REFINING 1970’S LAND-USE DATA WITH 1990

POPULATION DATA TO INDICATE NEW

RESIDENTIAL DEVELOPMENT

U.S. GEOLOGICAL SURVEY

Water-Resources Investigations Report 94–4250

NATIONAL WATER-QUALITY ASSESSMENT PROGRAM

National Water−Quality Assessment Study Unit
REFINING 1970’S LAND-USE DATA WITH 1990

POPULATION DATA TO INDICATE NEW

RESIDENTIAL DEVELOPMENT

By Kerie J. Hitt

U.S. GEOLOGICAL SURVEY

Water-Resources Investigations Report 94–4250

Reston, Virginia

1994
U.S. DEPARTMENT OF THE INTERIOR

BRUCE BABBITT, Secretary

U.S. GEOLOGICAL SURVEY

Gordon P. Eaton, Director

For additional information Copies of this report can be

write to: purchased from:

Chief, National Water-Quality U.S. Geological Survey

 Assessment Program Earth Science Information Center

U.S. Geological Survey Open-File Reports Section

12201 Sunrise Valley Drive Box 25286, Mail Stop 517

Mail Stop 413 Denver Federal Center

Reston, VA 22092 Denver, CO 80225

Information regarding the National Water-Quality Assessment (NAWQA) Program is available on

the Internet via the World Wide Web. You may connect to the NAWQA Home Page using the

Universal Resource Locator at <URL:http://wwwrvares.er.usgs.gov/nawqa/nawqa_home.html>.

The URL for this report is <URL:http://wwwrvares.er.usgs.gov/nawqa/WRI94-4250.html>.
FOREWORD

Foreword III

The mission of the U.S. Geological Survey

(USGS) is to assess the quantity and quality of the

earth resources of the Nation and to provide information that will assist resource managers and policymakers at Federal, State, and local levels in making sound

decisions. Assessment of water-quality conditions and

trends is an important part of this overall mission.

One of the greatest challenges faced by waterresources scientists is acquiring reliable information

that will guide the use and protection of the Nation’s

water resources. That challenge is being addressed by

Federal, State, interstate, and local water-resource

agencies and by many academic institutions. These

organizations are collecting water-quality data for a

host of purposes that include: compliance with permits

and water-supply standards; development of remediation plans for a specific contamination problem; operational decisions on industrial, wastewater, or watersupply facilities; and research on factors that affect

water quality. An additional need for water-quality

information is to provide a basis on which regional

and national-level policy decisions can be based. Wise

decisions must be based on sound information. As a

society we need to know whether certain types of

water-quality problems are isolated or ubiquitous,

whether there are significant differences in conditions

among regions, whether the conditions are changing

over time, and why these conditions change from

place to place and over time. The information can be

used to help determine the efficacy of existing waterquality policies and to help analysts determine the

need for and likely consequences of new policies.

To address these needs, the Congress appropriated funds in 1986 for the USGS to begin a pilot program in seven project areas to develop and refine the

National Water-Quality Assessment (NAWQA) Program. In 1991, the USGS began full implementation

of the program. The NAWQA Program builds upon an

existing base of water-quality studies of the USGS, as

well as those of other Federal, State, and local

agencies. The objectives of the NAWQA Program are

to:

•Describe current water-quality conditions for a

large part of the Nation’s freshwater streams,

rivers, and aquifers.

•Describe how water quality is changing over

time.

•Improve understanding of the primary natural

and human factors that affect water-quality

conditions.

This information will help support the development

and evaluation of management, regulatory, and monitoring decisions by other Federal, State, and local

agencies to protect, use, and enhance water resources.

The goals of the NAWQA Program are being

achieved through ongoing and proposed investigations

of 60 of the Nation’s most important river basins and

aquifer systems, which are referred to as study units.

These study units are distributed throughout the

Nation and cover a diversity of hydrogeologic settings. More than two-thirds of the Nation’s freshwater

use occurs within the 60 study units and more than

two-thirds of the people served by public water-supply

systems live within their boundaries.

National synthesis of data analysis, based on

aggregation of comparable information obtained from

the study units, is a major component of the program.

This effort focuses on selected water-quality topics

using nationally consistent information. Comparative

studies will explain differences and similarities in

observed water-quality conditions among study areas

and will identify changes and trends and their causes.

The first topics addressed by the national synthesis are

pesticides, nutrients, volatile organic compounds, and

aquatic biology. Discussions on these and other waterquality topics will be published in periodic summaries

of the quality of the Nation’s ground and surface water

as the information becomes available.

This report is an element of the comprehensive

body of information developed as part of the NAWQA

Program. The program depends heavily on the advice,

cooperation, and information from many Federal,

State, interstate, Tribal, and local agencies and the

public. The assistance and suggestions of all are

greatly appreciated.

Robert M. Hirsch

Chief Hydrologist
Contents V

CONTENTS

Foreword................................................................................................................................................................................ iii

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

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

Purpose and scope ....................................................................................................................................................... 2

Need for urban/suburban land-use information........................................................................................................... 2

Procedure for refining 1970’s land-use data to indicate new residential development ......................................................... 3

 Process national data bases

 Land use............................................................................................................................................................ 5

 Population......................................................................................................................................................... 5

 Census geography............................................................................................................................................. 6

Develop a classification scheme for defining “residential” land use........................................................................... 6

Produce maps of the 1990 population density at the block-group geographic level................................................... 9

Overlay the maps of 1990 population density on maps of 1970’s land-use data ........................................................ 9

Categorize land use in the derivative maps according to population density.............................................................. 9

Application and limitations of procedure .............................................................................................................................. 9

Conclusion............................................................................................................................................................................. 14

References cited..................................................................................................................................................................... 14

FIGURES

1–3. Maps showing:

1. Locations of National Water-Quality Assessment Program study units and their proposed implementation

 dates.................................................................................................................................................................. 2

2. Population change by county in the coterminous United States from 1970 to 1990......................................... 3

3. Use of population density to indicate urban development in the Atlanta, Georgia, area from the 1970’s to

 1990 .................................................................................................................................................................. 4

4. Graph showing the relation of housing unit density to population density ................................................................ 8

5–8. Maps showing:

5. Use of population density to indicate 1990 urban land use in the Potomac River Basin water-quality study

unit of the National Water-Quality Assessment ................................................................................................ 10

6. Use of population density to indicate 1990 urban land use in the Apalachicola–Chattahoochee–Flint Basin

water-quality study unit of the National Water-Quality Assessment ................................................................ 11

7. Use of population density to indicate 1990 urban land use in the Nevada Basin and Range water-quality

study unit of the National Water-Quality Assessment....................................................................................... 12

8. Use of population density to indicate 1990 urban land use in the White River Basin water-quality study

unit of the National Water-Quality Assessment ................................................................................................ 13

TABLES

1. Land use and land cover classification system for urban or built-up land.............................................................. 5

2. Selected Census geographic areas, 1990................................................................................................................. 5

3. Examples of land use classification systems for residential land used by Fairfax and Loudoun Counties,

Virginia, and Suffolk and Nassau Counties, New York......................................................................................... 7

4. Population density classes developed for indicating urban land use ...................................................................... 8
VI Contents

CONVERSION FACTORS

Multiply By To obtain

mile 1.609 kilometer

square mile 2.590 square kilometer

acre 4,047 square meter

people per square mile 0.386 people per square kilometer
Introduction 1

Refining 1970’s Land-Use Data With 1990 Population

Data to Indicate New Residential Development

By Kerie J. Hitt

Abstract

A procedure using a geographic information system was developed to define urban land

use representative of the 1990’s by overlaying

U.S. Bureau of the Census 1990 population density at the block group level on 1970’s digital

land-use data from 1:250,000- and 1:100,000-

scale maps. Any area having a population density

of 1,000 or more people per square mile is re-classified as “urban” land use in the derivative product. The procedure was applied to 20 study units

of the National Water-Quality Assessment program to provide what are considered reasonable

indications of urbanization that has occurred since

the 1970’s.

INTRODUCTION

The National Water-Quality Assessment

(NAWQA) Program was begun by the U.S. Geological

Survey in 1991 as a systematic assessment of the quality of the Nation’s water resources. The program will

describe the status and trends in the quality of a large,

representative part of the Nation’s surface-water and

ground-water resources and will define the primary

natural and human factors affecting the quality of

these resources. In meeting these goals, NAWQA will

produce information that will be useful to policy makers, managers, and the general public at the national,

state, and local levels. The building blocks of the

NAWQA program are 60 study-unit investigations that

include parts of most of the Nation’s major river

basins and aquifers (fig. 1). Leahy and others (1990)

discuss the design of the NAWQA Program in more

detail.

Characteristics for each of the 60 study units are

being incorporated into an overall “environmental

framework.” This framework consists of the common

natural and human factors at work across the Nation to

influence water quality and provides the basis for comparing and contrasting findings across study-unit

boundaries. For example, figure 2 shows population

change from 1970 to 1990 by county. Study units

where counties are increasing in population, such as

those in California and Florida, are areas where

increased urbanization and its corresponding effects

on water quality could be expected.

An integrating concept for the human factors

affecting water quality can be called “land use.” Land

use is perhaps the most important representation of the

human element of the environmental framework. Certain types of land use are associated with certain

human activities that affect water quality and can indicate the likely types and degree of human influence on

water quality. For example, land use defined as “cropland” could be associated with specific activities, such

as the application of pesticides and fertilizers, which

might move into surface or ground water. Land use

defined as “industrial” could be associated with the

use or disposal of toxic chemicals, which also could

wind up in streams or aquifers.

A nationally consistent, up-to-date (1990’s timeframe), land-use data base with the resolution and

detail required for NAWQA’s environmental framework is not available. Data that do exist at state and

local levels are not appropriate for national applications because the classification schemes are not uniform, data for whole study units sometimes do not

exist, and often the data are not available in digital format. Our strategy to fill the requirement for up-to-date

land use is to take the U.S. Geological Survey’s generalized national georeferenced data base on land use

available from the 1970’s and enhance it with current,

more detailed ancillary information on cropping prac-
2 Refining 1970’s Land-use Data with 1990 Population Data to Indicate New Residential Development

tices and population from the 1987 Census of Agriculture and the 1990 Census of Population, respectively.

In this way, numerical gradients of agricultural and

urban intensities can be used to evaluate the differences between study units in agricultural and urban/

suburban water-quality conditions. This approach for

characterizing land use is nationally consistent and

comparable, is current and detailed enough to support

at least part of NAWQA’s needs, relies on available

data that can be used in an automated geographic

information system (ARC/INFO1), and is realistic to

achieve.

Purpose and Scope

This paper describes a method used to update

residential land use using the 1990 Census of Population so that the extent of urban development within

NAWQA study units can be compared and contrasted.

1

Use of trade names in this report is for identification purposes only and does not constitute endorsement by the U.S. Geological Survey.

The procedure uses available national data bases of

land use and population, which are overlaid using the

capabilities of the automated geographic system. The

maps produced using this procedure illustrate where

urbanization has occurred over the last 20 years (fig.

3).

Need for Urban/Suburban Land-Use

Information

The NAWQA Program needs a nationally consistent definition of a broad category of “urban” land

use to document where urban land currently exists

(1990) and where urbanization has occurred over the

past 20 years, especially where agricultural or forest

uses have changed to urban uses. Under the land-use

classification system set forth by Anderson and others

(1976), urban or built-up land is defined as “areas of

intensive use with much of the land covered by structures.” This includes subcategories for residential,

commercial, industrial, and transportation uses. The

procedure attempts to use population data to refine

only the “residential” subcategory of urban land use,

Figure 1. Locations of National Water-Quality Assessment Program study units and their proposed starting dates.

EXPLANATION

Studies started in

 fiscal year 1991

Studies started in

 fiscal year 1994

Studies proposed for

 fiscal year 1997

Boundary of

 study unit
Procedure for Refining Land Use 3

which is the component of urban land use that

increased the most (50 percent) during the 1970’s,

according to Vesterby and others (1994).

Residential land uses range from high-density

multiple-unit structures, such as apartment buildings,

to low-density single-family houses on large lots typical of suburbia. In residential areas, septic tanks, sewage disposal systems, runoff from driveways and

parking lots, and fertilizers and pesticides applied for

lawn care can affect water quality. Various impacts of

runoff from urban land on water quality are described

in Tasker and Driver (1988).

Population became the index of residential

development that would supplement 1970’s land-use

data. Although housing unit density (dwelling units

per acre) often is used for land-use classification, population (people per square mile) was considered to be

a more direct measure of the potential impact on water

quality.

PROCEDURE FOR REFINING 1970’s

LAND-USE DATA TO INDICATE NEW

RESIDENTIAL DEVELOPMENT

The procedure for refining 1970’s residential

land use follows these general steps, which are

described in detail in the following pages:

1. Process national data bases on land use, population,

and Census geography.

2. Develop a classification scheme for defining “residential” land use that is based on population density.

3. Produce a map of the 1990 population density at the

block-group geographic level for each NAWQA

study unit.

4. Overlay the map of 1990 population density on the

map of 1970’s land-use data for each NAWQA

study unit.

5. Categorize land use in the derivative map according

to population density based on the definition of

“residential” land use developed in step 2.

Figure 2. Population change by county in the coterminous United States from 1970 to 1990.

EXPLANATION

Population change

NAWQA study unit

Increased by at least 100,000

Increased by 1 to 100,000

Decreased

Studies started in 1991
4 Refining 1970’s Land-use Data with 1990 Population Data to Indicate New Residential Development

This method to refine land-use data using population is similar to the “Census polygon land use

refinement” method described by Mynar and Hewitt

(1989) who evaluated seven traditional and alternative

(geographic information system) methods for estimating total population in the vicinity of industrial and

waste sites for the Environmental Protection Agency.

In what they called the “Census polygon land use

FULTON

COBB

PAULDING DE KALB

DOUGLAS

FAYETTE COWETA

GWINNETT

CLAYTON

HENRY

ROCKDALE

FORSYTH

CARROLL

A T L A N T A

FULTON

COBB

PAULDING DE KALB

DOUGLAS

FAYETTE COWETA

GWINNETT

CLAYTON

HENRY

ROCKDALE

FORSYTH

CARROLL

A T L A N T A

84°

34°

85°

EXPLANATION

Land Use

Urban and Built−Up

Non−Urban

New "residential"

(1990 population density

greater than 1,000

people per square mile)

NAWQA study unit boundary

Apalachicola−

Chattahoochee−

Flint NAWQA

Georgia−Florida

Coastal Plain

NAWQA

G E O R G I A

0 15 MILES

0 15 KILOMETERS

Figure 3. Use of population density to indicate urban development in the Atlanta, Georgia, area from the 1970’s to 1990.

refinement” method, population is associated with

areas of residential land use obtained from the 1970’s

land-use data by overlaying the land use polygons

with census block group/enumeration district boundaries. The population density of the residential land

use polygons is calculated as the total population for

the block group divided by the total area of residential

land use within the block group. The total population

in the area of risk around a facility is calculated as the

residential area within the buffer zone multiplied by

the population density for the residential area in the

block group. Mynar and Hewitt compared the population estimates derived using the seven methods against

a reference population estimate and ranked the “Census polygon land use refinement” method first in their

evaluation of the various estimation methods.
Procedure for Refining Land Use 5

Process national data bases

Land use

The only national data base of land use is the

land use and land cover digital data from 1:250,000-

and 1:100,000-scale maps (U.S. Geological Survey,

1990). NAWQA is using this data base for its initial

digital spatial land-use data. The land use and land

cover maps portray Level II categories of the land use

and land cover classification system developed by

Anderson and others (1976). The level I and level II

land use categories pertaining to urban land are shown

in table 1.

The data are stored in a digital format called the

Geographic Information Retrieval and Analysis System (GIRAS), which can be converted to ARC/INFO

format (polygon coverage). The data are organized by

1:250,000- or 1:100,000-scale quadrangles. The

source of the data is aerial photography from the

1970’s and mid-1980’s (U.S. Geological Survey,

1990).

Although the GIRAS land-use data is a nationally consistent data set that is uniform across the

nation, NAWQA’s use of the data is limited because

the compilation source materials are too old and the

resolution is too coarse. However, the data can be used

to show general patterns of land use and as a baseline

for measuring change since the 1970’s. A comparable

Table 1. Land use and land cover classification system for

urban or built-up land

[Source: Anderson and others (1976)]

Level I Classification Level

II Classification

1 Urban or built-up

land

11 Residential

12 Commercial and services

13 Industrial

14 Transportation, communications, and utilities

15 Industrial and commercial

complexes

16 Mixed urban or built-up

land

17 Other urban or built-up

land

nationwide data base for the 1990’s timeframe does

not exist, so this land-use data base is the foundation

for NAWQA’s refined land use classification scheme.

 Each of the first 20 NAWQA study units

obtained the GIRAS land use quadrangles for its area

and converted the data into ARC/INFO format (polygon coverages). Once in ARC/INFO format, the quadrangles were joined (with neatlines removed) and

clipped using the study unit boundary. The final result

is a polygon coverage for each study unit that contains

the Anderson Level II land use classifications clipped

to the study unit boundary.

Population

Counts and related information on population

and housing are portrayed in the 1990 Census of population and housing (Bureau of the Census, 1991a). The

Census furnishes the counts necessary to reapportion

seats in the U.S. House of Representatives and provides a wealth of information about social, economic,

and housing characteristics. The data are stored in

dBASE III Plus format. The Census Bureau supplies

programs to extract and summarize the data.

The basic data collection units of the Census are

“blocks,” which are small areas bounded on all sides

by physical features—such as streets, roads, streams,

and railroad tracks—and by legal boundaries—such as

city, town, and county limits and property lines. A

block is identified by a three-digit number. The Bureau

of the Census combines blocks into the geographical

hierarchy shown in table 2.

Table 2. Selected Census geographic areas, 1990

[Source: Bureau of the Census (1991b)]

Geographic area Estimated

number

Block 7,000,000

Block group 230,600

Census tract/Block numbering area (CTBNA) 62,000

County subdivision 6,000

County and equivalent area 3,286

State and equivalent area 60
6 Refining 1970’s Land-use Data with 1990 Population Data to Indicate New Residential Development

Approximately 7 million blocks are defined for

the United States and outlying areas. To make managing the data easier, the procedure summarizes population and housing unit digital data on the block group

level (summary level “740” in the programs supplied

by the Census Bureau). A block group is a cluster of

blocks having the same first digit of the 3-digit identifying numbers within a census tract/block numbering

area. The Census data base represents the location of

the block group by an internal point whose geographic

coordinates place it approximately at the center of the

block group. The point data for each block group

include statistical attributes such as area (square kilometers) and counts of population and housing units.

The boundaries of the block group are not included;

the only geographic reference is the internal point. To

perform spatial analysis on the Census data, the points

must be linked by the block group geographic key to

the 1990 TIGER/Line Census files, which are

described next (Bureau of the Census, 1990b).

An ARC/INFO point coverage was generated

from the locations of the block group population

points for the coterminous United States. To create a

population coverage for each study unit, the national

coverage was clipped using a 20,000 meter buffer

around the study unit boundary. Each point is referenced by a 12-digit block group identifier that is composed of state Federal Information Processing

Standard (FIPS) code, county FIPS code, and block

group number. The block group number is composed

of a 6-digit census tract/block numbering area code

and a 1-digit block group code. The 12-digit identifier

is used to relate the points in a study unit population

coverage to the polygons in a study unit TIGER/Line

file. Below is an example of a 12-digit block group

identifier and its components:

Census geography

The 1990 Census TIGER/Line files are digital

geographic data for all 1990 Census map features and

boundaries. The primary sources of the data base were

the U.S. Geological Survey’s 1:100,000-scale maps

and the Census Bureau’s 1980 GBF/DIME (Geographic Base File/Dual Independent Map Encoding)

files. The 100,000-scale source material covers about

98 percent of the land area of the United States, and

the GBF/DIME files cover the remaining 2 percent

(metropolitan areas). TIGER/Line files have their own

internal structure (Topologically Integrated Geographic Encoding and Referencing system). The files

are organized by county, and although block group

boundaries are not specifically coded, they can be

assembled from block boundaries. The TIGER/Line

files contain no statistical information; the files provide geographic layers for spatial analysis. The layers

are linked to data in the Census of population and

housing by the block group geographic key (Bureau of

the Census, 1990c,d).

The TIGER/Line files can be converted into

ARC/INFO format, although considerable computer

processing time and storage space are required.

NAWQA obtained TIGER/Line county files that

already had been converted into ARC/INFO format

and assembled into polygon coverages of block groups

by state. From the state coverages, a block group coverage was constructed for each study unit. If a study

unit falls in only one state, the block group boundaries

for that state are clipped to the study unit boundary. If

a study unit falls in more than one state, each of the

state block group polygon coverages is clipped to the

study unit boundary separately, and then all of the

pieces are joined into one block group coverage for the

whole study unit. Each polygon in the study unit block

group coverage is referenced by a 12-digit block group

identifier that is composed of state, county, and block

group numbers. The 12-digit identifier is used to relate

the polygons in a study unit TIGER/Line coverage to

the points in a study unit population coverage.

Develop a classification scheme for defining

“residential” land use

A nationally consistent way of classifying urban

or residential land use based on population or housing

unit density was not found during the research for this

project. The U.S. Bureau of the Census defines its

“Urbanized Areas” using a combination of total popu13/117/130100/1

Block group

Census tract/Block numbering area

County FIPS code

State FIPS code
Procedure for Refining Land Use 7

lation greater than 50,000 people and population density of 1,000 or more people per square mile (386 or

more people per square kilometer). However, this definition did not provide for gradations of “suburban” or

“rural” development that are of interest to NAWQA.

 A review of definitions of residential land use

categories compiled from several local agencies

showed that definitions for residential development

vary from place to place, but most are based on the

number of dwelling units per acre. However, what is

considered to be low-density residential development

in a predominantly suburban area may be considered

high-density development in a predominantly rural

area. The land use classification systems used by Fairfax and Loudoun counties in northern Virginia and

Suffolk and Nassau counties on Long Island, New

York, are shown in table 3. What is considered to be

suburban in Fairfax, Suffolk, and Nassau counties

(predominantly suburban areas) is considered to be

urban development in Loudoun county, which is less

developed than the other counties. Therefore, a composite definition of “residential” land use based on

population or housing unit density was needed.

Using categories of housing densities from

county classifications such as in table 3, five categories

that appear to meaningfully distinguish types of residential land (table 4) were determined. Housing densities were selected to differentiate two “non-urban” and

three “urban” classes. Then the relation between housing and population densities was used to define population density categories for the same five classes. The

split between “urban” and “non-urban” was set at the

1,000 person per square mile limit defined by the

Bureau of the Census. The other classes were determined using the relation on logarithmic scales

between housing density (in units per acre) and population density (in people per square mile) for the 8,556

block groups within Virginia (fig. 4).2

2

The outlier having a population density of almost 700,000

people per square mile is block group 515102024972 in Alexandria, Virginia. This is a very small block group created from a

sliver polygon that resulted when the census maps were digitized.

The sliver polygon really should have been a block that was incorporated into a larger census tract, but instead the sliver polygon

was given its own tract number. This “sliver tract” has an area that

is smaller in relation to its population than the area of a regular

block group has to its population, so the population and housing

unit densities are larger than the other block groups.

Table 3. Examples of land use classification systems for

residential land used by Fairfax and Loudoun Counties,

Virginia, and Suffolk and Nassau Counties, New York

 [Sources: Fairfax County from Fairfax County, Virginia, 1990; Loudoun

County from L. Stipek, oral commun., 1992; Suffolk and Nassau Counties

from Long Island Regional Planning Board, 1982]

The line shown in figure 4 is the “line of organic

correlation” or LOC (Helsel and Hirsch, 1992). This

line is more appropriate than linear regression when

the relation between two variables is of interest,

instead of a prediction of one variable from the other.

The LOC has been used in geomorphology to study

the relation between channel width and depth, and in

paleontology to model the relation between bone

length and thickness. In this study, either density variable could be considered as “causing” the other, and

no unique direction for predicting one variable from

the other is evident. Therefore the LOC is more appropriate to use than regression. The best fit line describing the relation between these variables has the

formula:

County Land-use

category Dwelling units per acre

Fairfax County,

Virginia.

Suburban................ 0.5 to 1.0

(single

family)

to

Up to 20

(multifamily)

Low-density

residential. 0.1 to 0.2 to 0.2 to 0.5

Suburban center...... 5 to 25

(noncore)to

15 to 35

(core)

Loudoun County,

Virginia. Farm........................ Less than .06

Farmette.................. .06 to .25

Rural development. .25 to 1

Urban ...................... 1 to 10

Dense urban............ More than 10

Suffolk and Nassau

Counties, New

York.

Low density

residential. 1 or fewer

Medium density

residential. 2 to 4

Intermediate

density

residential.

5 to 10

High density

residential. 11 or more
8 Refining 1970’s Land-use Data with 1990 Population Data to Indicate New Residential Development

log(housing density) = -3.18 + log(population density)(1)

or

log(population density) = 3.18 + log(housing density).(2)

Unlike linear regression, the same equation is valid

going in either direction. The slope of 1.0 (in log units)

is a consequence of this data set only.

Using these five population density classes, the

1970’s land-use data can be updated to reflect 1990

residential conditions. Block groups from the 1990

Census of Population having population density

greater than 1,000 people per square mile can be

labeled “urban.” Areas that have experienced significant population growth since the 1970’s could then

change from being labeled “non-urban” in the 1970’s

data base to being labeled “urban” in the derivative

map based on the update with population data.

Table 4. Population density classes developed for indicating

urban land use

Population

density, in

people per

square mile

Housing unit

density, in units

per acre

Assigned landuse category and

category number

“Non-urban” land use

Less than 130 Less than 0.2 “Rural development”

(1)

130 to 1,000 0.2 to 0.7 “Low density residential” (2)

“Urban” land use

1,000 to 5,180 0.7 to 3.4 “Medium density

residential” (3)

5,180 to 13,000 3.4 to 8.6 “High density residential” (4)

More than 13,000 More than 8.6 “Very high density

residential” (5)

0.0001

1,000

0.001

0.01

0.1

1

10

100

HOUSING UNIT DENSITY, IN UNITS PER ACRE

0.01 0.1 1 10 100 1,000 10,000 100,000 1,000,000

POPULATION DENSITY, IN PEOPLE PER SQUARE MILE

Figure 4. The relation of housing unit density to population density.
Application and Limitations of Procedure 9

Produce maps of the 1990 population density

at the block-group geographic level

The first step in producing maps of the 1990

population density is to summarize population and

land area for each block group in the study unit population coverage. (Block groups may be represented by

more than one population point in that data base.) The

total population for each block group is calculated by

summarizing the population for every point having the

same unique 12-digit block group identifier. The total

land area of the block group is calculated by summarizing the land area of every point having the same

unique block group identifier. The ARC/INFO “FREQUENCY” command accomplishes this. The frequency items (fields used to identify unique

combinations of codes) are state, county, census tract/

block numbering area, and block group. The summary

items (fields that are summarized for each unique

occurrence of the frequency items) are 1990 population and land area. The result is a lookup table listing

each unique 12-digit block group identifier, the total

population for each block group, and the total land

area for each block group.

 The next step is to calculate population density

for each block group, which equals the total population for the block group divided by the total land area

of the block group. (Block groups having zero land

area are excluded.) Each block group is assigned to

one of the five classes of population density defined in

step 2 and shown in table 4. The assigned density class

is stored in the lookup table by block group identifier.

Overlay the maps of 1990 population density

on maps of 1970’s land-use data

 The polygons in the land use coverage and

those in the block group coverage are overlaid using

ARC/INFO’s polygon-on-polygon overlay capabilities. Overlaying the polygons of the land use coverage

on the polygons of the block group coverage creates a

new coverage that is a combination of land use and

block group polygons. Both the land use and block

group attributes are contained in the new coverage so

that each polygon can be identified by an Anderson

Level II land use category as well as by a 12-digit

block group identifier.

Categorize land use in the derivative maps

according to population density

 The lookup table of population density calculated from 1990 Census data is linked to the derivative

land use map by the 12-digit block group identifier.

Each polygon in the derivative map is assigned the

population density for the corresponding block group

regardless of the polygon’s original land use.

When reclassifying the land use polygons, it is

assumed that any area that was called “urban or builtup” during the 1970’s remains “urban or built-up” land

(Level I, code 1) today since urban land is not likely to

revert to other uses. It also is assumed that any area

that was called “water” (Level I, code 5) remains

“water.” The urban and water areas are excluded when

the land use is re-classified by population density.

 Any polygon meeting the following criteria is

assigned a new land use of “residential:” 1) the original land use of the polygon is not urban, 2) the original

land use of the polygon is not water, and 3) the population density of the polygon is greater or equal to 1,000

people per square mile—density categories 3, 4, or 5.

APPLICATION AND LIMITATIONS OF

PROCEDURE

The technique for using population to define

residential land use was applied to the first 20

NAWQA study units. The results for four of the study

units are shown in figures 5–8. The maps of new “residential” land use show similar patterns of increased

areas of “residential” land surrounding areas previously defined as “urban or built-up.” This pattern is

one that would be expected where suburban areas are

expanding from a central urban core, which is illustrated by Atlanta, Georgia (Apalachicola–Chattahoochee–Flint study unit); Las Vegas, Nevada

(Nevada Basin and Range study unit); and Indianapolis, Indiana (White River Basin study unit). The maps

also show new “residential” land use where no urban

or built-up land use existed previously. People in the

NAWQA study units have verified that the results give

reasonable indications of urbanization that has

occurred in their study units since the 1970’s land-use

data were compiled.
10 Refining 1970’s Land-use Data with 1990 Population Data to Indicate New Residential Development

78°

40°

39°

38°

77°

79°

0 25 50 75 KILOMETERS

0 10 20 30 40 50 MILES

EXPLANATION

Land Use

Urban and Built−Up

Non−Urban

New "residential"

(1990 population density

greater than 1,000

people per square mile)

Figure 5. Use of population density to indicate 1990 urban land use in the Potomac River Basin study unit of the National

Water-Quality Assessment.
Application and Limitations of Procedure 11

31°

33°

34°

84°

32°

30°

85°

0 25 50 75 KILOMETERS

0 10 20 30 40 50 MILES

EXPLANATION

Land Use

Urban and Built−Up

Non−Urban

New "residential"

(1990 population density

greater than 1,000

people per square mile)

Figure 6. Use of population density to indicate 1990 urban land use in the Apalachicola–

Chattahoochee–Flint study unit of the National Water-Quality Assessment.
12 Refining 1970’s Land-use Data with 1990 Population Data to Indicate New Residential Development

39°

120°

36°

115°

119°

40°

0 25 50 75 KILOMETERS

0 10 20 30 40 50 MILES

EXPLANATION

Land Use

Urban and Built−Up

Non−Urban

New "residential"

(1990 population density

greater than 1,000

people per square mile)

Figure 7. Use of population density to indicate 1990 urban land use in the Nevada Basin and Range study unit

of the National Water-Quality Assessment.
Application and Limitations of Procedure 13

86°

40°

39°

87°

85°

0 25 50 75 KILOMETERS

0 10 20 30 40 50 MILES

EXPLANATION

Land Use

Urban and Built−Up

Non−Urban

New "residential"

(1990 population density

greater than 1,000

people per square mile)

Figure 8. Use of population density to indicate 1990 urban land use in the White River Basin study unit of the

National Water-Quality Assessment.
14 Refining 1970’s Land-use Data with 1990 Population Data to Indicate New Residential Development

The above classification system will enable

NAWQA scientists to relate today’s water quality

information to recent georeferenced information on

where people are living, rather than to data that are 20

years old. Water-quality information collected in

NAWQA study units will be related to variables representing human and natural influences. Some of those

will be population or housing densities, or information

derived from those densities. For example, a study by

Eckhardt and Stackelberg (1994) has shown that there

are generally different concentrations of groundwater

contaminants under low, medium, and high density

residential areas of Long Island. As a second example,

surface-water concentrations may be related to the percentage of the area upstream of the measuring site that

is in each of the population classes. Long-term measurements of quality at one site may be directly related

to the change in population density between the 1970’s

and 1990’s, with concentrations of contaminants

increasing as population has increased.

The use of population data to define residential

land use has several limitations. First, it represents

only one type of “urban” land use and does not

account for the commercial, industrial, or transportation components of “urban” land use. However, refining these uses with other ancillary data besides

population may be possible. Second, the density of

population does not measure structures on the land; it

measures only how many people are in a given area.

Third, the assumptions about excluding urban and

water categories of use when re-classifying polygons

could introduce error. Although unlikely, urban areas

possibly could have reverted to non-urban uses. Also,

water areas possibly could have been converted to

urban uses. Fourth, the procedure did not remove

block groups coded as “water” in the TIGER/Line

files. The procedure removed polygons coded as

“water” in the land-use data. Last, assigning population density and the corresponding land use classification by entire block groups might over-represent the

areal extent of new “residential” areas because the

actual extent of the new “residential” area could be

smaller than the whole block group.

CONCLUSION

Using this scheme based on population density,

one can refine residential land use in any National

Water-Quality Assessment study unit using the 1970’s

land use and 1990 population density. The technique

gives a uniform definition of “residential” land use

representative of the early 1990’s timeframe that is

useful in comparing and contrasting findings about

water-quality in NAWQA study units.

REFERENCES CITED

Anderson, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E.,

1976, A land use and land cover classification system

for use with remote sensor data: U.S. Geological Survey Professional Paper 964, 28 p.

Bureau of the Census, 1990, TIGER: The coast-to-coast

digital map data base: 18 p.

———1991a, Census of population and housing, 1990:

Public Law 94-171 data (United States) [machinereadable data files]: Washington, D.C., The Bureau

[producer and distributor].

———1991b, Census of population and housing, 1990:

Public Law 94-171 data technical documentation:

Washington, D.C., The Bureau.

———1991c, TIGER/Line Census Files, 1990 [machinereadable data files]: Washington, D.C., The Bureau

[producer and distributor].

———1991d, TIGER/Line Census Files, 1990 technical

documentation: Washington, D.C., The Bureau, 63 p.

Eckhardt, D.A.V., and Stackelberg, P.E., 1994, Relation of

ground-water quality to land use on Long Island, New

York: Ground Water Journal [in press].

Fairfax County, Virginia, 1990, Concept for future development and land classification system, 74 p.

Helsel, D.R., and Hirsch, R.M., 1992, Statistical methods in

water resources: New York, Elsevier, p. 276.

Leahy, P.P., Rosenshein, J.S., and Knopman, D.S, 1990,

Implementation plan for the National Water-Quality

Assessment Program: U.S. Geological Survey OpenFile Report 90-174, 10 p.

Leahy, P.P., and Thompson, T.H., 1994, National WaterQuality Assessment Program, U.S. Geological Survey

Open-File Report 94-70, 4 p.
References Cited 15

Long Island Regional Planning Board, 1992, Quantification

and analysis of land use for Nassau and Suffolk Counties, 1981: Hauppauge, N.Y., 47 p.

Mynar, F., II, and Hewitt, M. J., III, 1989, Population estimation for risk assessment: A comparison of methods,

in ESRI User Conference, 9th annual meeting, Palm

Springs, California, May 22-26, 1989, Proceedings:

Redlands, Calif., Environmental Systems Research

Inc., 7 p. [Each article separately paged.]

Tasker, G.D., and Driver, N.E., 1988, Nationwide regression

models for predicting urban runoff water quality at

unmonitored sites: Water Resources Bulletin, vol. 24,

no. 5, p. 1091-1101.

U.S. Geological Survey, 1990, Land use and land cover digital data from 1:250,000- and 1:100,000-scale maps,

Data user guide 4: Reston, Va., U.S. Geological Survey, 25 p.

Vesterby, M., Heimlich, R.E., and Krupa, K.S., 1994,

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