (classification level 1) chosen for this project is as follows: 

• Data by feature classes; 

• Data by jurisdictions; 

• County boundaries; 

• Nationwide data; 

• Quantitative results (spatial statistics); 

• Statewide data; and

• Merged data for the entire Richmond study area. 

In the next step, with the general, top level of the target data classification hierarchy 

defined, standards and conventions were established for naming files and fields, and for coding 

attributes. A plan was prepared for clipping data from the broad-extent files provided by the 

State of Virginia and by Federal agencies. These broad extents would be clipped to the 

boundaries of the smaller spatial extents (the counties and cities). A plan was also established for 

merging data for smaller extents into files that would cover the entire Richmond area.

As another step in the course of organizing and combining the data, the data were 

reprojected as necessary into a common system of geographic reference (Zeiler, 2010). The 

common system of spatial reference chosen for this effort was the Albers Equal-Area Conic 

projection. Specifications of the spatial reference chosen are given below. 

Projection: Albers Equal-Area Conic 

Horizontal datum: North American Datum of 1983 (NAD 83)

Standard parallel 1: 29.5° 

Standard parallel 2: 45.5° 

Central meridian: -96.0° 

Latitude of origin: 23.0° 

False easting: 0.0 

False northing: 0.0 

Linear unit: meter

The Albers Equal-Area Conic projection was chosen because it is the projection most 

frequently used in cell-based land-change modeling in USGS projects.

The combined data collection was intentionally designed to be redundant in order to 

assist users in finding and using the data they need as quickly and easily as possible. For those 

users who are most interested in specific features within the Richmond MSA, the collection 

organized by feature class provides ready access. The set of data by feature class also assists 

users by showing the available feature data for each of the jurisdictions within the MSA and 

letting users know when feature data of a particular type are not available for some of the 
17 

jurisdictions. As examples, forest data are available only for Louisa County and Richmond City; 

parks data are available for 18 out of the 24 jurisdictions; and roads data are available for all 24 

jurisdictions. Users who are more interested in one or more specific jurisdictions will find it 

convenient to access the sub-collection of data organized by jurisdiction. Finally, those users 

who require seamless data for the entire MSA will benefit from the merged data that provide 

seamless and (in many cases) wall-to-wall coverage of the Richmond MSA. 

For convenience, the collection retains the full statewide and nationwide datasets used in 

building the dataset for this MSA. The selection of feature types in the statewide and nationwide 

data collections is more limited than the feature types available from localities (counties, cities, 

or towns), but this limited set of features does fill in some data gaps within the MSA and also 

provides data for the geographic area surrounding the MSA. The nationwide data provide 

coverage for roads and population. The statewide data represent colleges, hospitals, wildlife 

management areas, and other features administered at the State level.

The work involved in merging heterogeneous data made up a major part of the effort of 

building a unified dataset for the Richmond area. There are two different data-merge processes, 

so it is important to understand in any context which is intended. The two processes are (1) 

creating mosaics of raster data, and (2) joining vector data across dataset boundaries. 

For greater clarity, the term “merge” should generally be used in the narrow sense of 

joining vector data, while the creation of mosaics should be called “mosaicking.” Of these two, 

the work with vector datasets required the greatest effort by far. In particular, the preparation of 

datasets for merging required a major commitment of time and effort. To prepare datasets for 

merging, the names of fields (columns) of attributes associated with geographic features needed 

to be made the same; and the data values used for attributes needed to be recoded into a single, 

consistent set of values (for example, Interstate Highway 95 needed to be designated with the 

same attribute value in all datasets for roads). Although the process of merging heterogeneous 

data could not be completely automated, Python programs were developed and proved effective 

in speeding up and facilitating this process, thus semi-automating the work of merging vector 

datasets. 
18 

Figure 3a. Screenshot image showing the top-level directory structure and file geodatabases (.gdb) of the 

unified geographic dataset for the Richmond, Va., Metropolitan Statistical Area (MSA). The top-level 

directories correspond to what is called “Classification level 1” in appendix 2, and the next level of 

directories or geodatabases (such as the geodatabases shown for jurisdictions and feature classes) 

correspond to what is called “Classification level 2” in appendix 2. 
19 

Figure 3b. Screenshot image showing the top-level structure and file geodatabases (.gdb) of the unified 

geographic dataset for the Richmond, Va., Metropolitan Statistical Area (MSA). 

Phase 3: Reformatting and Consolidating Data into Geodatabases

After compiling and unifying the dataset, most of the data were reformatted and 

consolidated into a collection of spatially referenced datasets that includes 69 file

geodatabases10 (.gdb files in figs. 3a and 3b and appendix 2). In appendix 2, “Classification 

level 2” mostly consists of these geodatabases, which serve to organize feature-class data. The 

file geodatabase is a proprietary format developed by Esri for its commercial GIS software. This 

format was chosen because of the following beneficial characteristics of the format:

 

10Although it may seem that there should be a specific technical term for a set or collection of geodatabases, the authors are not 

aware of any such term in widespread use. The definition in the “Glossary” for the term “geodatabase” distinguishes between its 

use in a general sense and the more widely used and specific meaning that refers to a file format introduced by Esri.
20 

1. Convenient packaging for sharing—A file geodatabase consists of a single computer 

file-system folder (directory) and the files it contains. Because a file geodatabase folder 

does not contain subfolders, and because the files in the file geodatabase folder have the 

same format under the Windows, UNIX, and Linux operating systems, a file geodatabase 

can be readily packaged for sharing with the “zip” or “tar” archive facilities.

2. Compression—A file geodatabase can be displayed and edited with Esri’s GIS software 

even while it is in its compressed form.

3. Performance—File geodatabases can be displayed and changed more quickly than the 

same data stored in other formats. 

4. Accessibility for various geographic information systems—Although Esri’s 

proprietary ArcGIS software product offers the most extensive features for working with 

file geodatabases, the application programming interface (API) for the file geodatabase 

format is available without charge for use with other software under the Windows and 

Linux operating systems, as is a higher-level programmer’s interface included in the 

freely available and widely used Geospatial Data Abstraction Library (GDAL) (GDAL, 

2016). 

The file geodatabase format also has a few disadvantages including the following: 

1. Not all GIS software is equipped to make full use of file geodatabases; and

2. A file geodatabase cannot be readily inspected through ordinary computer operatingsystem facilities.

Summary of Methods

The methods developed for building the Richmond area dataset and geodatabase

collection are summarized in this section. The methods apply to both vector and raster data, 

though most of the work involved in applying these methods involved vector data. 

Method 1: Unifying Data Names and Attribute Tables

Uniform naming and coding in feature-class files is a prerequisite for efficient and 

accurate merging. Conventions were developed and applied for file names, field names, and field 

types. First, all of the heterogeneous data for a feature-class type from the various jurisdictions 

(counties, cities, and towns) were reviewed in order to identify a common core of feature 

attributes. Then appropriate field names and conventions for attribute codes for features were 

selected or devised. Finally, the names and codes in all of the feature-class files were unified by 

deleting fields that were not common to all of the files, by recoding attributes, and by renaming

fields (that is, columns) in these feature-class files. A Python program was developed that 

partially automates the changes in field names and attribute codes. In order to have a more 

complete set of data for the various jurisdictions, some feature classes from the statewide and 

nationwide datasets were clipped using jurisdictional boundaries. 

Method 2: Eliminating Data Overlaps

In a few cases, data for some feature classes of vector data overlapped. This often 

occurred when working with data from multiple sources for the same jurisdiction. In these cases, 

the ArcGIS “Detect Feature Changes” tool allowed the differences among overlapping data to be 
21 

detected quickly and easily. Use of this tool enabled the evaluation of overlapping or duplicate 

data, discrimination between matching data and conflicting data, and choice of the most accurate 

data within each area of overlap.

Although the “Detect Feature Changes” tool was designed for detecting change over 

time, it proved to be an effective tool for eliminating data duplication. The tool effectively 

selects the most consistent, and usually most accurate, data in the area of overlap. This tool also 

provides information that is useful in making decisions about how to remove duplications in the 

overlapping data. It reports matches, new features, changes of location, deletions of features, and 

changes in attributes within the overlapping data.

Method 3: Merging Extents

In order to have seamless coverage of the Richmond MSA by the most important feature 

classes, the data for these feature classes for the individual jurisdictions were merged into MSAwide seamless datasets. The ArcGIS commands for combining multiple datasets into one are 

“merge” and “union.” The “merge” command is by far the most important in the work in the 

Richmond area.

A Python program processes lists of the files that should be merged and then it 

sequentially calls the merge process for these files within ArcGIS. The program runs this lengthy 

process without manual intervention. The automation of this procedure reduces errors by 

eliminating the repetitive manual selection of files and processes, instead requiring only the 

preparation of simple lists of input file names. The automated procedure also saves time by 

eliminating the delays that would occur between processes if ArcGIS were waiting for user input 

between calls to the merge command. 

Data Availability

Appendix 1 lists the restrictions on the distribution of the data collected and unified for 

the Richmond MSA. The data not subject to restrictions are available upon request. 

Summary and Recommendations

A data infrastructure to support land change modeling would need to recognize the different 

thematic data that are necessary; recognize their heterogeneous semantic, spatial, and 

temporal referencing; and develop a structured system for access and integration in the form 

of a global integrated land information system.

—National Research Council

(2014, Advancing Land Change Modeling, p. 97)

The Richmond, Va., Metropolitan Statistical Area (MSA) is an area well suited for the 

development, testing, and validation of process-based land-change models. This report has 

presented a case study of an effort to build a unified set of geospatial data for this area from 

independently developed, heterogeneous datasets in order to provide data that meet the 

requirements of process-based land-change models for detailed coverage of numerous land-use 

and land-cover (LULC) features.

At the basic level of data discovery, acquisition, compilation, and processing, much of 

the work that went into building a unified dataset for the Richmond area was similar to what GIS 

users and analysts do every day. Even so, because of the scope of the problem of unifying 
22 

heterogeneous data across an entire metropolitan statistical area, and because of the scale of the 

effort of unifying 30 categories of geographic features across data files from 24 individual data 

providers, the work highlighted the systemic need for standardization among data producers, and 

the need for sharing tools and techniques among data consumers.

This case study of data unification for the area of Richmond, Va., resulted in the 

following findings: 

1. Localities (counties, cities, and others) do not use common or standard data formats or 

conventions. 

2. Inconsistencies in the use of technical terms among geospatial data users and consumers

can lead to misunderstandings. 

3. When data producers do not use common standards and conventions, the resulting 

heterogeneous data often cannot be completely unified later.

4. Software tools (including software that operates within existing geographic information 

systems such as ArcGIS and the Geographic Resources Analysis Support System 

(GRASS)) for at least partially automating the work of unifying heterogeneous data from 

independent sources are not readily available or are not easily found. 

5. At this time, geographic information science and land-change modeling lack the common 

semantic framework and terminology (such as would be provided by an ontology) 

required for the development of effective data standards and conventions, which should 

be distributed and suggested to the many localities that currently produce geospatial data 

in a standards vacuum (National Research Council, 2014, p. 97). 

The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) 

has made the following contributions towards a geospatial data infrastructure for the USGS and 

national land-change modeling community: 

1. An operational definition of data unification that establishes goals for other efforts similar

to the building of a dataset for the area of Richmond, Va.; 

2. The core of a new uncopyrighted glossary for producers, distributors, and consumers of 

geospatial data;

3. The first entry into a potential series of reports that document data compiled for landchange modeling studies and the evolving data infrastructure for land-change modeling;

and

4. Descriptions of methods for handling heterogeneous geospatial data. 

Based on our experience in building unified geospatial data for land-change modeling for 

the area of Richmond, Va., we offer the following technical recommendations to the USGS 

subject to coordination with the national land-change modeling (LCM) community: 

• Build an actual (virtual) LCM data repository—The USGS should build an LCMspecific data repository with the intent to contribute it to a national LCM data repository. 

This repository would contain and distribute datasets or provide links to other sites that 

contain and distribute LCM data. 

• Develop and distribute data standards and data conventions—In cooperation with 

other groups of users of geospatial data, consideration should be given to incrementally 

developing freely distributable standards, examples, and templates for geospatial datasets. 

These materials would be provided for the categories of data that are often used in LCM 
23 

and would consist of self-contained documents, exemplars, and database structures or 

schema. Whenever possible, standards and conventions provided by data-domain 

specialists, such as State and Federal transportation departments, should be adopted. 

Unlike the abstract geospatial standards available from standards organizations, these 

standards and conventions would be concrete and compact.

• Develop and distribute standards for data documentation—In cooperation with other 

groups of users of geospatial data, consideration should be given to incrementally 

developing formats for data documentation. Unlike metadata, these documents would be 

structured for human reading, not for machine processing. 

• Maintain an evolving glossary for geospatial and geographic information science—

The USGS LCM community should maintain an evolving glossary that would be applied 

in publishing reports and documentation. 

• Network with data producers and other communities of geospatial data users—The 

USGS and national LCM communities need to engage with national and State

associations of county and city governments in order to promote the use of standards and 

conventions for data and data documentation.

• Publish reports on projects to build unified geospatial datasets—Members of the 

national LCM community need to be encouraged to publish reports on any major efforts 

to build unified geospatial datasets from heterogeneous sources.

• Publish computer code for data compilation and unification—The USGS LCM 

community should set an example by publishing computer code for compilation and 

unification of geospatial data on a shared repository site such as https://github.com/usgs. 

References Cited

[Data sources used in this report are provided in appendix 1 and are not listed here in the references cited.]

American National Standards Institute [ANSI], 2016, ANSI; American National Standards 

Institute: American National Standards Institute Web page, accessed June 14, 2016, at 

https://www.ansi.org/. 

Buccella, A., Cechich, A., and Fillottrani, P., 2009, Ontology-driven geographic information 

integration; A survey of current approaches: Computers & Geosciences, v. 35, no. 4, p. 710–

723. 

Butenuth, M., Gösseln, G.v., Tiedge, M., Heipke, C., Lipeck, U., and Sester, M., 2007, 

Integration of heterogeneous geospatial data in a federated database: ISPRS Journal of 

Photogrammetry and Remote Sensing, v. 62, no. 5, p. 328–346. [Also available at 

https://www.ipi.uni-hannover.de/uploads/tx_tkpublikationen/butenuth.isprsjournal.07.pdf.] 

Federal Geographic Data Committee [FGDC], 2015a, FGDC endorsed external standards: 

Federal Geographic Data Committee Web page, accessed August 9, 2016, at 

https://www.fgdc.gov/standards/fgdc-endorsed-external-standards. 

Federal Geographic Data Committee [FGDC], 2015b, 2014 Annual report; Federal Geographic 

Data Committee—National spatial data infrastructure strategic plan 2014–2016: Reston, Va., 

Federal Geographic Data Committee, 47 p. [Also available at 

http://www.fgdc.gov/resources/whitepapers-reports/annual%20reports/2014/index_html.] 
24 

Federal Geographic Data Committee [FGDC], 2016, Geospatial metadata standards and 

guidelines: Federal Geographic Data Committee Web page, accessed June 14, 2016, at 

https://www.fgdc.gov/metadata/geospatial-metadata-standards. 

Geospatial Data Abstraction Library [GDAL], 2016, GDAL—Geospatial Data Abstraction 

Library: Geospatial Data Abstraction Library Web page, accessed June 14, 2016, at 

http://www.gdal.org. 

Gotway, C.A., and Young, L.J., 2002, Combining incompatible spatial data: Journal of the 

American Statistical Association, v. 97, no. 458, p. 632–648. [Also available at 

http://www.tandfonline.com/toc/uasa20/97/458.] 

International Organization for Standardization [ISO], 2016, ISO-International Organization for 

Standardization: International Organization for Standardization Web page, accessed August 9, 

2016, at http://www.iso.org/iso/home.html. 

Lutz, M., Sprado, J., Klien, E., Schubert, C., and Christ, I., 2009, Overcoming semantic 

heterogeneity in spatial data infrastructures: Computers & Geosciences, v. 35, no. 4, p. 739–

752. [Also available at http://www.sciencedirect.com/science/journal/00983004/35/4.] 

National Research Council, 2014, Advancing land change modeling; Opportunities and research 

requirements: Washington, D.C., The National Academies Press, 142 p. [Also available at 

http://www.nap.edu/catalog/18385/advancing-land-change-modeling-opportunities-andresearch-requirements.] 

Podobnikar, T., and Ceh, M., 2012, Universal ontology of geographic space; Semantic 

enrichment for spatial data: Hershey, Pa., IGI Global, 304 p. 

Program Manager, Information Sharing Environment [PM-ISE], 2015, Information sharing 

environment; Geospatial interoperability reference architecture (GIRA): 141 p. and appendixes 

A–G [58 p.]. [Also available at https://www.ise.gov/sites/default/files/GIRA.pdf.] 

U.S. Census Bureau, 2009, Metropolitan and micropolitan statistical areas and components, 

December 2009, with codes [Dec. 2009 text file]: U.S. Census Bureau Web page, accessed 

October 1, 2015, at https://www.census.gov/population/metro/files/lists/2009/List1.txt. 

Vassiliadis, P., 2009, A Survey of extract–transform–load technology: International Journal of 

Data Warehousing and Mining, v. 5, no. 3, 27 p. 

Virginia Information Technologies Agency [VITA] and Virginia Geographic Information 

Network [VGIN], 2016a, VGIN Geospatial Services: Virginia Information Technologies

Agency (VITA) and Virginia Geographic Information Network (VGIN) Web page, accessed 

June 14, 2016, at http://www.vita.virginia.gov/isp/default.aspx?id=12096. 

Virginia Information Technologies Agency [VITA] and Virginia Geographic Information 

Network [VGIN], 2016b, Virginia GIS Clearinghouse: Virginia Information Technologies

Agency (VITA) and Virginia Geographic Information Network (VGIN) Web page, accessed 

June 14, 2016, at http://vgin.maps.arcgis.com/home/index.html. 

Williams, M.H., and Dreza, O., 2005, Combining heterogeneous spatial data from distributed 

sources, in Fisher, P.F., ed., Developments in spatial data handling; 11th International 

Symposium on Spatial Data Handling: Springer-Verlag, Berlin-Heidelberg, Germany, p. 59–

70. [Also available at http://link.springer.com/chapter/10.1007%2F3-540-26772-7_5#page-1.] 

Zeiler, M., 2010, Modelling our world; The ESRI guide to geodatabase concepts, 2nd ed.: 

Redlands, Calif., ESRI Press, 197 p. 

Zhao, T., Zhang, C., Wei, M., and Peng, Z.-R., 2008, Ontology-based geospatial data query and 

integration, in Cova, T., Miller, H., Beard, K., Frank, A., and Goodchild, M., eds., Geographic 

information science: Springer-Verlag, Berlin-Heidelberg, Germany, p. 370–392. 
25 

Ziegler, P., and Dittrich, K.R., 2004, Three decades of data integration—All problems solved?, 

in Jacquart, R., ed., Building the information society: IFIP International Federation for 

Information Processing, v. 156, p. 3–12. [Also available at 

http://link.springer.com/chapter/10.1007%2F978-1-4020-8157-6_1.] 
26 

Glossary

[Terms in the “Glossary” are shown in bold type upon first occurrence in the report]

This report makes use of vocabulary and technical terms from the fields of geographic 

information science and computer database technology. Since there are variations in the usage of 

some of these terms in professional writing and speaking, this glossary defines and explains the 

technical terms used in this report that are most likely to be misconstrued or misunderstood. 

Some of these explanatory definitions include annotations and comments provided to clarify the 

usage and concepts associated with the terms. The following list of terms is not meant to be a 

complete glossary for geographic information systems and technology. 

Area [noun] A connected two-dimensional part of the land surface. Examples include U.S. 

Census Bureau blocks, counties, and Metropolitan Statistical Areas (MSAs). In general, the 

terms “area,” “spatial extent,” and “region” are used semi-synonymously although some writers 

intend a region to be much larger than an area and a sub-region to be slightly larger than an area. 

Attribute [noun] (1) A column or field in a relational database table; (2) a value associated with 

a cell in a raster grid or with a feature in a feature class; (3) a characteristic of a database table or 

data file; or (4) a value for a specific column and row (record) of a database table. Since the term 

“attribute” is used in many different senses, can be a synonym for “field,” and can mean a value 

within a field in a specific table record, this term should only be used with adequate 

qualification. In ArcGIS, a relational table associated with a feature-class file is called an 

“attribute table,” and this usage implies that each item of data that describes one feature of a 

feature class is an “attribute” of that feature.

Call [noun] The action of temporarily or permanently passing control to another process (in 

computer software). This is a specialized term used in computer programming. Programmers

sometimes say that a computer program “makes a call to” some other program or part of a 

computer program, and that a line of code in a computer program “is a call to” another program 

or part of a program.

Call [verb] To pass control to another process in a computer program. Also see “call [noun].” 

Code [noun] (1) A number or short character string that represents a longer item of data. 

Examples include “CA” to stand for California and “80014” to represent the area to which this 

ZIP code is assigned. (2) Statements and directives making up the human-readable (source) 

statements of a computer program. 

Column [noun] The set of values in a fixed-field database table that occur in the same relative 

position in horizontal order in each record of the table. For example, all of the values that are 

third from the left in each row of a database table constitutes column. The terms “column” and 

“field” are synonyms in the context of relational database tables and other similar data files. 

Column name [noun] The name applied to a column (or field) of a database table or file. The 

terms “column name” and “field name” are synonyms in the context of relational database tables.

Cover [noun] (1) The geographic features that occur over an area of the Earth’s surface. This is 

also called “land cover.” (2) A loose synonym for the term “coverage.”

Cover [verb] To completely include a (two-dimensional) spatial extent. One spatial extent is 

said to “cover” a second extent if the second is completely included in the first spatial extent.
27 

Coverage [noun] (1) The state or condition of being covered by an area, region, or other spatial 

extent; (2) a particular type of GIS file as defined by Esri for use with its ArcInfo and ArcGIS 

products; or (3) a set of geospatial data files that cover an area, region, or other spatial extent. 

When used without qualification, this term refers to sense (1) of this definition. 

Database [noun] A collection of data organized according to a formal data model and stored in 

computer files that have specific formats and structures. Examples of types of databases are 

those based on the relational, hierarchical, or network data models. 

Data collection [noun] An organized group of machine-readable data files; or a computer data 

file. This term is a near synonym for “dataset.” 

Data fusion [noun] See “fusion.” 

Data integration [noun] See “integration.”

Dataset [noun] A collection or grouping of organized, machine-readable data. A dataset may be 

a single computer file such as a database table, or it may be a collection of multiple computer 

files. The latter meaning is used in this report, though some writers prefer “data collection” for 

the latter and reserve “dataset” for a single computer file of data. Note that some writers prefer 

“data set.” 

Data table [noun] Data organized into rows and a fixed number of columns; an attribute table.

The columns of a data table are also called “columns” or “fields.” Some data tables (but not all) 

are suited for use within a relational database.

Data type [noun] A general category of computer data. Examples include integer, Boolean, 

character, string, and binary large object.

Data unification [noun] See “unification” and “unified dataset.”

Extent See “spatial extent.” 

Feature [noun] A discrete entity found on the land surface, such as a river, building, city, 

county, park, monument, road, or mountain. 

Feature class [noun] A collection or set of features, or a computer file for use with GIS that 

includes more than one feature, usually of the same type. Examples include shape files that 

contain multiple features with the same geometric type, such as point or polygon. 

Feature type [noun] A category or class of features with the same geometric or spatial 

characteristics. “Feature type” is not the same as “feature class,” and is a broader and more 

abstract classification than “feature class.” Feature types include the point type and the areal 

type. By contrast, a feature class contains references to actual entities (physical or conceptual 

features) located on the Earth’s surface.

Field [noun] A location in a database or data table for a collection of data elements all of a

specific type and meaning. For example, one field of a feature class for data about roads might 

have the data type “integer” and contain data for a particular variety of road, such as two-lane 

highways or private gravel roads. The term “field” can also be used to refer to one data element 

within a record. Also see “column” and “record.”

Field name [noun] The name applied to a field of a database table, record, or file.

File [noun] An ordered repository of bytes that is named and accessible through a computer 

operating system. Some geographic information systems process collections of files as if they are 

single entities; for example, a shapefile is actually a collection of files as viewed through a 

computer operating system, such as Windows or Linux. 
28 

Fusion [noun] The act, process, or result of combining data or information in such a way as to 

create or synthesize a resulting set of data that are more consistent and compatible than the 

starting set. Although the term “data fusion” is used in varying senses that are context- or 

problem-domain-specific, in general, this term connotes the use of rules, transformations, and 

other processes that synthesize or derive new data or information from a starting set of data. By 

contrast, the term “data integration” connotes changes that retain the basic form and structure of 

.the starting data while changing names and codes to achieve consistency across the collection of 

data. The terms “data integration” and “data fusion” are not synonyms, but there is some overlap 

in their meanings. The term “information fusion” is a term of art in the field of satellite remotesensing data.

Geodatabase [noun] A proprietary Esri format for storing and disseminating a consolidated 

collection of GIS data; or a dataset stored in the geodatabase format. The term “geodatabase” can 

be used in a general sense to refer to any complex spatial database, but the term is most often 

used to refer to the proprietary format developed by Esri. 

Geographic information system [noun] A system of computer software designed and used for 

processing geospatial data and generating maps.

Geometry [noun] The abstract category of two- or three-dimensional figures to which a feature 

belongs. This term is used in expressions such as “the various features of a feature class usually 

share the same geometry.” Examples of geometries for features represented in GIS data files are 

points, lines, open polygons, closed polygons, and areas. 

Geospatial [adjective] Pertaining to, involving, or providing explicit coordinates for a specific 

location on the surface of the Earth.

GIS [adjective] Of or pertaining to one or more geographic information systems or the kinds of 

functions, processes, and data that are commonly encountered in the course of using a 

geographic information system. For example, the phrase “GIS data” refers to spatially referenced 

data in one or more of the (many) formats that are often processed with geographic information 

systems.

Heading [noun] A label or name for a field or column in a database table or file.

Information fusion [noun] See “fusion.” 

Integration [noun] The act, process, or result of combining data or software or both in such a 

way as to create more compatible, interoperable, and consistent data or software. This term is 

widely used in varying senses and contexts, so it does not have an agreed meaning or usage. 

Compare to “fusion” and “unify.” 

Land cover [noun] The geographic features that occur within an area of the Earth’s surface. 

This is a synonym for one sense of the term “cover.” 

Layer [noun] A set or collection of features that are grouped for presentation on a map. This is 

a cartographic term derived from the preparation of maps for printing that is now also applied in 

GIS. In GIS, layers are often associated one-to-one with feature-class files. In referring to a 

group of features, the term “layer” is applied when referring to features grouped for cartographic 

presentation; the term “feature class” is used in most other GIS contexts. 
29 

Merge [verb] To join raster or vector GIS data files for adjacent spatial extents in such a way as 

to reconnect or reassemble linear and areal features that cross or span the boundary between the 

extents. For example, in merging two counties, a lake that spans the county boundary would be 

reassembled; or a river that passes from one county to the other would be reconnected in the 

merged (that is, resultant) file. Note that “merge” is a general term and “mosaic” is a more 

specialized term. One may say either that raster images for two adjacent extents were “merged” 

or that they were “mosaicked.” The term “mosaic” does not, however, apply to vector data, nor 

to linear features such as road and rivers.

Metropolitan Statistical Area (MSA) [noun] An area as defined by the U.S. Census Bureau for a 

major city. An MSA consists of the major city and the surrounding area that is most strongly 

influenced by the city in population and economic activity. 

Mosaic [verb] To create a raster GIS file by merging two or more adjacent raster GIS files. 

Related verbal forms of the term are “mosaicking” and “mosaicked.” 

Ontology [noun] A document or set of documents (and in some cases, data) that provide a 

consistent and nonredundant vocabulary, set of names and codes, and set of concepts for 

scholarly and intellectual work in a specific, circumscribed area of human knowledge or interest. 

An ontology can assist those who develop and work with geospatial data by eliminating conflicts 

in names, terms, and concepts, hence supporting the development of compatible data files that 

use identical names and codes for the same entities. The term “ontology” has a variety of 

meanings, so some specialists would not agree with the definition provided here. 

Range [noun] The complete set of possible values that might occur within a column or field of 

a database table or data file. For example, the range of a field that identifies the State of the 

United States (excluding territories and the District of Columbia) might be an integer code 

ranging from 1 to 50, or it might be a two-letter code ranging from “AL” to “WY.” The word 

“range” is also used in a non-technical sense meaning “array” or “variety.” 

Raster [noun] A raster-data file. See “raster data.”

Raster [adjective] Of, being, or pertaining to an arrangement of data in association with an 

array of regularly spaced cells defined by a regular two-dimensional grid. 

Raster data [noun] GIS data consisting of a representation of one or more elements of data for 

each member, cell, or pixel of an array of regularly spaced cells defined by a regular twodimensional grid. 

Raster image [noun] A file defining an image or picture that is stored as raster data.

Record [noun] A set of values corresponding to a single row of a database table or data file.

(The word “record” is also used in a more general non-technical sense to refer to any of various 

documents and datasets.) 

Reformat [verb ] To change the names, codes, and other values in one or more computer files 

without altering the basic structure or meaning of the file(s). Related verbal forms are 

“reformatting” and “reformatted.”

Region [noun] See “area.” 

Regional [adjective] Of or pertaining to a geographic region. 

Seamless [noun] Having no artificial boundaries persisting after the merge of data for adjacent 

spatial extents.
30 

Shapefile [noun] A set of computer data files following a specific group of formats and 

conventions. The shapefile format was originally defined by Esri and is now widely used as a 

publicly specified format. Files in the shapefile format typically contain feature classes of vector 

data (such as points or polygons) along with attribute tables. While the term “shapefile,” as used 

to describe a single shapefile, is grammatically singular, a single shapefile actually consists of 

multiple files within a computer file system (such as the New Technology File System (NTFS), 

the file system commonly used with Windows).

Spatial extent [noun] (1) Any connected, holeless, two-dimensional part of the land surface; or 

(2) the size or areal coverage of an area. Unlike the terms “area” and “region,” the term “spatial 

extent” may be applied to a surface area of any size. A spatial extent might be a half-acre city 

parcel, or it might be the entire land area of a continent. In the second sense “(2),” the term is 

used as in “The number of parks depends on the spatial extent of the region being considered.” 

Spatial reference [noun] See “system of geographic reference” and “spatially referenced.”

Spatially referenced [adjective] Having coordinates that link features to specific locations on 

the surface of the Earth. This adjectival phrase is often applied to geospatial datasets.

System of geographic reference [noun] The combination of map projection, geodetic datum, 

and projection zone11 under which the coordinates of the points in a GIS dataset apply. See 

Zeiler (2010) for more information. 

Unification [noun] The process or quality of being unified (of a dataset). See the entries for 

“unified” and “unified data set” in this glossary.

Unified [adjective] Having most of a set of desirable properties including (1) availability of 

documentation; (2) completeness of coverage of an area by essential features; (3) consistency in 

use of codes, descriptions, field names, and other values; (4) equivalence of level of detail; (5) 

seamlessness; (6) absence of unnecessary redundancy; and (7) uniformity of accuracy and spatial 

reference (of a geospatial dataset).

Unified12 dataset [noun] A collection of GIS data files that share the same map projection and 

system of geographic reference as well as the same fields and codes for all vector and raster files. 

It is not correct, for example, to say that a dataset is unified if the data about roads in one part of 

the dataset use different classification codes than the codes used in another part of the dataset; 

nor may one say that a dataset is unified if there is an essential type of data available for one 

county covered by the set that is not available for other counties covered by the set. 

Union [noun] The set or collection resulting from selecting all elements of two or more starting 

sets or collections without duplication. Related expressions include “the union of [specified] 

sets” referring to the set or collection that is the union of the specified starting sets; and “to take 

the union of” two or more sets, meaning to create the union of these sets. 

 

11The terms “map projection,” “geodetic datum,” and “projection zone” are not defined here because of the specialized nature and 

complexity of the topic of map projections and geographic reference. Definitions of these terms are not necessary for an 

understanding of this report, but interested readers may wish to consult the book cited in the definition for more information on 

this topic.

12The term “synoptic data collection” (with “collection” used here to mean “the act of collecting”) has been used in professional 

writing to describe coordinated procedures intended to produce separate datasets in accordance with common standards and 

requirements so that these separate datasets, when finished, may be readily collected into a consistent and uniform aggregate

dataset. The related term “synoptic data” has not, however, come into widespread use for describing aggregated data that have 

been made consistent, perhaps because the term carries a strong connotation of data that constitute a summary, or synopsis. 

Another related term, “integrated,” has been used to describe aggregate datasets created by changing and compiling other 

(possibly heterogeneous) datasets. The term “integrated” would be an acceptable choice for use in this report, but it is arguably 

not the best choice because, in more widespread usage, this term does not clearly convey the idea of consistency down to the 

level of field names and attribute codes. The term “unified” has therefore been chosen and proposed for widespread use because 

it seems natural and appropriate, and because it is unencumbered by prior, conflicting usages.
31 

Vector data [noun] GIS data that describe spatially referenced, one-dimensional or linear 

features in the form of sequences of geographic or other spatial coordinates. The term “vector” is 

used to distinguish data containing sequences of coordinates from raster data. 

Vector dataset [noun] A dataset that contains vector data. See “dataset” and “vector data.”

Wall-to-wall [adjective] An informal term used to describe GIS data that cover an entire area 

without gaps, holes, or discontinuities. Wall-to-wall coverage is not necessarily seamless, but it 

may be in some cases.
32 

Appendixes
33 

Appendix 1. Data Sources and Data Restrictions for the Richmond Area

Table A1–1 lists the data sources for the Richmond Metropolitan Statistical Area (MSA) used in this report to build the unified 

geographic dataset that consists of a set of spatially referenced land-use and (or) land-cover data for the Richmond, Va., area. This 

table also lists the restrictions on the distribution of these data. The final six columns of table A1–1 (formatted in a series of questions) 

pertain to the public availability and the restrictions on the distribution of these data and are explained as follows: 

Standing public access?—Are these data readily available to the general public through a published source, such as a Web site?

No restrictions?—May these data be freely re-distributed without any limitations or constraints?

No redistribution?—Is redistribution of these data by a recipient prohibited by the original source? 

Includes proprietary data?—Are some of these data derived from privately-owned or otherwise restricted data or information? 

Re-sale prohibited?—Whether redistribution is entirely prohibited or not, is re-selling the data prohibited?

Acknowledgment required?—Must an acknowledgment of the originial source be included with redistributed data?

Table A1–1. Table of data sources and data restrictions for the Richmond Metropolitan Statistical Area (MSA). 

[--, “No” or not applicable]

Data provider Contact information and (or) data location Distribution

method

Standing

public

access?

Restrictions on re-sale or re-distribution

No 

restrictions?

No 

redistribution?

Includes 

proprietary 

data?

Re-sale 

prohibited?

Acknowledgment 

required?

Charles City

County

Web site: http://www.co.charles-city.va.us

Telephone: 804–652–4701

Email Yes -- Yes Yes Yes --

Chesterfield 

County

Web site: 

http://www.chesterfield.gov/content2.asp

x?id=14776

Telephone: 804–748–1503

Email Yes -- Yes Yes Yes Yes

Dinwiddie 

County

Data Company: World View Solutions

Web site: 

http://gis.worldviewsolutions.com/arcgis/r

est/services/dinwiddie

Email: helpdesk@dinwiddieva.us

Contractor-provided 

email

-- -- -- -- -- --
34 

Table A1–1. Table of data sources and data restrictions for the Richmond Metropolitan Statistical Area (MSA).—Continued

[--, “No” or not applicable]

Data provider Contact information and (or) data location Distribution

method

Standing

public

access?

Restrictions on re-sale or re-distribution

No 

restrictions?

No 

redistribution?

Includes 

proprietary 

data?

Re-sale 

prohibited?

Acknowledgment 

required?

Esri Web site: http://www.esri.com Online 

download

Yes -- -- -- -- --

Goochland Web site: http://www.co.goochland.va.us

Web site with GIS map request order form: 

www.goochlandva.us/DocumentCenter/V

iew/156

Email Yes -- -- Yes Yes --

Hannover 

County

Web site: http://www.hanovercountygis.org

Telephone: 804–501–5769

Email Yes -- -- Yes Yes --

Henrico 

County

Web site: http://henrico.us/gis/

Email: gis@co.hanover.va.us

Telephone: 804–365–6811

Email -- -- Yes Yes Yes --

James City 

County

Web site: 

http://www.jamescitycountyva.gov/395/G

IS-Mapping

Telephone: 757–253–6654

Online 

download

-- -- -- -- --

--

Louisa 

County

Web site: 

http://www.louisacounty.com/LCcommde

v/GIS.htm

Telephone: 540–967–3430

Email Yes -- -- -- -- --

National 

Park 

Service 

(NPS)

Web site: https://www.nps.gov/gis Online 

download

-- -- -- -- -- --
35 

Table A1–1. Table of data sources and data restrictions for the Richmond Metropolitan Statistical Area (MSA).—Continued

[--, “No” or not applicable]

Data provider Contact information and (or) data location Distribution

method

Standing

public

access?

Restrictions on re-sale or re-distribution

No 

restrictions?

No 

redistribution?

Includes 

proprietary 

data?

Re-sale 

prohibited?

Acknowledgment 

required?

New Kent 

County

Web site: http://www.co.newkent.va.us/index.aspx?nid=297

Telephone: 804–966–9861

Email Yes -- -- Yes Yes --

Powhatan 

County

Web site: 

http://www.powhatanva.gov/289/Geograp

hic-Information-System-GIS

Email Yes -- Yes Yes Yes --

Prince 

George 

County

Web site: 

http://www.princegeorgeva.org/business/

gis_information/index.php

Email: gis@princegeorgecountyva.gov

Telephone: 804–722–8702

Email -- -- Yes Yes Yes --

Richmond 

City

Web site: http://www.richmondgov.com/GIS

FTP site: ftp://ftp.ci.richmond.va.us/

Telephone: 804–646–6440

Online 

download

-- -- Yes Yes Yes --

Spotsylvania 

County

Web site: http://www.spotsylvania.va.us/GIS

Email: gis@spotsylvania.va.us

Telephone: 540–507–7432

Online 

download

Yes Yes -- -- -- --

Surry 

County

Data Company: World View Solutions

Web site: 

http://gis.worldviewsolutions.com/arcgis/r

est/services/Surry

Contractorprovided 

email

-- -- Yes Yes Yes --

The National 

Map

Web site: http://nationalmap.gov Online 

download

Yes -- -- -- -- --
36 

Table A1–1. Table of data sources and data restrictions for the Richmond Metropolitan Statistical Area (MSA).—Continued

[--, “No” or not applicable]

Data provider Contact information and (or) data location Distribution

method

Standing

public

access?

Restrictions on re-sale or re-distribution

No 

restrictions?

No 

redistribution?

Includes 

proprietary 

data?

Re-sale 

prohibited?

Acknowledgment 

required?

U.S. Census 

Bureau

Web site: 

https://www.census.gov/geo/mapsdata/data/tiger.html

Email: geo.geography@census.gov or 

geo.tiger@census.gov

Telephone: 301–763–1128

Online 

download

-- Yes -- -- -- Yes

U.S. Fish 

and 

Wildlife 

Service 

(USFWS)

Web site: 

http://www.fws.gov/gis/data/national/inde

x.html

Online 

download

-- -- -- -- -- --

U.S. Department of 

Agriculture 

(USDA) 

Forest 

Service

Web site: http://data.fs.usda.gov/geodata/

Email: data@fs.fed.us

Telephone: 804–786–7951

Online 

download

-- -- -- -- -- Yes

Virginia 

Department of 

Conservation and 

Recreation 

(DCR)

Web site: 

http://www.dcr.virginia.gov/natural_herit

age/clinfo.shtml

Online 

download

-- -- Yes -- -- --

Virginia 

Department of 

Forestry 

(VDOF)

Web site: http://www.dof.virginia.gov/gis/

Telephone: 434–977–6555

Online 

download

-- -- Yes -- Yes --
37 

Table A1–1. Table of data sources and data restrictions for the Richmond Metropolitan Statistical Area (MSA).—Continued

[--, “No” or not applicable]

Data provider Contact information and (or) data location Distribution

method

Standing

public

access?

Restrictions on re-sale or re-distribution

No 

restrictions?

No 

redistribution?

Includes 

proprietary 

data?

Re-sale 

prohibited?

Acknowledgment 

required?

Virginia 

Department of 

Game and 

Inland 

Fisheries 

(VDGIF)

Web site: http://www.dgif.virginia.gov/gis/ Online 

download

-- -- Yes -- Yes --

Virginia 

Geographic

Information 

Network 

(VGIN)

Web site: https://www.vita.virginia.gov

FTP site: https://ftp.vgingis.com/Download/Historical_RCL/

Telephone: 866–637–8482

FTP download/ 

email

-- Yes -- -- -- --
38 

Appendix 2. Contents of the Richmond Unified Geographic Dataset

Table A2–1 lists all of the feature-class files, both vector and raster, included in the unified 

geographic dataset for the Richmond, Va., area. “Classification level 1” is the broadest 

classification of the feature-class files and “Classification level 2” is the next broadest. The entries 

in “Classification level 2” are all geodatabases except for some folders in the 

“QuantitativeResults” folder of “Classification level 1.” Each cell of the “Classification level 3+” 

column lists either a single feature-class file name (with no dash “—”) or it lists the name of a 

group of feature classes followed by a dash “—” and then a single feature-class file name, 

depending on whether the geodatabase at “Classification level 2” contains a layer consisting of one 

or more feature classes, or just a single raster file. As examples of records with a dash (“—”), the 

“Boundaries” file geodatabase contains a layer named “LU_BU” that contains nine feature-class 

files. Examples of records without dashes are provided by the 26 raster files contained in the 

“LandCover” file geodatabase. As defined in the “Glossary” of this report, “feature class” may 

refer to a group of actual geographic features (such as roads, lakes, or elevations), or it may refer to 

a geospatial computer file that includes more than one feature, usually of the same type. Examples 

include shapefiles that contain multiple features with the same geometric type, such as point or 

polygon. 

Table A2‒1 was generated by software from the files in the unified geographic dataset, so 

although it is not visually appealing, it is an accurate and readily searchable reference for the 

contents of the dataset. Figure A2‒1 shows the Python code used to generate the contents of the 

table.
39 

Figure A2‒1. Screenshot image of the Python code used to create the list of the contents of the Richmond unified geographic dataset provided in 

table A2‒1.

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses AddressPoints.gdb DW_APts — DW_APts_C

ByFeatureClasses AddressPoints.gdb HN_APts — HN_APts_C

ByFeatureClasses AddressPoints.gdb LU_APts — LU_APts_C

ByFeatureClasses AddressPoints.gdb Study_APts — Study_APts_C

ByFeatureClasses AddressPoints.gdb NK_APts — NK_APts_C

ByFeatureClasses AddressPoints.gdb NW_APts — NW_APts_C

ByFeatureClasses AddressPoints.gdb RC_APts — RC_APts_C

ByFeatureClasses Boundaries.gdb RC_Bu — RC_Bu_C

ByFeatureClasses Boundaries.gdb NW_Bu — NW_Bu_C

ByFeatureClasses Boundaries.gdb LU_Bu — LU_Bu_C
40 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Boundaries.gdb LU_Bu — LU_Bu_FireDeptDistrict_C

ByFeatureClasses Boundaries.gdb LU_Bu — LU_Bu_HistoricGreenSpringsDistrict_C

ByFeatureClasses Boundaries.gdb LU_Bu — LU_Bu_louctyarea_C

ByFeatureClasses Boundaries.gdb LU_Bu — LU_Bu_MIN_C

ByFeatureClasses Boundaries.gdb LU_Bu — Lu_Bu_Ply_C

ByFeatureClasses Boundaries.gdb LU_Bu — Lu_Bu_PO_C

ByFeatureClasses Boundaries.gdb LU_Bu — LU_Bu_RescueSquadDistrict_C

ByFeatureClasses Boundaries.gdb LU_Bu — LU_Bu_TownOfLouisa_C

ByFeatureClasses Boundaries.gdb HN_Bu — HN_Bu_AshlandCorp_C

ByFeatureClasses Boundaries.gdb HN_Bu — HN_Bu_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_Fife_village_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_FifeVillage_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_HadensvilleVillage_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_ManakinVillage_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_OilvilleVillage_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_OLDVillage_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_RiverRoadVillage_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_SandyHookVillage_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_UrbanDevArea_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_VillageAll_C

ByFeatureClasses Boundaries.gdb GC_Bu — GC_Bu_VillageAllOLD_C

ByFeatureClasses Buildings.gdb SP_Bud — SP_Bud_Li_C

ByFeatureClasses Buildings.gdb SP_Bud — SP_Bud_Ply_C

ByFeatureClasses Buildings.gdb NK_Bud — NK_Bud_C

ByFeatureClasses Buildings.gdb LU_Bud — LU_Bud_C

ByFeatureClasses Buildings.gdb JCC_Bud — JCC_Bud_C

ByFeatureClasses Buildings.gdb HN_Bud — HN_Bud_C

ByFeatureClasses Buildings.gdb GC_Bud — GC_Bud_C

ByFeatureClasses Buildings.gdb Study_Bud — Study_Bud_Ply_C

ByFeatureClasses Contours.gdb RC_Co — RC_Co_C_Orig

ByFeatureClasses Contours.gdb LU_Co — LU_Co_C_Orig

ByFeatureClasses Contours.gdb HC_Co — HC_Co_C_Orig

ByFeatureClasses Contours.gdb GC_Co — GC_Co_C_2007

ByFeatureClasses Elevation.gdb RC_Elv — RC_Elv_BaseFloodElevation_C

ByFeatureClasses Elevation.gdb JCC_Elv — JCC_Elv_Spot_C

ByFeatureClasses FloodPlains.gdb RC_FlPl — RC_FlPl_500_C

ByFeatureClasses FloodPlains.gdb RC_FlPl — RC_FlPl_100_C

ByFeatureClasses FloodPlains.gdb PW_FlPL — PW_FLPl_FloodHazard_C
41 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses FloodPlains.gdb LU_FlPl — LU_FlPl_Flood100yr_C

ByFeatureClasses FloodPlains.gdb CF_FlPL — CF_floodp_C

ByFeatureClasses Forests.gdb LU_For — LU_For_VDF

ByFeatureClasses Forests.gdb RC_For — RC_For_C

ByFeatureClasses FutureLandUse.gdb LU_FLU — LU_FLU_C

ByFeatureClasses FutureLandUse.gdb Study_FLU — Study_FLU_C

ByFeatureClasses FutureLandUse.gdb PW_FLU — PW_FLU_C

ByFeatureClasses FutureLandUse.gdb RC_FLU — RC_FLU_C

ByFeatureClasses GolfCourses.gdb JCC_Gfc — JCC_Gfc_C

ByFeatureClasses LandCover.gdb AM_LC_CBP_2006

ByFeatureClasses LandCover.gdb CC_LC_CBP_2006

ByFeatureClasses LandCover.gdb CF_LC_CBP_2006

ByFeatureClasses LandCover.gdb CH_LC_CBP_2006

ByFeatureClasses LandCover.gdb CR_LC_CBP_2006

ByFeatureClasses LandCover.gdb DW_LC_CBP_2006

ByFeatureClasses LandCover.gdb GC_LC_CBP_2006

ByFeatureClasses LandCover.gdb HC_LC_CBP_2006

ByFeatureClasses LandCover.gdb HN_LC_CBP_2006

ByFeatureClasses LandCover.gdb HW_LC_CBP_2006

ByFeatureClasses LandCover.gdb JCC_LC_CBP_2006

ByFeatureClasses LandCover.gdb KG_LC_CBP_2006

ByFeatureClasses LandCover.gdb KQ_LC_CBP_2006

ByFeatureClasses LandCover.gdb KW_LC_CBP_2006

ByFeatureClasses LandCover.gdb LU_LC_CBP_2006

ByFeatureClasses LandCover.gdb NK_LC_CBP_2006

ByFeatureClasses LandCover.gdb NW_LC_CBP_2006

ByFeatureClasses LandCover.gdb PB_LC_CBP_2006

ByFeatureClasses LandCover.gdb PG_LC_CBP_2006

ByFeatureClasses LandCover.gdb PW_LC_CBP_2006

ByFeatureClasses LandCover.gdb RC_LC_CBP_2006

ByFeatureClasses LandCover.gdb SP_LC_CBP_2006

ByFeatureClasses LandCover.gdb SU_LC_CBP_2006

ByFeatureClasses LandCover.gdb Study_LC_CBP_2006

ByFeatureClasses LandCover.gdb SX_LC_CBP_2006

ByFeatureClasses LandCover.gdb Study_LC_CBP_2006b

ByFeatureClasses Landmarks.gdb AM_Lm — AM_Lm_CN_2013

ByFeatureClasses Landmarks.gdb CR_Lm — CR_Lm_CN_2013

ByFeatureClasses Landmarks.gdb CC_Lm — CC_Lm_CN_2013
42 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Landmarks.gdb CF_Lm — CF_Lm_CN_2013

ByFeatureClasses Landmarks.gdb CH_Lm — CH_Lm_CN_2013

ByFeatureClasses Landmarks.gdb DW_Lm — DW_Lm_CN_2013

ByFeatureClasses Landmarks.gdb GC_Lm — GC_Lm_CN_2013

ByFeatureClasses Landmarks.gdb HN_Lm — HN_Lm_CN_2013

ByFeatureClasses Landmarks.gdb HC_Lm — HC_Lm_CN_2013

ByFeatureClasses Landmarks.gdb HW_Lm — HW_Lm_CN_2013

ByFeatureClasses Landmarks.gdb JCC_Lm — JCC_Lm_CN_2013

ByFeatureClasses Landmarks.gdb KQ_Lm — KQ_Lm_CN_2013

ByFeatureClasses Landmarks.gdb KG_Lm — KG_Lm_CN_2013

ByFeatureClasses Landmarks.gdb KW_Lm — KW_Lm_CN_2013

ByFeatureClasses Landmarks.gdb LU_Lm — LU_Lm_CN_2013

ByFeatureClasses Landmarks.gdb NK_Lm — NK_Lm_CN_2013

ByFeatureClasses Landmarks.gdb NW_Lm — NW_Lm_CN_2013

ByFeatureClasses Landmarks.gdb PB_Lm — PB_Lm_CN_2013

ByFeatureClasses Landmarks.gdb PW_Lm — PW_Lm_CN_2013

ByFeatureClasses Landmarks.gdb PG_Lm — PG_Lm_CN_2013

ByFeatureClasses Landmarks.gdb RC_Lm — RC_Lm_CN_2013

ByFeatureClasses Landmarks.gdb SP_Lm — SP_Lm_CN_2013

ByFeatureClasses Landmarks.gdb SU_Lm — SU_Lm_CN_2013

ByFeatureClasses Landmarks.gdb Study_Lm — Study_Lm_CN_2013

ByFeatureClasses Landmarks.gdb SX_LM — SX_Lm_CN_2013

ByFeatureClasses Landmarks.gdb SX_LM — SX_Lm_USBGN

ByFeatureClasses LandUse.gdb CC_LUs — CC_LUs_C

ByFeatureClasses LandUse.gdb Study_LUs — Study_LUs_C

ByFeatureClasses LandUse.gdb PW_LUs — PW_LUs_C

ByFeatureClasses LandUse.gdb RC_LUs — RC_LUs_C

ByFeatureClasses Parcels.gdb GC_Par — GC_Par_C

ByFeatureClasses Parcels.gdb HN_Par — HN_Par_C

ByFeatureClasses Parcels.gdb JCC_Par — JCC_Par_C

ByFeatureClasses Parcels.gdb LU_Par — LU_Par_C

ByFeatureClasses Parcels.gdb Study_Par — Study_Par_C

ByFeatureClasses Parcels.gdb NK_Par — NKa_Par_C

ByFeatureClasses Parcels.gdb NK_Par — NKb_Par_C

ByFeatureClasses Parcels.gdb NW_Par — NW_Par_C

ByFeatureClasses Parcels.gdb PG_Par — PG_Par_C

ByFeatureClasses Parcels.gdb RC_Par — RC_Par_C

ByFeatureClasses Parcels.gdb SP_Par — SP_Par_C
43 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Parks.gdb CC_Pk — CC_Pk_C

ByFeatureClasses Parks.gdb CF_Pk — CFbp_Pk_C

ByFeatureClasses Parks.gdb CF_Pk — CF_Pk_C

ByFeatureClasses Parks.gdb CF_Pk — CF_Pk_VDCR

ByFeatureClasses Parks.gdb CF_Pk — CF_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb CF_Pk — CF_Pk_Union

ByFeatureClasses Parks.gdb GC_Pk — GC_Pk_C

ByFeatureClasses Parks.gdb HC_Pk — HC_Pk_C

ByFeatureClasses Parks.gdb HC_Pk — HC_Pk_VDCR

ByFeatureClasses Parks.gdb HC_Pk — HC_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb HC_Pk — HC_Pk_Union

ByFeatureClasses Parks.gdb JCC_Pk — JCC_Pk_C

ByFeatureClasses Parks.gdb JCC_Pk — JCC_Pk_VDCR

ByFeatureClasses Parks.gdb JCC_Pk — JCC_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb JCC_Pk — JCC_Pk_Union

ByFeatureClasses Parks.gdb Study_Pk — Study_Pk_Ply_C

ByFeatureClasses Parks.gdb Study_Pk — Study_Pk_Pts_C

ByFeatureClasses Parks.gdb Study_Pk — Study_Pk_Ply_VDCR

ByFeatureClasses Parks.gdb Study_Pk — Study_Pk_Ply_VDCRb_1

ByFeatureClasses Parks.gdb PG_Pk — PG_Pk_C

ByFeatureClasses Parks.gdb PG_Pk — PG_Pk_VDCR

ByFeatureClasses Parks.gdb PG_Pk — PG_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb PG_Pk — PG_Pk_Union

ByFeatureClasses Parks.gdb PW_Pk — PWb_Pk_C

ByFeatureClasses Parks.gdb PW_Pk — PWa_Pk_C

ByFeatureClasses Parks.gdb PW_Pk — PW_Pk_Union

ByFeatureClasses Parks.gdb RC_Pk — RC_Pk_C

ByFeatureClasses Parks.gdb RC_Pk — RC_Pk_VDCR

ByFeatureClasses Parks.gdb RC_Pk — RC_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb RC_Pk — RC_Pk_Union

ByFeatureClasses Parks.gdb SP_Pk — SP_Pk_C

ByFeatureClasses Parks.gdb SP_Pk — SP_Pk_VDCR

ByFeatureClasses Parks.gdb SP_Pk — SP_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb SP_Pk — SP_Pk_Union

ByFeatureClasses Parks.gdb CR_Pk — CR_Pk_VDCR

ByFeatureClasses Parks.gdb DW_Pk — DW_Pk_VDCR

ByFeatureClasses Parks.gdb DW_Pk — DW_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb DW_Pk — DW_Pk_Union
44 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Parks.gdb HN_Pk — HN_PK_VDCR

ByFeatureClasses Parks.gdb HN_Pk — HN_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb HN_Pk — HN_Pk_Union

ByFeatureClasses Parks.gdb HW_Pk — HW_Pk_VDCR

ByFeatureClasses Parks.gdb HW_Pk — HW_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb HW_Pk — HW_Pk_Union

ByFeatureClasses Parks.gdb PB_Pk — PB_Pk_VDCR

ByFeatureClasses Parks.gdb PB_Pk — PB_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb PB_Pk — PB_Pk_Union

ByFeatureClasses Parks.gdb SU_Pk — SU_Pk_VDCR

ByFeatureClasses Parks.gdb KG_Pk — KG_Pk_FromCensusLM_CN_2013

ByFeatureClasses Parks.gdb Study_Combined_Pk — Study_Pk_Ply_Union

ByFeatureClasses Parks.gdb Study_Combined_Pk — Study_Pk_Ply_Unionb

ByFeatureClasses Parks.gdb SX_Pk — SX_Pk_CN_2013

ByFeatureClasses Parks.gdb SX_Pk — SX_Pk_VDCR

ByFeatureClasses Population.gdb AM_Pop — AM_Pop_CN_2010

ByFeatureClasses Population.gdb CC_Pop — CC_Pop_CN_2010

ByFeatureClasses Population.gdb CF_Pop — CF_Pop_CN_2010

ByFeatureClasses Population.gdb CH_Pop — CH_Pop_CN_2010

ByFeatureClasses Population.gdb CR_Pop — CR_Pop_CN_2010

ByFeatureClasses Population.gdb DW_Pop — DW_Pop_CN_2010

ByFeatureClasses Population.gdb GC_Pop — GC_Pop_CN_2010

ByFeatureClasses Population.gdb HC_Pop — HC_Pop_CN_2010

ByFeatureClasses Population.gdb HN_Pop — HN_Pop_CN_2010

ByFeatureClasses Population.gdb HW_Pop — HW_Pop_CN_2010

ByFeatureClasses Population.gdb JCC_Pop — JCC_Pop_CN_2010

ByFeatureClasses Population.gdb KG_Pop — KG_Pop_CN_2010

ByFeatureClasses Population.gdb KQ_Pop — KQ_Pop_CN_2010

ByFeatureClasses Population.gdb KW_Pop — KW_Pop_CN_2010

ByFeatureClasses Population.gdb LU_Pop — LU_Pop_CN_2010

ByFeatureClasses Population.gdb Study_Pop — Study_Pop_CN_2010

ByFeatureClasses Population.gdb NK_Pop — NK_Pop_CN_2010

ByFeatureClasses Population.gdb NW_Pop — NW_Pop_CN_2010

ByFeatureClasses Population.gdb PB_Pop — PB_Pop_CN_2010

ByFeatureClasses Population.gdb PG_Pop — PG_Pop_CN_2010

ByFeatureClasses Population.gdb PW_Pop — PW_Pop_CN_2010
45 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Population.gdb RC_Pop — RC_Pop_CN_2010

ByFeatureClasses Population.gdb SP_Pop — SP_Pop_CN_2010

ByFeatureClasses Population.gdb SU_Pop — SU_Pop_CN_2010

ByFeatureClasses Properties.gdb GC_Prop — GC_Prop_VirginiaState_C

ByFeatureClasses Properties.gdb SX_Prop — SX_Prop_VEDP

ByFeatureClasses ProtectiveAreas.gdb CC_PA — CC_PA_C

ByFeatureClasses ProtectiveAreas.gdb CC_PA — CC_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb CF_PA — CF_PA_C

ByFeatureClasses ProtectiveAreas.gdb CF_PA — CF_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb JCC_PA — JCC_PA_C

ByFeatureClasses ProtectiveAreas.gdb JCC_PA — JCC_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb Study_PA — Study_PA_C

ByFeatureClasses ProtectiveAreas.gdb Study_PA — Study_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb Study_PA — Study_PA2_Ply_C

ByFeatureClasses ProtectiveAreas.gdb Study_PA — Study_PA_ESRIb

ByFeatureClasses ProtectiveAreas.gdb PW_PA — PW_PA_C

ByFeatureClasses ProtectiveAreas.gdb PW_PA — PW_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb RC_PA — RC_PA_C

ByFeatureClasses ProtectiveAreas.gdb RC_PA — RC_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb AM_PA — AM_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb CR_PA — CR_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb CH_PA — CH_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb DW_PA — DW_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb GC_PA — GC_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb HN_PA — HN_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb HC_PA — HC_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb HW_PA — HW_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb KQ_PA — KQ_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb KG_PA — KG_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb KW_PA — KW_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb LU_PA — LU_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb NK_PA — NK_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb NW_PA — NW_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb PB_PA — PB_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb PG_PA — PG_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb SP_PA — SP_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb SU_PA — SU_PA_ESRI

ByFeatureClasses ProtectiveAreas.gdb SX_PA — SX_PA_ESRI
46 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses RailRoads.gdb AM_RR — AM_RR_CN_2013

ByFeatureClasses RailRoads.gdb AM_RR — AM_RR_ESRI

ByFeatureClasses RailRoads.gdb AM_RR — AM_RR_combined

ByFeatureClasses RailRoads.gdb AM_RR — AM_RR_TNM

ByFeatureClasses RailRoads.gdb CR_RR — CR_RR_CN_2013

ByFeatureClasses RailRoads.gdb CR_RR — CR_RR_ESRI

ByFeatureClasses RailRoads.gdb CR_RR — CR_RR_Combined

ByFeatureClasses RailRoads.gdb CR_RR — CR_RR_TNM

ByFeatureClasses RailRoads.gdb CC_RR — CC_RR_CN_2013

ByFeatureClasses RailRoads.gdb CC_RR — CC_RR_ESRI

ByFeatureClasses RailRoads.gdb CC_RR — CC_RR_Combined

ByFeatureClasses RailRoads.gdb CC_RR — CC_RR_TNM

ByFeatureClasses RailRoads.gdb CF_RR — CF_RR_CN_2013

ByFeatureClasses RailRoads.gdb CF_RR — CF_RR_ESRI

ByFeatureClasses RailRoads.gdb CF_RR — CF_RR_Combined

ByFeatureClasses RailRoads.gdb CF_RR — CF_RR_TNM

ByFeatureClasses RailRoads.gdb CH_RR — CH_RR_CN_2013

ByFeatureClasses RailRoads.gdb CH_RR — CH_RR_ESRI

ByFeatureClasses RailRoads.gdb CH_RR — CH_RR_Combined

ByFeatureClasses RailRoads.gdb CH_RR — CH_RR_TNM

ByFeatureClasses RailRoads.gdb DW_RR — DW_RR_C

ByFeatureClasses RailRoads.gdb DW_RR — DW_RR_CN_2013

ByFeatureClasses RailRoads.gdb DW_RR — DW_RR_ESRI

ByFeatureClasses RailRoads.gdb DW_RR — DW_RR_Combined

ByFeatureClasses RailRoads.gdb DW_RR — DW_RR_TNM

ByFeatureClasses RailRoads.gdb GC_RR — GC_RR_C

ByFeatureClasses RailRoads.gdb GC_RR — GC_RR_CN_2013

ByFeatureClasses RailRoads.gdb GC_RR — GC_RR_ESRI

ByFeatureClasses RailRoads.gdb GC_RR — GC_RR_Combined

ByFeatureClasses RailRoads.gdb GC_RR — GC_RR_TNM

ByFeatureClasses RailRoads.gdb HN_RR — HN_RR_C

ByFeatureClasses RailRoads.gdb HN_RR — HN_RR_CN_2013

ByFeatureClasses RailRoads.gdb HN_RR — HN_RR_ESRI

ByFeatureClasses RailRoads.gdb HN_RR — HN_RR_TNM

ByFeatureClasses RailRoads.gdb HN_RR — HN_RR_combined

ByFeatureClasses RailRoads.gdb HC_RR — HC_RR_C

ByFeatureClasses RailRoads.gdb HC_RR — HC_RR_CN_2013

ByFeatureClasses RailRoads.gdb HC_RR — HC_RR_ESRI
47 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses RailRoads.gdb HC_RR — HC_RR_TNM

ByFeatureClasses RailRoads.gdb HC_RR — HC_RR_Combined

ByFeatureClasses RailRoads.gdb HW_RR — HW_RR_CN_2013

ByFeatureClasses RailRoads.gdb HW_RR — HW_RR_ESRI

ByFeatureClasses RailRoads.gdb HW_RR — HW_RR_TNM

ByFeatureClasses RailRoads.gdb HW_RR — HW_RR_Combined

ByFeatureClasses RailRoads.gdb JCC_RR — JCC_RR_C

ByFeatureClasses RailRoads.gdb JCC_RR — JCC_RR_CN_2013

ByFeatureClasses RailRoads.gdb JCC_RR — JCC_RR_ESRI

ByFeatureClasses RailRoads.gdb JCC_RR — JCC_RR_TNM

ByFeatureClasses RailRoads.gdb JCC_RR — JCC_RR_Combined

ByFeatureClasses RailRoads.gdb KG_RR — KG_RR_CN_2013

ByFeatureClasses RailRoads.gdb KG_RR — KG_RR_ESRI

ByFeatureClasses RailRoads.gdb KG_RR — KG_RR_TNM

ByFeatureClasses RailRoads.gdb KG_RR — KG_RR_Combined

ByFeatureClasses RailRoads.gdb KW_RR — KW_RR_CN_2013

ByFeatureClasses RailRoads.gdb KW_RR — KW_RR_ESRI

ByFeatureClasses RailRoads.gdb KW_RR — KW_RR_TNM

ByFeatureClasses RailRoads.gdb KW_RR — KW_RR_Combined

ByFeatureClasses RailRoads.gdb LU_RR — LU_RR_C

ByFeatureClasses RailRoads.gdb LU_RR — LU_RR_CN_2013

ByFeatureClasses RailRoads.gdb LU_RR — LU_RR_ESRI

ByFeatureClasses RailRoads.gdb LU_RR — LU_RR_TNM

ByFeatureClasses RailRoads.gdb LU_RR — LU_RR_Combined

ByFeatureClasses RailRoads.gdb NK_RR — NK_RR_CN_2013

ByFeatureClasses RailRoads.gdb NK_RR — NK_RR_ESRI

ByFeatureClasses RailRoads.gdb NK_RR — NK_RR_TNM

ByFeatureClasses RailRoads.gdb NK_RR — NK_RR_Combined

ByFeatureClasses RailRoads.gdb NW_RR — NW_RR_C

ByFeatureClasses RailRoads.gdb NW_RR — NW_RR_CN_2013

ByFeatureClasses RailRoads.gdb NW_RR — NW_RR_ESRI

ByFeatureClasses RailRoads.gdb NW_RR — NW_RR_TNM

ByFeatureClasses RailRoads.gdb NW_RR — NW_RR_Combined

ByFeatureClasses RailRoads.gdb PB_RR — PB_RR_CN_2013

ByFeatureClasses RailRoads.gdb PB_RR — PB_RR_ESRI

ByFeatureClasses RailRoads.gdb PB_RR — PB_RR_TNM

ByFeatureClasses RailRoads.gdb PB_RR — PB_RR_Combined

ByFeatureClasses RailRoads.gdb PW_RR — PW_RR_CN_2013
48 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses RailRoads.gdb PW_RR — PW_RR_ESRI

ByFeatureClasses RailRoads.gdb PW_RR — PW_RR_TNM

ByFeatureClasses RailRoads.gdb PW_RR — PW_RR_Combined

ByFeatureClasses RailRoads.gdb PG_RR — PG_RR_CN_2103

ByFeatureClasses RailRoads.gdb PG_RR — PG_RR_ESRI

ByFeatureClasses RailRoads.gdb PG_RR — PG_RR_TNM

ByFeatureClasses RailRoads.gdb PG_RR — PG_RR_Combined

ByFeatureClasses RailRoads.gdb RC_RR — RC_RR_CN_2013

ByFeatureClasses RailRoads.gdb RC_RR — RC_RR_ESRI

ByFeatureClasses RailRoads.gdb RC_RR — RC_RR_TNM

ByFeatureClasses RailRoads.gdb RC_RR — RC_RR_Combined

ByFeatureClasses RailRoads.gdb SP_RR — SP_RR_CN_2013

ByFeatureClasses RailRoads.gdb SP_RR — SP_RR_ESRI

ByFeatureClasses RailRoads.gdb SP_RR — SP_RR_TNM

ByFeatureClasses RailRoads.gdb SP_RR — SP_RR_Combined

ByFeatureClasses RailRoads.gdb SU_RR — SU_RR_CN_2013

ByFeatureClasses RailRoads.gdb SU_RR — SU_RR_ESRI

ByFeatureClasses RailRoads.gdb SU_RR — SU_RR_Combined

ByFeatureClasses RailRoads.gdb Study_RR — Study_RR_C

ByFeatureClasses RailRoads.gdb Study_RR — Study_RR_CN_2013

ByFeatureClasses RailRoads.gdb Study_RR — Study_RR_Combined

ByFeatureClasses RailRoads.gdb Study_RR — Study_RR_ESRI

ByFeatureClasses RailRoads.gdb Study_RR — Study_RR_TNM

ByFeatureClasses RailRoads.gdb SX_RR — SX_RR_ESRI

ByFeatureClasses RailRoads.gdb SX_RR — SX_RR_TNM

ByFeatureClasses RailRoads.gdb SX_RR — SX_RR_CN_2013

ByFeatureClasses RailRoads.gdb SX_RR — SX_RR_Combined

ByFeatureClasses Rivers.gdb NW_Riv — NW_RIV_Wb_C

ByFeatureClasses Rivers.gdb SX_Riv — SX_Riv_VDEQ

ByFeatureClasses Roads.gdb CC_Rds — CC_Rds_C

ByFeatureClasses Roads.gdb CC_Rds — CC_Rds_CN_2013

ByFeatureClasses Roads.gdb CC_Rds — CC_Rds_VD_1997

ByFeatureClasses Roads.gdb CC_Rds — CC_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb CC_Rds — CC_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb CC_Rds — CC_Rds_Combined

ByFeatureClasses Roads.gdb CC_Rds — CC_Rds_TNM

ByFeatureClasses Roads.gdb AM_Rds — AM_Rds_CN_2013

ByFeatureClasses Roads.gdb AM_Rds — AM_Rds_VD_1997
49 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Roads.gdb AM_Rds — AM_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb AM_Rds — AM_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb AM_Rds — AM_Rds_Combined

ByFeatureClasses Roads.gdb AM_Rds — AM_Rds_TNM

ByFeatureClasses Roads.gdb CF_Rds — CF_Rds_C

ByFeatureClasses Roads.gdb CF_Rds — CF_Rds_CN_2013

ByFeatureClasses Roads.gdb CF_Rds — CF_Rds_VD_1997

ByFeatureClasses Roads.gdb CF_Rds — CF_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb CF_Rds — CF_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb CF_Rds — CF_Rds_combined

ByFeatureClasses Roads.gdb CF_Rds — CF_Rds_TNM

ByFeatureClasses Roads.gdb CH_Rds — CH_Rds_CN_2013

ByFeatureClasses Roads.gdb CH_Rds — CH_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb CH_Rds — CH_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb CH_Rds — CH_Rds_combined

ByFeatureClasses Roads.gdb CH_Rds — CH_Rds_TNM

ByFeatureClasses Roads.gdb Study_Rds — Study_Rds_C

ByFeatureClasses Roads.gdb Study_Rds — Study_Rds_CN_2013

ByFeatureClasses Roads.gdb Study_Rds — Study_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb Study_Rds — Study_Rds_VD_1997

ByFeatureClasses Roads.gdb Study_Rds — Study_Rds_VG_2014Q1b_1

ByFeatureClasses Roads.gdb Study_Rds — Study_Rds_TNMb_1

ByFeatureClasses Roads.gdb CR_Rds — CR_Rds_CN_2013

ByFeatureClasses Roads.gdb CR_Rds — CR_Rds_VD_1997

ByFeatureClasses Roads.gdb CR_Rds — CR_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb CR_Rds — CR_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb CR_Rds — CR_Rds_Combined

ByFeatureClasses Roads.gdb CR_Rds — CR_Rds_TNM

ByFeatureClasses Roads.gdb DW_Rds — DW_Rds_C

ByFeatureClasses Roads.gdb DW_Rds — DW_Rds_CN_2013

ByFeatureClasses Roads.gdb DW_Rds — DW_Rds_VD_1997

ByFeatureClasses Roads.gdb DW_Rds — DW_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb DW_Rds — DW_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb DW_Rds — DW_Rds_Combined

ByFeatureClasses Roads.gdb DW_Rds — DW_Rds_TNM

ByFeatureClasses Roads.gdb GC_Rds — GCa_Rds_C

ByFeatureClasses Roads.gdb GC_Rds — GCb_Rds_C

ByFeatureClasses Roads.gdb GC_Rds — GC_Rds_CN_2013
50 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Roads.gdb GC_Rds — GC_Rds_VD_1997

ByFeatureClasses Roads.gdb GC_Rds — GC_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb GC_Rds — GC_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb GC_Rds — GC_Rds_Combined

ByFeatureClasses Roads.gdb GC_Rds — GC_Rds_TNM

ByFeatureClasses Roads.gdb HC_Rds — HC_Rds_C

ByFeatureClasses Roads.gdb HC_Rds — HC_Rds_CN_2013

ByFeatureClasses Roads.gdb HC_Rds — HC_Rds_VD_1997

ByFeatureClasses Roads.gdb HC_Rds — HC_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb HC_Rds — HC_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb HC_Rds — HC_Rds_Combined

ByFeatureClasses Roads.gdb HC_Rds — HC_Rds_TNM

ByFeatureClasses Roads.gdb HN_Rds — HNa_Rds_C

ByFeatureClasses Roads.gdb HN_Rds — HNb_Rds_C

ByFeatureClasses Roads.gdb HN_Rds — HN_Rds_CN_2013

ByFeatureClasses Roads.gdb HN_Rds — HN_Rds_VD_1997

ByFeatureClasses Roads.gdb HN_Rds — HN_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb HN_Rds — HN_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb HN_Rds — HN_Rds_Combined

ByFeatureClasses Roads.gdb HN_Rds — HN_Rds_TNM

ByFeatureClasses Roads.gdb HW_Rds — HW_Rds_CN_2013

ByFeatureClasses Roads.gdb HW_Rds — HW_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb HW_Rds — HW_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb HW_Rds — HW_Rds_Combined

ByFeatureClasses Roads.gdb HW_Rds — HW_Rds_TNM

ByFeatureClasses Roads.gdb JCC_Rds — JCC_Rds_C

ByFeatureClasses Roads.gdb JCC_Rds — JCC_Rds_CN_2013

ByFeatureClasses Roads.gdb JCC_Rds — JCC_Rds_VD_1997

ByFeatureClasses Roads.gdb JCC_Rds — JCC_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb JCC_Rds — JCC_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb JCC_Rds — JCC_Rds_Combined

ByFeatureClasses Roads.gdb JCC_Rds — JCC_Rds_TNM

ByFeatureClasses Roads.gdb KG_Rds — KG_Rds_CN_2013

ByFeatureClasses Roads.gdb KG_Rds — KG_Rds_VD_1997

ByFeatureClasses Roads.gdb KG_Rds — KG_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb KG_Rds — KG_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb KG_Rds — KG_Rds_Combined

ByFeatureClasses Roads.gdb KG_Rds — KG_Rds_TNM
51 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Roads.gdb KQ_Rds — KQ_Rds_CN_2013

ByFeatureClasses Roads.gdb KQ_Rds — KQ_Rds_VD_1997

ByFeatureClasses Roads.gdb KQ_Rds — KQ_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb KQ_Rds — KQ_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb KQ_Rds — KQ_Rds_Combined

ByFeatureClasses Roads.gdb KQ_Rds — KQ_Rds_TNM

ByFeatureClasses Roads.gdb KW_Rds — KW_Rds_CN_2013

ByFeatureClasses Roads.gdb KW_Rds — KW_Rds_VD_1997

ByFeatureClasses Roads.gdb KW_Rds — KW_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb KW_Rds — KW_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb KW_Rds — KW_Rds_Combined

ByFeatureClasses Roads.gdb KW_Rds — KW_Rds_TNM

ByFeatureClasses Roads.gdb LU_Rds — LU_Rds_C

ByFeatureClasses Roads.gdb LU_Rds — LU_Rds_CN_2013

ByFeatureClasses Roads.gdb LU_Rds — LU_Rds_VD_1997

ByFeatureClasses Roads.gdb LU_Rds — LU_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb LU_Rds — LU_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb LU_Rds — LU_Rds_Combined

ByFeatureClasses Roads.gdb LU_Rds — LU_Rds_TNM

ByFeatureClasses Roads.gdb NK_Rds — NK_Rds_C

ByFeatureClasses Roads.gdb NK_Rds — NK_Rds_CN_2013

ByFeatureClasses Roads.gdb NK_Rds — NK_Rds_VD_1997

ByFeatureClasses Roads.gdb NK_Rds — NK_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb NK_Rds — NK_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb NK_Rds — NK_Rds_Combined

ByFeatureClasses Roads.gdb NK_Rds — NK_Rds_TNM

ByFeatureClasses Roads.gdb NW_Rds — NW_Rds_C

ByFeatureClasses Roads.gdb NW_Rds — NW_Rds_CN_2013

ByFeatureClasses Roads.gdb NW_Rds — NW_Rds_VD_1997

ByFeatureClasses Roads.gdb NW_Rds — NW_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb NW_Rds — NW_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb NW_Rds — NW_Rds_Combined

ByFeatureClasses Roads.gdb NW_Rds — NW_Rds_TNM

ByFeatureClasses Roads.gdb PB_Rds — PB_Rds_CN_2013

ByFeatureClasses Roads.gdb PB_Rds — PB_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb PB_Rds — PB_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb PB_Rds — PB_Rds_Combined

ByFeatureClasses Roads.gdb PB_Rds — PB_Rds_TNM
52 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Roads.gdb PG_Rds — PG_Rds_C

ByFeatureClasses Roads.gdb PG_Rds — PG_Rds_CN_2013

ByFeatureClasses Roads.gdb PG_Rds — PG_Rds_VD_1997

ByFeatureClasses Roads.gdb PG_Rds — PG_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb PG_Rds — PG_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb PG_Rds — PG_Rds_Combined

ByFeatureClasses Roads.gdb PG_Rds — PG_Rds_TNM

ByFeatureClasses Roads.gdb PW_Rds — PW_Rds_C

ByFeatureClasses Roads.gdb PW_Rds — PW_Rds_CN_2013

ByFeatureClasses Roads.gdb PW_Rds — PW_Rds_VD_1997

ByFeatureClasses Roads.gdb PW_Rds — PW_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb PW_Rds — PW_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb PW_Rds — PW_Rds_Combined

ByFeatureClasses Roads.gdb PW_Rds — PW_Rds_TNM

ByFeatureClasses Roads.gdb RC_Rds — RC_Rds_C

ByFeatureClasses Roads.gdb RC_Rds — RC_Rds_CN_2013

ByFeatureClasses Roads.gdb RC_Rds — RC_Rds_VD_1997

ByFeatureClasses Roads.gdb RC_Rds — RC_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb RC_Rds — RC_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb RC_Rds — RC_Rds_Combined

ByFeatureClasses Roads.gdb RC_Rds — RC_Rds_TNM

ByFeatureClasses Roads.gdb SP_Rds — SP_Rds_C

ByFeatureClasses Roads.gdb SP_Rds — SP_Rds_CN_2013

ByFeatureClasses Roads.gdb SP_Rds — SP_Rds_VD_1997

ByFeatureClasses Roads.gdb SP_Rds — SP_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb SP_Rds — SP_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb SP_Rds — SP_Rds_Combined

ByFeatureClasses Roads.gdb SP_Rds — SP_Rds_TNM

ByFeatureClasses Roads.gdb SU_Rds — SU_Rds_C

ByFeatureClasses Roads.gdb SU_Rds — SU_Rds_CN_2013

ByFeatureClasses Roads.gdb SU_Rds — SU_Rds_VD_1997

ByFeatureClasses Roads.gdb SU_Rds — SU_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb SU_Rds — SU_Rds_VG_2008Q3

ByFeatureClasses Roads.gdb SU_Rds — SU_Rds_Combined

ByFeatureClasses Roads.gdb SU_Rds — SU_Rds_TNM

ByFeatureClasses Roads.gdb Study_Combined — Study_Rds_Combined

ByFeatureClasses Roads.gdb SX_Rds — SX_Rds_FS

ByFeatureClasses Roads.gdb SX_Rds — SX_Rds_TNM
53 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Roads.gdb SX_Rds — SX_Rds_VG_2014Q1

ByFeatureClasses Roads.gdb SX_Rds — SX_Rds_CN_2104

ByFeatureClasses Roads.gdb SX_Rds — SX_Rds_CN_2013

ByFeatureClasses Roads.gdb SX_Rds — SX_Rds_Combined

ByFeatureClasses Roads.gdb SX_Rds — SX_Rds_VG_2008Q3

ByFeatureClasses Schools.gdb SP_Sch — SP_Sch_C

ByFeatureClasses Schools.gdb RC_Sch — RC_Sch_Public_C

ByFeatureClasses Slope.gdb GC_Sl — GC_Sl_SteepSlope_C

ByFeatureClasses Soils.gdb HN_So — HN_So_C

ByFeatureClasses Soils.gdb LU_So — LU_So_C

ByFeatureClasses Soils.gdb RC_So — RC_So_C

ByFeatureClasses Streams.gdb CF_St — CF_St_C

ByFeatureClasses Streams.gdb HC_St — HC_St_C

ByFeatureClasses Streams.gdb JCC_St — JCC_St_C

ByFeatureClasses Streams.gdb PG_St — PG_St_C

ByFeatureClasses Streams.gdb PW_St — PW_St_C

ByFeatureClasses Streams.gdb SU_St — SU_St_C

ByFeatureClasses Streams.gdb Study_St — Study_St_C

ByFeatureClasses Topography.gdb HN_Tp — HN_Tp_C

ByFeatureClasses Transit.gdb JCC_Tr — JCC_TransitLines_C

ByFeatureClasses Transit.gdb JCC_Tr — JCC_TransitStops_C

ByFeatureClasses Transit.gdb SX_Transit — SX_Apts_TNM

ByFeatureClasses TreeCoverage.gdb HN_TC — HN_TC_C

ByFeatureClasses Utilities.gdb Study_Ut — Study_Ut_C

ByFeatureClasses Utilities.gdb HN_Utility — HN_Ut_C

ByFeatureClasses Utilities.gdb LU_Ut — LU_Ut_C

ByFeatureClasses Waterbodies.gdb CC_Wb — CC_Wb_Ply_C

ByFeatureClasses Waterbodies.gdb CC_Wb — CC_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb CC_Wb — CC_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb CF_Wb — CFb_Wb_Li_C

ByFeatureClasses Waterbodies.gdb CF_Wb — CFa_Wb_Li_C

ByFeatureClasses Waterbodies.gdb CF_Wb — CF_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb CF_Wb — CF_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb DW_Wb — DW_Wb_Li_C

ByFeatureClasses Waterbodies.gdb DW_Wb — DW_Wb_Ply_C

ByFeatureClasses Waterbodies.gdb DW_Wb — DW_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb DW_Wb — DW_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb GC_Wb — GC_Wb_Li_C
54 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Waterbodies.gdb GC_Wb — GC_Wb_Ply_C

ByFeatureClasses Waterbodies.gdb GC_Wb — GC_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb GC_Wb — GC_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb HC_Wb — HC_Wb_Li_C

ByFeatureClasses Waterbodies.gdb HC_Wb — HC_Wb_Ply_C

ByFeatureClasses Waterbodies.gdb HC_Wb — HC_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb HC_Wb — HC_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb JCC_Wb — JCC_Wb_Li_C

ByFeatureClasses Waterbodies.gdb JCC_Wb — JCC_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb JCC_Wb — JCC_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb LU_Wb — LU_Wb_Ply_C

ByFeatureClasses Waterbodies.gdb LU_Wb — LU_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb LU_Wb — LU_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb Study_Wb — Study_Wb_Ply_C

ByFeatureClasses Waterbodies.gdb Study_Wb — Study_Wb_Li_C

ByFeatureClasses Waterbodies.gdb Study_Wb — Study_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb Study_Wb — Study_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb NW_Wb — NW_Wb_Li_C

ByFeatureClasses Waterbodies.gdb NW_Wb — NW_Wb_Ply_C

ByFeatureClasses Waterbodies.gdb NW_Wb — NW_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb NW_Wb — NW_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb PG_Wb — PG_Wb_Ply_C

ByFeatureClasses Waterbodies.gdb PG_Wb — PG_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb PG_Wb — PG_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb PW_Wb — PWa_Wb_Li_C

ByFeatureClasses Waterbodies.gdb PW_Wb — PWb_Wb_Li_C

ByFeatureClasses Waterbodies.gdb PW_Wb — PW_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb PW_Wb — PW_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb SU_Wb — SU_Wb_Li_C

ByFeatureClasses Waterbodies.gdb SU_Wb — SU_Wb_Ply_C

ByFeatureClasses Waterbodies.gdb SU_Wb — SU_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb SU_Wb — SU_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb AM_Wb — AM_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb AM_Wb — AM_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb CH_Wb — CH_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb CH_Wb — CH_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb CR_Wb — CR_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb CR_Wb — CR_Wb_Ply_NHD_USGS
55 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses Waterbodies.gdb HN_Wb — HN_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb HN_Wb — HN_WB_C_Orig

ByFeatureClasses Waterbodies.gdb HN_Wb — HN_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb HW_Wb — HW_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb HW_Wb — HW_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb KG_Wb — KG_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb KG_Wb — KG_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb KQ_Wb — KQ_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb KQ_Wb — KQ_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb KW_Wb — KW_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb KW_Wb — KW_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb NK_Wb — NK_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb NK_Wb — NK_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb PB_Wb — PB_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb PB_Wb — PB_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb RC_Wb — RC_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb RC_Wb — RC_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb SP_Wb — SP_Wb_Li_CN_2013

ByFeatureClasses Waterbodies.gdb SP_Wb — SP_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb SX_Wb — SX_Wb_Ply_NHD_USGS

ByFeatureClasses Waterbodies.gdb SX_Wb — SX_Wb_Li_CN_2014

ByFeatureClasses Waterbodies.gdb SX_Wb — SX_Wb_Li_CN_2013

ByFeatureClasses WaterSheds.gdb RC_WaS — RC_WaS_Sub_C

ByFeatureClasses WaterSheds.gdb RC_WaS — RC_WaS_C

ByFeatureClasses WaterSheds.gdb GC_WaS — GC_WaS_MajorSub_C

ByFeatureClasses WetlandsSwamps.gdb CF_WS — CF_WS_C

ByFeatureClasses WetlandsSwamps.gdb DW_WS — DW_WS_C

ByFeatureClasses WetlandsSwamps.gdb JCC_WS — JCC_WS_C

ByFeatureClasses WetlandsSwamps.gdb Study_WS — Study_WS_C

ByFeatureClasses WetlandsSwamps.gdb PG_WS — PG_WS_C

ByFeatureClasses WetlandsSwamps.gdb PW_WS — PW_WS_C

ByFeatureClasses WetlandsSwamps.gdb RC_WS — RC_WS_C

ByFeatureClasses WetlandsSwamps.gdb SU_WS — SUa_WS_C

ByFeatureClasses WetlandsSwamps.gdb SU_WS — SUb_WS_C

ByFeatureClasses ZipCodes.gdb RC_Zip — RC_Zip_C

ByFeatureClasses ZipCodes.gdb RC_Zip — RC_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb NW_Zip — NW_Zip_C

ByFeatureClasses ZipCodes.gdb NW_Zip — NW_Zip_CN_2013
56 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByFeatureClasses ZipCodes.gdb AM_Zip — AM_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb CC_Zip — CC_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb CF_Zip — CF_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb CH_Zip — CH_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb CR_Zip — CR_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb DW_Zip — DW_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb GC_Zip — GC_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb HC_Zip — HC_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb HN_Zip — HN_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb HW_Zip — HW_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb JCC_Zip — JCC_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb KG_Zip — KG_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb KQ_Zip — KQ_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb KW_Zip — KW_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb LU_Zip — LU_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb Study_Zip — Study_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb NK_Zip — NK_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb PB_Zip — PB_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb PG_Zip — PG_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb PW_Zip — PW_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb SP_Zip — SP_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb SU_Zip — SU_Zip_CN_2013

ByFeatureClasses ZipCodes.gdb SX_Zip — SX_Zip_CN_2013

ByFeatureClasses Zoning.gdb Study_Zo — Study_Zo_C

ByFeatureClasses Zoning.gdb DW_Zo — DW_Zo_C

ByFeatureClasses Zoning.gdb GC_Zo — GC_Zo_C

ByFeatureClasses Zoning.gdb HN_Zo — HN_Zo_C

ByFeatureClasses Zoning.gdb JCC_Zo — JCC_Zo_C

ByFeatureClasses Zoning.gdb LU_Zo — LUb_Zo_C

ByFeatureClasses Zoning.gdb LU_Zo — LUa_Zo_C

ByFeatureClasses Zoning.gdb Min_Zo — Min_Zo_C

ByFeatureClasses Zoning.gdb NK_Zo — NK_Zo_C

ByFeatureClasses Zoning.gdb RC_Zo — RC_Zo_C

ByJurisdictions Amelia.gdb AM_Rds — AM_Rds_CN_2013

ByJurisdictions Amelia.gdb AM_Rds — AM_Rds_VD_1997

ByJurisdictions Amelia.gdb AM_Rds — AM_Rds_VG_2008Q3

ByJurisdictions Amelia.gdb AM_Rds — AM_Rds_VG_2014Q1

ByJurisdictions Amelia.gdb AM_Rds — AM_Rds_TNM
57 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Amelia.gdb AM_Rds — AM_Rds_Combined

ByJurisdictions Amelia.gdb AM_Lm — AM_Lm_CN_2013

ByJurisdictions Amelia.gdb AM_PA — AM_PA_ESRI

ByJurisdictions Amelia.gdb AM_RR — AM_RR_CN

ByJurisdictions Amelia.gdb AM_RR — AM_RR_ESRI

ByJurisdictions Amelia.gdb AM_RR — AM_RR_TNM

ByJurisdictions Amelia.gdb AM_RR — AM_RR_combined

ByJurisdictions Amelia.gdb AM_Borders — AM_Border

ByJurisdictions Amelia.gdb AM_Wb — AM_Wb_Li_CN_2013

ByJurisdictions Amelia.gdb AM_Wb — AM_Wb_Ply_NHD_USGS

ByJurisdictions Amelia.gdb AM_Zip — AM_Zip_CN_2013

ByJurisdictions Caroline.gdb CR_Rds — CR_Rds_CN_2013

ByJurisdictions Caroline.gdb CR_Rds — CR_Rds_VD_1997

ByJurisdictions Caroline.gdb CR_Rds — CR_Rds_VG_2008Q3

ByJurisdictions Caroline.gdb CR_Rds — CR_Rds_VG_2014Q1

ByJurisdictions Caroline.gdb CR_Rds — CR_Rds_Combined

ByJurisdictions Caroline.gdb CR_Rds — CR_Rds_TNM

ByJurisdictions Caroline.gdb CR_Lm — CR_Lm_CN_2013

ByJurisdictions Caroline.gdb CR_Pk — CR_Pk_VDCR

ByJurisdictions Caroline.gdb CR_PA — CR_PA_ESRI

ByJurisdictions Caroline.gdb CR_Borders — CR_Border

ByJurisdictions Caroline.gdb CR_Wb — CR_Wb_Li_CN_2013

ByJurisdictions Caroline.gdb CR_Wb — CR_Wb_Ply_NHD_USGS

ByJurisdictions Caroline.gdb CR_Zip — CR_Zip_CN_2013

ByJurisdictions Caroline.gdb CR_RR — CR_RR_CN_2013

ByJurisdictions Caroline.gdb CR_RR — CR_RR_Combined

ByJurisdictions Caroline.gdb CR_RR — CR_RR_ESRI

ByJurisdictions Caroline.gdb CR_RR — CR_RR_TNM

ByJurisdictions CharlescityCounty.gdb CC_Rds — CC_Rds_C_Orig

ByJurisdictions CharlescityCounty.gdb CC_Rds — CC_Rds_C_New

ByJurisdictions CharlescityCounty.gdb CC_Rds — CC_Rds_CN_2013

ByJurisdictions CharlescityCounty.gdb CC_Rds — CC_Rds_VD_1997

ByJurisdictions CharlescityCounty.gdb CC_Rds — CC_Rds_VG_2008Q3

ByJurisdictions CharlescityCounty.gdb CC_Rds — CC_Rds_VG_2014Q1

ByJurisdictions CharlescityCounty.gdb CC_Rds — CC_Rds_TNM

ByJurisdictions CharlescityCounty.gdb CC_Rds — CC_Rds_Combined

ByJurisdictions CharlescityCounty.gdb CC_Wb — CC_Wb_Ply_C_Orig

ByJurisdictions CharlescityCounty.gdb CC_Wb — CC_Wb_Ply_C_New
58 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions CharlescityCounty.gdb CC_Wb — CC_Wb_Li_CN_2013

ByJurisdictions CharlescityCounty.gdb CC_Wb — CC_Wb_Ply_NHD_USGS

ByJurisdictions CharlescityCounty.gdb CC_LUs — CC_LUs_C_Orig

ByJurisdictions CharlescityCounty.gdb CC_LUs — CC_LUs_C_New

ByJurisdictions CharlescityCounty.gdb CC_Pk — CC_Pk_C_Orig

ByJurisdictions CharlescityCounty.gdb CC_Pk — CC_Pk_C_New

ByJurisdictions CharlescityCounty.gdb CC_PA — CC_PA_C_Orig

ByJurisdictions CharlescityCounty.gdb CC_PA — CC_PA_C_New

ByJurisdictions CharlescityCounty.gdb CC_PA — CC_PA_ESRI

ByJurisdictions CharlescityCounty.gdb CC_Other — CC_BND_C

ByJurisdictions CharlescityCounty.gdb CC_Lm — CC_Lm_CN_2013

ByJurisdictions CharlescityCounty.gdb CC_RR — CC_RR_CN_2013

ByJurisdictions CharlescityCounty.gdb CC_RR — CC_RR_ESRI

ByJurisdictions CharlescityCounty.gdb CC_RR — CC_RR_Combined

ByJurisdictions CharlescityCounty.gdb CC_RR — CC_RR_TNM

ByJurisdictions CharlescityCounty.gdb CC_Borders — CC_Border

ByJurisdictions CharlescityCounty.gdb CC_Zip — CC_Zip_CN_2013

ByJurisdictions Chesterfield.gdb CF_Rds — CF_Rds_C_Orig

ByJurisdictions Chesterfield.gdb CF_Rds — CF_Rds_C_New

ByJurisdictions Chesterfield.gdb CF_Rds — CF_Rds_CN_2013

ByJurisdictions Chesterfield.gdb CF_Rds — CF_Rds_VD_1997

ByJurisdictions Chesterfield.gdb CF_Rds — CF_Rds_VG_2008Q3

ByJurisdictions Chesterfield.gdb CF_Rds — CF_Rds_VG_2014Q1

ByJurisdictions Chesterfield.gdb CF_Rds — CF_Rds_combined

ByJurisdictions Chesterfield.gdb CF_Rds — CF_Rds_TNM

ByJurisdictions Chesterfield.gdb CF_Pk — CFbp_Pk_C_Orig

ByJurisdictions Chesterfield.gdb CF_Pk — CF_Pk_C_Orig

ByJurisdictions Chesterfield.gdb CF_Pk — CF_Pk_C_New

ByJurisdictions Chesterfield.gdb CF_Pk — CF_Pk_VDCR

ByJurisdictions Chesterfield.gdb CF_Pk — CFbp_Pk_C_New

ByJurisdictions Chesterfield.gdb CF_Pk — CF_Pk_FromCensusLM_CN_2013

ByJurisdictions Chesterfield.gdb CF_Wb — CF_lakesp_WB_C_Orig

ByJurisdictions Chesterfield.gdb CF_Wb — CF_riverbdy_WB_C_Orig

ByJurisdictions Chesterfield.gdb CF_Wb — CFa_Wb_Li_C_New

ByJurisdictions Chesterfield.gdb CF_Wb — CFb_Wb_Li_C_New

ByJurisdictions Chesterfield.gdb CF_Wb — CF_Wb_Li_CN_2013

ByJurisdictions Chesterfield.gdb CF_Wb — CF_Wb_Ply_NHD_USGS

ByJurisdictions Chesterfield.gdb CF_Res — CF_Res_C
59 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Chesterfield.gdb CF_PA — CF_PA_C_Orig

ByJurisdictions Chesterfield.gdb CF_PA — CF_PA_C_New

ByJurisdictions Chesterfield.gdb CF_PA — CF_PA_ESRI

ByJurisdictions Chesterfield.gdb CF_St — CF_St_C_Orig

ByJurisdictions Chesterfield.gdb CF_St — CF_St_C_New

ByJurisdictions Chesterfield.gdb CF_WS — CF_WS_C_Orig

ByJurisdictions Chesterfield.gdb CF_WS — CF_WS_C_New

ByJurisdictions Chesterfield.gdb CF_Lm — CF_Lm_CN_2013

ByJurisdictions Chesterfield.gdb CF_RR — CF_RR_CN_2013

ByJurisdictions Chesterfield.gdb CF_RR — CF_RR_ESRI

ByJurisdictions Chesterfield.gdb CF_RR — CF_RR_Combined

ByJurisdictions Chesterfield.gdb CF_RR — CF_RR_TNM

ByJurisdictions Chesterfield.gdb CF_Borders — CF_Border

ByJurisdictions Chesterfield.gdb CF_FlPL — CF_floodp_C

ByJurisdictions Chesterfield.gdb CF_Zip — CF_Zip_CN_2013

ByJurisdictions ColonialHeights.gdb CH_Rds — CH_Rds_VG_2014Q1

ByJurisdictions ColonialHeights.gdb CH_Rds — CH_Rds_CN_2013

ByJurisdictions ColonialHeights.gdb CH_Rds — CH_Rds_VG_2008Q3

ByJurisdictions ColonialHeights.gdb CH_Rds — CH_Rds_combined

ByJurisdictions ColonialHeights.gdb CH_Rds — CH_Rds_TNM

ByJurisdictions ColonialHeights.gdb CH_Lm — CH_Lm_CN_2013

ByJurisdictions ColonialHeights.gdb CH_PA — CH_PA_ESRI

ByJurisdictions ColonialHeights.gdb CH_RR — CH_RR_CN_2013

ByJurisdictions ColonialHeights.gdb CH_RR — CH_RR_ESRI

ByJurisdictions ColonialHeights.gdb CH_RR — CH_RR_Combined

ByJurisdictions ColonialHeights.gdb CH_RR — CH_RR_TNM

ByJurisdictions ColonialHeights.gdb CH_Borders — CH_Border

ByJurisdictions ColonialHeights.gdb CH_Wb — CH_Wb_Li_CN_2013

ByJurisdictions ColonialHeights.gdb CH_Wb — CH_Wb_Ply_NHD_USGS

ByJurisdictions ColonialHeights.gdb CH_Zip — CH_Zip_CN_2013

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_Rds_C_New

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_Rds_CN

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_Rds_VG_2014Q1

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_Rds_VD_97

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_Ancillary_Rds_C

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_Rds_C_Orig

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_DBO_Auxillary_Rds_C

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_Rds_VG_2008Q3
60 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_Rds_Combined

ByJurisdictions Dinwiddie.gdb DW_Rds — DW_Rds_TNM

ByJurisdictions Dinwiddie.gdb DW_Wb — DW_Hydro_Wb_Ply_C_Orig

ByJurisdictions Dinwiddie.gdb DW_Wb — DW_Hydro_Wb_Li_C_Orig

ByJurisdictions Dinwiddie.gdb DW_Wb — DW_Wb_Li_C_new

ByJurisdictions Dinwiddie.gdb DW_Wb — DW_Wb_Ply_C_New

ByJurisdictions Dinwiddie.gdb DW_Wb — DW_Wb_Li_CN_2013

ByJurisdictions Dinwiddie.gdb DW_Wb — DW_Wb_Ply_NHD_USGS

ByJurisdictions Dinwiddie.gdb DW_RR — DW_RR_C_Orig

ByJurisdictions Dinwiddie.gdb DW_RR — DW_RR_C_new

ByJurisdictions Dinwiddie.gdb DW_RR — DW_RR_CN

ByJurisdictions Dinwiddie.gdb DW_RR — DW_RR_ESRI

ByJurisdictions Dinwiddie.gdb DW_RR — DW_RR_Combined

ByJurisdictions Dinwiddie.gdb DW_RR — DW_RR_TNM

ByJurisdictions Dinwiddie.gdb DW_APts — DW_APts_C_Orig

ByJurisdictions Dinwiddie.gdb DW_APts — DW_APts_C_New

ByJurisdictions Dinwiddie.gdb DW_WS — DW_Swamps_WS_C_Orig

ByJurisdictions Dinwiddie.gdb DW_WS — DW_WS_C_New

ByJurisdictions Dinwiddie.gdb DW_Zo — Dw_Zo_C_Orig

ByJurisdictions Dinwiddie.gdb DW_Zo — DW_Zo_C_New

ByJurisdictions Dinwiddie.gdb DW_Lm — DW_Lm_CN_2013

ByJurisdictions Dinwiddie.gdb DW_Pk — DW_Pk_VDCR

ByJurisdictions Dinwiddie.gdb DW_Pk — DW_Pk_FromCensusLM_CN_2013

ByJurisdictions Dinwiddie.gdb DW_PA — DW_PA_ESRI

ByJurisdictions Dinwiddie.gdb DW_Borders — DW_Border

ByJurisdictions Dinwiddie.gdb DW_Zip — DW_Zip_CN_2013

ByJurisdictions Goochland.gdb GC_Lm — GC_Lm_CN_2013

ByJurisdictions Goochland.gdb GC_Par — GC_Par_C_New

ByJurisdictions Goochland.gdb GC_Par — GC_Par_C_Orig

ByJurisdictions Goochland.gdb GC_Par — GC_Par_FireRescue_C

ByJurisdictions Goochland.gdb GC_Par — GC_Par_FifeVillage_C

ByJurisdictions Goochland.gdb GC_Par — GC_Par_FifeClip_C

ByJurisdictions Goochland.gdb GC_Par — GC_Par_FifeOLD_C

ByJurisdictions Goochland.gdb GC_Par — GC_Par_Hadensville_C

ByJurisdictions Goochland.gdb GC_Par — GC_Par_Manakin_C

ByJurisdictions Goochland.gdb GC_Par — GC_Par_Oilville_C

ByJurisdictions Goochland.gdb GC_Par — GC_Par_SandyHook_C

ByJurisdictions Goochland.gdb GC_Par — GC_Par_StateFarm_C
61 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Goochland.gdb GC_Par — GC_Par_VillageAreasAll_C

ByJurisdictions Goochland.gdb GC_Par — GC_Par_VillageAreasAllOLD_C

ByJurisdictions Goochland.gdb GC_Pk — GC_Pk_C_New

ByJurisdictions Goochland.gdb GC_Pk — GC_Pk_C_Orig

ByJurisdictions Goochland.gdb GC_Pk — GC_Pk_FutureParkAtRiver_C

ByJurisdictions Goochland.gdb GC_PA — GC_PA_ESRI

ByJurisdictions Goochland.gdb GC_RR — GC_RR_C_New

ByJurisdictions Goochland.gdb GC_RR — GC_RR_CN_2013

ByJurisdictions Goochland.gdb GC_RR — GC_RR_ESRI

ByJurisdictions Goochland.gdb GC_RR — GC_RR_C_Orig

ByJurisdictions Goochland.gdb GC_RR — GC_RR_Combined

ByJurisdictions Goochland.gdb GC_RR — GC_RR_TNM

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_CN_2013

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_VD_1997

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_VG_2008Q3

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_VG_2014Q1

ByJurisdictions Goochland.gdb GC_Rds — GCa_Rds_C_New

ByJurisdictions Goochland.gdb GC_Rds — GCb_Rds_C_New

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_CentervilleRoadCloverleaf_C

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_FutureTransportation_C

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_OilvilleRoadCloverleaf_C

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_OilvilleRoadsForCloverleaf_C_Orig

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_RiverRoad_C_Orig

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_TNM

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_Combined

ByJurisdictions Goochland.gdb GC_Rds — GC_Rds_C_Orig

ByJurisdictions Goochland.gdb GC_Wb — GC_Wb_Li_C_New

ByJurisdictions Goochland.gdb GC_Wb — GC_Wb_Ply_C_New

ByJurisdictions Goochland.gdb GC_Wb — GC_Wb_Li_CN_2013

ByJurisdictions Goochland.gdb GC_Wb — GC_Wb_Ply_NHD_USGS

ByJurisdictions Goochland.gdb GC_Zo — GC_Zo_C_New

ByJurisdictions Goochland.gdb GC_Zo — GC_Zo_C_Orig

ByJurisdictions Goochland.gdb GC_Other — GC_FireStation5MileBuffer_C

ByJurisdictions Goochland.gdb GC_Other — GC_FireStation5MileBuffer_Ply_C

ByJurisdictions Goochland.gdb GC_Other — Gc_GeorgesTavernFireStation_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_Fife_village_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_FifeVillage_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_HadensvilleVillage_C
62 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_ManakinVillage_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_OilvilleVillage_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_OLDVillage_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_RiverRoadVillage_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_SandyHookVillage_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_VillageAll_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_UrbanDevArea_C

ByJurisdictions Goochland.gdb GC_Bu — GC_Bu_VillageAllOLD_C

ByJurisdictions Goochland.gdb GC_Co — GC_Co_C_2007

ByJurisdictions Goochland.gdb GC_WaS — GC_WaS_MajorSub_C

ByJurisdictions Goochland.gdb GC_Sl — GC_Sl_SteepSlope_C

ByJurisdictions Goochland.gdb GC_LUs — GC_LUs_OLDOriginal_C

ByJurisdictions Goochland.gdb GC_SW — GC_SW_Li_C

ByJurisdictions Goochland.gdb GC_SW — GC_SW_Ply_C

ByJurisdictions Goochland.gdb GC_Borders — GC_Border

ByJurisdictions Goochland.gdb GC_Prop — GC_Prop_VirginiaState_C

ByJurisdictions Goochland.gdb GC_Bud — GC_Bud_C

ByJurisdictions Goochland.gdb GC_Zip — GC_Zip_CN_2013

ByJurisdictions Hanover.gdb HN_APts — HN_APts_C_Orig

ByJurisdictions Hanover.gdb HN_APts — HN_APts_C_New

ByJurisdictions Hanover.gdb HN_Lm — HN_Lm_CN_2013

ByJurisdictions Hanover.gdb HN_Par — HN_Par_C_New

ByJurisdictions Hanover.gdb HN_Par — HN_Par_C_Orig

ByJurisdictions Hanover.gdb HN_Pk — HN_PK_VDCR

ByJurisdictions Hanover.gdb HN_Pk — HN_Pk_FromCensusLM_CN_2013

ByJurisdictions Hanover.gdb HN_PA — HN_PA_ESRI

ByJurisdictions Hanover.gdb HN_RR — HN_RR_C_New

ByJurisdictions Hanover.gdb HN_RR — HN_RR_CN_2013

ByJurisdictions Hanover.gdb HN_RR — HN_RR_ESRI

ByJurisdictions Hanover.gdb HN_RR — HN_RR_C_Orig

ByJurisdictions Hanover.gdb HN_RR — HN_RR_TNM

ByJurisdictions Hanover.gdb HN_RR — HN_RR_combined

ByJurisdictions Hanover.gdb HN_Rds — HN_Rds_CN_2013

ByJurisdictions Hanover.gdb HN_Rds — HN_Rds_VD_1997

ByJurisdictions Hanover.gdb HN_Rds — HN_Rds_VG_2008Q3

ByJurisdictions Hanover.gdb HN_Rds — HN_Rds_VG_2014Q1

ByJurisdictions Hanover.gdb HN_Rds — HNa_Rds_C_New

ByJurisdictions Hanover.gdb HN_Rds — HNb_Rds_C_New
63 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Hanover.gdb HN_Rds — HN_Rds_TNM

ByJurisdictions Hanover.gdb HN_Rds — HN_Rds_Combined

ByJurisdictions Hanover.gdb HN_Rds — HN_Rds_C_Orig

ByJurisdictions Hanover.gdb HN_Ut — HN_Ut_C_New

ByJurisdictions Hanover.gdb HN_Ut — HN_Ut_C_Orig

ByJurisdictions Hanover.gdb HN_Zo — HN_Zo_C_New

ByJurisdictions Hanover.gdb HN_Zo — HN_Zo_C_orig

ByJurisdictions Hanover.gdb HN_Bu — HN_Bu_C

ByJurisdictions Hanover.gdb HN_Bu — HN_Bu_AshlandCorp_C

ByJurisdictions Hanover.gdb HN_Other — HN_row_arc_C

ByJurisdictions Hanover.gdb HN_Other — HN_fences_arc_C

ByJurisdictions Hanover.gdb HN_Other — HN_Election_Ply_C

ByJurisdictions Hanover.gdb HN_Other — HN_easement_C

ByJurisdictions Hanover.gdb HN_Other — HN_cuplines_C

ByJurisdictions Hanover.gdb HN_Other — HN_Cup_Ply_C

ByJurisdictions Hanover.gdb HN_So — HN_So_C

ByJurisdictions Hanover.gdb HN_TC — HN_TC_C

ByJurisdictions Hanover.gdb HN_Borders — HN_Border_Li_C

ByJurisdictions Hanover.gdb HN_Borders — HN_Border

ByJurisdictions Hanover.gdb HN_Bud — HN_Bud_C

ByJurisdictions Hanover.gdb HN_Tp — HN_Tp_C

ByJurisdictions Hanover.gdb HN_Wb — HN_WB_C_Orig

ByJurisdictions Hanover.gdb HN_Wb — HN_Wb_Li_CN_2013

ByJurisdictions Hanover.gdb HN_Wb — HN_Wb_Ply_NHD_USGS

ByJurisdictions Hanover.gdb HN_Zip — HN_Zip_CN_2013

ByJurisdictions Henrico.gdb HC_Lm — HC_Lm_CN_2013

ByJurisdictions Henrico.gdb HC_Pk — HC_Pk_C_New

ByJurisdictions Henrico.gdb HC_Pk — HC_Pk_VDCR

ByJurisdictions Henrico.gdb HC_Pk — HC_Pk_C_Orig

ByJurisdictions Henrico.gdb HC_Pk — HC_Pk_FromCensusLM_CN_2013

ByJurisdictions Henrico.gdb HC_PA — HC_PA_ESRI

ByJurisdictions Henrico.gdb HC_RR — HC_RR_C_New

ByJurisdictions Henrico.gdb HC_RR — HC_RR_CN_2013

ByJurisdictions Henrico.gdb HC_RR — HC_RR_ESRI

ByJurisdictions Henrico.gdb HC_RR — HC_RR_C_Orig

ByJurisdictions Henrico.gdb HC_RR — HC_RR_TNM

ByJurisdictions Henrico.gdb HC_RR — HC_RR_Combined

ByJurisdictions Henrico.gdb HC_Rds — HC_Rds_C_New
64 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Henrico.gdb HC_Rds — HC_Rds_CN_2013

ByJurisdictions Henrico.gdb HC_Rds — HC_Rds_VD_1997

ByJurisdictions Henrico.gdb HC_Rds — HC_Rds_VG_2008Q3

ByJurisdictions Henrico.gdb HC_Rds — HC_Rds_VG_2014Q1

ByJurisdictions Henrico.gdb HC_Rds — HC_Rds_TNM

ByJurisdictions Henrico.gdb HC_Rds — HC_Rds_Combined

ByJurisdictions Henrico.gdb HC_Rds — HC_Rds_C_Orig

ByJurisdictions Henrico.gdb HC_St — HC_St_C_New

ByJurisdictions Henrico.gdb HC_St — HC_St_C_Orig

ByJurisdictions Henrico.gdb HC_Wb — HC_Wb_Li_C_New

ByJurisdictions Henrico.gdb HC_Wb — HC_Wb_Ply_C_New

ByJurisdictions Henrico.gdb HC_Wb — HC_Wb_C_Orig

ByJurisdictions Henrico.gdb HC_Wb — HC_Wb_Li_CN_2013

ByJurisdictions Henrico.gdb HC_Wb — HC_Wb_Ply_NHD_USGS

ByJurisdictions Henrico.gdb HC_Co — HC_Co_C_Orig

ByJurisdictions Henrico.gdb HC_Borders — HC_Border

ByJurisdictions Henrico.gdb HC_Zip — HC_Zip_CN_2013

ByJurisdictions HopeWellCity.gdb HW_Lm — HW_Lm_CN_2013

ByJurisdictions HopeWellCity.gdb HW_Pk — HW_Pk_VDCR

ByJurisdictions HopeWellCity.gdb HW_Pk — HW_Pk_FromCensusLM_CN_2013

ByJurisdictions HopeWellCity.gdb HW_PA — HW_PA_ESRI

ByJurisdictions HopeWellCity.gdb HW_RR — HW_RR_CN_2013

ByJurisdictions HopeWellCity.gdb HW_RR — HW_RR_ESRI

ByJurisdictions HopeWellCity.gdb HW_RR — HW_RR_TNM

ByJurisdictions HopeWellCity.gdb HW_RR — HW_RR_Combined

ByJurisdictions HopeWellCity.gdb HW_Rds — HW_Rds_CN_2013

ByJurisdictions HopeWellCity.gdb HW_Rds — HW_Rds_VG_2008Q3

ByJurisdictions HopeWellCity.gdb HW_Rds — HW_Rds_VG_2014Q1

ByJurisdictions HopeWellCity.gdb HW_Rds — HW_Rds_TNM

ByJurisdictions HopeWellCity.gdb HW_Rds — HW_Rds_Combined

ByJurisdictions HopeWellCity.gdb HW_Borders — HW_Border

ByJurisdictions HopeWellCity.gdb HW_Wb — HW_Wb_Li_CN_2013

ByJurisdictions HopeWellCity.gdb HW_Wb — HW_Wb_Ply_NHD_USGS

ByJurisdictions HopeWellCity.gdb HW_Zip — HW_Zip_CN_2013

ByJurisdictions JamesCityCounty.gdb JCC_Lm — JCC_Lm_CN_2013

ByJurisdictions JamesCityCounty.gdb JCC_Par — JCC_Par_C_New

ByJurisdictions JamesCityCounty.gdb JCC_Par — JCC_Par_C_Orig

ByJurisdictions JamesCityCounty.gdb JCC_Pk — JCC_Pk_C_New
65 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions JamesCityCounty.gdb JCC_Pk — JCC_Pk_VDCR

ByJurisdictions JamesCityCounty.gdb JCC_Pk — JCC_Pk_C_Orig

ByJurisdictions JamesCityCounty.gdb JCC_Pk — JCC_Pk_FromCensusLM_CN_2013

ByJurisdictions JamesCityCounty.gdb JCC_PA — JCC_PA_C_New

ByJurisdictions JamesCityCounty.gdb JCC_PA — JCC_PA_ESRI

ByJurisdictions JamesCityCounty.gdb JCC_PA — JCC_PA_C_Orig

ByJurisdictions JamesCityCounty.gdb JCC_RR — JCC_RR_C_New

ByJurisdictions JamesCityCounty.gdb JCC_RR — JCC_RR_CN_2013

ByJurisdictions JamesCityCounty.gdb JCC_RR — JCC_RR_ESRI

ByJurisdictions JamesCityCounty.gdb JCC_RR — JCC_RR_C_Orig

ByJurisdictions JamesCityCounty.gdb JCC_RR — JCC_RR_TNM

ByJurisdictions JamesCityCounty.gdb JCC_RR — JCC_RR_Combined

ByJurisdictions JamesCityCounty.gdb JCC_Rds — JCC_Rds_C_New

ByJurisdictions JamesCityCounty.gdb JCC_Rds — JCC_Rds_CN_2013

ByJurisdictions JamesCityCounty.gdb JCC_Rds — JCC_Rds_VD_1997

ByJurisdictions JamesCityCounty.gdb JCC_Rds — JCC_Rds_VG_2008Q3

ByJurisdictions JamesCityCounty.gdb JCC_Rds — JCC_Rds_VG_2014Q1

ByJurisdictions JamesCityCounty.gdb JCC_Rds — JCC_Rds_C_Orig

ByJurisdictions JamesCityCounty.gdb JCC_Rds — JCC_Rds_TNM

ByJurisdictions JamesCityCounty.gdb JCC_Rds — JCC_Rds_Combined

ByJurisdictions JamesCityCounty.gdb JCC_St — JCC_St_C_New

ByJurisdictions JamesCityCounty.gdb JCC_Wb — JCC_Wb_Li_C_New

ByJurisdictions JamesCityCounty.gdb JCC_Wb — JCC_Wb_C_Orig

ByJurisdictions JamesCityCounty.gdb JCC_Wb — JCC_Wb_Li_CN_2013

ByJurisdictions JamesCityCounty.gdb JCC_Wb — JCC_Wb_Ply_NHD_USGS

ByJurisdictions JamesCityCounty.gdb JCC_WS — JCC_WS_C_New

ByJurisdictions JamesCityCounty.gdb JCC_WS — JCC_WS_Marsh_C

ByJurisdictions JamesCityCounty.gdb JCC_Zo — JCC_Zo_C_New

ByJurisdictions JamesCityCounty.gdb JCC_Zo — JCC_Zo_C_Orig

ByJurisdictions JamesCityCounty.gdb JCC_Other — jcc_bmps_C

ByJurisdictions JamesCityCounty.gdb JCC_Other — JCC_EdgeOfPavement_C

ByJurisdictions JamesCityCounty.gdb JCC_Other — JCC_Subdivisions_C

ByJurisdictions JamesCityCounty.gdb JCC_Other — jcc_afd_C

ByJurisdictions JamesCityCounty.gdb JCC_Gfc — JCC_Gfc_C

ByJurisdictions JamesCityCounty.gdb JCC_Tr — JCC_TransitLines_C

ByJurisdictions JamesCityCounty.gdb JCC_Tr — JCC_TransitStops_C

ByJurisdictions JamesCityCounty.gdb JCC_Borders — JCC_Border

ByJurisdictions JamesCityCounty.gdb JCC_Bud — JCC_Bud_C
66 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions JamesCityCounty.gdb JCC_Elv — JCC_Elv_Spot_C

ByJurisdictions JamesCityCounty.gdb JCC_Zip — JCC_Zip_CN_2013

ByJurisdictions KingAndQueen.gdb KQ_Lm — KQ_Lm_CN_2013

ByJurisdictions KingAndQueen.gdb KQ_PA — KQ_PA_ESRI

ByJurisdictions KingAndQueen.gdb KQ_Rds — KQ_Rds_CN_2013

ByJurisdictions KingAndQueen.gdb KQ_Rds — KQ_Rds_VD_1997

ByJurisdictions KingAndQueen.gdb KQ_Rds — KQ_Rds_VG_2008Q3

ByJurisdictions KingAndQueen.gdb KQ_Rds — KQ_Rds_VG_2014Q1

ByJurisdictions KingAndQueen.gdb KQ_Rds — KQ_Rds_TNM

ByJurisdictions KingAndQueen.gdb KQ_Rds — KQ_Rds_Combined

ByJurisdictions KingAndQueen.gdb KQ_Borders — KQ_Border

ByJurisdictions KingAndQueen.gdb KQ_Wb — KQ_Wb_Li_CN_2013_1

ByJurisdictions KingAndQueen.gdb KQ_Wb — KQ_Wb_Ply_NHD_USGS

ByJurisdictions KingAndQueen.gdb KQ_Zip — KQ_Zip_CN_2013

ByJurisdictions KingGeorge.gdb KG_Lm — KG_Lm_CN_2013

ByJurisdictions KingGeorge.gdb KG_PA — KG_PA_ESRI

ByJurisdictions KingGeorge.gdb KG_RR — KG_RR_CN_2013

ByJurisdictions KingGeorge.gdb KG_RR — KG_RR_ESRI

ByJurisdictions KingGeorge.gdb KG_RR — KG_RR_TNM

ByJurisdictions KingGeorge.gdb KG_RR — KG_RR_Combined

ByJurisdictions KingGeorge.gdb KG_Rds — KG_Rds_CN_2013

ByJurisdictions KingGeorge.gdb KG_Rds — KG_Rds_VD_1997

ByJurisdictions KingGeorge.gdb KG_Rds — KG_Rds_VG_2008Q3

ByJurisdictions KingGeorge.gdb KG_Rds — KG_Rds_VG_2014Q1

ByJurisdictions KingGeorge.gdb KG_Rds — KG_Rds_Combined

ByJurisdictions KingGeorge.gdb KG_Rds — KG_Rds_TNM

ByJurisdictions KingGeorge.gdb KG_Borders — KG_Border

ByJurisdictions KingGeorge.gdb KG_Wb — KG_Wb_Li_CN_2013

ByJurisdictions KingGeorge.gdb KG_Wb — KG_Wb_Ply_NHD_USGS

ByJurisdictions KingGeorge.gdb KG_Zip — KG_Zip_CN_2013

ByJurisdictions KingGeorge.gdb KG_Pk — KG_Pk_FromCensusLM_CN_2013

ByJurisdictions KingWilliam.gdb KW_Lm — KW_Lm_CN_2013

ByJurisdictions KingWilliam.gdb KW_PA — KW_PA_ESRI

ByJurisdictions KingWilliam.gdb KW_RR — KW_RR_CN_2013

ByJurisdictions KingWilliam.gdb KW_RR — KW_RR_ESRI

ByJurisdictions KingWilliam.gdb KW_RR — KW_RR_TNM

ByJurisdictions KingWilliam.gdb KW_RR — KW_RR_Combined

ByJurisdictions KingWilliam.gdb KW_Rds — KW_Rds_CN_2013
67 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions KingWilliam.gdb KW_Rds — KW_Rds_VD_1997

ByJurisdictions KingWilliam.gdb KW_Rds — KW_Rds_VG_2008Q3

ByJurisdictions KingWilliam.gdb KW_Rds — KW_Rds_VG_2014Q1

ByJurisdictions KingWilliam.gdb KW_Rds — KW_Rds_Combined

ByJurisdictions KingWilliam.gdb KW_Rds — KW_Rds_TNM

ByJurisdictions KingWilliam.gdb KW_Borders — KW_Border

ByJurisdictions KingWilliam.gdb KW_Wb — KW_Wb_Li_CN_2013

ByJurisdictions KingWilliam.gdb KW_Wb — KW_Wb_Ply_NHD_USGS

ByJurisdictions KingWilliam.gdb KW_Zip — KW_Zip_CN_2013

ByJurisdictions Lousia.gdb DW_APts — LU_APts_C_Orig

ByJurisdictions Lousia.gdb DW_APts — LU_APts_C_New

ByJurisdictions Lousia.gdb LU_For — LU_For_VDF

ByJurisdictions Lousia.gdb LU_FLU — LU_FLU_C_New

ByJurisdictions Lousia.gdb LU_FLU — LU_FLU_TownOfLouisa_C_Orig

ByJurisdictions Lousia.gdb LU_Lm — LU_Lm_CN_2013

ByJurisdictions Lousia.gdb LU_Par — LU_Par_C_New

ByJurisdictions Lousia.gdb LU_Par — LU_Par_C_Orig

ByJurisdictions Lousia.gdb LU_PA — LU_PA_ESRI

ByJurisdictions Lousia.gdb LU_RR — LU_RR_C_new

ByJurisdictions Lousia.gdb LU_RR — LU_RR_CN_2013

ByJurisdictions Lousia.gdb LU_RR — LU_RR_ESRI

ByJurisdictions Lousia.gdb LU_RR — LU_RR_C_Orig

ByJurisdictions Lousia.gdb LU_RR — LU_RR_TNM

ByJurisdictions Lousia.gdb LU_RR — LU_RR_Combined

ByJurisdictions Lousia.gdb LU_Rds — LU_Rds_C_New

ByJurisdictions Lousia.gdb LU_Rds — LU_Rds_CN_2013

ByJurisdictions Lousia.gdb LU_Rds — LU_Rds_VD_1997

ByJurisdictions Lousia.gdb LU_Rds — LU_Rds_VG_2008Q3

ByJurisdictions Lousia.gdb LU_Rds — LU_Rds_VG_2014Q1

ByJurisdictions Lousia.gdb LU_Rds — LU_Rds_MainRoads_C

ByJurisdictions Lousia.gdb LU_Rds — LU_Rds_Combined

ByJurisdictions Lousia.gdb LU_Rds — LU_Rds_TNM

ByJurisdictions Lousia.gdb LU_Rds — LU_Rds_C_Orig

ByJurisdictions Lousia.gdb LU_Ut — LU_Ut_C_New

ByJurisdictions Lousia.gdb LU_Ut — LU_Ut_C_Orig

ByJurisdictions Lousia.gdb LU_Wb — LU_Wb_Ply_C_New

ByJurisdictions Lousia.gdb LU_Wb — LUb_Wb_C_Orig

ByJurisdictions Lousia.gdb LU_Wb — LUa_Wb_C_Orig
68 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Lousia.gdb LU_Wb — LU_Wb_Li_CN_2013

ByJurisdictions Lousia.gdb LU_Wb — LU_Wb_Ply_NHD_USGS

ByJurisdictions Lousia.gdb LU_Zo — LUa_Zo_C_New

ByJurisdictions Lousia.gdb LU_Zo — LUb_Zo_C_New

ByJurisdictions Lousia.gdb LU_Zo — LU_Zo_C_Orig

ByJurisdictions Lousia.gdb LU_Zo — LU_Zo_MIN_C

ByJurisdictions Lousia.gdb LU_Zo — LUb_Zo_TownOfLouisa_C_Orig

ByJurisdictions Lousia.gdb LU_Other — LU_AFD_C

ByJurisdictions Lousia.gdb LU_Other — LU_AirStrip_C

ByJurisdictions Lousia.gdb LU_Other — LU_Bridge_C

ByJurisdictions Lousia.gdb LU_Other — LU_DRIVEWAY_C

ByJurisdictions Lousia.gdb LU_Other — LU_FloodIndex_C

ByJurisdictions Lousia.gdb LU_Other — LU_GrowthArea_C

ByJurisdictions Lousia.gdb LU_Other — LU_Hydrants_C

ByJurisdictions Lousia.gdb LU_Other — LU_PrecinctVotingLocations_C

ByJurisdictions Lousia.gdb LU_Other — LU_TaxMapGrid_C

ByJurisdictions Lousia.gdb LU_Other — LU_VotingDistricts_C

ByJurisdictions Lousia.gdb LU_Other — LU_VotingPrecincts_C

ByJurisdictions Lousia.gdb LU_Borders — LU_Border

ByJurisdictions Lousia.gdb LU_Bu — LU_Bu_C

ByJurisdictions Lousia.gdb LU_Bu — LU_Bu_FireDeptDistrict_C

ByJurisdictions Lousia.gdb LU_Bu — LU_Bu_HistoricGreenSpringsDistrict_C

ByJurisdictions Lousia.gdb LU_Bu — LU_Bu_louctyarea_C

ByJurisdictions Lousia.gdb LU_Bu — LU_Bu_MIN_C

ByJurisdictions Lousia.gdb LU_Bu — Lu_Bu_Ply_C

ByJurisdictions Lousia.gdb LU_Bu — Lu_Bu_PO_C

ByJurisdictions Lousia.gdb LU_Bu — LU_Bu_RescueSquadDistrict_C

ByJurisdictions Lousia.gdb LU_Bu — LU_Bu_TownOfLouisa_C

ByJurisdictions Lousia.gdb LU_Bud — LU_Bud_C

ByJurisdictions Lousia.gdb LU_Co — LU_Co_C_Orig

ByJurisdictions Lousia.gdb LU_So — LU_So_C

ByJurisdictions Lousia.gdb LU_St — LU_St_C_Orig

ByJurisdictions Lousia.gdb LU_FlPl — LU_FlPl_Flood100yr_C

ByJurisdictions Lousia.gdb LU_Zip — LU_Zip_CN_2013

ByJurisdictions NewKent.gdb NK_APts — NK_APts_C_Orig

ByJurisdictions NewKent.gdb NK_APts — NK_APts_C_new

ByJurisdictions NewKent.gdb NK_Lm — NK_Lm_CN_2013

ByJurisdictions NewKent.gdb NK_Par — NK_Par_AFD_C
69 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions NewKent.gdb NK_Par — NK_Par_Septic_C

ByJurisdictions NewKent.gdb NK_Par — NK_Par_Joined2VISION_C

ByJurisdictions NewKent.gdb NK_Par — NK_Par_C_Orig

ByJurisdictions NewKent.gdb NK_Par — NKa_Par_C_New

ByJurisdictions NewKent.gdb NK_Par — NKb_Par_C_New

ByJurisdictions NewKent.gdb NK_PA — NK_PA_C_Orig

ByJurisdictions NewKent.gdb NK_PA — NK_PA_ESRI

ByJurisdictions NewKent.gdb NK_RR — NK_RR_C_Orig

ByJurisdictions NewKent.gdb NK_RR — NK_RR_CN_2013

ByJurisdictions NewKent.gdb NK_RR — NK_RR_ESRI

ByJurisdictions NewKent.gdb NK_RR — NK_RR_TNM

ByJurisdictions NewKent.gdb NK_RR — NK_RR_Combined

ByJurisdictions NewKent.gdb NK_Rds — NK_Rds_Scenic_C

ByJurisdictions NewKent.gdb NK_Rds — NK_Rds_Anno_C

ByJurisdictions NewKent.gdb NK_Rds — NK_Rds_C_New

ByJurisdictions NewKent.gdb NK_Rds — NK_Rds_CN_2013

ByJurisdictions NewKent.gdb NK_Rds — NK_Rds_VD_1997

ByJurisdictions NewKent.gdb NK_Rds — NK_Rds_VG_2008Q3

ByJurisdictions NewKent.gdb NK_Rds — NK_Rds_VG_2014Q1

ByJurisdictions NewKent.gdb NK_Rds — NK_Rds_TNM

ByJurisdictions NewKent.gdb NK_Rds — NK_Rds_Combined

ByJurisdictions NewKent.gdb NK_Zo — NK_Zo_C_Orig

ByJurisdictions NewKent.gdb NK_Zo — NK_Zo_OverlayDistricts_C

ByJurisdictions NewKent.gdb NK_Zo — NK_Zo_C_New

ByJurisdictions NewKent.gdb NK_Other — NK_BrickshireESAYellow_C

ByJurisdictions NewKent.gdb NK_Other — NK_BrickshireESAOrange_C

ByJurisdictions NewKent.gdb NK_Other — NK_BrickshireESARed_C

ByJurisdictions NewKent.gdb NK_Other — NK_BrickshireESAGreen_C

ByJurisdictions NewKent.gdb NK_Other — NK_BrickshireESABlue_C

ByJurisdictions NewKent.gdb NK_Other — NK_BikeThreeQuarterCenturyRide_C

ByJurisdictions NewKent.gdb NK_Other — NK_RrPDC_RCL_C

ByJurisdictions NewKent.gdb NK_Other — NK_MileMarkers_C

ByJurisdictions NewKent.gdb NK_Other — NK_Impedence_C

ByJurisdictions NewKent.gdb NK_Other — NK_FirestationBuff8_C

ByJurisdictions NewKent.gdb NK_Other — NK_FirestationBuff6_C

ByJurisdictions NewKent.gdb NK_Other — NK_BikeQuarterCenturyRide_C

ByJurisdictions NewKent.gdb NK_Other — NK_Driveway_C

ByJurisdictions NewKent.gdb NK_Other — NK_BikesSigns_C
70 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions NewKent.gdb NK_Other — NK_BikeMetricCenturyRide_C

ByJurisdictions NewKent.gdb NK_Other — NK_BikeHalfCenturyRide_C

ByJurisdictions NewKent.gdb NK_Other — NK_BikeFamilyFunRide_C

ByJurisdictions NewKent.gdb NK_Other — NK_BikeCenturyRide_C

ByJurisdictions NewKent.gdb NK_Other — NK_Bridge_C

ByJurisdictions NewKent.gdb NK_Other — NK_Ancillary_C

ByJurisdictions NewKent.gdb NK_Other — NK_AddressGrid_C

ByJurisdictions NewKent.gdb NK_Other — NK_AddressErrata_C

ByJurisdictions NewKent.gdb NK_Other — NK_ESZ_C

ByJurisdictions NewKent.gdb NK_Other — NK_AddressAnno_C

ByJurisdictions NewKent.gdb NK_Other — NK_AddressAccess_Li_C

ByJurisdictions NewKent.gdb NK_Other — NK_VBMP_2009_C

ByJurisdictions NewKent.gdb NK_Other — NK_VA24kQuad_C

ByJurisdictions NewKent.gdb NK_Other — NK_OrthoIndex_C

ByJurisdictions NewKent.gdb NK_Other — NK_VADOQQ_C

ByJurisdictions NewKent.gdb NK_Other — NK_CBay_C

ByJurisdictions NewKent.gdb NK_Other — NK_Index600Scale_C

ByJurisdictions NewKent.gdb NK_Other — NK_Index200Scale_C

ByJurisdictions NewKent.gdb NK_Other — NK_Subdivisions_C

ByJurisdictions NewKent.gdb NK_Other — NK_BlockCuts_C

ByJurisdictions NewKent.gdb NK_Other — NK_Inserts_C

ByJurisdictions NewKent.gdb NK_Other — NK_Hooks_C

ByJurisdictions NewKent.gdb NK_Other — NK_Blocks_C

ByJurisdictions NewKent.gdb NK_Borders — NK_Border

ByJurisdictions NewKent.gdb NK_Bud — NK_Bud_C

ByJurisdictions NewKent.gdb NK_Wb — NK_Wb_Li_CN_2013

ByJurisdictions NewKent.gdb NK_Wb — NK_Wb_Ply_NHD_USGS

ByJurisdictions NewKent.gdb NK_Zip — NK_Zip_CN_2013

ByJurisdictions Nottoway.gdb NW_APts — NW_APts_C_New

ByJurisdictions Nottoway.gdb NW_APts — NW_APts_C_Orig

ByJurisdictions Nottoway.gdb NW_Lm — NW_Lm_CN_2013

ByJurisdictions Nottoway.gdb NW_Par — NW_Par_C_New

ByJurisdictions Nottoway.gdb NW_Par — NW_Par_C_Orig

ByJurisdictions Nottoway.gdb NW_PA — NW_PA_ESRI

ByJurisdictions Nottoway.gdb NW_RR — NW_RR_C_New

ByJurisdictions Nottoway.gdb NW_RR — NW_RR_CN_2013

ByJurisdictions Nottoway.gdb NW_RR — NW_RR_ESRI

ByJurisdictions Nottoway.gdb NW_RR — NW_RR_C_Orig
71 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Nottoway.gdb NW_RR — NW_RR_TNM

ByJurisdictions Nottoway.gdb NW_RR — NW_RR_Combined

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_C_New

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_CN_2013

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_VD_1997

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_VG_2008Q3

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_VG_2014Q1

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_C_Orig

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_RightofWay_C

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_Unamed_C

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_Combined

ByJurisdictions Nottoway.gdb NW_Rds — NW_Rds_TNM

ByJurisdictions Nottoway.gdb NW_Wb — NW_Wb_Li_C_new

ByJurisdictions Nottoway.gdb NW_Wb — NW_Wb_Ply_C_New

ByJurisdictions Nottoway.gdb NW_Wb — NW_Wb_C_2

ByJurisdictions Nottoway.gdb NW_Wb — NW_Wb_Lakes_C_Orig

ByJurisdictions Nottoway.gdb NW_Wb — NW_Wb_Li_CN_2013

ByJurisdictions Nottoway.gdb NW_Wb — NW_Wb_Ply_NHD_USGS

ByJurisdictions Nottoway.gdb NW_Other — NW_Areas_C

ByJurisdictions Nottoway.gdb NW_Other — NW_Bridge_Pts_C

ByJurisdictions Nottoway.gdb NW_Other — NW_Cartography_C

ByJurisdictions Nottoway.gdb NW_Other — NW_CartographyInserts_C

ByJurisdictions Nottoway.gdb NW_Other — NW_CellL_C

ByJurisdictions Nottoway.gdb NW_Other — NW_Cemetery_C

ByJurisdictions Nottoway.gdb NW_Other — NW_Church_C

ByJurisdictions Nottoway.gdb NW_Other — NW_ESNS_C

ByJurisdictions Nottoway.gdb NW_Other — NW_Grid_C

ByJurisdictions Nottoway.gdb NW_Other — NW_GridInserts_C

ByJurisdictions Nottoway.gdb NW_Other — NW_HouseCompleteReverseGeocod_C

ByJurisdictions Nottoway.gdb NW_Other — NW_HouseGrid_C

ByJurisdictions Nottoway.gdb NW_Other — NW_LandHook_C

ByJurisdictions Nottoway.gdb NW_Other — NW_LotAnno_C

ByJurisdictions Nottoway.gdb NW_Other — NW_LotAnnoInserts_C

ByJurisdictions Nottoway.gdb NW_Other — NW_VbmpTiles_2009

ByJurisdictions Nottoway.gdb NW_Borders — NW_Border

ByJurisdictions Nottoway.gdb NW_Bu — NW_Bu_C

ByJurisdictions Nottoway.gdb NW_Riv — NW_RIV_Wb_C

ByJurisdictions Nottoway.gdb NW_Zip — NW_Zip_C
72 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Nottoway.gdb NW_Zip — NW_Zip_CN_2013

ByJurisdictions Petersburg.gdb PB_Lm — PB_Lm_CN_2013

ByJurisdictions Petersburg.gdb PB_Pk — PB_Pk_VDCR

ByJurisdictions Petersburg.gdb PB_Pk — PB_Pk_FromCensusLM_CN_2013

ByJurisdictions Petersburg.gdb PB_PA — PB_PA_ESRI

ByJurisdictions Petersburg.gdb PB_RR — PB_RR_CN_2013

ByJurisdictions Petersburg.gdb PB_RR — PB_RR_ESRI

ByJurisdictions Petersburg.gdb PB_RR — PB_RR_TNM

ByJurisdictions Petersburg.gdb PB_RR — PB_RR_Combined

ByJurisdictions Petersburg.gdb PB_Rds — PB_Rds_CN_2013

ByJurisdictions Petersburg.gdb PB_Rds — PB_Rds_VG_2008Q3

ByJurisdictions Petersburg.gdb PB_Rds — PB_Rds_VG_2014Q1

ByJurisdictions Petersburg.gdb PB_Rds — PB_Rds_TNM

ByJurisdictions Petersburg.gdb PB_Rds — PB_Rds_Combined

ByJurisdictions Petersburg.gdb PB_Borders — PB_Border

ByJurisdictions Petersburg.gdb PB_Wb — PB_Wb_Li_CN_2013

ByJurisdictions Petersburg.gdb PB_Wb — PB_Wb_Ply_NHD_USGS

ByJurisdictions Petersburg.gdb PB_Zip — PB_Zip_CN_2013

ByJurisdictions Powhantan.gdb PW_FLU — PW_FLU_C_Orig

ByJurisdictions Powhantan.gdb PW_FLU — PW_FLU_C_New

ByJurisdictions Powhantan.gdb PW_Lm — PW_Lm_CN_2013

ByJurisdictions Powhantan.gdb PW_LUs — PW_LUs_C_Orig

ByJurisdictions Powhantan.gdb PW_LUs — PW_LUs_C_New

ByJurisdictions Powhantan.gdb PW_Pk — PWb_Pk_PowhatanStatePark_C_Orig

ByJurisdictions Powhantan.gdb PW_Pk — PWa_Pk_FightingCreek_C_Orig

ByJurisdictions Powhantan.gdb PW_Pk — PWa_Pk_C_New

ByJurisdictions Powhantan.gdb PW_Pk — PWb_Pk_C_New

ByJurisdictions Powhantan.gdb PW_PA — PW_PA_C_Orig

ByJurisdictions Powhantan.gdb PW_PA — PW_PA_C_New

ByJurisdictions Powhantan.gdb PW_PA — PW_PA_ESRI

ByJurisdictions Powhantan.gdb PW_RR — PW_RR_CN_2013

ByJurisdictions Powhantan.gdb PW_RR — PW_RR_ESRI

ByJurisdictions Powhantan.gdb PW_RR — PW_RR_TNM

ByJurisdictions Powhantan.gdb PW_RR — PW_RR_Combined

ByJurisdictions Powhantan.gdb PW_Rds — PW_Rds_C_Orig

ByJurisdictions Powhantan.gdb PW_Rds — PW_Rds_C_New

ByJurisdictions Powhantan.gdb PW_Rds — PW_Rds_CN_2013

ByJurisdictions Powhantan.gdb PW_Rds — PW_Rds_VD_1997
73 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Powhantan.gdb PW_Rds — PW_Rds_VG_2008Q3

ByJurisdictions Powhantan.gdb PW_Rds — PW_Rds_VG_2014Q1

ByJurisdictions Powhantan.gdb PW_Rds — PW_Rds_Combined

ByJurisdictions Powhantan.gdb PW_Rds — PW_Rds_TNM

ByJurisdictions Powhantan.gdb PW_St — PW_St_C_Orig

ByJurisdictions Powhantan.gdb PW_St — PW_St_C_New

ByJurisdictions Powhantan.gdb PW_Wb — PWa_Wb_Li_C_New

ByJurisdictions Powhantan.gdb PW_Wb — PWb_Wb_Li_C_New

ByJurisdictions Powhantan.gdb PW_Wb — PW_Wb_Li_C_Orig

ByJurisdictions Powhantan.gdb PW_Wb — PW_Wb_Li_CN_2013

ByJurisdictions Powhantan.gdb PW_Wb — PW_Wb_Ply_NHD_USGS

ByJurisdictions Powhantan.gdb PW_WS — PW_WS_C_Orig

ByJurisdictions Powhantan.gdb PW_WS — PW_WS_C_New

ByJurisdictions Powhantan.gdb PW_Other — PW_PWMA_C

ByJurisdictions Powhantan.gdb PW_Borders — PW_Border

ByJurisdictions Powhantan.gdb PW_FlPL — PW_FLPl_FloodHazard_C

ByJurisdictions Powhantan.gdb PW_Zip — PW_Zip_CN_2013

ByJurisdictions PrinceGeorge.gdb PG_Lm — PG_Lm_CN_2013

ByJurisdictions PrinceGeorge.gdb PG_Par — PG_Par_C_Orig

ByJurisdictions PrinceGeorge.gdb PG_Par — PG_Par_C_new

ByJurisdictions PrinceGeorge.gdb PG_Pk — PG_Pk_Pts_C_Orig

ByJurisdictions PrinceGeorge.gdb PG_Pk — PG_Pk_C_New

ByJurisdictions PrinceGeorge.gdb PG_Pk — PG_Pk_VDCR

ByJurisdictions PrinceGeorge.gdb PG_Pk — PG_Pk_FromCensusLM_CN_2013

ByJurisdictions PrinceGeorge.gdb PG_PA — PG_PA_ESRI

ByJurisdictions PrinceGeorge.gdb PG_RR — PG_RR_CN_2103

ByJurisdictions PrinceGeorge.gdb PG_RR — PG_RR_ESRI

ByJurisdictions PrinceGeorge.gdb PG_RR — PG_RR_TNM

ByJurisdictions PrinceGeorge.gdb PG_Rds — PG_Rds_C_Orig

ByJurisdictions PrinceGeorge.gdb PG_Rds — PG_Rds_C_New

ByJurisdictions PrinceGeorge.gdb PG_Rds — PG_Rds_CN_2013

ByJurisdictions PrinceGeorge.gdb PG_Rds — PG_Rds_VD_1997

ByJurisdictions PrinceGeorge.gdb PG_Rds — PG_Rds_VG_2008Q3

ByJurisdictions PrinceGeorge.gdb PG_Rds — PG_Rds_VG_2014Q1

ByJurisdictions PrinceGeorge.gdb PG_Rds — PG_Rds_TNM

ByJurisdictions PrinceGeorge.gdb PG_Rds — PG_Rds_Combined

ByJurisdictions PrinceGeorge.gdb PG_St — PG_St_C_Orig

ByJurisdictions PrinceGeorge.gdb PG_St — PG_St_C_New
74 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions PrinceGeorge.gdb PG_Wb — PG_Wb_Ply_C_Orig

ByJurisdictions PrinceGeorge.gdb PG_Wb — PG_Wb_Ply_C_new

ByJurisdictions PrinceGeorge.gdb PG_Wb — PG_Wb_Li_CN_2013

ByJurisdictions PrinceGeorge.gdb PG_Wb — PG_Wb_Ply_NHD_USGS

ByJurisdictions PrinceGeorge.gdb PG_WS — PG_WS_C_Orig

ByJurisdictions PrinceGeorge.gdb PG_WS — PG_WS_C_new

ByJurisdictions PrinceGeorge.gdb PG_Borders — PG_Border

ByJurisdictions PrinceGeorge.gdb PG_Zip — PG_Zip_CN_2013

ByJurisdictions RichmondCity.gdb RC_APts — RC_APts_C_Orig

ByJurisdictions RichmondCity.gdb RC_APts — RC_APts_C_new

ByJurisdictions RichmondCity.gdb RC_FLU — RC_FLU_C_Orig

ByJurisdictions RichmondCity.gdb RC_FLU — RC_FLU_C_New

ByJurisdictions RichmondCity.gdb RC_Lm — RC_Lm_Pts_C

ByJurisdictions RichmondCity.gdb RC_Lm — RC_Lm_C

ByJurisdictions RichmondCity.gdb RC_Lm — RC_Lm_CN_2013

ByJurisdictions RichmondCity.gdb RC_LUs — RC_LUs_2008_C_Orig

ByJurisdictions RichmondCity.gdb RC_LUs — RC_LUs_C_New

ByJurisdictions RichmondCity.gdb RC_Par — RC_Par_C_Orig

ByJurisdictions RichmondCity.gdb RC_Par — RC_Par_C_New

ByJurisdictions RichmondCity.gdb RC_Pk — RC_Pk_C_Orig

ByJurisdictions RichmondCity.gdb RC_Pk — RC_Pk_C_New

ByJurisdictions RichmondCity.gdb RC_Pk — RC_Pk_VDCR

ByJurisdictions RichmondCity.gdb RC_Pk — RC_Pk_FromCensusLM_CN_2013

ByJurisdictions RichmondCity.gdb RC_PA — RC_PA_C_Orig

ByJurisdictions RichmondCity.gdb RC_PA — RC_PA_C_New

ByJurisdictions RichmondCity.gdb RC_PA — RC_PA_ESRI

ByJurisdictions RichmondCity.gdb RC_RR — RC_RR_CN_2013

ByJurisdictions RichmondCity.gdb RC_RR — RC_RR_ESRI

ByJurisdictions RichmondCity.gdb RC_RR — RC_RR_TNM

ByJurisdictions RichmondCity.gdb RC_Rds — RC_Rds_C_Orig

ByJurisdictions RichmondCity.gdb RC_Rds — RC_Rds_C_New

ByJurisdictions RichmondCity.gdb RC_Rds — RC_Rds_CN_2013

ByJurisdictions RichmondCity.gdb RC_Rds — RC_Rds_VD_1997

ByJurisdictions RichmondCity.gdb RC_Rds — RC_Rds_VG_2008Q3

ByJurisdictions RichmondCity.gdb RC_Rds — RC_Rds_VG_2014Q1

ByJurisdictions RichmondCity.gdb RC_Rds — RC_Rds_TNM

ByJurisdictions RichmondCity.gdb RC_Rds — RC_Rds_Combined

ByJurisdictions RichmondCity.gdb RC_WS — RC_WS_C_Orig
75 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions RichmondCity.gdb RC_WS — RC_WS_C_New

ByJurisdictions RichmondCity.gdb RC_Zo — RC_Zo_C_Orig

ByJurisdictions RichmondCity.gdb RC_Zo — RC_Zo_C_New

ByJurisdictions RichmondCity.gdb RC_Other — RC_VotingStations_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_VoterPrecinct_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_TrafficZone2000_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_Tract2000_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_StateSenateDistrict_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_StateHouseDistrict_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_SpecialUsePermit_Pts_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_SchoolZoneMiddle_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_SchoolZoneHigh_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_SchoolZoneElementary_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_RedevConservation_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_PoliceSector_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_PolicePrecinct_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_PlanningDistrict_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_NIBTarget_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_NIBImpact_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_Neighborhood_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_HousingOpportunityArea_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_HistoricDistrictNR_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_HistoricDistrictCity_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_FireDistrict_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_EnterpriseZone_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_DispatchZone_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_DesignOverlay_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_CouncilDistrict_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_CongressDistrict_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_CommunityUnitPlan_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_CodeInspection_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_CivicAssociation_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_CDBG_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_CARE_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_BlockGroup2000_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_Block2000_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_CommunityCenters_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_RMA_C
76 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions RichmondCity.gdb RC_Other — RC_IDA_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_DFIRM_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_CrossSection_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_BikePath_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_WalkingPath_C

ByJurisdictions RichmondCity.gdb RC_Other — RC_ExportOutput_C

ByJurisdictions RichmondCity.gdb RC_So — RC_So_C

ByJurisdictions RichmondCity.gdb RC_Sch — RC_Sch_Public_C

ByJurisdictions RichmondCity.gdb RC_FlPl — RC_FlPl_500_C

ByJurisdictions RichmondCity.gdb RC_FlPl — RC_FlPl_100_C

ByJurisdictions RichmondCity.gdb RC_Zip — RC_Zip_C

ByJurisdictions RichmondCity.gdb RC_Zip — RC_Zip_CN_2013

ByJurisdictions RichmondCity.gdb RC_Bu — RC_Bu_C

ByJurisdictions RichmondCity.gdb RC_Borders — RC_Border

ByJurisdictions RichmondCity.gdb RC_Co — RC_Co_C_Orig

ByJurisdictions RichmondCity.gdb RC_Elv — RC_Elv_BaseFloodElevation_C

ByJurisdictions RichmondCity.gdb RC_For — RC_For_C

ByJurisdictions RichmondCity.gdb RC_WaS — RC_WaS_Sub_C

ByJurisdictions RichmondCity.gdb RC_WaS — RC_WaS_C

ByJurisdictions RichmondCity.gdb RC_Wb — RC_Wb_Li_CN_2013

ByJurisdictions RichmondCity.gdb RC_Wb — RC_Wb_Ply_NHD_USGS

ByJurisdictions Spotsylvania.gdb SP_APts — SP_APts_C_Orig

ByJurisdictions Spotsylvania.gdb SP_Lm — SP_Lm_CN_2013

ByJurisdictions Spotsylvania.gdb SP_Par — SP_Par_C_New

ByJurisdictions Spotsylvania.gdb SP_Par — SP_Par_C_Orig

ByJurisdictions Spotsylvania.gdb SP_Pk — SP_Pk_C_New

ByJurisdictions Spotsylvania.gdb SP_Pk — SP_Pk_VDCR

ByJurisdictions Spotsylvania.gdb SP_Pk — SP_Pk_LakeAnnaStatePark_C_Orig

ByJurisdictions Spotsylvania.gdb SP_Pk — SP_Pk_FromCensusLM_CN_2013

ByJurisdictions Spotsylvania.gdb SP_PA — SP_PA_ESRI

ByJurisdictions Spotsylvania.gdb SP_PA — SP_PA_C_Orig

ByJurisdictions Spotsylvania.gdb SP_RR — SP_RR_CN_2013

ByJurisdictions Spotsylvania.gdb SP_RR — SP_RR_ESRI

ByJurisdictions Spotsylvania.gdb SP_RR — SP_RR_C_Orig

ByJurisdictions Spotsylvania.gdb SP_RR — SP_RR_TNM

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_C_New

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_CN_2013

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_VD_1997
77 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_VG_2008Q3

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_VG_2014Q1

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_C_Orig

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_Casings_C

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_Intersections_C

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_TNM

ByJurisdictions Spotsylvania.gdb SP_Rds — SP_Rds_Combined

ByJurisdictions Spotsylvania.gdb SP_Borders — SP_Border

ByJurisdictions Spotsylvania.gdb SP_Bud — SP_Bud_Li_C

ByJurisdictions Spotsylvania.gdb SP_Bud — SP_Bud_Ply_C

ByJurisdictions Spotsylvania.gdb SP_Other — SP_Cemeteries_C

ByJurisdictions Spotsylvania.gdb SP_Other — SP_DevelopmentDistricts_C

ByJurisdictions Spotsylvania.gdb SP_Other — SP_FireRescueStations_C

ByJurisdictions Spotsylvania.gdb SP_Other — SP_MileMarkers_C

ByJurisdictions Spotsylvania.gdb SP_Other — SP_TAZ_C

ByJurisdictions Spotsylvania.gdb SP_FLU — SP_FLU_C_Orig

ByJurisdictions Spotsylvania.gdb SP_Sch — SP_Sch_C

ByJurisdictions Spotsylvania.gdb SP_St — SP_St_C_Orig

ByJurisdictions Spotsylvania.gdb SP_Wb — SP_Wb_C_Orig

ByJurisdictions Spotsylvania.gdb SP_Wb — SP_Wb_Li_CN_2013

ByJurisdictions Spotsylvania.gdb SP_Wb — SP_Wb_Ply_NHD_USGS

ByJurisdictions Spotsylvania.gdb SP_Zo — SP_Zo_C_Orig

ByJurisdictions Spotsylvania.gdb SP_Zip — SP_Zip_CN_2013

ByJurisdictions Surry.gdb SU_Lm — SU_Lm_CN_2013

ByJurisdictions Surry.gdb SU_Pk — SU_Pk_VDCR

ByJurisdictions Surry.gdb SU_PA — SU_PA_ESRI

ByJurisdictions Surry.gdb SU_RR — SU_RR_CN_2013

ByJurisdictions Surry.gdb SU_RR — SU_RR_ESRI

ByJurisdictions Surry.gdb SU_Rds — SU_Rds_C_New

ByJurisdictions Surry.gdb SU_Rds — SU_Rds_CN_2013

ByJurisdictions Surry.gdb SU_Rds — SU_Rds_VD_1997

ByJurisdictions Surry.gdb SU_Rds — SU_Rds_VG_2008Q3

ByJurisdictions Surry.gdb SU_Rds — SU_Rds_VG_2014Q1

ByJurisdictions Surry.gdb SU_Rds — SU_Rds_C_Orig

ByJurisdictions Surry.gdb SU_Rds — SU_Rds_TNM

ByJurisdictions Surry.gdb SU_Rds — SU_Rds_Combined

ByJurisdictions Surry.gdb SU_St — SU_St_C_New

ByJurisdictions Surry.gdb SU_St — SU_St_C_Orig
78 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Surry.gdb SU_Wb — SU_Wb_Li_C_new

ByJurisdictions Surry.gdb SU_Wb — SU_Wb_Ply_C_New

ByJurisdictions Surry.gdb SU_Wb — SU_Wb_Ply_C_Orig

ByJurisdictions Surry.gdb SU_Wb — SU_Wb_Li_CN_2013

ByJurisdictions Surry.gdb SU_Wb — SU_Wb_Ply_NHD_USGS

ByJurisdictions Surry.gdb SU_WS — SUa_WS_C_New

ByJurisdictions Surry.gdb SU_WS — SUb_WS_C_New

ByJurisdictions Surry.gdb SU_WS — SU_WS_Swamps_C_Orig

ByJurisdictions Surry.gdb SU_WS — SU_WS_Wetlands_C_Orig

ByJurisdictions Surry.gdb SU_Borders — SU_Border

ByJurisdictions Surry.gdb SU_Zip — SU_Zip_CN_2013

ByJurisdictions Sussex.gdb SX_Lm — SX_Lm_CN_2013

ByJurisdictions Sussex.gdb SX_Lm — SX_Lm_USBGN

ByJurisdictions Sussex.gdb SX_Borders — SX_Border

ByJurisdictions Sussex.gdb SX_PA — SX_PA_ESRI

ByJurisdictions Sussex.gdb SX_Pk — SX_Pk_CN_2013

ByJurisdictions Sussex.gdb SX_Pk — SX_Pk_VDCR

ByJurisdictions Sussex.gdb SX_Rds — SX_Rds_TNM

ByJurisdictions Sussex.gdb SX_Rds — SX_Rds_VG_2014Q1

ByJurisdictions Sussex.gdb SX_Rds — SX_Rds_FS

ByJurisdictions Sussex.gdb SX_Rds — SX_Rds_CN_2104

ByJurisdictions Sussex.gdb SX_Rds — SX_Rds_CN_2013

ByJurisdictions Sussex.gdb SX_Rds — SX_Rds_Combined

ByJurisdictions Sussex.gdb SX_Rds — SX_Rds_VG_2008Q3

ByJurisdictions Sussex.gdb SX_RR — SX_RR_ESRI

ByJurisdictions Sussex.gdb SX_RR — SX_RR_TNM

ByJurisdictions Sussex.gdb SX_RR — SX_RR_CN_2013

ByJurisdictions Sussex.gdb SX_RR — SX_RR_Combined

ByJurisdictions Sussex.gdb SX_Wb — SX_Wb_Ply_NHD_USGS

ByJurisdictions Sussex.gdb SX_Wb — SX_Wb_Li_CN_2013

ByJurisdictions Sussex.gdb SX_Wb — SX_Wb_Li_CN_2014

ByJurisdictions Sussex.gdb SX_Zip — SX_Zip_CN_2013

ByJurisdictions Sussex.gdb SX_Prop — SX_Prop_VEDP

ByJurisdictions Sussex.gdb SX_Res — SX_Res_VDEQ

ByJurisdictions Sussex.gdb SX_Riv — SX_Riv_VDEQ

ByJurisdictions Sussex.gdb SX_SWF — SX_SWF_VDEQ

ByJurisdictions Sussex.gdb SX_SWM — SX_SWM_VDCR

ByJurisdictions Sussex.gdb SX_St — SX_St_TNM
79 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

ByJurisdictions Sussex.gdb SX_Tr — SX_Apts_TNM

CountyBoundaries CountyBorders.gdb AMborder

CountyBoundaries CountyBorders.gdb CRBorder

CountyBoundaries CountyBorders.gdb CCBorder

CountyBoundaries CountyBorders.gdb CFBorder

CountyBoundaries CountyBorders.gdb CHBorder

CountyBoundaries CountyBorders.gdb DWBorder

CountyBoundaries CountyBorders.gdb GCBorder

CountyBoundaries CountyBorders.gdb HNBorder

CountyBoundaries CountyBorders.gdb HCBorder

CountyBoundaries CountyBorders.gdb HWBorder

CountyBoundaries CountyBorders.gdb JCCBorder

CountyBoundaries CountyBorders.gdb KQBorder

CountyBoundaries CountyBorders.gdb KGBorder

CountyBoundaries CountyBorders.gdb KWBorder

CountyBoundaries CountyBorders.gdb LUBorder

CountyBoundaries CountyBorders.gdb NKBorder

CountyBoundaries CountyBorders.gdb NWBorder

CountyBoundaries CountyBorders.gdb PBBorder

CountyBoundaries CountyBorders.gdb PWBorder

CountyBoundaries CountyBorders.gdb PGBorder

CountyBoundaries CountyBorders.gdb RCBorder

CountyBoundaries CountyBorders.gdb SPBorder

CountyBoundaries CountyBorders.gdb SUBorder

CountyBoundaries CountyBorders.gdb State_CountyBorders_GC

CountyBoundaries CountyBorders.gdb SXBorder

CountyBoundaries CountyBorders.gdb Study_countyborders

NationWideData Borders.gdb counties

NationWideData ByFeatureClass.gdb RailRoad — Nation_RR_CN_2013

NationWideData ByFeatureClass.gdb Roads — Nation_Rds_FS

NationWideData ByFeatureClass.gdb ZipCode — Nation_Zip_CN_2013

NationWideData BySource.gdb Census — Nation_RR_CN_2013

NationWideData BySource.gdb Census — Nation_Zip_CN_2013

NationWideData BySource.gdb USForestService — Nation_Rds_FS

QuantitativeResults ChangeDetection CC_Rds_CcgCN_VG_100.shp

QuantitativeResults ChangeDetection CC_Rds_C_CN_100.shp

QuantitativeResults ChangeDetection DW_VG_C_100.shp

QuantitativeResults ChangeDetection HC_VG2014Q1_VG2008Q3_100MChange.shp
80 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

QuantitativeResults ChangeDetection PW_CN_C_50M.shp

QuantitativeResults ChangeDetection PW_CN_VG2014Q1_1M.shp

QuantitativeResults ChangeDetection PW_CN_VG2014Q1_50M.shp

QuantitativeResults ChangeDetection PW_CN_VG2014Q1_5M.shp

QuantitativeResults ChangeDetection PW_C_VG2014Q1_50m.shp

QuantitativeResults ChangeDetection RC_VG2014Q1_VG2008Q3_50MChange.shp

QuantitativeResults Cluster Analysis HN_Par_C_Cluster.shp

QuantitativeResults DensityAnalyses densityrdscom

QuantitativeResults DensityAnalyses density_rds_c

QuantitativeResults DensityAnalyses dw_apts_dens

QuantitativeResults HotSpotAnalyses HN_Bud_C_Elevationheight.shp

QuantitativeResults HotSpotAnalyses HN_Par_C_Acres_HotSpot.shp

QuantitativeResults HotSpotAnalyses VGIN_speed_hotspot.shp

StateWideData ByFeatureClass.gdb Roads — State_Rds_VG_2014Q1

StateWideData ByFeatureClass.gdb Roads — State_Rds_TNM

StateWideData ByFeatureClass.gdb Colleges — State_CL_VEDP

StateWideData ByFeatureClass.gdb RailRoads — State_RR_ESRI

StateWideData ByFeatureClass.gdb RailRoads — State_RR_TNM

StateWideData ByFeatureClass.gdb Hospitals — State_HP_VEDP

StateWideData ByFeatureClass.gdb Properties — State_Prop_VEDP

StateWideData ByFeatureClass.gdb ProtectedAreas — State_PA_ESRI

StateWideData ByFeatureClass.gdb Reserviors — State_Res_VDEQ

StateWideData ByFeatureClass.gdb Rivers — State_Riv_VDEQ

StateWideData ByFeatureClass.gdb SolidWasteFacilities — State_SWF_VDEQ

StateWideData ByFeatureClass.gdb StateWildlifeManagementArea — State_SWM_VDCR

StateWideData ByFeatureClass.gdb Parks — State_Pk_VDCR

StateWideData ByFeatureClass.gdb Parks — Parks_FromCensusLM_CN_2013

StateWideData ByFeatureClass.gdb Landmarks — State_Lm_CN_2013

StateWideData ByFeatureClass.gdb Landmarks — State_Lm_USBGN

StateWideData ByFeatureClass.gdb Structures_NationalMap — Struct_Point

StateWideData ByFeatureClass.gdb TransitFromNationalMap — State_Trail_NM

StateWideData ByFeatureClass.gdb TransitFromNationalMap — State_Airports_Pts_NM

StateWideData ByFeatureClass.gdb TransitFromNationalMap — State_Runways_NM

StateWideData ByFeatureClass.gdb Waterbodies — State_Wb_Ply_NHD_USGS

StateWideData BySource.gdb VGIN — State_Rds_VG_2014Q1

StateWideData BySource.gdb VGIN — State_Rds_VG_2008Q3

StateWideData BySource.gdb VGIN — Study_Rds_VG_2008Q3_Clip

StateWideData BySource.gdb VirginiaEconomicDevelopmentPartnership — State_CL_VEDP
81 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

StateWideData BySource.gdb VirginiaEconomicDevelopmentPartnership — State_HP_VEDP

StateWideData BySource.gdb VirginiaEconomicDevelopmentPartnership —

State_Prop_VEDP

StateWideData BySource.gdb ESRI — State_PA_ESRI

StateWideData BySource.gdb ESRI — State_RR_ESRI

StateWideData BySource.gdb VirginiaDeptOfConservationAndRecreation —

State_SWM_VDCR

StateWideData BySource.gdb VirginiaDeptOfConservationAndRecreation —

State_NPS_VDCR

StateWideData BySource.gdb VirginiaDeptOfEnvironmentalQuality — State_Res_VDEQ

StateWideData BySource.gdb VirginiaDeptOfEnvironmentalQuality — State_Riv_VDEQ

StateWideData BySource.gdb VirginiaDeptOfEnvironmentalQuality — State_SWF_VDEQ

StateWideData BySource.gdb Census — State_Lm_CN_2013

StateWideData BySource.gdb Census — Parks_FromCensusLM_CN_2013

StateWideData BySource.gdb Census — State_Pop_CN_2010

StateWideData BySource.gdb Census — State_Pop_CN_2010new

StateWideData BySource.gdb USBGN — State_Lm_USBGN

StateWideData BySource.gdb TheNationalMap — State_RR_TNM

StateWideData BySource.gdb TheNationalMap — State_Rds_TNM

StateWideData BySource.gdb USGS — State_Wb_Ply_NHD_USGS

StudyAreaWideData BySource.gdb County_City — Study_Zo_C

StudyAreaWideData BySource.gdb County_City — Study_WS_C

StudyAreaWideData BySource.gdb County_City — Study_PA_Li_C

StudyAreaWideData BySource.gdb County_City — Study_PA2_Ply_C

StudyAreaWideData BySource.gdb County_City — Study_Ut_C

StudyAreaWideData BySource.gdb County_City — Study_Rds_C

StudyAreaWideData BySource.gdb County_City — Study_RR_C

StudyAreaWideData BySource.gdb County_City — Study_PA_Ply_C

StudyAreaWideData BySource.gdb County_City — Study_Pk_Ply_C

StudyAreaWideData BySource.gdb County_City — Study_Pk_Pts_C

StudyAreaWideData BySource.gdb County_City — Study_Par_C

StudyAreaWideData BySource.gdb County_City — Study_LUs_C

StudyAreaWideData BySource.gdb County_City — Study_FLU_C

StudyAreaWideData BySource.gdb County_City — Study_APts_C

StudyAreaWideData BySource.gdb County_City — Study_St_C

StudyAreaWideData BySource.gdb County_City — Study_Wb_Li_C

StudyAreaWideData BySource.gdb County_City — Study_Wb_Ply_C

StudyAreaWideData BySource.gdb County_City — Study_Bud_Ply_C
82 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

StudyAreaWideData BySource.gdb County_City — Study_FlPl_C

StudyAreaWideData BySource.gdb County_City — Study_Co_C

StudyAreaWideData BySource.gdb Census — Study_Rds_CN_2013

StudyAreaWideData BySource.gdb Census — Study_RR_CN_2013

StudyAreaWideData BySource.gdb Census — Study_Lm_CN_2013

StudyAreaWideData BySource.gdb Census — Study_Wb_Li_CN_2013

StudyAreaWideData BySource.gdb Census — Study_Pop_CN_2010

StudyAreaWideData BySource.gdb Census — Study_Zip_CN_2013

StudyAreaWideData BySource.gdb Census — Study_Zip_CN_2013b

StudyAreaWideData BySource.gdb VDOT — Study_Rds_VD_1997

StudyAreaWideData BySource.gdb VGIN — Study_Rds_VG_2014Q1

StudyAreaWideData BySource.gdb VGIN — Study_Rds_VG_2008Q3

StudyAreaWideData BySource.gdb ESRI — Study_PA_ESRI

StudyAreaWideData BySource.gdb ESRI — Study_RR_ESRI

StudyAreaWideData BySource.gdb ESRI — Study_RR_ESRIb

StudyAreaWideData BySource.gdb VDCR — Study_Pk_Ply_VDCR

StudyAreaWideData BySource.gdb TheNationalMap — Study_RR_TNM

StudyAreaWideData BySource.gdb TheNationalMap — Study_Rds_TNM

StudyAreaWideData BySource.gdb USGS — Study_Wb_Ply_NHD_USGS

StudyAreaWideData BySource.gdb USGS — Study_Wb_Ply_NHD_USGSb

StudyAreaWideData ByType.gdb Roads — Study_Rds_C

StudyAreaWideData ByType.gdb Roads — Study_Rds_VD_1997

StudyAreaWideData ByType.gdb Roads — Study_Rds_TNM

StudyAreaWideData ByType.gdb Roads — Study_Rds_Combined

StudyAreaWideData ByType.gdb Roads — Study_Rds_VG_2014Q1

StudyAreaWideData ByType.gdb Roads — Study_Rds_TNMb_1

StudyAreaWideData ByType.gdb Roads — Study_Rds_2013_CN

StudyAreaWideData ByType.gdb Roads — Study_Rds_VG_2008Q3

StudyAreaWideData ByType.gdb Roads — topology1

StudyAreaWideData ByType.gdb AddressPoints — Study_APts_C

StudyAreaWideData ByType.gdb FutureLandUse — Study_FLU_C

StudyAreaWideData ByType.gdb LandUse — Study_LUs_C

StudyAreaWideData ByType.gdb ProtectiveAreas — Study_PA_Li_C

StudyAreaWideData ByType.gdb ProtectiveAreas — Study_PA_Ply_C

StudyAreaWideData ByType.gdb ProtectiveAreas — Study_PA2_Ply_C

StudyAreaWideData ByType.gdb ProtectiveAreas — Study_PA_ESRI

StudyAreaWideData ByType.gdb Parcels — Study_Par_C

StudyAreaWideData ByType.gdb Parks — Study_Pk_Ply_C
83 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

StudyAreaWideData ByType.gdb Parks — Study_Pk_Pts_C

StudyAreaWideData ByType.gdb Parks — Study_Pk_Ply_VDCR

StudyAreaWideData ByType.gdb Parks — Study_Pk_Ply_Union

StudyAreaWideData ByType.gdb RailRoads — Study_RR_C

StudyAreaWideData ByType.gdb RailRoads — Study_RR_CN_2013

StudyAreaWideData ByType.gdb RailRoads — Study_RR_ESRI

StudyAreaWideData ByType.gdb RailRoads — Study_RR_TNM

StudyAreaWideData ByType.gdb RailRoads — Study_RR_Combined

StudyAreaWideData ByType.gdb RailRoads — Study_RR_Combined_b

StudyAreaWideData ByType.gdb Streams — Study_St_C

StudyAreaWideData ByType.gdb Utility — Study_Ut_C

StudyAreaWideData ByType.gdb Wetlands_Swamps — Study_WS_C

StudyAreaWideData ByType.gdb Zoning — Study_Zo_C

StudyAreaWideData ByType.gdb LandMarks — Study_Lm_CN_2013

StudyAreaWideData ByType.gdb WaterBodies — Study_Wb_Ply_C

StudyAreaWideData ByType.gdb WaterBodies — Study_Wb_Li_C

StudyAreaWideData ByType.gdb WaterBodies — Study_Wb_Ply_NHD_USGS

StudyAreaWideData ByType.gdb WaterBodies — Study_Wb_Li_CN_2013

StudyAreaWideData ByType.gdb Buildings — Study_Bud_Ply_C

StudyAreaWideData ByType.gdb Population — Study_Pop_CN_2010

StudyAreaWideData ByType.gdb Contours — Study_Co_C

StudyAreaWideData ByType.gdb floodplains — Study_FlPl_C

StudyAreaWideData ByType.gdb ZipCodes — Study_Zip_CN_2013

StudyAreaWideData Combined Datasets.gdb Study_Pk_Ply_Union

StudyAreaWideData Combined Datasets.gdb Study_Rds_Combined

StudyAreaWideData Combined Datasets.gdb Study_RR_Combined

StudyAreaWideData DataWithEntireDatasets.gdb Study_Pop_CN_2010

StudyAreaWideData DataWithEntireDatasets.gdb Study_Rds_C

StudyAreaWideData DataWithEntireDatasets.gdb Study_Pk_Ply_VDCR

StudyAreaWideData DataWithEntireDatasets.gdb Study_Rds_VD_1997

StudyAreaWideData DataWithEntireDatasets.gdb Study_Lm_CN_2013

StudyAreaWideData DataWithEntireDatasets.gdb Study_PA_ESRI

StudyAreaWideData DataWithEntireDatasets.gdb Study_Rds_2013_CN

StudyAreaWideData DataWithEntireDatasets.gdb Study_Rds_TNM

StudyAreaWideData DataWithEntireDatasets.gdb Study_Rds_VG_2014Q1

StudyAreaWideData DataWithEntireDatasets.gdb Study_Zip_CN_2013

StudyAreaWideData DataWithEntireDatasets.gdb Study_Wb_Ply_NHD_USGS

StudyAreaWideData DataWithEntireDatasets.gdb Study_Wb_Li_CN_2013
84 

Table A2–1. Table listing the file names and classification levels of the Richmond unified geographic dataset.—Continued

Classification level 1 Classification level 2 Classification level 3+

StudyAreaWideData DataWithEntireDatasets.gdb Study_RR_TNM

StudyAreaWideData DataWithEntireDatasets.gdb Study_RR_ESRI

StudyAreaWideData DataWithEntireDatasets.gdb Study_RR_CN_2013

StudyAreaWideData DataWithEntireDatasets.gdb Study_Rds_VG_2008Q3

StudyAreaWideData Rasters.gdb Study_LC_CBP_2006

StudyAreaWideData Study_Borders.gdb Study_countyborders
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Edited by David A. Shields 

Layout by Cathy Knutson 

Web support by Angela Hall 

For more information concerning this report, contact: 

Director, Eastern Geographic Science Center

U.S. Geological Survey 

521 National Center 

12201 Sunrise Valley Drive

Reston, VA 20192 

http://egsc.usgs.gov/ 
ISSN 2331-1258 (online) 

https://doi.org/10.3133/ofr20161176