Description and Testing of the Geo Data Portal: 

A Data Integration Framework and Web Processing Services 

for Environmental Science Collaboration

Open-File Report 2011–1157

U.S. Department of the Interior

U.S. Geological Survey

Description and Testing of the Geo Data Portal: 

A Data Integration Framework and Web 

Processing Services for Environmental 

Science Collaboration

By David L. Blodgett, Nathaniel L. Booth, Thomas C. Kunicki, Jordan I. Walker, 

and Roland J. Viger

Open-File Report 2011–1157

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2011

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Blodgett, D.L., Booth, N.L., Kunicki, T.C., Walker, J.L., and Viger, R.J., 2011, Description and testing of the 

Geo Data Portal: Data integration framework and Web processing services for environmental science collaboration: 

U.S. Geological Survey Open-File Report 2011–1157, 9 p.
iii

Contents

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

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

Description of the Data Integration Framework ......................................................................................2

Metadata................................................................................................................................................2

Gridded Data..........................................................................................................................................3

Vector Polygon Feature Data..............................................................................................................3

Geo Data Portal Implementation Details ..................................................................................................3

Web Feature Service and Time Period Specification.....................................................................4

Gridded Data Access ...........................................................................................................................4

Geo Data Portal Web Processing Service Overview......................................................................4

Area-Weighted Statistics Generation...............................................................................................4

Categorical Coverage Fraction Computation...................................................................................4

Testing of Geo Data Portal Web Processing Services ...........................................................................5

Testing of Area-Weighted Statistics Generation.............................................................................5

Summary of Datasets Used for Testing GDP Web Processing Services ....................................6

Gridded Meteorological Observations Dataset......................................................................6

River Forecasting Center Quantitative Precipitation Estimates Dataset............................7

National Land Cover Dataset.....................................................................................................7

National Elevation Dataset Digital Elevation Dataset ...........................................................7

Testing Results ......................................................................................................................................8

Summary..........................................................................................................................................................8

References Cited............................................................................................................................................9

Glossary...........................................................................................................................................................9

Figures

1. Typical Geo Data Portal Web processing workflow.........................................................................1

2. Examples of more accurate definition of gridded polygon features by resampling a 

gridded dataset to finer resolutions ..........................................................................................5

3. Feature data used for the gridded meteorological test case shown converted to the 

grid resolution for analysis..........................................................................................................6

4. Feature data used for the River Forecasting Center Quantitative Precipitation Estimate 

test case shown converted to the grid resolution for analysis.............................................7

Tables

1. Results of testing show small errors between spatial averages derived using 

ArcGIS 9.3.1 and the GDP Web processing service. ..............................................................8
iv

Conversion Factors and Abbreviations

SI to Inch/Pound 

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

meter (m) 1.094 yard (yd) 

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

The World Geodetic System 1984 (WGS 84) defines a reference frame for the Earth for use in 

geodesy and navigation.

Abbreviations 

DIF Data Integration Framework

GDP Geo Data Portal

GIS Geographic Information System

NED National Elevation Dataset 

NLCD National Land Cover Dataset

OGC Open Geospatial Consortium

OPeNDAP Open-source Project for a Network Data Access Protocol

URI Uniform Resource Identifier

USGS U.S. Geological Survey

WCS Web Coverage Service

WFS Web Feature Service

WMS Web Map Service

WPS Web Processing Service

XML Extensible Markup Language
Description and Testing of the Geo Data Portal: A Data 

Integration Framework and Web Processing Services for 

Environmental Science Collaboration

By David L. Blodgett, Nathaniel L. Booth, Thomas C. Kunicki, Jordan I. Walker, and Roland J. Viger

Abstract

Interest in sharing interdisciplinary environmental modeling results and related data is increasing among scientists. The 

U.S. Geological Survey Geo Data Portal project enables data 

sharing by assembling open-standard Web services into an 

integrated data retrieval and analysis Web application design 

methodology that streamlines time-consuming and resourceintensive data management tasks. Data-serving Web services 

allow Web-based processing services to access Internetavailable data sources. The Web processing services developed 

for the project create commonly needed derivatives of data in 

numerous formats. Coordinate reference system manipulation 

and spatial statistics calculation components implemented for 

the Web processing services were confirmed using ArcGIS 

9.3.1, a geographic information science software package. 

Outcomes of the Geo Data Portal project support the rapid 

development of user interfaces for accessing and manipulating 

environmental data. 

Introduction

Interest in interdisciplinary data sharing is increasing 

among scientists from the U.S. Geological Survey (USGS), 

other agencies, and academia. For example, watershed 

modelers need downscaled general circulation model climate projections and land-cover data summaries to predict 

streamflow for various climate scenarios. In turn, ecological 

modelers may be interested in these streamflow predictions for 

various climate scenarios as they study the effects of altered 

streamflow conditions on the habitat of biotic communities. As 

a result, environmental modelers from fields of study including climatology, hydrology, geology, and ecology rely on common, cross-discipline data sources and processing methods.

The Geo Data Portal (GDP) project’s purpose was to 

establish consistency and interoperability among common 

methods of data access, analysis, and formatting, to assist in 

model parameterization, model coupling, and data integration. 

The GDP project achieved this by adopting existing software 

that implements broadly accepted scientific communitydriven standards and by developing connections between 

them. For example, Web processing services that integrate the 

geographic reference systems of the climate-and-forecasting 

community and the geographic scientific community were 

developed to allow interoperable access to data from either. 

Thus, the services developed and refined by the GDP project 

streamline the time-consuming and resource-intensive tasks 

associated with data access and manipulation that can inhibit 

the sharing of interdisciplinary environmental science data.

The typical GDP Web processing workflow is illustrated 

in figure 1. Although the steps shown here may vary in practice, they are representative of a common workflow. The steps 

represented in the figure 

are as follows.

1. A user interacts with 

a user interface to specify 

required process parameters: 1) a set of polygon 

features for analysis, 2) 

a pointer to the gridded 

dataset to be analyzed, 

and 3) parameters to 

configure the GDP Web 

processing service.

Data

User Interface

Data

User 

Specifications

Remote Server

Processing

1

2

4 Formatted 

output

3 5

Geo Data 

Portal Web 

Processing 

Parameters

Meta 

Figure 1. Typical Geo Data Portal Web processing workflow.
2 Geo Data Portal: A Data Integration Framework and Web Processing Services for Environmental Science Collaboration

2. To assist selection of process parameters in step 1, the 

user interface accesses discovery metadata from a catalog 

held on a remote server. Internal dataset metadata, such as 

data types and time ranges, are found by direct inspection 

of data resources on remote servers.

3. Based on user input from step 1 and 2, the user interface 

application generates a request that is submitted to the 

GDP Web processing services.

4. The GDP Web processing service accesses data and performs calculations and/or transformations as requested in 

step 3. 

5. The GDP Web processing service responds to the user 

interface application with processing status and, eventually, a file containing the results or a record of a processing error.

Two separate but complementary components of the GDP 

are described in this report: (1) a data-integration strategy and 

(2) Web processing and data service software. The data-integration strategy is implemented as a comprehensive system 

of data-integration standards referred to herein as the Data 

Integration Framework (DIF). The Web processing and data 

services software documented in this report implements and 

enhances existing implementations of standards used in the 

DIF.

The first section below explains how the components 

of the DIF work together at a conceptual level. The second 

section describes implementation details of the Web processing services, and it provides details for readers who want 

to develop their own software to interact with GDP project 

services and standards. The final section describes the tests 

that were performed to confirm that the GDP Web processing 

services geospatial calculations are consistent with geographic 

information science software and provides a summary of the 

results.

Description of the Data Integration 

Framework 

The DIF includes three general types of data: metadata, 

gridded data, and vector polygon feature data. Web processing 

services rely on metadata to discover the existence of a gridded dataset and to get a description of the contents of a gridded or feature dataset. Gridded data are the data source that is 

summarized according to a vector polygon feature dataset. The 

following sections explain the roles of the three data types in 

the DIF.

Metadata

The DIF relies on two categories of metadata, (1) data 

discovery metadata that is used to find data Web services and 

(2) internal dataset metadata that is found by direct inspection of a data Web service. There are provided by metadata 

catalogs and gridded data servers respectively. At a minimum, 

discovery metadata includes a dataset name and a Uniform 

Resource Identifier (URI) Web address that identifies the location of the dataset. At its most rich, discovery metadata takes 

the form of an International Organization for Standardization 

standard metadata record, which is made available by an Open 

Geospatial Consortium (OGC) standard catalog service that 

allows a user to search and evaluate various metadata records. 

Discovery metadata is then used to find internal dataset metadata by querying a remote server about a particular dataset of 

interest. The names of variables within a dataset and available 

time ranges are examples of internal dataset metadata.

In contrast to approaches in which each time step of data 

in a dataset is made available as a separate URI and a separate 

data Web service, a dataset within the DIF approach is referenced with a URI that resolves to a single data Web service. In 

the DIF, for example, a gridded dataset holding a time series 

of temperature, wind, and precipitation can be represented by 

one URI, and all of the time steps of all of the dataset variables 

(temperature, wind, precipitation) are accessible through the 

single URI-resolved data Web service. The use of a single URI 

per dataset enables the use of the following workflow for data 

discovery and access: 

1. A user interface application is used to query a catalog of 

discovery metadata to find a data Web service URI.

2. The application uses the data Web service to query the 

dataset for variable names (like temperature) and content 

characteristics (like available time range).

3. A Web processing service can then request the user-specified portion of the dataset.

The DIF offers users flexibility in accessing discovery 

metadata. A simple user-interface implementation using the 

DIF could offer Web processing services related to a single 

data source, and in that case no formal datasource catalog is 

required. For compiling small data-source catalogs, a list of 

dataset URIs and accompanying discovery metadata made 

available by a user-interface application may satisfy data discovery needs. To allow users to search for and combine large 

data-source catalogs, user-interface applications can implement the OGC Catalogue Service for the Web (Open Geospatial Consortium, Inc., 2007a) standard for metadata search and 

retrieval. It is important to note that the DIF uses discovery 

metadata only for data Web service URI discovery.
Geo Data Portal Implementation Details 3

Applications implementing the DIF should rely on data 

Web service introspection rather than discovery metadata 

services to access internal dataset metadata. Given a data Web 

service URI, a user interface implementing the DIF retrieves 

internal dataset metadata by use of a dynamic query to the data 

Web service rather than relying on static or cataloged metadata. This is useful if the dataset tends to change frequently, 

thus making cataloged internal metadata obsolete.

Gridded Data

Commonly, a modeler or analyst needs to determine statistics about gridded data for a set of polygons related to their 

modeling or analysis. The following paragraphs summarize 

the types of gridded data included in the DIF and the data Web 

service protocols used to access them.

Gridded data are collections of cells referenced to 

geographic axes. The geometry and arrangement of cells in 

gridded data accessible to GDP Web processing services may 

be defined in several ways: 1) as an origin and cell size; 2) 

as a list of distances from the origin of each axis; 3) as two 

two-dimensional grids of explicitly defined positions so that 

each grid cell is defined as a point. The first and second cases 

are regular and rectilinear grids, respectively; the third is 

actually a set of points that happens to be laid out in a grid not 

aligned with a particular axis. This third grid type is especially 

useful if a grid is defined in a projection unknown to processing services, because grid cell locations can be redefined and 

analyzed using a known coordinate reference system without 

the need to resample to new grid geometry.

A gridded dataset can contain time-invariant or timeseries data. These two data types require special processing 

considerations. High resolution time-invariant gridded datasets, like digital elevation models, that exceed server capacity 

must be resampled to a lower resolution or processed using 

a less demanding analysis method, like tiling. Time-series 

gridded datasets are generally not as spatially massive per time 

step as time-invariant gridded datasets, but time-series gridded 

datasets can be extremely large with regard to the time dimension. Time-series data require functionality to handle specification of time period processing parameters and processing 

services capable of handling time series appropriately. Two 

standard Web service protocols have been adopted by the DIF 

to handle spatially massive or temporally extensive gridded 

data sources: the OGC Web Coverage Service (WCS) (Open 

Geospatial Consortium, Inc., 2006a) and the Open-source 

Project for a Network Data Access Protocol (OPeNDAP; 

Open-source Project for a Network Data Access Protocol, Inc., 

2010). Requests for data from the WCS allow spatial subsetting and resampling of gridded datasets in case they are too 

large. The OPeNDAP allows flexible access to time-series 

gridded datasets but requires the requesting Web processing 

service to request data by grid index position rather than by 

location or time. Data servers that implement the WCS and 

OPeNDAP standards, in most cases, also support the OGC 

Web Map Service (WMS) protocol (Open Geospatial Consortium, Inc., 2006b). Distinct from the WCS and OPeNDAP, 

the WMS provides a rendered screen-resolution image of the 

requested dataset. Web processing services do not use Web 

map services, but applications implementing DIF components 

are able to take advantage of the WMS for visualization and 

verification of data.

Vector Polygon Feature Data

Vector polygon feature data have explicit geometry, like 

points or polygons, which are described using geographic 

coordinate pairs. The GDP Web processing services analyze 

gridded data on a per-polygon-feature basis, but the DIF 

adopted standards are capable of handling any vector features. 

The shapefile format is an example of a widely used encoding 

for feature data, like watershed polygons or sampling points. 

Shapefile is a storage file format that works seamlessly with 

virtually all geographic-information software and servers. This 

allows a shapefile created with desktop geographic information system (GIS) software to be loaded into a geospatial 

server without special preparation or considerations. The DIF 

uses the OGC Web Feature Service (WFS) protocol (Open 

Geospatial Consortium Inc., 2005) with the feature data 

encoded in the Geography Markup Language simple features 

profile (Open Geospatial Consortium, Inc., 2010). Similar 

to the OPeNDAP and WCS servers, the geospatial servers 

that support the WFS also tend to implement the WMSs for 

visualization. Applications implementing the DIF can access 

the geospatial server WMS directly to allow users to verify 

that feature data were transferred to a geospatial server as 

expected. 

Geo Data Portal Implementation 

Details 

Throughout design and implementation of the GDP DIF 

and Web processing service, a generalized graphical user interface and utility services were implemented as proof of concept 

to ensure the utility of the services and ease of specification of 

processing parameters in a Web browser-based user interface 

application. Important details about the Web processing services and findings from research and development of them are 

presented below.
4 Geo Data Portal: A Data Integration Framework and Web Processing Services for Environmental Science Collaboration

Web Feature Service and Time Period 

Specification

The GDP implementation provides a shapefile upload 

service that loads a user’s shapefile into a geospatial server 

to make it available as a WFS for processing. A fully formed 

WFS request is provided to the Web processing service as an 

input parameter. 

Feature data is retrieved by the Web processing service. 

Processing is performed using the coordinate reference system 

of the user-supplied feature data. The start and end of the 

analysis time period is specified using the Extensible Markup 

Language (XML) Schema standard date-time data type (World 

Wide Web Consortium, 2004). 

Gridded Data Access

Gridded data must be available from a data Web service 

that meets the OPeNDAP or WCS standard to be accessible 

to a GDP Web processing service. A data Web service URI 

and a data-type identifier are required for access because that 

information is used with information from the WFS and time 

period specifications to formulate a full request for the subset 

of gridded data being analyzed. When these requirements are 

met, Web processing services can then retrieve the gridded 

data for the user during process execution.

Geo Data Portal Web Processing Service 

Overview

Geo Data Portal Web processing services are made available using the OGC Web Processing Service (WPS) standard (Open Geospatial Consortium, Inc., 2007b). The WPS 

standard specifies a language for describing Web processing 

algorithms. It provides a mechanism for explicit specification of formats and encodings of input data, output data, and 

process parameters. By using the WPS and DIF standards, 

software developers are able to rapidly create user interfaces 

to parameterize and execute Web processing services utilizing 

remote computer resources and decentralized data repositories. 

As outlined above, datasets for processing are provided as 

URIs where Web processing services can retrieve them. The 

WPS requests can be executed 1) synchronously for immediate mapping or analysis, or 2) asynchronously, in which 

the processing status is provided and the output file is made 

available upon process completion. This is best described with 

examples. A request to upload a shapefile is made synchronously; the Web browser user interface application uploads a 

file and waits for a server response. A request to run a large 

analysis is made asynchronously; the Web browser user interface application sends a processing request and receives a link 

to a processing status document that it can check periodically.

Area-Weighted Statistics Generation

The GDP Web processing services include an algorithm 

to generate area-weighted statistics of a gridded dataset for a 

set of vector polygon features. Several parameters discussed in 

the preceding sections are required in a request to execute this 

service: 

1. a fully specified WFS request, 

2. a feature attribute name to label output data, 

3. a gridded dataset URI, 

4. a data type to be accessed from the gridded dataset, and

5. an optional time range. 

By use of the bounding box that encloses the feature data 

and the time range, if provided, a subset of the gridded dataset 

is requested from the remote gridded data server. Polygon representations are generated for cells in the retrieved grid. The 

polygon grid-cell representations are projected to the feature 

data coordinate reference system and used to calculate per 

grid-cell feature coverage fractions. Area-weighted statistics, 

mean, minimum, maximum, standard deviation, variance, 

count, and weighted sum are then calculated for each feature 

using the grid values and fractions as weights (West, 1979). 

The last step is repeated for each time step within the specified 

time range or all time steps if a time range was not supplied.

Categorical Coverage Fraction Computation

This GDP Web processing service is used with categorical gridded data to assess the coverage fraction of each 

category in the gridded data for each feature. For example, 

a gridded dataset of land cover types can be summarized to 

percentage of each land cover type in each feature provided. 

The WPS request for computation of the categorical coverage 

fraction contains similar parameters to those for the request for 

area-weighted statistics:

1. a fully specified WFS request, 

2. a feature attribute name to label output data, 

3. a gridded dataset URI, and

4. a data type to be accessed from the gridded dataset. 

This service does not process gridded time series data. 

By use of the feature dataset bounding box, a subset of the 

gridded dataset is requested from the remote gridded data 

server. The location of each grid-cell center is then projected 

to the feature dataset coordinate reference system. For each 

grid cell in the subsetted grid, the grid-cell center is tested for 

inclusion in each feature in the feature dataset. If the grid-cell 

center is in a given feature, the count for that cell’s category is 

incremented for that feature. After all the grid-cell centers are 

processed, the coverage fraction for each category is calculated for each feature. 
Testing of Geo Data Portal Web Processing Services 5

Testing of Geo Data Portal Web 

Processing Services 

To show the GDP Web processing services perform 

geospatial calculations consistent with geographic information science software, several tests have been developed as 

examples that are intended to demonstrate a broad range of 

service functionality. The GIS processing for these examples 

were carried out using ArcGIS 9.3.1 desktop software, and the 

results of the tests of the GDP Web processing services and 

ArcGIS 9.3.1 were compared. ArcGIS 9.3.1 was used owing 

to its wide use and a general consensus that it represents the 

geographic information science standard and is applicable for 

the selected use cases. It was necessary for the user doing the 

testing to download and manipulate raw data into appropriate 

formats for use with ArcGIS 9.3.1, while these same data were 

accessed directly from data Web services by the GDP Web 

processing service.

Testing of Area-Weighted Statistics Generation

The generation of area-weighted statistics of a gridded 

dataset for a set of polygon features was tested with the GDP 

processing services and ArcGIS 9.3.1. The method that ArcGIS 9.3.1 uses to calculate statistics of gridded data per feature 

is substantially different than the method associated with the 

GDP Web processing services. ArcGIS 9.3.1 first converts 

the feature data into a gridded representation of the features 

with the same layout (for instance, cell size) as the gridded 

dataset and then performs analysis on a raster basis. The GDP 

area weighted statistics Web processing service automatically 

converts all data to a vector representation. When the cell size 

of the gridded dataset is very small relative to the size of the 

features, ArcGIS 9.3.1 and the GDP Web processing service 

return very similar results; however, as the size of the gridded 

data cells approach or exceed the size of the (vector) features, 

major differences are likely to occur in the derived statistics. 

To achieve equivalent results using the different analysis 

methods, the gridded dataset was resampled 

to a fine enough resolution to accurately 

define the feature geometry using a gridded 

representation.

Figure 2 illustrates the process of resampling the coarse gridded dataset to a finer 

resolution so that polygon features can be 

more accurately defined. Figure 2A shows 

an example set of polygon features overlaying a grid whose cell size is so coarse 

relative to the size of the features that the 

ArcGIS method would produce different 

results than the GDP method. Figure 2B

shows the polygon features converted to 

the coarse cell size of the gridded data; note 

the pixilation in the feature boundaries. 

Figure 2C shows the entire analysis zone 

resampled to a grid 100 times finer than the 

original input gridded data. Figure 2D is an 

exploded view showing that the fine resolution version of the features more accurately 

represents the detail of the input feature 

data polygons.

1:700K A B

1:700K C 1:100K D

1:700K

Figure 2. Examples of more accurate 

definition of gridded polygon features 

by resampling a gridded dataset to finer 

resolutions. A, The original 0.125-degree grid 

with feature polygons. B, Feature polygons 

resampled to 0.125 degree grid. C, Feature 

polygons resampled to a 0.00125-degree grid. 

D, View showing a detail of a 0.00125-degree 

grid that can more accurately reproduce zone 

polygons for analysis.
6 Geo Data Portal: A Data Integration Framework and Web Processing Services for Environmental Science Collaboration

The general workflow used in ArcGIS 9.3.1 was as follows:

1. Gridded data were downloaded.

2. The gridded data were imported from their native format 

into ArcGIS GRID format.

3. The gridded data were resampled to a very fine resolution.

4. The resampled gridded data were projected into an Albers 

equal area projection.

5. The (vector) feature data were projected into an Albers 

equal area projection.

6. The feature data were rasterized using the same grid 

layout as the resampled gridded data to verify consistent 

polygon geometry reproduction.

7. Statistics, mean, minimum, maximum, and standard 

deviation of the gridded data were calculated for the feature data using a built in ArcGIS 9.3.1 tool.

The general workflow used with the GDP Web processing 

service was as follows:

1. Feature data were uploaded to the GDP WFS.

2. Gridded data URIs and processing parameters were 

selected.

3. The request was submitted to the Web processing service.

Summary of Datasets Used for Testing GDP Web 

Processing Services

Tests of the ability of the GDP Web processing services 

to perform coordinate reference system conversions and pass 

data between software components were conducted. The 

weighted statistics analysis outlined in the area-weighted 

statistics generation section was applied to three datasets, each 

with different cell sizes and spatial referencing characteristics. 

The categorical coverage fraction analysis was applied to one 

dataset. 

Gridded Meteorological Observations Dataset

One of the primary uses that the area-weighted statistics Web processing service serves is the analysis of gridded 

meteorological and/or downscaled general circulation model 

climate change scenarios according to a modeler’s areas of 

interest. As previously mentioned, watershed modelers often 

use data resulting from this analysis to predict streamflow 

for various climate scenarios. The Gridded Meteorological 

Observations dataset (Maurer and others, 2002) uses a 12-kilometer (km) cell size, with position expressed as the latitude 

and longitude of the cell centers. This “unprojected” data lacks 

datum information. For grids lacking datum specification, 

both ArcGIS 9.3.1 and the GDP processing services presented 

here assume latitude-longitude pairs using the World Geodetic 

System 1984 geodetic datum. Weighted statistics analysis 

was applied to the dataset using the GDP processing service 

and ArcGIS 9.3.1. For the ArcGIS 9.3.1 analysis, the grid 

was resampled from its native 0.125-degree resolution to a 

projected resolution of about 140 meters (m). The feature data 

used for this analysis are shown converted to the analysis grid 

in figure 3.

Figure 3. Feature data used for the gridded 

meteorological test case shown converted to the grid 

resolution for analysis.
Testing of Geo Data Portal Web Processing Services 7

River Forecasting Center Quantitative 

Precipitation Estimates Dataset

Radar rainfall estimates can be attributed to watersheds 

using the GDP area-weighted statistics generation service. 

Multisensor quantitative precipitation estimates are derived 

by the National Weather Service River Forecasting Centers 

and distributed by the National Precipitation Verification Unit 

(National Precipitation Verification Unit, 2010). The positions 

of these data are expressed on a polar stereographic projected 

grid with a projected spatial resolution of 4.762 km. Projected 

to an Albers Equal Area projection, the grid has a resolution of 

about 4.3 km in the vicinity of the analysis presented here (89 

degrees west and 43 degrees north) (Fulton, 1998). Weighted 

statistics analysis was applied to the dataset using the GDP 

processing service and ArcGIS 9.3.1. The 4.3-km grid was 

resampled to a resolution of about 43 m. The feature data used 

for this analysis were rasterized according to the layout of the 

analysis grid (shown in grey) in the background of figure 4.

National Land Cover Dataset

The National Land Cover Dataset (NLCD; U.S. Environmental Protection Agency Multi-Resolution Land Characteristics Consortium, n.d.) is the best available nationally 

contiguous source for land-cover classes, impervious surface 

estimates, and forest canopy information. As was mentioned 

previously, watershed modelers often analyze land cover data 

and create land cover data summaries to predict streamflow 

for various climate scenarios in areas of interest. The NLCD 

is a 30-m resolution raster dataset projected using an Albers 

equal area projection based on the North American Datum 

of 1983 (NAD 83). For the test presented here, computation 

of the categorical coverage fraction was applied to one land 

cover class, deciduous-forest, by using the GDP processing 

service and ArcInfo 9.3.1. While the GDP Web processing 

service returns all classes, for testing purposes the output from 

one was considered. Because the categorical coverage fraction 

Web process calculates statistics based on grid cell centers, the 

NLCD grid was not resampled prior to calculating statistics in 

ArcGIS. In addition, comparability of analysis results would 

not be hampered since the NLCD cell size was sufficiently fine 

relative to the feature sizes.

National Elevation Dataset Digital Elevation 

Dataset 

The National Elevation Dataset (NED) uses high precision ground surface elevation data for the United States and 

is a 1/9th arc-second (roughly 3-m) resolution grid based on 

the NAD 83 datum (U.S. Geological Survey, n.d.). Weighted 

statistics analysis was applied to the dataset using the GDP 

processing service and ArcGIS 9.3.1. Because the NED is of 

such high resolution, no resampling was performed prior to 

calculating weighted statistics in ArcGIS.

Figure 4. Feature data used for the River Forecasting 

Center Quantitative Precipitation Estimate test case 

shown converted to the grid resolution for analysis.
8 Geo Data Portal: A Data Integration Framework and Web Processing Services for Environmental Science Collaboration

Testing Results

The testing results presented in table 1 demonstrate that 

analyses performed using the GDP Web processing service 

produce results that are equivalent to analyses performed using 

standard GIS software. The area weighted mean difference 

of spatial means in table 1 represents the average difference 

of the GPD Web processing service analysis and the ArcGIS 

9.3.1 processing using the area of the feature to weight its 

contribution to the average. The mean of spatial means is the 

overall average value of the set of features used for the test. 

The mean of spatial means and area weighted mean difference of spatial means were used to calculate the standard error 

between the two. A pooled two-sample T-test was conducted 

for each set of analysis output to test for a statistical difference 

between the GPD Web processing service and ArcGIS 9.3.1 

analysis results. The T distribution value was used to calculate 

the T distribution probability (p-value) associated with that T 

value. A p-value less than or equal to 0.05 was considered to 

represent a significant difference in the results of the analyses. 

The largest standard error of results found using the two 

processing methods, 2 percent, was the result of the test of the 

categorical coverage fraction algorithm. Three factors may 

have contributed to the relatively large difference of that test 

case: the feature dataset had quite small polygon features, the 

land cover dataset was binary (the variables were “forested” or 

“not forested”), and the ArcGIS 9.3.1 and GDP Web processing service analysis methods differed slightly. Test results 

of the area-weighted statistics generation services showed 

standard errors of less than 1 percent. All T-test p-values are 

much greater than the 0.05 statistical difference threshold. 

These very small standard errors and high p-values indicate 

that the GDP Web processing service’s geospatial calculations 

are consistent with standard geospatial methods.

Table 1. Results of testing show small errors between spatial averages derived using ArcGIS 9.3.1 and the 

GDP Web processing service.

[mm/d, millimeters per day; mm, millimeters; m, meters; %, percent]

Dataset

Area weighted mean 

difference of 

spatial means

Mean of 

spatial means Standard error T-test p-values

Gridded Meteorological 

Observations

0.0196 mm/day 23.0 mm/day 0.085 % 0.997

River Forecasting 

Center Quantitative 

Precipitation Estimates

.0320 mm 12.6 mm .254 % 0.993

National Land Cover 

Dataset Categorical 

Percent Coverage

.0057 % .28 % 2.028 % 0.962

National Elevation 

Dataset Digital 

Elevation Model

.0127 m 286.8 m .004 % 0.999

Summary

The GDP data integration framework and Web processing 

services were developed based on the open-standard data and 

Web service specifications that were found to best facilitate 

interdisciplinary environmental data integration. The GDP 

Web processing services allow environmental modelers and 

other environmental data users to extract aerially weighted and 

categorical coverage statistics from gridded data sources for 

polygonal features of interest. 

The software has been implemented to enhance scientific 

community-supported open-source software from the geographic, atmospheric, and oceanographic science communities. Adoption of open standards and technology from multiple 

scientific communities has allowed development of generalized spatial processing services and user interface implementations that are capable of accessing any dataset made available 

by one of the framework standards. GDP Web processing 

service GIS calculations were confirmed to be consistent with 

ArcGIS 9.3.1 geospatial analysis software.
Glossary 9

References Cited

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Glossary

Area-weighted statistics—Statistics based on a sample population weighted by the fraction of each sample intersecting the 

area being used for analysis.

Categorical coverage fraction—The area of a given analysis 

region covered by a single category in a categorical data type 

normalized against the total area of the analysis region.

Dataset bounding box—A geospatial rectangle that fully 

encloses a given dataset.

Data Web service—An Internet available method of describing and providing access to data resources.

Derivative data—A calculated summary of a dataset.

Discovery metadata—Information used to describe a dataset’s 

contents and location or access method.

Downscaling—A process used to refine global climate model 

projections of future conditions from course grid and time 

resolution to finer grid and time resolution.

Implementation—A piece of software developed to conform 

to, or implement, a particular standard or design guideline.

Internal dataset metadata—Information inherent to a dataset 

that is found by inspecting the dataset directly.

Polygon feature—A spatially distinct entity defined using 

many precise location pairs arranged in a line forming a closed 

ring.

Uniform Resource Identifier—A universally unique Internet 

address. 

User interface application—A computer program, often 

embedded in a Web page, meant to provide human access to 

programmatic tools.

Web processing service—An Internet available method of 

describing and providing access to processing algorithms and 

other server resources.
Publishing support provided by the U.S. Geological Survey

Science Publishing Network, Columbus Publishing Service Center

For more information concerning the research in this report, contact the

Director, Wisconsin Water Science Center

U.S. Geological Survey

8505 Research Way

Middleton, Wisconsin 53562

http://wi.water.usgs.gov/

Blodgett and others— Geo Data Portal: A Data Integration Framework and Web Processing Services for Environmental Science Collaboration—Open-File Report 2011–1157