National Water-Quality Assessment Program

Derivation of Nationally Consistent Indices

Representing Urban Intensity within and across 

Nine Metropolitan Areas of the Conterminous 

United States

Scientific Investigations Report 2008–5095
Cover. Left: Logan River, Utah. Right: Urbanization in the Salt Lake basin, Utah (photographs taken by Elise M. Giddings, formerly with the 

U.S. Geological Survey).
Derivation of Nationally Consistent Indices 

Representing Urban Intensity within and 

across Nine Metropolitan Areas of the 

Conterminous United States

By Thomas F. Cuffney and James A. Falcone

National Water-Quality Assessment Program

Scientific Investigations Report 2008–5095

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2009

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Suggested citation:

Cuffney, T.F., and Falcone, J.A., 2009, Derivation of nationally consistent indices representing urban intensity 

within and across nine metropolitan areas of the conterminous United States: U.S. Geological Survey Scientific 

Investigations Report 2008–5095, 36 p. 

[Available online at http://pubs.water.usgs.gov/sir2008-5095]
Foreword

The U.S. Geological Survey (USGS) is committed to providing the Nation with credible 

scientific information that helps to enhance and protect the overall quality of life and 

that facilitates effective management of water, biological, energy, and mineral resources 

(http://www.usgs.gov/). Information on the Nation’s water resources is critical to ensuring 

long-term availability of water that is safe for drinking and recreation and is suitable for 

industry, irrigation, and fish and wildlife. Population growth and increasing demands for 

water make the availability of that water, now measured in terms of quantity and quality, 

even more essential to the long-term sustainability of our communities and ecosystems.

The USGS implemented the National Water-Quality Assessment (NAWQA) Program in 

1991 to support national, regional, State, and local information needs and decisions related 

to water-quality management and policy (http://water.usgs.gov/nawqa). The NAWQA 

Program is designed to answer: What is the condition of our Nation’s streams and ground 

water? How are conditions changing over time? How do natural features and human 

activities affect the quality of streams and ground water, and where are those effects most 

pronounced? By combining information on water chemistry, physical characteristics, 

stream habitat, and aquatic life, the NAWQA Program aims to provide science-based 

insights for current and emerging water issues and priorities. From 1991 to 2001, the 

NAWQA Program completed interdisciplinary assessments and established a baseline 

understanding of water-quality conditions in 51 of the Nation’s river basins and aquifers, 

referred to as Study Units (http://water.usgs.gov/nawqa/studyu.html). 

Multiple national and regional assessments are ongoing in the second decade (2001–2012) 

of the NAWQA Program as 42 of the 51 Study Units are reassessed. These assessments 

extend the findings in the Study Units by determining status and trends at sites that 

have been consistently monitored for more than a decade, and filling critical gaps in 

characterizing the quality of surface water and ground water. For example, increased 

emphasis has been placed on assessing the quality of source water and finished water 

associated with many of the Nation’s largest community water systems. During the second 

decade, NAWQA is addressing five national priority topics that build an understanding of 

how natural features and human activities affect water quality, and establish links between 

sources of contaminants, the transport of those contaminants through the hydrologic 

system, and the potential effects of contaminants on humans and aquatic ecosystems. 

Included are topics on the fate of agricultural chemicals, effects of urbanization on 

stream ecosystems, bioaccumulation of mercury in stream ecosystems, effects of nutrient 

enrichment on aquatic ecosystems, and transport of contaminants to public-supply wells. 

These topical studies are conducted in those Study Units most affected by these issues; 

they comprise a set of multi-Study-Unit designs for systematic national assessment. In 

addition, national syntheses of information on pesticides, volatile organic compounds 

(VOCs), nutrients, selected trace elements, and aquatic ecology are continuing. 

The USGS aims to disseminate credible, timely, and relevant science information to 

address practical and effective water-resource management and strategies that protect and 

restore water quality. We hope this NAWQA publication will provide you with insights 

and information to meet your needs, and will foster increased citizen awareness and 

involvement in the protection and restoration of our Nation’s waters. 
The USGS recognizes that a national assessment by a single program cannot address all 

water-resource issues of interest. External coordination at all levels is critical for costeffective management, regulation, and conservation of our Nation’s water resources. The 

NAWQA Program, therefore, depends on advice and information from other agencies—

Federal, State, regional, interstate, Tribal, and local—as well as nongovernmental 

organizations, industry, academia, and other stakeholder groups. Your assistance and 

suggestions are greatly appreciated.

 Matthew C. Larsen

Associate Director for Water
v

Contents

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

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

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

Purpose and Scope ..............................................................................................................................2

Methods...........................................................................................................................................................2

Compilation of GIS Data.......................................................................................................................3

Derivation of Metropolitan Area Urban Intensity Indices (MA-UII).............................................7

Derivation of National Urban Intensity Indices (MA-NUII, NUII) .................................................7

Derivation and Comparison of Urban Intensity Indices ..........................................................................8

Metropolitan Area Urban Intensity Indices (MA-UII).....................................................................8

Selection of Variables for National Urban Intensity Indices .......................................................10

Metropolitan Area National Urban Intensity Index (MA-NUII)...................................................15

National Urban Intensity Index (NUII).............................................................................................15

Comparison of the MA-UII, MA-NUII, and NUII ............................................................................18

Summary and Conclusions.........................................................................................................................21

Characteristics of a National Index.................................................................................................21

Future Development of Urban Intensity Indices............................................................................22

Literature Cited.............................................................................................................................................22

Figures

1. Map showing locations of the nine metropolitan areas included in the EUSE studies ....2

2. Conceptual model showing examples of how land-use and population 

characteristics associated with urbanization can affect physical, chemical, 

and biological characteristics of streams................................................................................3

3–8. Graphs showing:

3. Response of the (A) HUDEN and (B) PPURBAN census variables in relation 

to population density (POPDEN00) for the Milwaukee-Green Bay (MGB) 

metropolitan area ............................................................................................................10

4. Slopes (mean with 95 percent confidence interval) relating MA-NUII 

variables and POPDEN00 before and after adjusting for differences in the 

response rates among metropolitan areas.................................................................16

5. Linear regressions relating percentage developed land (P_NLCD1_2) to 

population density in Denver (DEN) and Raleigh (RAL) (A) before and 

(B) after adjusting the data for differences in response rates and maximum 

population density ...........................................................................................................17

6. Maximum population density (km-2) for sampling sites in each of the nine 

metropolitan areas ..........................................................................................................17

7. Distribution of sites in each metropolitan area based on the (A) MA-UII 

and MA-NUII indices or the (B) NUII index ................................................................19

8. Relation between the MA-UII and MA-NUII indices for the nine metropolitan 

areas showing (A) linear and (B) curvilinear responses ..........................................20
vi

Tables

1. GIS-derived variables that were considered in the development of urban intensity 

indices (A) nationally and (A and B) for individual metropolitan areas...............................4

2. Hypothetical example illustrating the calculation of the national urban intensity 

indices (MA-UII, MA-NUII) for a metropolitan area with 30 sites........................................8

3. Urban intensity indices and variables used to calculate the national urban 

intensity indices (MA-NUII, NUII) for nine metropolitan areas ..........................................25

4. Census, land-cover, and infrastructure variables that were used to construct 

the metropolitan area urban intensity index (MA-UII) for each metropolitan area ..........9

5. Spearman rank correlations between 2000 population density (POPDEN00) and 

variables considered in the derivation of nationally consistent urban intensity 

indices (MA-NUII, NUII) ............................................................................................................11

6. National land-cover data (NLCD), census, and infrastructure variables that 

showed consistent linear responses to changes in 2000 population density 

for nine metropolitan areas.......................................................................................................15

7. Linear regressions used to project the value of the urban intensity index 

variables at the maximum level of population density (2,174.5) observed 

in nine urban studies (projected value) ..................................................................................18

8. Percentage of variables used in the construction of the metropolitan area 

urban intensity index (MA-UII) that showed a linear, curvilinear, 

or indeterminate relation with population density ................................................................20

Conversion Factors, Acronyms, Abbreviations, and 

Definitions

SI to Inch/Pound

Multiply By To obtain

Length

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) 

Area

square meter (m2

) 0.0002471 acre 

square kilometer (km2

) 247.1 acre

square meter (m2

) 10.76 square foot (ft2) 

hectare (ha) 0.003861 square mile (mi2

) 

square kilometer (km2

) 0.3861 square mile (mi2)

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

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

Elevation, as used in this report, refers to distance above the vertical datum.
vii

Metropolitan area abbreviations and land-cover definitions

ATL Atlanta, Georgia (GA), metropolitan area

BIR Birmingham, Alabama (AL), metropolitan area

BOS Boston, Massachusetts (MA), metropolitan area

DEN Denver, Colorado (CO), metropolitan area

DFW Dallas-Fort Worth, Texas (TX), metropolitan area

EUSE Effects of Urbanization on Stream Ecosystems Studies

GIS Geographic information system

HUDEN Housing-unit density

LDI Landscape Development Intensity

MGB Milwaukee-Green Bay, Wisconsin (WI), metropolitan area

NAWQA National Water-Quality Assessment Program

NHD National Hydrography Data

NLCD01 National Land Cover Data 2001

NOAA National Oceanic and Atmospheric Administration

NPDES National Pollutant Discharge Elimination System

P_NLCD1_2 Percentage of developed land in basin

POR Portland, Oregon (OR), metropolitan area

RAL Raleigh-Winston Salem, North Carolina (NC), metropolitan area

ROADDEN Road density

SLC Salt Lake City, Utah (UT), metropolitan area

TRI Toxics release inventory

USGS U.S. Geological Survey

USEPA U.S. Environmental Protection Agency

Statistical and mathematical abbreviations and definitions

MA-NUII Metropolitan area national urban intensity index

MA-UII Metropolitan area urban intensity index

NUII National urban intensity index

R2 Regression coefficient

≥ Greater than or equal to

> Greater than

< Less than

% Percentage

ρ Spearman rank correlation coefficient

|ρ| Absolute value of the Spearman rank correlation coefficient

Abstract

Two nationally consistent multimetric indices of urban 

intensity were developed to support studies of the effects of 

urbanization on streams in nine metropolitan areas of the 

conterminous United States: Atlanta, Georgia; Birmingham, 

Alabama; Boston, Massachusetts; Dallas-Fort Worth, Texas; 

Denver, Colorado; Milwaukee-Green Bay, Wisconsin; 

Portland, Oregon; Raleigh, North Carolina; and Salt Lake 

City, Utah. These studies were conducted as a part of the 

U.S. Geological Survey’s National Water-Quality Assessment 

Program. These urban intensity indices were used to define 

gradients of urbanization and to interpret biological, physical, 

and chemical changes along these gradients. Ninety census, 

land-cover, and infrastructure variables obtained from nationally available databases were evaluated. Only variables that 

exhibited a strong and consistent linear relation with 2000 

population density were considered for use in the indices. 

Housing-unit density (HUDEN), percentage of basin area in 

developed land (P_NLCD1_2), and road density (ROADDEN) 

were selected as the best representatives of census, land-cover, 

and infrastructure variables. The metropolitan area national 

urban intensity index (MA-NUII) was scaled to represent 

urban intensity within each metropolitan area and ranged 

from 0 (little or no urban) to 100 (maximum urban) for sites 

within each metropolitan area. The national urban intensity 

index (NUII) was scaled to represent urban intensity across all 

nine metropolitan areas and ranged from 0 to 100 for all sites. 

The rates at which HUDEN, P_NLCD1_2, and ROADDEN 

changed with changes in population density varied among 

metropolitan areas. Therefore, these variables were adjusted 

to obtain a more uniform rate of response across metropolitan 

areas in the derivation of the NUII. The NUII indicated that 

maximum levels of urban intensity occurred in the West 

and Midwest rather than in the East primarily because small 

inner-city streams in eastern metropolitan areas are buried 

and converted to storm drains or sewers and because of higher 

density development in the Western and Central United States. 

The national indices (MA-NUII, NUII) were compared to 

indices that were derived independently for each metropolitan 

area (MA-UII) based on variables that were of local interest. 

The MA-UIIs, which were based on 5 to 40 variables, tended 

to overestimate urban intensity relative to the national indices 

particularly when the MA-UII was composed of large numbers 

of variables that were not linearly related to population density 

as in Denver, Dallas-Fort Worth, and Milwaukee-Green Bay. 

Introduction

The U.S. Geological Survey (USGS) is studying the 

effects of urbanization on the biological, physical, and 

chemical characteristics of streams as a part of the Effects of 

Urbanization on Stream Ecosystems (EUSE) studies of the 

National Water-Quality Assessment (NAWQA) Program. Nine 

EUSE studies (fig. 1) have been completed in metropolitan 

areas associated with Atlanta, GA (ATL); Birmingham, AL 

(BIR); Boston, MA (BOS); Dallas-Fort Worth, TX (DFW); 

Denver, CO (DEN); Milwaukee-Green Bay, WI (MGB); 

Portland, OR (POR); Raleigh, NC (RAL); and Salt Lake 

City, UT (SLC). Biological, physical, and chemical responses 

to urbanization were studied using a common experimental 

design (Coles and others, 2004; Tate and others, 2005) that 

selects representative sites along a gradient of urban intensity 

from rural to highly urbanized in each metropolitan area. 

Confounding effects of natural environmental factors were 

minimized by limiting each study to an area with relatively 

homogeneous natural environmental features (for example, 

similar stream size, ecoregion [Omernik, 1987], climate, and 

potential natural vegetation). 

The urban gradient is defined using a multimetric index 

of urban intensity based on census, land-cover, and infrastructure variables (McMahon and Cuffney, 2000; Coles and others, 

2004; Tate and others, 2005) obtained from local and national 

data (Tate and others, 2005; Falcone and others, 2007). The 

use of a multimetric index emphasizes the multivariate nature 

of urbanization and compensates for variability in the derivation of the characteristics of urbanization. For example, census 

data are derived from census blocks that vary in size from 

very large in rural areas (counties) to very small (city blocks) 

in heavily populated urban areas. Consequently, basin-level 

census values (for example, population density, housing 

Derivation of Nationally Consistent Indices Representing 

Urban Intensity within and across Nine Metropolitan 

Areas of the Conterminous United States

By Thomas F. Cuffney and James A. Falcone
2 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

density, median income) were derived from a larger number 

of census blocks in urban areas than in rural areas (Falcone 

and others, 2007). In contrast, national land-cover data derived 

from satellite images with a constant pixel size provide a 

more consistent spatial scale across the country. A multimetric 

approach tends to offset errors in any one measurement and 

has been employed to great effect in biomonitoring and is 

applicable to non-biological variables (Karr, 1981, 1993; 

Yoder and Rankin, 1995; Barbour and others, 1996). 

The urban intensity index is meant to characterize the 

changes that occur as an increasing population alters the 

landscape and directly and indirectly changes the physical, 

chemical, and biological characteristics of streams (fig. 2). 

Construction of buildings, roads, sewers, and other elements 

of urban infrastructure can lead to deforestation, increased 

sedimentation, increased imperviousness, reduced infiltration, 

and increased runoff, which can alter the structure of the riparian zone leading to changes in light penetration, bank stability, 

organic matter inputs, and water temperature. Sedimentation 

and changes in hydrology can alter instream habitats by burying substrates or changing the timing, magnitude, and duration 

of high and low flows. Increased residential and commercial 

chemical use can add contaminants, such as pesticides and 

metals, to water and sediment. Collectively, these changes can 

affect the biota of a stream leading to a loss of biodiversity and 

ecosystem function (McDonnell and Pickett, 1990; Sala and 

others, 2000; Paul and Meyer, 2001; Fitzpatrick and others, 

2004; Brown and others, 2005; Sprague and others, 2006).

Population density was chosen as the central determinant 

of urban intensity in the EUSE studies because urban effects 

arise primarily from the goods and services (for example, 

roads, housing, utilities, and 

transportation) associated with 

supporting an increasing population density. Consequently, each 

of the nine urban studies derived 

a multimetric index of urban 

intensity based on a set of census, 

land-cover, and infrastructure 

variables that were correlated 

with population density and 

deemed appropriate for studying 

the process of urbanization in the 

respective metropolitan area. This 

metropolitan area urban intensity 

index (MA-UII) was constructed 

from as few as 5 (Atlanta, Raleigh) 

to as many as 40 (Dallas-Fort 

Worth) variables depending on 

the aspects of urbanization that 

were emphasized within each 

metropolitan area (Coles and 

others, 2004; Tate and others, 

2005; Sprague and others, 2006; 

Gregory and Calhoun, 2007; 

K.D. Richards and others, U.S. 

Geological Survey, written commun., 2007; Moring, 2008; 

Waite and others, 2008). While the locally derived MA-UII 

was suitable for representing the urban gradient in individual 

studies, it was not appropriate for comparing responses to 

urbanization among metropolitan areas or across the Nation 

because of differences in the composition of the MA-UII. This 

report addresses this issue by developing two national urban 

intensity indices—metropolitan (MA-NUII) and national 

(NUII)—from nationally consistent variables that were scaled 

to represent urban intensity within (MA-NUII) and across 

(NUII) metropolitan areas.

Purpose and Scope

The purpose of this report is to describe the derivation of 

nationally consistent urban intensity indices for the NAWQA 

Program EUSE studies and to compare these national indices 

(MA-NUII, NUII) to locally derived urban intensity indices 

(MA-UII) for the nine metropolitan areas studied. These 

indices provide a consistent framework in which to interpret 

and compare changes in physical, chemical, and biological 

characteristics along gradients of urban intensity both locally 

and nationally. 

Methods

The nine urban studies were conducted using a common 

study design (McMahon and Cuffney, 2000; Coles and others, 

2004; Cuffney and others, 2005; Tate and others, 2005) 

Figure 1. Location of the nine metropolitan areas included in the EUSE studies. The shaded 

areas show the spatial extent of each study area. Open circles designate eastern metropolitan 

areas, solid circles designate central metropolitan areas, and open diamonds designate 

western metropolitan areas.

0 250 500 KILOMETERS

0 250 500 MILES
Methods 3

derived from nationally available geographic information 

system (GIS) variables (Falcone and others, 2007) to define 

a population of candidate basins (typically basins drained 

by second to third order streams) from which 28–30 basins 

were selected to represent a gradient of urbanization. Local 

and national GIS variables that represented the natural 

environmental setting (for example, ecoregion, climate, 

elevation, stream size) were used to minimize the effects of 

environmental variability by dividing candidate basins into 

groups with relatively homogenous environmental features. 

GIS variables that were associated with urbanization (for 

example, census, land cover, and infrastructure) were used to 

derive a multimetric index of urban intensity (MA-UII) that 

ranged from 0 (little or no urban) to 100 (maximum urban) in 

each metropolitan area. Study sites were selected to provide 

a relatively uniform distribution across the urban gradient 

(0 to 100) within as homogeneous an environmental setting 

as possible. The urban intensity index used to select sites 

(MA-UII) was derived independently for each urban study and 

incorporated from 5 to 40 variables (Coles and others, 2004; 

Tate and others, 2005; Sprague and others, 2006; Gregory and 

Calhoun, 2007; K.D. Richards and others, U.S. Geological 

Survey, written commun., 2007; Moring, 2008; Waite and 

others, 2008) depending on the urban factors that were deemed 

of interest in the study area. Selected variables from the GIS 

data developed by each urban study were compiled, updated, 

and used to derive two national urban intensity indices. Unlike 

the MA-UII, the variables used to derive the national indices 

did not vary among metropolitan areas. The metropolitan area 

national urban intensity index (MA-NUII) was scaled independently for each urban area, and the national urban intensity 

index (NUII) was scaled to represent urban intensity nationally 

(that is, scaled across the 265 sites from the 9 metropolitan 

areas).

Compilation of GIS Data

National urban intensity indices were derived from 90 

GIS variables (table 1A) for drainage basins with boundaries 

that were developed primarily from USGS 30-meter (m) 

National Elevation Data (U.S. Geological Survey, 2005a); a 

small number of basin boundaries were refined using higherresolution data (Coles and others, 2004; Tate and others, 2005; 

Falcone and others, 2007). Variables were derived for the 

entire basin (basin-level statistics) except the riparian landcover variables, which were calculated as a 100-m corridor 

on either side of the stream midline. Streams were defined on 

the basis of the USGS National Hydrography Data (NHD) 

1:100,000 stream set (U.S. Geological Survey, 2005b). 

Population density was calculated from 2000 census 

block data (GeoLytics, 2001). All other census variables 

were calculated from 2000 census block-group data. Road 

characteristics (RDLENGTH, ROADDEN, RDARDEN, 

Figure 2. Conceptual model showing examples of how land-use and population characteristics associated with 

urbanization can affect physical, chemical, and biological characteristics of streams.
4 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 1. GIS-derived variables that were considered in the development of urban intensity indices (A) nationally and (A and 

B) for individual metropolitan areas. The variables used in the construction of the national urban intensity indices (MA-NUII 

and NUII) are shown in bold. — Continued

[%, percentage; km2, square kilometer; <, less than; >, greater than; km, kilometer; m, meter; mm/hr, millimeter per hour]

A. National variables (available in all metropolitan areas)

Category Abbreviation Description

Census data

 Energy source

 PHUT Occupied housing units using utility gas (natural gas) as fuel (%)

PHLP Occupied housing units using liquid petroleum gas as fuel (%)

PHEL Occupied housing units using electricity as fuel (%)

PHOIL Occupied housing units using oil as fuel (%)

PHWOOD Occupied housing units using wood as fuel (%)

 Ethnicity

 PPWHITE Proportion of population race, white

PPBLACK Proportion of population race, black

PPNAM Proportion of population race, Native American

PPASIA Proportion of population race, Asian

P_US Proportion of citizens born in United States

P_INSTAT Proportion of citizens born in State of residence

P_OUTST Proportion of citizens born in other States in the United States

P_NONUS Proportion of citizens not born in United States

PC_CTY95 Proportion of citizens living in same county more than 5 years

PC_ST95 Proportion of citizens living in same State more than 5 years

 Housing age

PHU_L5 Proportion of housing units built between 1995 and 2000 

PHU_L10 Proportion of housing units built between 1990 and 2000 

PHU_L20 Proportion of housing units built between 1980 and 2000

PHU_G20 Proportion of housing units built prior to 1979 

PHU_G30 Proportion of housing units built prior to 1969 

PHU_G40 Proportion of housing units built prior to 1959 

PHU_G50 Proportion of housing units built prior to 1949 

PHU_G60 Proportion of housing units built prior to 1939

 Housing occupancy

 HHDEN Household density (occupied housing units/km2

)

HUDEN Density of housing units (housing units/km2

)

PHO_G4P Proportion of households occupied by 4 or more people

PP_SH95 Percent of population living in same house as in 1995

P_OWN Proportion of occupied housing units that are owner occupied

P_RENT Proportion of occupied housing units that are renter occupied

PHFAM Proportion of households occupied by a family

PHNONFAM Proportion of households occupied by a non-family

PHO_L3P Proportion of households occupied by less than 3 people

P_OCCUPY Proportion of housing units that are occupied

P_VACANT Proportion of housing units that are vacant

PH_1PERS Proportion of households occupied by 1 person

PH_2PERS Proportion of households occupied by 2 persons

PH_3PERS Proportion of households occupied by 3 persons

PH_4PERS Proportion of households occupied by 4 persons

PH_5PERS Proportion of households occupied by 5 persons

PH_6PERS Proportion of households occupied by 6 persons

PH_7PERS Proportion of households occupied by 7 or more persons
Methods 5

Table 1. GIS-derived variables that were considered in the development of urban intensity indices (A) nationally and (A and 

B) for individual metropolitan areas. The variables used in the construction of the national urban intensity indices (MA-NUII 

and NUII) are shown in bold. — Continued

[%, percentage; km2, square kilometer; <, less than; >, greater than; km, kilometer; m, meter; mm/hr, millimeter per hour]

Category Abbreviation Description

Census data (continued)

 Income

PHHI_L10 Proportion of households with annual income < $10,000

PHHI_L20 Proportion of households with annual income < $20,000

PHHI_L30 Proportion of households with annual income < $30,000

MEDHHI Median household income (dollars)

PM_GT16E Proportion of male population > 16 years of age and employed

PF_GT16E Proportion of female population > 16 years of age and employed

PPOP_POV Proportion of population with income below the poverty level

PFAM_POV Proportion of families with income below the poverty level

PHH_POV Proportion of households with income below the poverty level

PERCAPIN Per Capita income

MFAMINC Median family household income

MNFAMINC Median non-family household income

 Population

POPDEN00 2000 population density (people/km2)

POP90_00 Proportional change in population from 1990 to 2000

PPURBAN Proportion of population living in urban area

PPRURAL Proportion of population living in rural area

PP_L5Y Proportion of population < 5 years old

PHS_G25 Proportion of population > 25 years old with high school degree

PBCH_G25 Proportion of population > 25 years old with bachelors degree

POCC_G65 Proportion of housing units occupied by persons > 65 years of age

PPMALE Proportion of population that is male

PPFEMALE Proportion of population that is female

PM_GT25Y Proportion of male population > 25 years of age

PF_GT25Y Proportion of female population > 25 years of age

NLCD01 Level 1 land cover

 Percentage of basin area

P_NLCD1_1 Water 

P_NLCD1_2 Developed 

P_NLCD1_3 Barren 

P_NLCD1_4 Forest 

P_NLCD1_5 Shrubland 

P_NLCD1_7 Herbaceous upland natural/semi-natural vegetation (grassland) 

P_NLCD1_8 Crops and pasture 

P_NLCD1_9 Wetlands 

NLCD1_IS Mean percent impervious surface in the basin (NLCD data)

NOAA_1KM_IS Mean percent impervious surface in the basin (NOAA 1 km data)

 Percentage of riparian buffer (approximately 100-m area each side of stream centerline)

P_NLCD1_B1 Water 

P_NLCD1_B2 Developed 

P_NLCD1_B3 Barren 

P_NLCD1_B4 Forest 

P_NLCD1_B5 Shrubland 

P_NLCD1_B7 Herbaceous upland natural/semi-natural vegetation (grassland) 

P_NLCD1_B8 Crops and pasture 

P_NLCD1_B9 Wetlands 

NLCD1_BIS Mean percent impervious surface in the riparian zone
6 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 1. GIS-derived variables that were considered in the development of urban intensity indices (A) nationally and (A and 

B) for individual metropolitan areas. The variables used in the construction of the national urban intensity indices (MA-NUII 

and NUII) are shown in bold. — Continued

[%, percentage; km2, square kilometer; <, less than; >, greater than; km, kilometer; m, meter; mm/hr, millimeter per hour]

Category Abbreviation Description

Infrastructure

RDLENGTH Length of TIGER roads in basin (km) in basin

ROADDEN Road density in basin (km/km2

)

RDARDEN Road area index density (km/km2) 

RDTRDEN Road traffic index density (km/km2)

D_PSCOUNT Point-source dischargers in basin (number/100 km2)

D_TRICOUNT Toxics Release Inventory sites in basin (number/100 km2)

B. Local variables (available in some metropolitan areas, but not nationally)

Category Abbreviation Description

Census data

 Ethnicity

P_NWHT99 Percent of 1999 population that is non-white 

 Housing age

PHOUSL80 Percent of 1990 housing units built before 1980

 Housing occupancy

PHH2 Proportion of households that are two-person households

P_HU3RM Proportion of total housing units that have three bedrooms

P_FHHF90 Percent of families with female head of household 

 Population

POP90_99 Percentage change in population density between 1990 and 1999

SEI_2 Socio-economic index 2

SEI_3 Socio-economic index 3

SEI_4 Socio-economic index 4

SEI_5 Socio-economic index 5

NLCD01 Level 2 land cover

 Percentage of basin area

P_NLCD1_21 Low-intensity residential

P_NLCD1_22 High-intensity residential

P_NLCD1_23 Commercial/industrial/transportation

P_NLCD1_41 Deciduous forest

P_NLCD1_42 Evergreen forest

P_NLCD1_43 Mixed forest

P_NLCD1_81 Pasture/hay

P_NLCD1_82 Row cops

P_NLCD1_85 Urban/recreational grasses

P_NLCD1_95 Wetlands, emergent herbaceous wetlands

Infrastructure

P_SEWER Percent of housing units on public sewer

DAMDEN Dam density (number/100 hectares)

Miscellaneous

PHSG2 Hydrologic soil group B, minimum infiltration rate 4-8 mm/hr (percent basin area)

PLITH_146 Percent of basin area in lake sediment and playa

WELLPCT Proportion of watershed with well-drained soils
Methods 7

RDTRDEN) were derived from Census 2000 TIGER roads 

(GeoLytics, 2001). Point-source discharge count density 

(DPSCOUNT, number of discharge permits per 100 square 

kilometers (km2) was derived from the U.S. Environmental 

Protection Agency (USEPA) National Pollutant Discharge 

Elimination System (NPDES) (U.S. Environmental Protection 

Agency, 2005a). Toxics release inventory (TRI) count density 

(D_TRICOUNT, number of TRI dischargers per 100 km2) was 

derived from the USEPA Toxics Release Inventory database 

(U.S. Environmental Protection Agency, 2005b). 

Land-cover data for ATL, BOS, BIR, RAL, and SLC 

were based on the National Land Cover Data 2001 (NLCD01) 

dataset (U.S. Geological Survey, 2005c). Land-cover data for 

POR were derived (Falcone and others, 2007) by using the 

NLCD01 class structure to process data from the National 

Oceanic and Atmospheric Administration (NOAA) Coastal 

Change Analysis Program (National Oceanic and Atmospheric 

Administration, 2005). NOAA land-cover classes were 

recoded to match the NLCD01 classes. Land-cover data for 

DEN, DFW, and MGB were derived using identical methods 

and protocols as the NLCD01 program (Falcone and Pearson, 

2006). The 16 NLCD01 land-cover classes (Multi-Resolution 

Land Characteristics Consortium, 2008) were aggregated into 

8 Anderson Level 1 classes. For example, “deciduous forest,” 

“evergreen forest,” and “mixed forest” were aggregated into 

“forest” (Anderson and others, 1976) because the broader 

Level 1 classes were deemed to be more reliable than the 

Level 2 classes (Falcone and others, 2007).

Some of the GIS variables used to develop the MA-UII 

were not used in the derivation of national indices (table 1B) 

because these variables were not available nationally (for 

example, P_HU3RM and P_FHHF90), were thought to be 

inconsistent among metropolitan areas (for example, Level 2 

NLCD01 data), or were not amenable to calculation at the 

national level (for example, socioeconomic indices SEI_2, 

SEI_3, SEI_5 [McMahon and Cuffney, 2000], which are 

compilations of census variables obtained from principal 

components analysis within each metropolitan area). The 

BOS, BIR, and SLC studies were conducted before 2000 

census and 2001 NLCD data were available so the MA-UII 

for these metropolitan areas (Coles and others, 2004; Tate and 

others, 2005) was based on projected 1999 census and 1992 

NLCD data. The national indices (MA-NUII, NUII) for BOS, 

BIR, and SLC, however, are based on 2000 census and 2001 

NLCD data. The SLC MA-UII was unique in that it included 

soil and lithographic characteristics (PHSG2, PLITH_146, 

WELLPCT) of the drainage basins.

Derivation of Metropolitan Area Urban Intensity 

Indices (MA-UII)

The MA-UII indices were derived independently for 

each metropolitan area on the basis of a compilation of GIS 

variables (census, land cover, infrastructure) that were correlated (Spearman rank correlation, |ρ| ≥ 0.5) with population 

density (SPSS, Inc., 1999). The variables that were used to 

create the MA-UII varied among metropolitan areas depending 

on which variables were deemed important for understanding 

urbanization. Each variable was range standardized as follows:

 Xrs = [(Xi

-Xmin)/(Xmax-Xmin)]*100

where: 

Xi = value of variable (for example, HHDEN) at 

site

 Xmin = minimum value of X over all sites

Xmax = maximum value of X over all sites

Xrs = range standardized Xi

Variables that were negatively correlated with population density were adjusted by subtracting the range-standardized value 

from 100. This ensured that all range-standardized variables 

increased with increasing population density. Values of all 

range-standardized variables used in the MA-UII (for example, 

HHDEN, P_NLCD1_2, ROADDEN) were averaged for each 

site to produce a preliminary index. The preliminary index values were range standardized to produce a final urban intensity 

index (MA-UII) that ranged from 0 (little or no urban) to 100 

(maximum urban) for each metropolitan area (McMahon and 

Cuffney, 2000). The calculation of the MA-UII is illustrated in 

table 2 for a hypothetical set of data. 

Derivation of National Urban Intensity Indices 

(MA-NUII, NUII)

The MA -NUII and NUII indices were derived by 

identifying census, land-cover, and infrastructure variables 

that were strongly correlated (|ρ| ≥ 0.7) with population 

density (POPDEN00) for each of the nine urban studies. 

These variables were plotted against POPDEN00 to determine 

the form and consistency of the response across the range of 

population density. Variables that were strongly correlated 

with POPDEN00 and that exhibited a consistent linear 

response across the entire range of population density were 

further evaluated by examining the relation (linear regression) 

among variables within each data source (census, land cover, 

infrastructure). Redundant (that is, highly related) variables 

were eliminated to obtain a subset with equal representation 

of each data source. Linear regressions and correlations were 

calculated using SYSTAT 9 (SPSS, Inc., 1999).

Once a consistent set of variables was identified, the MANUII was calculated separately for each metropolitan area by 

using the same procedures as were used to derive the MA-UII 

(range standardization, adjustment of negatively correlated 

variables, averaging the range-standardized variables, and 

range standardizing the averages). This produced an index 

that ranged from 0 (little or no urban) to 100 (highest urban) 

for each metropolitan area based on a nationally consistent set 

of variables. 
8 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

The derivation of the NUII involved the same variables 

and range standardization techniques as were used to derive 

the MA-NUII. However, the range standardization occurred 

over all sites from the nine metropolitan areas, and variables 

were adjusted to account for differences in the rates at which 

the index variables responded to changes in population 

density among metropolitan areas. Variables were adjusted 

by deriving regression equations that related the value of 

each variable to population density in each urban area. These 

regression equations were used to predict the value of the 

variable at the maximum population density observed over all 

nine urban studies. The observed values were expressed as a 

percentage of the predicted value and then range standardized 

over all sites (nine metropolitan areas combined) following 

the procedures used for MA-UII and MA-NUII (range 

standardization, adjustment of negatively correlated variables, 

averaging the range-standardized variables, and range 

standardizing the averages) to produce an index (NUII) that 

varied from 0 to 100 over all sites from the nine metropolitan 

areas rather than separately for each area. As a result, the NUII 

ranged from 0 (little or no urban) to 100 (highest urban), but 

each metropolitan area did not necessarily encompass sites 

that ranged from 0 to 100.

Derivation and Comparison of Urban 

Intensity Indices

This report focuses on the derivation of the national urban 

intensity indices (MA-NUII, NUII); consequently, only brief 

overviews of the GIS data and MA-UII results are presented 

here. A more detailed presentation of the GIS data and the 

methods used to derive these data can be found in Falcone 

and others (2007). The MA-UII results that are reported here 

(table 3, p. 25) are detailed in Coles and others (2004), Tate 

and others (2005), Sprague and others (2006), Gregory and 

Calhoun (2007), K.D. Richards and others, U.S. Geological 

Survey, written commun. (2007), Moring (2008), and Waite 

and others (2008). 

Metropolitan Area Urban Intensity Indices 

(MA-UII)

The urban intensity indices that were derived independently for each metropolitan area (MA-UII) differed 

substantially in the number of variables that were incorporated 

into the index (table 4) with as many as 40 (DFW) and as 

Table 2. Hypothetical example illustrating the calculation of the national urban intensity indices (MA-UII, MA-NUII) for a metropolitan 

area with 30 sites (data are shown for nine sites). The index variables (VAR1–VAR3) are correlated with 2000 population density (POPDEN00) 

and range standardized using the maximum and minimum values for each variable. Variables that are negatively correlated with population 

density (VAR3) are adjusted by subtracting the unadjusted value from 100. The range-standardized values are averaged to form the 

preliminary index, and the preliminary index is range standardized to form the MA-UII or MA-NUII.

Range-standardized variables

Values of index variables

VAR1 VAR2

VAR3 Preliminary

index

MA-UII or

Site POPDEN00 VAR1 VAR2 VAR3 Unadjusted Adjusted MA-NUII

1 30.42 12.90 4.36 10.55 0.94 1.42 100.00 0.00 0.79 0.0

2 408.23 130.80 52.98 4.48 18.78 52.38 32.29 67.71 46.3 46.6

3 148.65 65.88 20.00 7.10 8.95 17.81 61.48 38.52 21.8 21.5

: : : : : : : : : : : 

14 1,235.1 541.5 94.2 1.6 80.9 95.5 0.3 99.7 92.0 93.4

15 1,212.4 667.7 98.4 2.0 100.0 100.0 4.7 95.3 98.4 100.0

16 789.9 281.0 72.2 3.8 41.5 72.5 25.1 74.9 63.0 63.7

: : : : : : : : : : : 

28 472.6 166.8 55.9 4.6 24.2 55.5 33.6 66.4 48.7 49.0

29 292.5 116.8 38.8 5.8 16.7 37.5 46.8 53.2 35.8 35.8

30 1,083.3 446.8 96.9 1.8 66.6 98.4 2.0 98.0 87.7 88.9

Maximum: 667.7 98.4 10.6 100.0 100.0 100.0 100.0 98.4 100.0

Minimum: 6.7 3.0 1.6 0.0 0.0 0.0 0.0 0.8 0.0

Correlation with 

POP-DEN00 0.98 0.89 -0.97
Derivation and Comparison of Urban Intensity Indices 9

Table 4. Census, land-cover, and infrastructure variables that were used to construct the metropolitan area urban intensity 

index (MA-UII) for each metropolitan area. Abbreviations are described in table 1.

Metropolitan area Variables incorporated into MA-UII

Boston (BOS)

Census P_FHHF90, PHOUSL80, P_RENT, P_NWHT99, POP90_99, SEI_2, SEI_3, SEI_5

Basin land cover P_NLCD1_2, P_NLCD1_4, P_NLCD1_21, P_NLCD1_22, P_NLCD1_23, P_NLCD1_42, P_NLCD1_43, P_NLCD1_82, 

P_NLCD1_85

Riparian land cover P_NLCD1_B2, P_NLCD1_B4

Infrastructure ROADDEN, D_PSCOUNT, DAMDEN, D_TRICOUNT

Raleigh (RAL)

Census POPDEN00, HHDEN

Basin land cover P_NLCD1_2

Riparian land cover P_NLCD1_B2

Infrastructure ROADDEN

Atlanta (ATL)

Census HUDEN

Basin land cover P_NLCD1_2, P_NLCD1_4

Riparian land cover P_NLCD1_B2

Infrastructure ROADDEN

Birmingham (BIR)

Census SEI_2 

Basin land cover P_NLCD1_2, P_NLCD1_4, P_NLCD1_21, P_NLCD1_22, P_NLCD1_23, P_NLCD1_41, P_NLCD1_43, P_NLCD1_81, 

P_NLCD1_85

Riparian land cover P_NLCD1_B2, P_NLCD1_B4

Infrastructure ROADDEN, P_SEWER

Milwaukee-Green Bay (MGB)

Census PHEL, PHOIL, PHWOOD, PPBLACK, PPWHITEPPASIA, P_US, P_INSTAT, P_OUTST, P_NONUS, PHU_G50, PHU_G60, 

HHDEN, HUDEN, PP_SH95, P_OWN, P_RENT, PF_GT16E, SEI_2, SEI_4, PPURBAN, PPRURAL, PHS_G25, PBCH_G25, 

PPMALE, PPFEMALE

Basin land cover P_NLCD1_2, P_NLCD1_5, P_NLCD1_8, NLCD1_IS

Riparian land cover P_NLCD1_B5, P_NLCD1_B8, NLCD1_BIS

Infrastructure RDARDEN, RDTRDEN

Denver (DEN)

Census HUDEN, PPURBAN, POP90_00, PC_ST95, PM_GT16E, PHH2, P_HU3RM, PH_2PERS, PHU_G60, PHWOOD, PHLP,

Basin land cover P_NLCD1_2, P_NLCD1_7, P_NLCD1_8, P_NLCD1_95

Riparian land cover

Infrastructure RDARDEN

Dallas-Fort Worth (DFW)

Census PHLP, PHWOOD, PHUT, PPWHITE, PPASIA, P_US, P_INSTAT, P_OUTST, PHU_G50, PHU_G60, HHDEN, HUDEN, 

PHO_L3P, P_OCCUPY, P_VACANT, PP_SH95, PH_2PERS, POCC_G65, PPURBAN, PPRURAL, MNFAMINC, SEI_2, 

SEI_3, SEI_4, PBCH_G25, PHS_G25

Basin land cover P_NLCD1_2, P_NLCD1_5, P_NLCD1_7, P_NLCD1_8, NLCD1_IS

Riparian land cover P_NLCD1_B2, P_NLCD1_B5, P_NLCD1_B7, P_NLCD1_B8, NLCD1_BIS

Infrastructure ROADDEN, RDARDEN, RDTRDEN, D_TRICOUNT

Salt Lake City (SLC)

Census SEI _2, POP90_99

Basin land cover P_NLCD1_2, P_NLCD1_4, P _NLCD1_5

Riparian land cover P_NLCD1_B2, P_NLCD1_B4, P_NLCD1_B5

Infrastructure ROADDEN, P_SEWER

Other PLITH_146, PHSG2, WELLPCT

Portland (POR)

Census PHLP, PHWOOD, PHUT, PASIA, P_US, PHU_G60, HHDEN, HUDEN, PPURBAN, PPRURAL, PP_SH95, SEI_3

Basin land cover P_NLCD1_2, P_NLCD1_3, P_NLCD1_4, P_NLCD1_5, NLCD1_IS

Riparian land cover P_NLCD1_B2, P_NLCD1_B3, P_NLCD1_B4, P_NLCD1_B5, NLCD1_BIS

Infrastructure ROADDEN, RDTRDEN
10 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

few as 5 (ATL, RAL) variables defining urban intensity. The 

distribution of variables among the major sources of data 

(census, land cover, and infrastructure) varied widely, with 

some metropolitan areas having nearly equal distribution 

(ATL, RAL) and others (DEN, DFW, MGB) having a preponderance (65–75 percent) of census variables. The greatest 

commonality among MA-UII variables used by the various 

studies was for land cover (P_NLCD1_2, included in nine 

of nine metropolitan areas) and infrastructure (ROADDEN, 

included in seven of nine metropolitan areas). There was 

considerably less commonality among the census variables 

that were incorporated into the MA-UII. Measures of housing 

density (HHDEN or HUDEN) and socioeconomic indices 

were included most often (six of nine metropolitan areas). 

The RAL MA-UII included population density in the index 

whereas all other indices excluded population density as a 

component of the index (table 4).

Selection of Variables for National Urban 

Intensity Indices

The census, land-cover, and infrastructure variables 

that were strongly correlated with population density varied 

among metropolitan areas (table 5). The largest numbers 

of strong correlations were seen among the land-cover and 

infrastructure variables. Census variables showed surprisingly few consistent correlations with population density. 

Variables that were strongly correlated with population density 

generally showed one of two forms of response—either a 

consistent linear response across the range of population 

density (HUDEN, fig. 3A) or a curvilinear response in which 

the variable responded only over a portion of the population 

gradient (PPURBAN, fig. 3B). Variables that showed a nearly 

linear response provided a consistent representation of change 

across the entire population density gradient and were suitable 

Figure 3. Response of the (A) HUDEN and (B) PPURBAN census variables in relation to population density (POPDEN00) for the 

Milwaukee-Green Bay (MGB) metropolitan area. Abbreviations are explained in table 1.

A. B.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

PPURBAN (proportion of population)

0

200

400

600

800

1,000

1,200

0 500 1,000 1,500 2,000 2,500

POPDEN00 (km-2)

0 500 1,000 1,500 2,000 2,500

POPDEN00 (km-2)

HUDEN (km-2)
Derivation and Comparison of Urban Intensity Indices 11

Table 5A. Spearman rank correlations between 2000 population density (POPDEN00) and variables considered in the derivation of 

nationally consistent urban intensity indices (MA-NUII, NUII). Bold entries indicate a correlation (|ρ|) ≥ 0.7. — Continued

[|ρ|, absolute value of Spearman rank correlation; ≥, greater than or equal to]

Abbreviation

(see table 5B) BOS RAL ATL BIR MGB DEN DFW SLC POR

Census data

 Energy source

 PHUT 0.81 0.84 0.88 0.47 0.81 0.47 0.64 0.52 0.78

 PHLP -0.73 -0.92 -0.94 -0.69 -0.89 -0.58 -0.93 -0.45 -0.78

 PHEL 0.70 -0.45 -0.34 -0.23 0.62 0.31 0.28 -0.30 0.00

 PHOIL -0.73 -0.30 -0.30 -0.42 -0.82 -0.21 -0.09 -0.14 -0.41

 PHWOOD -0.92 -0.83 -0.86 -0.51 -0.90 -0.62 -0.73 0.08 -0.90

 Ethnicity

 PPWHITE -0.84 0.00 -0.26 -0.51 -0.80 -0.26 -0.53 -0.46 -0.62

 PPBLACK 0.77 -0.13 0.12 0.53 0.76 0.47 0.40 0.57 0.65

 PPNAM -0.20 0.36 0.25 -0.25 0.20 0.07 -0.09 0.32 0.29

 PPASIA 0.68 0.42 0.65 0.58 0.84 0.36 0.77 0.44 0.74

 P_US -0.82 -0.65 -0.80 -0.41 -0.80 -0.38 -0.52 -0.45 -0.81

 P_INSTAT 0.73 -0.34 -0.84 -0.14 -0.79 0.24 -0.73 -0.34 -0.56

 P_OUTST -0.80 0.15 0.84 0.13 0.72 -0.40 0.59 0.14 0.20

 P_NONUS 0.19 0.26 0.59 0.25 0.66 0.34 0.48 0.66 0.64

 PC_CTY95 -0.01 0.36 0.46 0.51 0.44 0.04 0.45 0.04 0.41

 PC_ST95 -0.26 -0.40 -0.27 -0.19 0.02 0.63 -0.26 -0.01 0.49

 Housing age

 PHU_L5 -0.43 -0.42 -0.10 -0.36 0.03 -0.47 -0.02 -0.14 0.41

 PHU_L10 -0.36 -0.41 -0.11 -0.46 0.11 -0.41 -0.01 -0.20 0.30

 PHU_L20 -0.69 -0.32 0.09 -0.49 0.10 -0.30 0.09 -0.17 0.17

 PHU_G20 0.69 0.32 -0.09 0.48 -0.11 0.30 -0.10 0.17 -0.17

 PHU_G30 0.62 0.31 -0.29 0.32 -0.11 0.13 -0.28 0.01 -0.04

 PHU_G40 0.53 0.30 -0.55 0.17 -0.30 -0.05 -0.48 -0.07 -0.25

 PHU_G50 0.29 0.24 -0.71 -0.10 -0.74 -0.42 -0.79 -0.38 -0.58

 PHU_G60 0.15 0.09 -0.83 -0.22 -0.80 -0.54 -0.86 -0.46 -0.73

 Housing occupancy

 HHDEN 1.00 0.99 0.98 0.97 0.99 0.99 0.99 0.90 0.99

 HUDEN 1.00 0.99 0.99 0.97 0.99 0.98 0.98 0.88 0.99

 PHO_G4P -0.19 -0.21 0.07 -0.54 -0.37 -0.27 0.25 -0.27 -0.36

 PP_SH95 0.40 -0.52 -0.54 -0.37 -0.78 -0.24 -0.62 0.07 -0.80

 P_OWN -0.59 -0.63 -0.14 -0.39 -0.60 -0.41 -0.40 -0.22 -0.51

 PHFAM -0.40 -0.58 -0.12 -0.49 -0.46 -0.44 -0.08 -0.20 -0.47

 PHNONFAM 0.40 0.58 0.12 0.49 0.46 0.44 0.08 0.20 0.47

 PHO_L3P -0.58 -0.77 -0.01 -0.28 -0.32 -0.41 -0.63 0.17 -0.15

 P_OCCUPY 0.68 0.19 0.79 0.07 0.13 0.35 0.64 0.57 0.42

 P_VACANT -0.68 -0.19 -0.79 -0.07 -0.19 -0.35 -0.66 -0.56 -0.42

 P_RENT 0.59 0.63 0.14 0.39 0.60 0.41 0.40 0.22 0.51

 PH_1PERS 0.62 0.60 0.01 0.44 0.43 0.48 0.04 0.20 0.45

 PH_2PERS -0.46 -0.39 -0.22 0.01 -0.23 -0.54 -0.59 0.16 -0.40

 PH_3PERS -0.28 -0.55 0.25 -0.58 0.06 0.20 0.25 0.44 0.43

 PH_4PERS -0.24 -0.37 0.11 -0.35 -0.28 -0.26 0.20 0.08 -0.31

 PH_5PERS 0.03 -0.21 -0.11 -0.54 -0.45 -0.32 0.23 -0.19 -0.40

 PH_6PERS -0.20 0.01 0.08 -0.02 -0.48 0.10 0.18 -0.39 -0.23

 PH_7PERS 0.05 0.23 0.07 0.13 -0.27 -0.04 0.09 -0.21 -0.55

 Income

 PHHI_L10 0.29 0.06 -0.66 -0.14 0.16 0.21 -0.46 -0.03 0.03

 PHHI_L20 0.44 0.04 -0.64 -0.03 0.01 0.34 -0.50 -0.08 -0.06

 PHHI_L30 0.17 0.03 -0.65 -0.03 0.02 0.37 -0.48 0.12 -0.05
12 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 5A. Spearman rank correlations between 2000 population density (POPDEN00) and variables considered in the derivation of 

nationally consistent urban intensity indices (MA-NUII, NUII). Bold entries indicate a correlation (|ρ|) ≥ 0.7. — Continued

[|ρ|, absolute value of Spearman rank correlation; ≥, greater than or equal to]

Abbreviation

(see table 5B) BOS RAL ATL BIR MGB DEN DFW SLC POR

 Income (continued)

 MEDHHI 0.18 0.04 0.67 0.06 0.21 -0.26 0.49 0.16 0.17

 PM_GT16E -0.55 -0.06 0.28 -0.12 -0.39 -0.23 0.49 -0.15 0.13

 PF_GT16E -0.68 -0.20 0.59 0.16 -0.52 -0.19 0.47 -0.25 0.23

 PPOP_POV 0.23 0.17 -0.43 -0.08 0.05 0.10 -0.33 -0.06 0.33

 PFAM_POV 0.41 0.07 -0.43 -0.13 0.20 0.22 -0.40 -0.08 0.38

 PHH_POV 0.15 0.10 -0.56 -0.17 0.08 0.07 -0.42 -0.07 0.08

 PERCAPIN 0.30 0.18 0.75 0.24 0.41 -0.28 0.45 0.33 0.37

 MFAMINC 0.31 0.12 0.69 0.08 0.35 -0.25 0.44 0.23 0.25

 MNFAMINC 0.11 0.22 0.67 0.32 0.46 -0.16 0.59 0.35 0.21

 Population

 POP90_00 -0.60 -0.33 0.03 -0.51 0.21 -0.22 0.00 -0.05 0.24

 PPURBAN 0.96 0.93 0.96 0.90 0.96 0.86 0.92 0.68 0.92

 PPRURAL -0.96 -0.93 -0.96 -0.90 -0.96 -0.86 -0.92 -0.68 -0.92

 PP_L5Y 0.07 -0.08 0.11 -0.43 0.09 -0.10 0.45 -0.36 0.53

 PHS_G25 -0.11 -0.36 -0.57 -0.41 -0.82 0.08 -0.70 -0.08 -0.65

 PBCH_G25 0.15 0.25 0.85 0.41 0.59 -0.12 0.50 0.31 0.48

 POCC_G65 0.65 0.16 -0.53 0.34 0.24 0.48 -0.57 0.21 0.11

 PPMALE -0.57 -0.08 0.02 -0.51 -0.60 -0.41 -0.26 -0.58 -0.30

 PPFEMALE 0.57 0.08 -0.02 0.51 0.59 0.41 0.20 0.58 0.30

 PM_GT25Y -0.68 -0.06 -0.13 -0.56 -0.65 -0.35 -0.22 -0.51 -0.61

 PF_GT25Y 0.68 0.06 0.13 0.56 0.64 0.35 0.08 0.51 0.61

2001 NLCD Level 1 basin land cover

 Percentage of basin area

 P_NLCD1_1 0.30 -0.38 -0.05 -0.37 -0.03 0.15 -0.44 0.32 0.32

 P_NLCD1_2 0.97 0.94 0.98 0.94 0.98 0.95 0.91 0.89 0.98

 P_NLCD1_3 -0.29 -0.63 -0.15 -0.31 -0.18 -0.42 0.37 -0.12 -0.76

 P_NLCD1_4 -0.92 -0.85 -0.91 -0.83 -0.47 0.29 0.01 -0.84 -0.91

 P_NLCD1_5 -0.82 -0.88 -0.68 -0.72 0.73 -0.21 -0.82 -0.81 -0.85

 P_NLCD1_7 -0.36 -0.89 -0.85 -0.75 -0.36 -0.81 -0.70 -0.06 -0.31

 P_NLCD1_8 -0.65 -0.90 -0.58 -0.77 -0.86 -0.70 -0.62 -0.06 0.00

 P_NLCD1_9 0.20 -0.57 -0.30 -0.40 -0.62 -0.36 0.02 0.05 -0.03

 NLCD1_IS 0.94 0.80 0.97 0.92 0.97 0.94 0.93 0.94 0.98

 NOAA_1KM_IS 0.98 0.88 0.98 0.96 0.95 0.94 0.98 0.91 0.97

 Percentage of riparian buffer

 P_NLCD1_B1 0.27 -0.29 0.13 -0.23 -0.06 0.30 -0.13 0.33 0.17

 P_NLCD1_B2 0.94 0.90 0.96 0.89 0.94 0.91 0.84 0.80 0.97

 P_NLCD1_B3 -0.52 -0.42 0.05 -0.07 -0.01 -0.43 0.31 -0.11 -0.84

 P_NLCD1_B4 -0.87 -0.82 -0.82 -0.70 -0.34 0.27 0.08 -0.77 -0.83

 P_NLCD1_B5 -0.79 -0.79 -0.57 -0.60 0.77 -0.32 -0.83 -0.55 -0.87

 P_NLCD1_B7 -0.07 -0.67 -0.64 -0.47 -0.38 -0.82 -0.70 0.00 -0.31

 P_NLCD1_B8 -0.17 -0.85 -0.30 -0.68 -0.82 -0.71 -0.62 0.02 -0.01

 P_NLCD1_B9 0.29 -0.44 -0.25 -0.31 -0.40 -0.27 0.13 0.22 -0.09

 NLCD1_BIS 0.90 0.79 0.95 0.86 0.93 0.91 0.86 0.90 0.97

Infrastructure

 RDLENGTH 0.89 0.47 0.85 0.63 0.65 0.32 0.72 0.74 0.65

 ROADDEN 0.99 0.96 0.98 0.84 0.94 0.95 0.94 0.89 0.95

 RDARDEN 0.97 0.93 0.98 0.8 0.94 0.95 0.94 0.76 0.95

 RDTRDEN 0.96 0.88 0.95 0.79 0.93 0.92 0.93 0.68 0.95

 D_PSCOUNT 0.63 0.09 0.33 0.3 0.33 0.35 -0.05 -0.09 0.15

 D_TRICOUNT 0.87 0.46 0.74 0.23 0.15 0.16 0.79 0.31 0.62
Derivation and Comparison of Urban Intensity Indices 13

Table 5B. Description of GIS-derived variables used to develop nationally consistent urban intensity indices. — Continued

[km/km2, kilometer per square kilometer; km2, square kilometer]

Category Abbreviation Description

Census data

 Energy source 

PHUT Percent of occupied housing units using utility gas (natural gas) as fuel

PHLP Percent of occupied housing units using liquid petroleum gas as fuel

PHEL Proportion of occupied housing units using electricity as fuel

PHOIL Percent of occupied housing units using oil as fuel

PHWOOD Percent of occupied housing units using wood as fuel

 Ethnicity

PPWHITE Proportion of population race = white

PPBLACK Proportion of population race = black

PPNAM Proportion of population race = native american

PPASIA Proportion of population race = asian

P_US Proportion of citizens born in US

P_INSTAT Proportion of citizens born in State of Residence

P_OUTST Proportion of citizens born in other States in the US

P_NONUS Proportion of citizens not born in US

PC_CTY95 Proportion of citizens living in same county more more than 5 years (since 1995)

PC_ST95 Proportion of citizens living in same State more more than 5 years (since 1995)

 Housing age

PHU_L5 Proportion of housing units built between 1995-2000 

PHU_L10 Proportion of housing units built between 1990-2000 

PHU_L20 Prorportion of housing units built between 1980-2000

PHU_G20 Proportion of housing units built prior to 1979 (1939 or earlier to 1979)

PHU_G30 Proportion of housing units built prior to 1969 (1939 or earlier to 1969)

PHU_G40 Proportion of housing units built prior to 1959 (1939 or earlier to 1959)

PHU_G50 Proportion of housing units built prior to 1949 (1939 or earlier to 1949)

PHU_G60 Proportion of housing units built prior to 1939

 Housing occupancy

HHDEN Household density (occupied housing units per square kilometer)

PHO_G4P Proportion of households occupied by 4 or more people

PP_SH95 Percent of population living in same house as in 1995

P_OWN Proportion of occupied housing units that are owner occupied, in 2000 (%)

HUDEN Density of housing units (housing units/square kilometer)

PHFAM Proportion of households occupied by a family

PHNONFAM Proportion of households occupied by a non-family

PHO_L3P Proportion of households ocuupied by less than 3 people

P_OCCUPY Proportion of housing units that are occupied

P_VACANT Proportion of housing units that are vacant

P_RENT Proportion of occupied housing units that are renter occupied, in 2000 (%)

PH_1PERS Proportion of households occupied by 1 person

PH_2PERS Proportion of households occupied by 2 persons

PH_3PERS Proportion of households occupied by 3 persons

PH_4PERS Proportion of households occupied by 4 persons

PH_5PERS Proportion of households occupied by 5 persons

PH_6PERS Proportion of households occupied by 6 persons

PH_7PERS Proportion of households occupied by 7 or more persons

 Income

PHHI_L10 Proportion of households with income less than $10,000, in 2000

PHHI_L20 Proportion of households with household income less than $20,000 per year

PHHI_L30 Proportion of households with income less than $30,000, in 2000

MEDHHI Median household income, in 2000 (dollars)

PM_GT16E Proportion of male population greater than 16 years of age that are employed

PF_GT16E Proportion of female population greater than 16 years of age that are employed

PPOP_POV Proportion of population with income below the poverty level

PFAM_POV Proportion of families with income below the poverty level

PHH_POV Proportion of households with income below the poverty level

PERCAPIN Per capita income

MFAMINC Median family household income

MNFAMINC Median non-family household income
14 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 5B. Description of GIS-derived variables used to develop nationally consistent urban intensity indices. — Continued

[km/km2, kilometer per square kilometer; km2, square kilometer]

Category Abbreviation Description

Census data (continued)

 Population

POPDEN00 2000 population density (people/square kilometer)

POP90_00 Proportional change in population from 1990 – 2000

PPURBAN Proportion of population living in urban area

PPRURAL Proportion of population living in rural area

PP_L5Y Proportion of population less than 5 years old

PHS_G25 Proportion of population greater than 25 years old that have High School degrees

PBCH_G25 Proportion of population greater than 25 years old that have Bachelors degree

POCC_G65 Proportion of housing units occupied by persons greater than 65 years of age

PPMALE Proportion of population that is male

PPFEMALE Proportion of population that is female

PM_GT25Y Proportion of male population greater than 25 years of age

PF_GT25Y Proportion of female population greater than 25 years of age

2001 NLCD Level I basin land cover

 Percentage of basin

 area

P_NLCD1_1 Aggregated NLCD 2001 “level 1” category: water 

P_NLCD1_2 Aggregated NLCD 2001 “level 1” category: developed 

P_NLCD1_3 Aggregated NLCD 2001 “level 1” category: barren (includes all level 2 barren and unconsolidated categories) 

P_NLCD1_4 Aggregated NLCD 2001 “level 1” category: forest 

P_NLCD1_5 Aggregated NLCD 2001 “level 1” category: shrubland (includes all level 2 shrub and scrub 

categories)

P_NLCD1_7 Aggregated NLCD 2001 “level 1” category: herbaceous upland natural/semi-natural vegetation (grassland) 

P_NLCD1_8 Aggregated NLCD 2001 “level 1” category: crops and pasture 

P_NLCD1_9 Aggregated NLCD 2001 “level 1” category: wetlands 

NLCD1_IS NLCD 2001 mean percent impervious surface in the basin

NOAA_1KM_IS Mean percent impervious surface in the basin derived from NOAA 1 kilometer resolution data 

for 1990s

 Percentage of riparian

 buffer (approxi-

 mately 100 m 

 area each side of 

 stream centerline)

P_NLCD1_B1 Aggregated NLCD 2001 “level 1” category: water 

P_NLCD1_B2 Aggregated NLCD 2001 “level 1” category: developed 

P_NLCD1_B3 Aggregated NLCD 2001 “level 1” category: barren (includes all level 2 barren and unconsolidated categories) 

P_NLCD1_B4 Aggregated NLCD 2001 “level 1” category: forest 

P_NLCD1_B5 Aggregated NLCD 2001 “level 1” category: shrubland (includes all level 2 shrub and scrub 

categories)

P_NLCD1_B7 Aggregated NLCD 2001 “level 1” category: herbaceous upland natural/semi-natural vegetation (grassland) 

P_NLCD1_B8 Aggregated NLCD 2001 “level 1” category: crops and pasture 

P_NLCD1_B9 Aggregated NLCD 2001 “level 1” category: wetlands 

NLCD1_BIS NLCD 2001 mean percent impervious surface in the riparian zone

Infrastructure

RDLENGTH Length of TIGER roads in watshed (km), excludes non-paved or access roads, bike paths, etc.

ROADDEN Road density in watershed (km/km2

)

RDARDEN Road area index density (road length multiplied by an area factor and normalized by watershed 

area: km/km2

) 

RDTRDEN Road traffic index density. Road length multiplied by a traffic factor (by type of road) normalized by watershed area (km/km2

) 

D_PSCOUNT Density (no./100 km2

) of point source dischargers in watershed (EPA database - NPDES)

D_TRICOUNT Density (no./100 km2

) of Toxics Release Inventory sites in watershed
Derivation and Comparison of Urban Intensity Indices 15

for index development. Curvilinear responses represented 

change over only a portion of the gradient and were not 

suitable for index development. Only 9 of the 89 variables that 

were examined showed a consistent linear response across the 

population density gradient in all nine urban areas (table 6) 

and were candidates for inclusion in the MA-NUII and 

NUII indices. 

Of the nine candidate variables, two were census 

(HHDEN, HUDEN), five were land-cover (P_NLCD1_2, 

NLCD1_IS, NOAA_1KM_IS, P_NLCD1_B2, NLCD1_BIS), 

and two were infrastructure (ROADDEN, RDARDEN) 

variables (table 6). The HUDEN and HHDEN census variables 

were strongly related to one another (R2 ≥ 0.99 for linear 

regression relating HUDEN to HHDEN) for all nine urban 

studies indicating that these variables provide essentially 

identical information. HUDEN was selected to represent 

census variables in the national indices because the derivation 

of HUDEN was less complex and less prone to error than the 

derivation of HHDEN, which involved both the estimation of 

housing density (HUDEN) and the proportion of housing units 

that are occupied. 

Of the five land-cover (NLCD01) variables that were 

strongly related to POPDEN00 in all nine metropolitan areas, 

the percentage of basin area in developed land (P_NLCD1_2) 

was consistently more strongly related to changes in population density (table 6) than were the measures of impervious 

surface (NLCD1_IS, NOAA_1KM_IS, and NLCD1_BIS) or 

developed land in the stream buffer (P_NLCD1_B2). This 

was particularly noticeable in the RAL urban study where 

correlations between population density and measures of 

impervious surface were lower than correlations with measures of developed land (table 5). Measures of developed land 

in the basin (P_NLCD1_2) and stream buffer (P_NLCD1_B2) 

provided essentially the same information as evidenced by the 

strong linear relation among these two variables (R2

 for linear 

regressions ranged from 0.90 to 0.98 for the nine studies). 

Consequently, P_NLCD1_2 was chosen to represent land 

cover in the derivation of the national indices.

The two infrastructure variables that were consistently 

and strongly related to population density (ROADDEN and 

RDARDEN) showed a very strong relation (R2

 for linear 

regression of ROADDEN with RDARDEN ranged from 

0.98 to 1.00 for the nine studies) indicating that these two 

variables convey the same information regarding changes with 

population density. ROADDEN was selected for inclusion in 

the national indices because the derivation of this variable was 

less complex than RDARDEN, which required estimating road 

length and width.

Metropolitan Area National Urban Intensity 

Index (MA-NUII)

The metropolitan area national urban intensity index 

(MA-NUII) derived for each of the nine urban areas based on 

the census (HUDEN), land-cover (P_NLCD1_2), and infrastructure (ROADDEN) variables showed strong and consistent 

relations with POPDEN00 for each of the nine metropolitan 

areas. The MA-NUII was individually scaled (range standardized) for each metropolitan area and ranged from 0 (lowest 

urban) to 100 (highest urban) for each metropolitan area 

(table 3). This is the same scaling that was used to produce the 

MA-UII (table 2).

National Urban Intensity Index (NUII)

While urban intensity can be described by a nationally 

consistent set of census, land-cover, and infrastructure 

variables (HUDEN, P_NLCD1_2, and ROADDEN), the rates 

at which these variables change in response to changes in 

population density varied among metropolitan areas (fig. 4A, 

C, and E). Developed land (P_NLCD1_2) and road density 

(ROADDEN) showed the most variability, and housing-unit 

Table 6. National land-cover data (NLCD), census, and infrastructure variables that showed 

consistent linear responses to changes in 2000 population density for nine metropolitan areas. 

The range in slopes (b), regression coefficients (R2

), and significance levels (p) across the nine 

metropolitan areas are shown. Abbreviations are described in table 1.

[<, less than]

Variable group Variable b R2 p

Census HHDEN 0.329–0.429 0.87–0.99 <0.001

HUDEN 0.342–0.447 0.86–0.99 <0.001

NLCD-basins P_NLCD1_2 0.045–0.072 0.75–0.99 <0.001

NLCD1_IS 0.020–0.030 0.52–0.98 <0.001

NOAA_1KM_IS 0.023–0.033 0.71–0.97 <0.001

NLCD-riparian P_NLCD1_B2 0.035–0.068 0.65–0.98 <0.001

NLCD1_BIS 0.015–0.024 0.50–0.97 <0.001

Infrastructure ROADDEN 0.004–0.007 0.67–0.98 <0.001

RDARDEN 0.005–0.007 0.62–0.98 <0.001
16 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

density (HUDEN) showed the least. Examination of the plots 

used to develop these regressions (fig. 5A and B) showed that 

the rates (regression slopes) differed (0.072 for RAL and 0.045 

for DEN) as did the maximum population density observed in 

each metropolitan area (1,860 km-2 for DEN and 1,294 km-2

for RAL). Surprisingly, maximum population densities 

measured in the eastern metropolitan areas (ATL, BIR, BOS, 

RAL) were lower than those in the central (DEN, DFW, 

MGB) and western (SLC, POR) metropolitan areas (fig. 6) 

even though the East has a much longer history of urban 

development. This indicates that development in the EUSE 

basins of the Central and Western United States tends to occur 

on a smaller proportion of the river basin than it does in the 

East. That is, for a given level of population density, there is a 

larger amount of developed land in the East than in the central 

or western metropolitan areas. This indicates that in terms of 

two of the dimensions of landscape pattern that can be used to 

describe sprawl—population density and percent developed 

land—the eastern metropolitan areas show a relatively high 

level of sprawl compared with the western studies (Galster and 

others, 2006).

The derivation of the NUII takes into account the 

differences that were observed in response rates and maximum 

population densities among metropolitan areas. Linear 

regressions (table 7) were used to convert each NUII variable (HUDEN, P_NLCD1_2, ROADDEN) to a percentage 

of the value that was projected to occur at the maximum 

population density observed for all sites in the nine urban 

studies (2,174.5 km-2 for MGB). For example, the regressions developed for P_NLCD1_2 in figure 5A were used to 

predict the value that would occur at a population density of 

2,174.5 km-2 (table 7 projected values of 170 for RAL and 112 

for DEN). The P_NLCD1_2 values were then expressed as a 

percentage of the projected value. The rescaled P_NLCD1_2 

data yielded response slopes that were then comparable 

among metropolitan areas (fig. 4B and 5B). The HUDEN and 

ROADDEN data were converted in the same way to produce 

response rates that were comparable among metropolitan 

areas (fig. 4D and F). The NUII was calculated by range 

standardizing these adjusted values (percentages of projected 

values) over the data for all nine metropolitan areas (table 3). 

This produced an urban intensity index (NUII) that ranged 

Figure 4. Slopes (mean with 95 percent confidence interval) relating MA-NUII variables and POPDEN00 

before and after adjusting for differences in the response rates among metropolitan areas. Abbreviations are 

explained in table 1.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.00

0.01

0.02

0.03

0.04

0.05

0.06

 

SLOPES, MEAN WITH 95 PERCENT CONFIDENCE INTERVAL

A: P_NLCD1_2 

0.00

0.01

0.02

0.03

0.04

0.05

0.06

B: P_NLCD1_2-adjusted 

BOS RAL ATL BIR MGB DEN DFW SLC POR BOS RAL ATL BIR MGB DEN DFW SLC POR

0.00

0.02

0.04

0.06

0.08

0.10

C: ROADDEN D: ROADDEN-adjusted

0.00

0.01

0.02

0.03

0.04

0.05

0.06

BOS RAL ATL BIR MGB DEN DFW SLC POR BOS RAL ATL BIR MGB DEN DFW SLC POR

E: HUDEN F: HUDEN-adjusted

BOS RAL ATL BIR MGB DEN DFW SLC POR BOS RAL ATL BIR MGB DEN DFW SLC POR

0.00

0.02

0.04

0.06

0.08

0.10
Derivation and Comparison of Urban Intensity Indices 17

Figure 5. Linear regressions relating percentage developed land (P_NLCD1_2) to 

population density in Denver (DEN) and Raleigh (RAL) (A) before and (B) after adjusting 

the data for differences in response rates and maximum population density.

Figure 6. Maximum population density (km-2) for sampling sites in each of 

the nine metropolitan areas.

0

10

20

30

40

50

60

70

80

90

100

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000

PERCENTAGE OF BASIN IN DEVELOPED LAND

A.

RAL : y = 0.072x + 12.10R2 = 0.84

DEN : y = 0.045x + 14.46R2 = 0.81

RAL : y = 0.043x + 7.14R2 = 0.84

DEN : y = 0.040x + 12.90R2 = 0.81

0

10

20

30

40

50

60

70

80

90

100

PERCENTAGE OF PROJECTED MAXIMUM

B.

POPULATION DENSITY (km-2)

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000

POPULATION DENSITY (km-2)

0

500

1,000

1,500

2,000

2,500

BOS RAL ATL BIR MGB DEN DFW SLC POR

Eastern Central Western

MAXIMUM POPULATION DENSITY (km-2)
18 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 7. Linear regressions used to project the value of the urban intensity index variables at the maximum level of 

population density (2,174.5) observed in nine urban studies (projected value). The projected values are expressed as 

a percentage of the maximum value observed in each study (percentage of observed maximum). 

[Y = a + bX, a = intercept, b = slope; R2, regression coefficient; P, significance value for regression; N, number of sites; <, less than]

Variable Urban 

area

a b R2 P N Projected 

value

Percentage 

of observed 

maximum

HUDEN: housing density

BOS -6.65 0.4392 0.99 <0.001 30 948 48

RAL -9.10 0.4418 0.96 <0.001 30 952 70

ATL -8.57 0.4166 0.98 <0.001 30 897 64

BIR -10.11 0.4474 0.95 <0.001 30 963 76

MGB -5.11 0.4180 0.99 <0.001 30 904 109

DEN -7.08 0.4416 0.95 <0.001 28 953 108

DFW -4.50 0.3929 0.98 <0.001 29 850 84

SLC 9.39 0.3421 0.86 <0.001 30 753 113

POR -2.09 0.4084 0.99 <0.001 28 886 83

P_NLCD1_2: percent developed land in basin

BOS 1.74 0.0697 0.95 <0.001 30 153 50

RAL 12.09 0.0724 0.84 <0.001 30 170 58

ATL 8.40 0.0661 0.94 <0.001 30 152 56

BIR 14.50 0.0688 0.83 <0.001 30 164 58

MGB 13.65 0.0508 0.75 <0.001 30 124 80

DEN 14.46 0.0449 0.81 <0.001 28 112 81

DFW 3.67 0.0529 0.99 <0.001 29 119 75

SLC 24.85 0.0416 0.77 <0.001 30 115 84

POR 6.34 0.0541 0.91 <0.001 28 124 79

ROADDEN: road density

BOS 1.43 0.0060 0.96 <0.001 30 14 54

RAL 1.95 0.0066 0.89 <0.001 30 16 71

ATL 1.64 0.0050 0.91 <0.001 30 13 57

BIR 2.09 0.0060 0.67 <0.001 30 15 78

MGB 1.99 0.0043 0.88 <0.001 30 11 92

DEN 1.67 0.0049 0.92 <0.001 28 12 89

DFW 1.38 0.0049 0.98 <0.001 29 12 79

SLC 2.18 0.0057 0.84 <0.001 30 15 93

POR 1.89 0.0049 0.94 <0.001 28 13 80

from 0 to 100 over the 265 sites in the nine metropolitan areas 

rather than from 0 to 100 in each metropolitan area as did the 

MA-UII and MA-NUII. The NUII expresses urban intensity 

as a percentage of the value that would, theoretically, occur at 

the maximum observed population density based on observed 

response rates.

Comparison of the MA-UII, MA-NUII, and NUII

The levels of urban intensity obtained for each site 

differed depending on the index used to characterize urbanization (fig. 7). With the exception of DEN, DFW, and MGB, 

however, the three indices were all linearly related 

(R2

 = 0.84–0.99, b = 0.93–1.03, P < 0.001) and the relations among sites that had been originally defined by the 

MA-UII were preserved (fig. 8A). DEN, DFW, and MGB 

(fig. 8B) showed a non-linear relation with MA-NUII with 

little response in MA-NUII until MA-UII > 25. This pattern 

resulted from including large numbers of variables in the 

MA-UII that had a curvilinear or indeterminate relation 

with population density (table 8). Variables that exhibited 

a curvilinear response tended to change rapidly at low to 

moderate levels of population density but only very slowly or 

not at all at moderate to high levels (for example, PPURBAN 
Derivation and Comparison of Urban Intensity Indices 19

evident in DFW where the national 

index (MA-NUII) revealed a large 

gap in the representation of the urban 

intensity gradient (no sites between 22 

and 67) that did not appear with the 

MA-UII (12 sites between 22 and 67, 

fig. 7A). 

The MA-NUII and NUII 

differ only in how they are scaled 

(independently for each metropolitan 

area and collectively for all nine 

metropolitan areas). Rescaling to form 

the NUII is based on the regression of 

each of the index variables (HUDEN, 

P_NLCD1_2, and ROADDEN) with 

population density. Since the slopes 

of these regressions differ among the 

three variables within and among 

metropolitan areas, this rescaling can 

change how sites rank along the urban 

gradient. However, the correlation 

(Spearman rank) between NUII and 

MA-NUII index scores within each 

metropolitan area ranged from 0.996 

to 1.000, indicating that rescaling 

had little effect on the relation among 

sites within a metropolitan area. The 

major difference produced by the 

national scaling is the maximum level 

of urbanization that is achieved in 

each metropolitan area (fig. 7B). For 

example, the maximum value for NUII 

in BOS is 52 (site BOS_aber) compared to 100 in SLC (site SLC_m300) 

even though both of these sites had 

MA-UII and MA-NUII values of 100 

(table 3). The population density associated with BOS_aber (1,042 km-2) is 

far less than the maximum population 

density observed among all nine 

metropolitan areas (site MGB_linc 

2,174.5 km-2). In contrast, the 

SLC_m300 site had a population density (2,136 km-2) that 

was close to the maximum for all nine metropolitan areas. 

All of the eastern metropolitan areas (BOS, RAL, ATL, BIR) 

had lower maximum population densities than did the central 

or western metropolitan areas (fig. 6); consequently, the 

maximum NUII in the eastern metropolitan areas was lower 

than in the central or western areas (fig. 7B). Therefore, on the 

basis of the NUII, small streams (second to third order) in the 

eastern metropolitan areas encompassed a smaller proportion 

of the national urban intensity gradient than did similar 

streams in the Western and Central United States. 

Figure 7. Distribution of sites in each metropolitan area based on the (A) MA-UII and 

MA-NUII indices or the (B) NUII index.

0

20

40

60

80

100

10

URBAN INTENSITY INDEX (UII)

0

20

40

60

80

100

NATIONAL URBAN INTENSITY INDEX (NUII)

MA-UII

MA-NUII

10

EXPLANATION

A.

B.

BOS RAL ATL BIR MGB DEN DFW SLC POR

BOS RAL ATL BIR MGB DEN DFW SLC POR

in fig. 3B). Variables with an indeterminate response were 

significantly correlated with population density, but the form 

of the response was poorly defined. Inclusion of large numbers 

of variables with curvilinear or indeterminate responses to 

population density in DEN, DFW, and MGB (≥ 63 percent 

of MA-UII variables) overestimated urban intensity at low 

to moderate levels of population density compared to indices 

derived from nationally consistent variables (MA-NUII and 

NUII). Consequently, the urban gradients in MGB, DEN, and 

DFW have far more sites at the lower end of the gradient when 

urban intensity is expressed as MA-NUII or NUII than when 

it is expressed as MA-UII (fig. 8B). This was particularly 
20 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Figure 8. Relation between the MA-UII and MA-NUII indices for the nine 

metropolitan areas showing (A) linear and (B) curvilinear responses.

Table 8. Percentage of variables used in the construction of the metropolitan area 

urban intensity index (MA-UII) that showed a linear, curvilinear, or indeterminate relation 

with population density.

Metropolitan 

area

Number of 

variables in 

MA-UII

Percentage of variables with response form:

Linear Curvilinear Indeterminate

BOS 23 69.6 17.4 13.0

RAL 5 100.0 0.0 0.0

ATL 5 100.0 0.0 0.0

BIR 14 92.9 7.1 0.0

MGB 35 34.3 42.9 22.9

DEN 16 31.3 37.5 31.3

DFW 40 37.5 45.0 17.5

SLC 13 61.5 15.4 23.1

POR 24 50.0 37.5 12.5

A.

DFW

DEN

MGB

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

MA-UII

0 20 40 60 80 100

MA-UII

MA-NUII

B.

0

10

20

30

40

50

60

70

80

90

100

MA-NUII

BOS

ATL

RAL

BIR

SLC

POR
Summary and Conclusions 21

Summary and Conclusions

The indices that were derived to characterize urban 

intensity (MA-UII, MA-NUII, and NUII) are multimetric 

representations of changes in population density. Population 

density was chosen as the focus of these studies because the 

effects of urbanization arise primarily from the goods and 

services required to maintain a population within a drainage 

basin. These goods and services represent a complex, interacting set of factors, such as transportation (roads, railroads, 

airports), housing (building and maintaining homes and 

yards), manufacturing, retail sales, and utilities (water, sewer, 

electricity, gas), that directly or indirectly affect the biological, 

chemical, and physical characteristics of streams. Population 

density provides a useful surrogate for these and other changes 

associated with urbanization and has the advantage of having 

been monitored consistently for a long period of time. We 

chose a multimetric representation of population density 

(housing density, percentage of developed land, and road 

density) in order to capture part of the multivariate nature of 

urbanization, to address the inconsistent spatial scale of census 

data (census blocks are large in rural areas, small in urban 

areas), and to address situations where basins may have been 

highly modified for human use without having a large resident 

population (for example, airports, rail yards, and 

large factories). 

The most common representations of urban intensity 

in the literature focus on single variables such as population 

density, percent impervious surface, road density, and percent 

developed or urban lands. Population density, road density, and 

developed or urban lands are directly addressed in the urban 

intensity indices (MA-NUII and NUII) either because they are 

a component of the index (ROADDEN, P_NLCD1_2) or are 

strongly correlated with the index (POPDEN00). Measures 

of imperviousness are not directly addressed in the indices 

though they were considered in the development of these 

indices. Impervious surface appears frequently in the literature 

as a surrogate for urban intensity (Arnold and Gibbons, 1996; 

Center for Watershed Protection, 2003; Morse, Huryn, and 

Cronan, 2003) that provides a direct linkage between changes 

in stream hydrology and effects on stream biota and ecological 

processes (Poff and others, 1997; Konrad and Booth, 2005). 

Even though impervious surface (NLCD1_IS, NLCD1_BIS, 

NOAA_1KM_IS) was strongly correlated with population 

density, it was not included in the national urbanization indices 

(MA-NUII, NUII) because it was essentially redundant with 

percentage of developed land (P_NLCD1_2), and in the RAL 

area developed land was more closely associated with population density than was impervious surface. Impervious surface 

was included in some MA-UIIs (MGB, DFW, POR) even 

though it was highly correlated with other variables. 

Multimetric indices of urbanization are far less commonly used to represent urbanization than are single variables. 

The Landscape Development Intensity (LDI) index (Brown 

and Vivas, 2005) is one of the few multimetric urban intensity 

indices that have been used to interpret the effects of urbanization. The LDI index quantifies urbanization as an aggregation 

of human activity derived by combining land-cover classes 

with the amount of nonrenewable energies and related services 

that are associated with each land-cover class. The information 

necessary for calculating the LDI index was not available at 

the national level, so an LDI index was not calculated that 

could be compared with the indices used to characterize 

urban intensity. 

Characteristics of a National Index 

Ideally, a national urban intensity index should be 

capable of representing changes in urban intensity that are 

consistent across metropolitan areas; that is, a given change 

in the index (for example, five intensity units) should indicate 

equivalent changes in all metropolitan areas. The index should 

be constructed from nationally consistent variables that are 

important indicators of urbanization across the country, that 

represent changes in urban intensity across the entire urban 

gradient, that have similar rates of response in all urban areas, 

and that are nationally scaled. Most urban studies concentrate 

on urbanization within a single area and cannot directly 

address these issues. The EUSE studies provided a unique 

opportunity to develop national urban intensity indices by 

addressing all these issues in multiple urban areas. 

The indices of urban intensity that were originally 

developed independently for each metropolitan area (MA-UII) 

cannot provide a national characterization of urban intensity 

because of the diversity of variables used to construct the 

index, inconsistency in the form of the response to population 

density (linear, curvilinear, indeterminate), and inappropriate 

scaling. Compared to the national indices (MA-NUII, NUII), 

the MA-UII tended to overestimate urban intensity, and the 

resulting distributions of sites along the urban gradient were 

skewed toward the low end of the gradient or contained gaps 

(DFW). The MA-NUII provided a better national representation of changes along the population gradient because it was 

based on a nationally consistent set of variables (HUDEN, 

P_NLCD1_2, ROADDEN) that had a consistent linear 

response to population density and that were evenly distributed 

among categories of urban variables. The MA-NUII, however, 

was scaled to represent urbanization only within a metropolitan area and could not represent differences in urban intensity 

among metropolitan areas. 

The NUII addressed the deficiencies in the MA-NUII by 

scaling across all sites in the nine metropolitan areas and by 

adjusting for differences in the rates at which index variables 

(HUDEN, P_NLCD1_2, ROADDEN) respond to changes in 

population density among metropolitan areas (slopes of the 

regression with POPDEN00). Expressing the index variables 

as a percentage of the value that was predicted to occur at the 

maximum population density observed over all nine metropolitan areas successfully adjusted the variables so that the rate 

of change was nearly constant among metropolitan areas 
22 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

(fig. 4B, D, and F). However, there are a number of drawbacks 

associated with this approach that make it less than an ideal 

method of deriving a nationally consistent index. First, this 

approach requires estimating values at population densities 

well beyond what were actually measured, which is a 

statistically tenuous situation. Second, the rescaled (that is, 

predicted) values sometimes do not make sense (for example, 

P_NLCD1_2 values in excess of 100 percent, table 7). 

Last, scaling the values to the maximum population density 

observed among all sites results in an index that is dependent 

on that maximum value. That is, the index values could 

change if the maximum population density changes, which 

might occur if urban areas are added, deleted, or monitored 

over time. Given these drawbacks, the current NUII can best 

be described as a work in progress and we continue to explore 

approaches for deriving a nationally consistent urban intensity 

index that is capable of representing urbanization across 

the country.

Even though the NUII is not a perfect representation 

of urban intensity, it did reveal patterns in urban intensity 

that were not evident with MA-UII or MA-NUII. That is, 

maximum urban intensities in eastern metropolitan areas 

(BOS, RAL, ATL, BIR) were much lower relative to that 

of metropolitan areas in the Central and Western United 

States (fig. 7B). This was an unexpected result that seemed 

to imply that similar levels of urbanization in the East are 

associated with lower population density and less compact 

development than in the Central and Western United States. 

However, these results need to be interpreted in light of the 

design of the EUSE studies, which looked at relatively small 

streams (second to third order) that were still connected to 

the landscape (that is, were above ground and not confined 

to manmade channels). In the inner-city areas of many of the 

eastern metropolitan areas where population density is high 

(for example, downtown Boston, MA), there are no small 

streams to study because they have been effectively converted 

to sewers either by burying the streams in culverts (Elmore 

and Kaushal, 2008) or placing them in cement-lined channels. 

This restricts the study of streams in these areas to suburban 

areas with lower population density. In addition, the scarcity 

of water in the West may lead to higher density and more 

compact suburban development than in the East because of the 

expense of maintaining large lawns. Estimates of population 

densities in eastern cities may also have been reduced because 

of the greater integration of commercial and industrial areas 

within these cities and the movement of population from city 

centers to suburban and rural areas. 

Future Development of Urban Intensity Indices

The urban intensity indices that are described in this 

report (MA-UII, MA-NUII, NUII) were developed to provide 

a mechanism for objectively selecting sites that represent a 

gradient of urban intensity and interpreting biological, chemical, and physical responses. The progression from MA-UII 

to MA-NUII to NUII constituted a refinement, in terms of 

consistency and scaling, in the development of the urban intensity index with the objective of providing a more nationally 

comparable representation of urban intensity. However, the 

indices and their component variables are not directly linked 

to physical, chemical, or biological changes in streams. Such 

linkages are a major objective of the analyses conducted in 

the EUSE studies, and further development of urban intensity 

indices will be based on urban variables that have been shown 

to drive physical, chemical, and biological responses rather 

than on population density. The goal of this effort is to derive 

an index that can represent equivalent changes in stream 

conditions across the Nation. That is, a change of 10 urban 

intensity units would correspond to equivalent changes in indicators of response such as hydrologic variability, invertebrate 

taxa richness, or percentage of eutrophic algae. Development 

of this index will more directly tie urban intensity to its effect 

and should be a valuable contribution to managers and policy 

makers that must deal with the consequences of development.

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Table 3 25

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

BOS 01072540 Little R nr Lebanon, ME BOS_lime 34.73 17.81 2.45 1.23 0.00 1.40 3.44

BOS 01072650 Greatworks R nr North 

Berwick, ME

BOS_grea 103.26 40.10 8.04 2.20 14.00 10.42 7.88

BOS 01072845 Isinglass R Batchelder Rd 

nr center Strafford, NH

BOS_isin 34.60 16.59 1.83 1.36 6.80 1.70 3.58

BOS 01072904 Bellamy R at Bellamy Rd 

nr Dover, NH

BOS_bell 88.40 37.12 7.98 1.94 19.20 8.83 7.11

BOS 01073260 Lamprey R Cotton Rd nr 

Deerfield, NH

BOS_lamp 31.46 11.79 1.15 1.15 1.40 0.00 2.75

BOS 01073458 North R at Route 152 nr 

Nottingham, NH

BOS_nort 36.89 14.74 2.55 1.47 5.20 2.44 3.94

BOS 010734833 Little R at Cartland Rd at 

Lee, NH

BOS_linh 45.75 18.89 2.39 1.66 4.00 3.61 4.51

BOS 01089743 Little Suncook R Blackhall 

Rd at Epsom, NH

BOS_lsun 37.69 22.57 3.28 1.41 6.10 3.05 4.25

BOS 01090477 Black Brook Dunbarton 

Rd nr Manchester, NH

BOS_blab 57.55 25.79 0.93 1.53 1.10 2.84 4.12

BOS 01094005 Baboosic R Bedford Rd nr 

Merrimack, NH

BOS_babo 117.72 42.21 4.92 2.08 9.70 8.60 6.96

BOS 01095220 Stillwater R nr Sterling, 

MA

BOS_stil 55.50 22.45 3.16 1.65 15.20 4.18 4.80

BOS 01096544 Stony Brook at School 

Street at Chelmsford, 

MA

BOS_ston 227.66 82.97 21.50 3.36 37.80 25.34 15.28

BOS 010965852 Beaver Brook at North 

Pelham, NH

BOS_beav 354.77 129.60 30.63 3.76 38.70 34.86 20.02

BOS 01096710 Assabet R at Allen Street 

at Northborough, MA

BOS_assa 381.66 136.59 35.69 3.76 60.00 37.62 21.42

BOS 01096945 Elizabeth Brook off White 

Pond Rd nr Stow, MA

BOS_eliz 110.66 37.46 10.34 2.50 28.20 12.71 9.01

BOS 01097270 Fort Pond Brook at R Rd 

nr South Acton, MA

BOS_fort 255.83 94.17 18.34 3.34 37.00 24.67 14.92

BOS 01097476 Sudbury R at Concord 

Street at Ashland, MA

BOS_sudb 257.48 98.77 24.40 3.09 38.00 26.45 15.86

BOS 01101500 Ipswich R at South 

Middleton, MA

BOS_ipsw 454.80 169.53 43.29 4.83 62.20 48.77 26.92

BOS 01102345 Saugus R at Saugus Ironworks at Saugus, MABOS_saug 865.75 375.16 64.45 7.00 87.40 84.30 44.46

BOS 01102500 Aberjona R (head of Mystic River) at Winchester, 

MA

BOS_aber 1,041.96 458.03 76.43 7.84 100.00 100.00 52.24

BOS 011032058 Charles R at Maple Street 

at North Bellingham, 

MA

BOS_char 479.01 212.24 34.74 4.26 52.60 45.35 25.18

BOS 01105000 Neponset R at Norwood, 

MA

BOS_nepo 460.63 184.58 38.56 4.21 57.00 44.72 24.92

BOS 01105500 East Branch Neponset at 

Canton Junction, MA

BOS_enep 574.24 243.00 44.68 4.98 61.30 55.64 30.30

BOS 01105581 Monatiquot R at River 

Street at Braintree, MA

BOS_mona 699.78 296.52 53.68 5.72 73.40 67.30 36.07

BOS 01106468 Matfield R at North 

Central Street at East 

Bridgewater, MA

BOS_matf 1,068.02 458.62 60.13 6.59 92.60 86.60 45.56
26 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

BOS 01109000 Wading R (head of 

Threemile River) nr 

Norton, MA

BOS_wade 230.63 88.02 23.49 2.84 39.50 23.98 14.66

BOS 01109595 Middle R off Sutton Lane 

at Worcester, MA

BOS_midd 483.79 221.87 28.53 4.16 56.60 42.84 23.89

BOS 01110000 Quinsigamond R at North 

Grafton, MA

BOS_quin 693.85 337.58 50.20 5.79 87.00 69.19 36.96

BOS 01112262 Mill R at Summer Street 

nr Blackstone, MA

BOS_mill 208.45 80.96 15.44 2.73 23.40 19.39 12.33

BOS 01193340 Blackledge R abv Lyman Brook nr North 

Westchester, CT

BOS_blal 89.75 33.18 6.38 1.81 19.80 7.19 6.30

RAL 02081190 Tar R nr Berea, NC RAL_tar 9.40 6.70 3.01 1.03 0.00 0.00 2.33

RAL 02081510 Foundry Branch at mouth 

nr Oxford, NC

RAL_fondry 408.23 130.80 52.98 5.78 38.89 38.82 27.19

RAL 0208500600 Cates Creek nr Hillsborough, NCRAL_cates 148.65 65.88 20.00 2.94 14.54 15.00 12.03

RAL 0208501535 Strouds Cr at St Marys Rd 

nr Hillsborough, NC

RAL_stroud 70.70 32.74 12.42 2.45 7.21 9.12 8.22

RAL 02085430 Deep Creek nr Moriah, NC RAL_deep 30.42 12.90 4.36 1.58 1.95 2.55 4.01

RAL 0208725055 Black Cr at Weston Parkway nr Cary, NCRAL_black 1,293.49 564.74 78.96 8.22 79.56 77.54 53.54

RAL 0208726370 Richlands Creek at Schenk 

Forest nr Cary, NC

RAL_rschen 182.46 85.26 60.14 4.32 33.16 34.39 23.90

RAL 0208726995 Hare Snipe Creek at SR 

1822 nr Leesville, NC

RAL_hare 1,151.87 530.83 76.09 7.70 73.62 73.18 50.62

RAL 0208730725 Beaverdam Creek at 

Glenwood Avenue at 

Raleigh, NC

RAL_beaver 1,235.15 541.50 94.16 10.21 90.80 88.00 60.04

RAL 0208732610 Pigeon House Br at Crabtree Blvd at Raleigh, 

NC

RAL_pigeon 1,212.39 667.68 98.41 11.52 100.00 100.00 68.29

RAL 02087580 Swift Creek nr Apex, NC RAL_swift 789.91 280.99 72.19 6.49 58.55 55.36 38.12

RAL 0208758440 Dutchmans Br at SR 1386 

nr McCullers Crossrds, 

NC

RAL_dutch 380.39 139.73 39.29 4.60 28.61 30.74 22.21

RAL 0208794025 Camp Branch abv SR 

1390 nr Holly Springs, 

NC

RAL_camp 162.56 82.82 18.22 2.65 12.88 14.33 11.68

RAL 0209517912 N Buffalo Creek at 

Greensboro, NC

RAL_nbuff 1,000.92 490.22 96.91 9.26 85.59 83.35 56.71

RAL 0209647280 Service Creek abv Dry 

Creek at Burlington, 

NC

RAL_serv 561.65 226.35 65.65 5.96 49.29 48.65 33.67

RAL 0209647295 Dry Cr abv Service Creek 

at Burlington, NC

RAL_dry 1,141.07 453.53 96.81 10.55 89.17 85.58 58.10

RAL 0209651815 Branch Creek blw NC 54 

nr Graham, NC

RAL_branch 858.36 300.58 92.63 9.05 76.36 71.62 48.46

RAL 0209665940 Rock Cr Trib at Stoney Cr 

Golf Course nr Sedalia, 

NC

RAL_rtrib 93.61 29.47 20.67 2.60 12.18 12.32 10.12

RAL 0209665990 Rock Cr abv Rock Cr Trib 

nr Whitsett, NC

RAL_rock 47.43 23.81 7.28 1.77 3.85 4.70 5.40
Table 3 27

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

RAL 0209679804 Little Alamance Cr at SR 

2309 nr Graham, NC

RAL_lalam 695.14 306.51 82.12 7.73 64.36 64.08 43.73

RAL 0209695780 Brooks Cr at Eddie Perry 

Rd nr Bynum, NC

RAL_brooks 27.39 16.63 3.14 1.61 2.14 2.40 3.96

RAL 0209697900 Pokeberry Creek nr Pittsboro, NCRAL_poke 69.46 30.10 6.00 2.20 4.44 5.97 6.30

RAL 02097355 Bolin Cr abv Franklin St 

nr Chapel Hill NC

RAL_bolin 790.72 263.25 44.35 5.37 42.46 41.19 29.39

RAL 02097464 Morgan Creek nr White 

Cross, NC

RAL_morgan70.55 27.94 5.19 2.40 4.93 6.21 6.47

RAL 0209750881 Wilson Cr at mouth nr 

Chapel Hill, NC

RAL_wilson 295.14 169.27 23.73 3.83 24.14 24.36 18.47

RAL 02099238 Bull Run at NC 29/70 nr 

Jamestown, NC

RAL_bull 669.41 273.07 66.81 4.78 51.82 47.64 33.09

RAL 02099480 Richland Creek nr Archdale, NCRAL_rich 597.14 243.08 76.39 7.10 56.83 56.85 38.89

RAL 02100295 Hasketts Cr blw SR 2149 

nr Central Falls, NC

RAL_hasket 472.59 166.77 55.91 5.77 41.86 41.64 29.10

RAL 02100634 Vestal Creek nr Asheboro, 

NC

RAL_vestal 292.52 116.80 38.78 4.48 27.85 29.02 21.01

RAL 0211583580 Bowen Branch nr mouth at 

Winston-Salem, NC

RAL_bowen 1,083.27 446.77 96.91 9.59 85.05 82.21 55.82

ATL 02204230 Big Cotton Indian Cr at 

GA 138 nr Stockbridge, 

GA

ATL_bci 471.98 166.45 43.21 4.40 46.44 46.59 27.85

ATL 02204468 Walnut Creek at Airline 

Road nr Mcdonough, 

GA

ATL_wal 218.02 68.09 24.80 3.02 29.96 25.47 15.98

ATL 02206314 Jackson Creek at Lester 

Road nr Lilburn, GA

ATL_jac 1,015.41 377.48 67.00 5.90 74.12 77.33 45.62

ATL 02208150 Alcovy R at New Hope 

Road nr Grayson, GA

ATL_alc 277.86 89.91 39.67 3.15 40.21 33.69 20.59

ATL 02213450 Little Tobesofkee Creek nr 

Bolingbroke, GA

ATL_ltob 7.32 5.14 3.59 1.11 5.85 2.47 3.40

ATL 02217293 Little Mulberry R at GA 

211 nr Hoschton, GA

ATL_lmul 192.78 57.95 20.38 2.73 26.07 21.48 13.79

ATL 02217471 Beech Creek at GA 211 nr 

Statham, GA

ATL_bch 123.17 32.53 16.38 2.61 23.70 17.61 11.55

ATL 02218700 Apalachee R nr Bethlehem, GAATL_apa 131.81 43.96 17.84 2.34 23.53 17.42 11.57

ATL 02221000 Murder Creek nr Monticello, GA

ATL_mur 6.86 6.41 3.81 1.17 4.84 2.97 3.67

ATL 02334885 Suwanee Creek at Suwanee, GAATL_suw 267.63 102.49 42.56 3.43 41.05 37.18 22.52

ATL 02335870 Sope Creek nr Marietta, 

GA

ATL_sop 901.05 348.69 72.51 7.12 83.00 84.57 49.17

ATL 02335910 Rottenwood Cr (Interstate 

N Pkwy) nr Smyrna, 

GA

ATL_rot 1,197.72 577.23 85.41 6.45 100.00 100.00 59.09

ATL 02336635 Nickajack Creek at US 

78/278, nr Mableton, 

GA

ATL_nic 899.66 357.05 66.17 6.55 75.04 79.32 46.45
28 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

ATL 02336728 Utoy Creek at Great 

Southwest Pkwy nr 

Atlanta, GA

ATL_uto 915.15 369.47 60.56 5.92 66.99 74.28 43.89

ATL 02336822 Mill Creek at Morning 

Side Dr nr Hiram, GA

ATL_mil 202.11 66.78 22.06 3.26 26.64 25.61 16.00

ATL 02336876 Powder Springs Cr 

Oglesby Rd Powder 

Springs, GA

ATL_pow 312.51 106.70 35.56 3.83 37.69 36.68 22.19

ATL 02336968 Noses Creek at Powder 

Springs Rd Powder 

Springs, GA

ATL_nos 513.28 182.52 43.08 4.51 46.01 48.10 28.74

ATL 02337395 Dog R at North Helton 

Road nr Winston, GA

ATL_dog 71.40 34.07 13.45 2.42 16.27 15.45 10.41

ATL 02338280 Whooping Creek at GA 5 

nr Whitesburg, GA

ATL_whp 56.13 25.29 7.28 1.77 9.87 8.83 6.87

ATL 02338375 Centralhatchee Cr 

Armstrong Mill Rd, 

Ctrlhtche, GA

ATL_cen 16.66 6.90 4.77 1.47 8.62 5.03 4.74

ATL 02338523 Hillabahatchee Creek 

at Thaxton Rd nr 

Franklin,GA

ATL_hil 7.75 4.62 2.81 0.91 0.86 1.04 2.65

ATL 02339480 Oseligee Creek at County 

Rd 92 nr Fredonia, AL

ATL_ose 21.67 5.88 5.91 1.33 4.44 4.67 4.56

ATL 02340282 House Creek at GA 103 nr 

Whitesville, GA

ATL_hou 10.91 5.78 4.94 1.15 5.88 3.32 3.85

ATL 02344340 Morning Creek at GA 54 

nr Fayetteville, GA

ATL_mor 352.43 133.09 38.35 3.54 39.28 37.88 23.06

ATL 02344480 Shoal Creek nr Griffin, GA ATL_sho 186.24 70.79 22.91 2.96 26.09 24.53 15.49

ATL 02344737 Whitewater Cr at Willow 

Pond Rd nr Fayetteville, 

GA

ATL_whw 186.65 67.15 25.08 2.97 27.59 25.26 15.87

ATL 02344797 White Oak Creek at Cannon Road nr Raymond, 

GA

ATL_who 153.43 59.57 25.73 2.62 25.92 23.18 14.76

ATL 02344887 Red Oak Creek at GA 362 

nr Gay, GA

ATL_rdo 24.34 7.70 4.46 1.31 4.00 4.09 4.26

ATL 02346358 Turnpike Creek nr Milner, 

GA

ATL_tur 48.00 24.69 10.96 2.23 17.65 12.81 8.95

ATL 02347748 Auchumpkee Cr at Allen 

Rd nr Roberta, GA

ATL_auc 5.06 2.58 2.33 0.78 0.00 0.00 2.09

BIR 02388518 Little Dry Creek at US 27 

at Rome, GA

BIR_ldry 460.47 146.34 55.76 5.07 43.00 37.14 28.09

BIR 02397939 Chappel Creek at Long 

Branch Rd nr Trion, GA

BIR_chapp 28.48 24.08 4.30 1.52 0.00 1.74 4.57

BIR 02398001 Town Branch nr Summerville, GA

BIR_town 409.66 177.16 57.72 7.44 45.00 46.69 35.10

BIR 02400675 Unnamed Trib to Big 

Wills Cr at SR 35 nr 

Fort Payne, AL

BIR_utrwil 332.19 112.71 49.27 7.31 40.60 40.23 30.68

BIR 02400725 Mush Creek nr Portersville, ALBIR_mush 8.92 6.59 5.29 1.10 0.74 0.00 3.20

BIR 02400800 Little Wills Cr at Collins 

Chapel Rd at Collinsville, AL

BIR_lwill 58.16 9.43 8.87 1.91 6.60 3.93 5.91
Table 3 29

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

BIR 02401350 Big Canoe Cr at Canoe 

Creek Rd nr Springville, AL

BIR_bigca 34.93 24.22 2.77 1.46 1.80 1.03 4.13

BIR 02401355 Unnamed Trib to Big 

Canoe Cr nr Springville, AL

BIR_utrbc 93.35 15.71 12.67 3.42 4.50 10.32 10.44

BIR 02401749 Williams Branch nr Jacksonville, ALBIR_willi 307.88 97.02 40.18 4.86 34.10 28.55 22.53

BIR 02401760 Little Tallaseehatchee Cr 

nr Weaver, AL

BIR_ltal 81.62 42.13 9.62 2.62 11.20 7.97 8.91

BIR 02403380 Snow Cr blw Anniston 

, AL

BIR_snow 413.80 211.65 52.16 6.23 50.00 42.51 32.38

BIR 02406930 Shirtee Creek nr Odena, 

AL

BIR_shirt 245.13 104.76 42.43 4.83 31.20 29.65 23.22

BIR 02423120 Cahaba R abv Trussville, 

AL

BIR_cahar 112.37 51.01 13.50 2.32 9.00 8.83 9.35

BIR 0242339580 Little Cahaba R nr Markeeta, ALBIR_lcabr 286.35 65.53 26.52 3.89 19.60 19.11 16.25

BIR 02423397 Little Cahaba R blw 

Leeds, AL

BIR_lcale 275.21 84.86 36.33 3.31 25.60 21.77 17.71

BIR 02423515 Patton Creek nr Bluff Park 

blw Patton Chapel, AL

BIR_patto 911.51 378.74 83.44 7.95 64.20 66.96 48.99

BIR 02423536 Buck Creek at Buck Creek 

Rd at Alabaster, AL

BIR_buck 254.23 90.27 33.28 3.54 16.40 21.63 17.78

BIR 0242354650 Cahaba Valley Cr at Indian Trail Rd nr Indian 

Springs, AL

BIR_cvci 319.71 106.90 31.72 3.65 16.80 22.18 18.30

BIR 0242354750 Cahaba Valley Cr at Cross 

Creek Rd at Pelham, 

AL

BIR_cvc 266.21 104.00 30.10 3.30 16.30 20.38 17.05

BIR 02423576 Shades Cr at Lakeshore 

Drive nr Mountain 

Brook, AL

BIR_shade 490.04 225.18 53.93 5.20 40.80 40.56 30.86

BIR 02423581 Shades Cr at Samford 

University at Homewood, AL

BIR_shasam 562.05 268.95 59.30 1.81 46.10 33.94 25.73

BIR 02423590 Unnamed Trib to Shades 

Creek nr Oxmoor, AL

BIR_utsha 597.78 400.26 85.15 6.38 71.90 63.68 46.51

BIR 02423620 Little Shades Cr at State 

Highway 150 nr Bessemer, AL

BIR_lshad 28.09 13.56 26.56 1.40 2.80 8.95 8.64

BIR 02423729 Dry Creek at Spring Cr Rd 

nr Montevallo, AL

BIR_dry 96.15 39.53 13.47 2.27 12.60 8.12 8.80

BIR 0242372950 Spring Cr at County Rd 16 

nr Moores Crossroads, 

AL

BIR_sprin 57.67 33.74 11.74 1.90 7.50 6.08 7.38

BIR 02456900 Fivemile Creek at Fivemile 

Rd nr Huffman, AL

BIR_fivfi 1,016.07 387.39 76.53 7.52 65.20 63.52 46.85

BIR 02456980 Fivemile Creek at Lawson 

Rd nr Tarrant City, AL

BIR_filaw 905.69 347.55 69.85 3.46 58.30 46.59 34.63

BIR 02458150 Village Creek at East Lake 

in Birmingham, AL

BIR_villa 809.51 339.96 79.23 8.46 74.00 65.22 47.88
30 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

BIR 02461200 Valley Creek at Cleburn 

Avenue at Powderly, 

AL

BIR_valle 1,545.30 729.81 95.72 11.88 100.00 100.00 73.35

BIR 02461670 Five Mile Creek at Nevel 

Rd nr McCalla, AL

BIR_fivne 174.13 60.52 36.46 3.23 16.20 20.41 16.65

MGB 04072233 Lancaster Brook at 

Shawano Avenue at 

Howard, WI

MGB_lanc 114.69 40.69 11.66 2.91 33.33 9.97 13.08

MGB 04078085 Black Otter Creek nr 

Hortonville, WI

MGB_blot 67.76 21.82 5.36 1.67 26.49 2.57 6.76

MGB 04081897 Sawyer Creek at Westhaven Rd at Oshkosh, 

WI

MGB_sawy 111.73 39.07 13.80 1.75 32.60 6.41 10.05

MGB 04084429 Mud Creek at Spencer Rd 

at Appleton, WI

MGB_mudc 465.35 190.00 58.83 5.29 62.31 40.22 39.35

MGB 04084468 Garners Creek at Park 

Street at Kaukauna, WI

MGB_garn 580.74 206.80 68.95 5.90 60.03 46.58 44.73

MGB 04085046 Apple Creek at Sniderville, WIMGB_appl 121.42 46.96 17.59 2.70 33.29 11.48 14.34

MGB 040850683 Ashwaubenon Creek at 

South Bridge Rd nr 

Depere, WI

MGB_ashw 23.16 10.21 5.70 1.62 21.51 2.11 6.26

MGB 040851235 Bower Creek Trib at Lime 

Kiln Rd nr Bellevue, 

WI

MGB_bowr 176.74 67.16 24.40 3.07 37.97 15.90 18.16

MGB 040851325 Baird Creek at Superior 

Rd at Green Bay, WI

MGB_bair 43.05 25.39 5.48 1.42 19.20 1.81 6.16

MGB 04085188 Rio Creek at Pheasant Rd 

nr Rio Creek, WI

MGB_rioc 11.74 7.01 3.53 1.28 10.48 0.00 4.48

MGB 040851932 Kewaunee R Trib at Lowell Rd nr Luxemburg, 

WI

MGB_kewa 16.29 10.82 4.37 1.81 13.78 2.38 6.51

MGB 04085270 Jambo Creek at Jambo 

Creek Rd nr Mishicot, 

WI

MGB_jamb 10.91 5.25 3.71 1.53 0.00 0.93 5.25

MGB 040853145 Black Creek at Curran Rd 

nr Denmark, WI

MGB_blak 10.28 4.45 3.23 1.42 4.18 0.31 4.72

MGB 04085322 Devils R at Rosencrans Rd 

nr Maribel, WI

MGB_devl 16.47 10.72 4.00 1.76 10.81 2.05 6.23

MGB 040854395 Point Creek at Ucker Point 

Rd nr Newton, WI

MGB_poin 14.24 9.74 5.02 1.45 19.50 1.23 5.52

MGB 04085455 Meeme R at Washington 

Rd nr Cleveland, WI

MGB_meme 16.33 6.02 5.04 1.56 6.96 1.53 5.73

MGB 04086699 Pigeon Creek at Williamsburg Dr at Theinsville, 

WI

MGB_pign 143.07 64.03 14.92 2.36 51.83 9.88 13.19

MGB 040869415 Lincoln Creek at 47th 

Street at Milwaukee, 

WI

MGB_linc 2,174.50 857.33 97.56 9.32 100.00 91.26 88.28

MGB 04087030 Menomonee R at Menomonee Falls, WIMGB_meno 310.10 121.74 30.39 3.75 49.47 22.35 24.04

MGB 0408703164 Lily Creek at Good Hope 

Rd nr Menomonee 

Falls, WI

MGB_lily 471.74 151.81 77.99 6.19 60.29 48.91 46.03
Table 3 31

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

MGB 04087070 Little Menomonee R at 

Milwaukee, WI

MGB_ltme 564.27 204.98 44.28 3.95 71.54 30.76 31.74

MGB 040870856 Underwood Cr at Watertown Plank Rd at Elm 

Grove, WI

MGB_undw 555.67 209.85 86.05 6.78 74.82 55.85 52.33

MGB 04087118 Honey Creek nr Portland 

Avenue at Wauwatosa, 

WI

MGB_hony 2,143.96 984.88 99.06 10.38 85.24 100.00 96.86

MGB 04087204 Oak Creek at South Milwaukee, WIMGB_oakc 600.92 252.20 62.93 4.63 76.37 41.37 40.88

MGB 04087213 Root R at Layton Avenue 

at Greenfield, WI

MGB_root 1,197.98 504.03 92.84 8.08 89.25 73.03 69.56

MGB 040872393 Hoods Creek at Brook Rd 

nr Franksville, WI

MGB_hood 92.61 35.78 16.30 2.24 31.26 8.97 12.14

MGB 04087258 Pike R at Cth A nr 

Kenosha, WI

MGB_pikr 198.14 83.24 27.32 2.37 53.05 14.88 17.43

MGB 04087270 Pike Creek at 43rd Street 

at Kenosha, WI

MGB_pikc 1,319.13 503.10 87.25 8.62 83.23 73.02 69.61

MGB 05527729 Kilbourn Ditch at 6Oth 

Street nr Kenosha, WI

MGB_kilb 24.30 12.21 7.43 1.67 27.36 2.98 6.99

MGB 055437901 Fox R at River Rd nr Sussex, WIMGB_foxr 140.35 65.99 20.15 2.36 39.99 11.78 14.74

DEN 06713500 Cherry Creek at Denver, 

CO

DEN_cherry 1,859.76 1,033.29 79.61 9.11 99.24 97.34 87.22

DEN 393557105033101 Dutch Cr at Weaver Park 

nr Columbine Valley, 

CO

DEN_dutch 1,211.47 437.46 72.02 7.20 97.03 66.61 57.78

DEN 393613104511401 Cottonwood Cr ab Newark 

WY at Greenwood Village, CO

DEN_cotton 135.44 86.58 59.05 3.63 83.21 36.30 30.92

DEN 393948105053501 Bear Creek blw Estes Rd 

at Lakewood, CO

DEN_breste 534.07 221.68 38.01 4.44 68.34 35.41 31.62

DEN 394107105021001 Sanderson Gulch abv 

Lowell Ave at Denver, 

CO

DEN_sander 1,851.26 797.86 90.42 10.93 93.65 100.00 87.09

DEN 394409105020501 Lakewood Gulch abv 

Knox Street at Denver, 

CO

DEN_lakew 1,576.03 737.84 89.26 9.67 100.00 92.91 80.99

DEN 394553105075101 Lena Gulch at Lewis 

Meadows Park at Wheat 

Ridge, CO

DEN_lena 648.48 307.68 68.76 7.35 81.76 61.30 52.46

DEN 394629105063101 Clear Creek blw Kipling at 

Wheat Ridge, CO

DEN_clear 581.98 251.58 62.57 6.20 73.54 52.68 45.25

DEN 394919105074601 Ralston Creek abv Simms 

at Arvada, CO

DEN_ralst 436.25 144.03 41.40 4.23 53.64 33.32 29.25

DEN 394921105015701 Little Dry Ck blw Lowell 

St nr Westminster, CO

DEN_litdry 1,764.34 646.07 90.37 10.07 97.25 91.60 79.12

DEN 395324105035001 Big Dry Ck blw Hyland Cr 

at Westminster, CO

DEN_bigdry 459.61 181.37 33.27 3.47 53.24 28.60 25.95

DEN 395554105085601 Rock Creek abv Rock 

Creek Pkwy, at Superior, CO

DEN_rock 175.36 54.37 17.40 2.19 37.26 13.13 12.80

DEN 395707105100401 Coal Creek abv Mccaslin 

Rd at Superior, CO

DEN_coal 8.09 8.62 2.80 1.36 18.22 2.66 4.29
32 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

DEN 395958105113501 Dry Creek abv Baseline 

Rd nr Boulder CO

DEN_drybas 91.66 36.08 19.21 2.57 37.24 14.59 13.77

DEN 400000105125400 South Boulder Cr at Baseline Rd nr Boulder ColoDEN_sbould 72.94 50.14 5.82 1.09 23.89 4.36 5.98

DEN 400023105142301 Bear Cr abv Wellman 

Feeder Canal at Boulder, CO

DEN_brwell 975.86 452.49 58.90 7.20 64.25 61.80 54.25

DEN 400217105123701 Boulder Creek blw 61st 

Street nr Boulder, CO

DEN_bould 986.84 419.82 49.38 5.50 60.07 50.71 45.33

DEN 400607105094401 Dry Creek blw Niwot Rd 

at Niwot, CO

DEN_dryniw 171.40 77.06 22.18 2.25 39.76 16.10 15.29

DEN 400810105071301 Left Hand Creek abv Pike 

Rd at Longmont, CO

DEN_lhand 87.79 33.93 10.44 2.23 28.22 9.77 10.03

DEN 400855105090501 Dry Creek blw Airport Rd 

nr Longmont, CO

DEN_dryair 34.56 10.80 8.68 1.28 17.05 4.84 5.97

DEN 400925105023201 Spring Gulch at Sandstone 

Ranch Park nr Longmont, CO

DEN_spgulc 163.22 79.13 25.15 2.39 44.90 17.88 16.69

DEN 402549105043101 Dry Creek at US 287 at 

Loveland, CO

DEN_dry287 285.89 142.81 20.94 2.61 52.31 19.18 18.32

DEN 403035105035301 Mail Creek nr mouth at 

Fort Collins, CO

DEN_mail 1,022.80 293.14 88.05 8.48 92.61 72.63 61.07

DEN 403048105042701 Fossil Cr at College Ave at 

Fort Collins, CO

DEN_fossil 134.48 49.70 16.60 2.20 53.02 12.69 12.42

DEN 403308105001601 Boxelder Creek at mouth 

nr Fort Collins, CO

DEN_boxel 13.44 5.91 4.42 1.18 2.97 2.59 4.20

DEN 403356105024001 Spring Creek at Edora 

Park at Fort Collins, CO

DEN_spcree 1,232.16 538.17 63.49 7.43 81.30 67.50 59.46

DEN 410714104480101 Crow Cr abv Morrie Ave 

at Cheyenne, WY

DEN_crow 35.76 13.18 8.72 1.20 0.00 4.64 5.84

DEN 413659104370001 Bear Cr abv Little Bear Cr 

nr Phillips, WY

DEN_brphil 0.22 0.49 1.46 0.85 0.61 0.00 2.14

DFW 08049490 Johnson Cr nr Duncan 

Perry Rd Grand Prairie, 

TX

DFW_johnsn 1,591.25 662.31 88.80 9.10 96.63 100.00 78.78

DFW 08049580 Mountain Cr nr Venus, TX DFW_mount 89.94 25.52 4.79 1.74 34.17 5.27 6.79

DFW 08049955 Fish Cr at Belt Line Rd 

Grand Prairie, TX

DFW_fish 1,320.93 433.80 70.22 8.04 91.21 77.11 60.93

DFW 08052740 Doe Branch at Fishtrap Rd 

nr Prosper, TX

DFW_doe 34.78 11.68 6.71 1.76 31.69 5.45 6.85

DFW 08057200 White Rock Cr at Greenville Ave Dallas, TXDFW_white 1,514.71 716.78 83.94 8.18 98.37 96.95 76.92

DFW 08057431 Fivemile Cr nr Simpson 

Stuart Rd Dallas, TX

DFW_5mile 1,131.16 401.55 61.60 6.87 73.99 67.30 53.69

DFW 08057475 Parsons Slough nr Davis 

Rd nr Crandall, TX

DFW_parsn 317.40 109.60 21.28 3.19 46.02 21.88 19.26

DFW 08059530 Tickey Cr nr CR 400 nr 

Princeton, TX

DFW_tickey 105.09 34.80 9.37 1.98 26.81 8.52 9.20

DFW 08059571 Wilson Cr nr Gray Branch 

Rd nr Mckinney, TX

DFW_wilson 25.91 13.75 6.49 1.54 34.48 4.53 6.21

DFW 08061536 Spring Cr at Naaman 

School Rd nr Garland, 

TX

DFW_spring 1,483.12 548.86 86.01 9.45 100.00 94.81 74.36
Table 3 33

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

DFW 08061740 Duck Cr at Town East 

Blvd nr Mesquite, TX

DFW_duck 1,474.19 553.43 78.89 7.76 85.36 85.25 67.54

DFW 08061780 Buffalo Cr nr Trinity Rd at 

Forney, TX

DFW_buftri 100.27 31.66 15.16 2.31 44.33 12.05 11.73

DFW 08061952 South Mesquite Cr at Lawson Rd nr Mesquite, TXDFW_smesq 1,270.01 473.87 71.94 7.71 78.25 78.40 62.12

DFW 08061995 Mustang Cr at Fm 2757 nr 

Crandall, TX

DFW_muscra138.09 29.57 17.35 2.54 36.69 13.75 12.95

DFW 08062020 Buffalo Cr nr Fm 148 nr 

Crandall, TX

DFW_bufcra 64.18 15.50 7.21 1.68 29.72 5.50 6.91

DFW 08062090 Red Oak Cr nr Hampton 

Rd nr Red Oak, TX

DFW_redoak 226.57 77.53 15.26 3.26 50.72 18.21 16.40

DFW 08062525 Walker Cr nr Oil Field Rd 

nr Rosser, TX

DFW_walker 13.40 7.43 2.32 1.18 16.25 1.09 3.69

DFW 08062550 Bois D’Arc Creek nr CR 

4072 nr Rosser, TX

DFW_bois 34.57 8.40 4.56 1.74 17.65 4.32 6.01

DFW 08062600 Grays Cr at Cr 1603 nr 

Rice, TX

DFW_grays 5.24 3.46 2.48 0.94 12.94 0.00 2.89

DFW 08062805 Williams Cr nr Fm 1836 nr 

Kemp, TX

DFW_will 23.82 8.24 1.56 1.01 23.65 0.16 3.03

DFW 08063047 Bynum Cr nr Fm 308 nr 

Malone, TX

DFW_bynum 5.63 2.46 2.75 1.37 0.00 1.81 4.17

DFW 08063300 Pin Oak Cr nr Fm 73 nr 

Coolidge, TX

DFW_pinoak 8.56 5.46 3.11 1.38 5.29 2.15 4.43

DFW 08063510 Little Pin Oak Cr nr Ih 45 

nr Richland, TX

DFW_lpin 3.28 1.50 5.13 1.37 4.43 2.72 4.83

DFW 08063555 S Fk Chambers Cr nr CR 

102 nr Maypearl, TX

DFW_sfkcha 16.16 6.91 3.34 1.39 16.22 2.33 4.58

DFW 08063565 Mill Cr at Lowell Rd nr 

Milford, TX

DFW_mill 11.22 2.44 2.80 1.27 6.82 1.42 3.90

DFW 08063574 Big Onion Cr at Feaster 

Rd nr Bardwell, TX

DFW_bigoni 11.79 3.65 2.55 1.06 18.30 0.52 3.27

DFW 08063595 South Prong Cr at Fm 876 

nr Waxahachie, TX

DFW_sprong 19.68 11.01 2.28 1.06 29.05 0.77 3.48

DFW 08063692 Mustang Cr at Moseley Rd 

nr Ennis, TX

DFW_

musenn

131.72 57.41 13.87 2.10 30.78 11.90 11.78

DFW 08064695 Tehuacana Cr at Rural Rd 

27 nr Wortham, TX

DFW_tehuac 24.95 10.08 6.94 1.49 7.28 4.35 6.06

SLC 10167800 L Cottonwood Cr at Crestwood Park at Salt Lake 

City, UT

SLC_lccre 1,418.90 460.13 90.83 9.32 80.80 71.89 69.99

SLC 400927111354501 Hobble Creek at 800 East 

at Springville, UT

SLC_h800 367.85 104.67 33.41 4.21 40.80 25.43 24.18

SLC 400959111363201 Hobble Creek at Center 

Street at Springville, 

UT

SLC_hcen 496.33 145.40 39.63 4.91 45.50 30.91 29.59

SLC 401442111402201 Provo R at 800 North at 

Salt Lake City, UT

SLC_p800 1,259.36 338.32 72.26 7.47 71.10 56.11 54.39

SLC 401653111400301 Provo R at 3700 North at 

Provo, UT

SLC_p3700 542.86 149.81 43.26 4.55 51.30 31.45 30.04

SLC 401850111392201 Provo R at Highway 189 at 

Provo, UT

SLC_p189 14.15 43.98 20.54 3.34 30.90 16.44 15.44
34 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

SLC 403707111463701 Big Cottonwood Cr abv 

WWTP at Salt Lake 

City, UT

SLC_bcwtp 0.00 5.56 48.15 0.90 0.00 18.44 16.17

SLC 403755111514201 L Cottonwood Cr at 

Wheeler Farm at Salt 

Lake City, UT

SLC_lcwh 1,504.37 520.77 93.37 9.55 84.90 75.74 74.10

SLC 403927111523601 L Cottonwood Cr at Murray Park at Salt Lake 

City, UT

SLC_lcmu 1,760.71 607.82 96.84 12.15 90.10 86.78 85.34

SLC 403945111501001 Big Cottonwood Cr at Cottonwood Mall Salt Lake 

City, UT

SLC_bcmal 981.06 379.61 85.29 9.60 71.30 67.49 65.29

SLC 404000111515801 Big Cottonwood Cr at 

900 East at Salt Lake 

City, UT

SLC_bc900 1,297.26 522.69 91.94 11.05 78.80 79.01 77.33

SLC 404140111481601 Mill Creek at 3060 East at 

Salt Lake City, UT

SLC_m3060 1,218.08 431.56 67.72 10.37 89.40 65.37 64.22

SLC 404143111500101 Mill Creek at 2000 East 

Salt Lake City, UT

SLC_m2000 1,256.70 492.50 65.26 10.90 88.40 68.23 67.54

SLC 404218111525601 Mill Creek at 300 East at 

Salt Lake City, UT

SLC_m300 2,136.14 849.45 96.91 13.62 100.00 100.00 100.00

SLC 404317111503601 Parleys Cr at Sugarhouse 

Park at Salt Lake City, 

UT

SLC_parl 501.54 316.55 65.46 7.34 58.20 52.59 51.05

SLC 404349111512201 Emigration Cr at 1200 

East at Salt Lake City, 

UT

SLC_ewmin 1,011.13 448.29 67.75 8.29 76.20 60.94 60.07

SLC 404430111495301 Emigration Cr at 1300 

South at Salt Lake City, 

UT

SLC_e1300 596.63 228.54 56.68 5.60 59.10 41.78 40.20

SLC 405854111534801 Farmington Cr at Frontage 

Rd at Farmington, UT

SLC_farm 470.53 125.48 50.52 6.02 48.80 36.61 34.60

SLC 410041111581101 Baer Creek at Frontage Rd 

at Kaysville, UT

SLC_bafr 671.06 201.25 76.77 3.97 63.40 43.64 41.13

SLC 410148111535301 Baer Creek at Fruit 

Heights, UT

SLC_ba1800 34.00 15.40 0.00 0.00 15.10 0.00 0.00

SLC 410231111565001 Holmes Creek at Main 

Street at Layton, UT

SLC_holm 1,023.04 314.38 65.29 10.16 76.60 59.37 57.59

SLC 410250111571501 North Fork of Holmes 

Cr at Main Street at 

Layton, UT

SLC_nkho 807.06 236.04 58.65 5.42 67.00 42.32 40.71

SLC 410342111574201 Kays Creek at Layton, UT SLC_kayl 1,164.57 281.08 67.63 8.42 76.00 54.57 52.61

SLC 410453111570001 Kays Creek at 1000 East at 

Layton, UT

SLC_k1000 1,066.79 227.07 62.25 7.70 70.60 48.81 46.80

SLC 410501111555201 South Fork of Kays Creek 

at Layton, UT

SLC_sfk 1,036.67 256.32 69.58 8.37 72.10 54.14 51.94

SLC 410522111541201 SF Kays Cr at Fernwood 

Picnic Area at Salt Lake 

City, UT

SLC_fern 0.63 17.75 0.00 0.00 1.30 0.09 0.11

SLC 411407111580501 Ogden R at Washington 

Avenue at Ogden, UT

SLC_owas 598.43 400.31 57.63 6.26 47.70 50.55 49.99

SLC 411413111554601 Ogden R at Valley Drive 

Ogden, UT

SLC_oval 75.78 15.68 26.77 5.44 3.80 22.62 20.99
Table 3 35

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

SLC 411413111564101 Ogden R at Harrison Avenue at Ogden, UTSLC_ohar 501.49 239.52 39.97 4.77 35.60 34.42 33.71

SLC 414258111502001 Logan R at Golf Course 

Rd at Salt Lake City, 

UT

SLC_log 1,144.24 443.90 74.35 8.53 71.50 63.64 62.42

POR 14199710 Nate Creek nr Colton, OR POR_nate 21.46 15.11 1.89 1.73 15.23 4.39 5.20

POR 14205400 East Fork Dairy Cr nr 

Meacham Corner, OR

POR_efdar 3.59 2.96 0.53 1.24 0.00 1.54 3.01

POR 14206347 Rock Creek at Quatama 

Rd nr Hillsboro, OR

POR_rocor 441.93 182.72 28.22 3.53 50.88 28.21 24.15

POR 14206435 Beaverton Creek at SW 

216th Ave nr Orenco, 

OR

POR_beave 1,468.55 603.50 82.89 8.79 100.00 86.80 70.58

POR 14206750 Chicken Creek nr Sherwood, ORPOR_chick 246.00 64.85 22.03 3.52 44.67 20.50 17.78

POR 14206950 Fanno Creek at Durham, 

OR

POR_fanno 1,512.27 641.50 84.32 9.99 96.09 93.60 75.82

POR 14211315 Tryon Creek blw Nettle 

Creek nr Lake Oswego, 

OR

POR_tryon 1,105.79 489.99 59.55 9.25 72.44 75.01 60.85

POR 434745123040200 Silk Creek nr Cottage 

Grove, OR

POR_silk 52.58 23.27 3.46 2.48 23.57 8.16 8.06

POR 435212122483300 Lost Creek nr Dexter, OR POR_lost 3.67 0.76 0.16 1.51 6.45 2.31 3.56

POR 440257123103200 Amazon Creek nr Danebo 

Rd at Eugene, OR

POR_amazo 1,237.51 559.28 61.94 8.15 76.72 74.98 61.19

POR 443326123165200 Oak Creek at Corvallis, 

OR

POR_oak 107.09 65.33 7.13 2.03 39.03 9.71 9.48

POR 445029122592600 Battle Creek nr Turner, OR POR_battl 497.42 207.57 32.99 4.07 58.26 33.09 27.98

POR 445551123015800 Pringle Creek at Salem, 

OR

POR_pring 1,282.44 534.56 88.71 8.88 88.27 85.95 69.76

POR 450022123012400 Claggett Creek at Keizer, 

OR

POR_clagg 1,958.20 731.10 97.81 9.32 99.63 100.00 81.24

POR 450955122291200 Milk Creek at Camp 

Adams, OR

POR_milk 26.96 6.17 2.27 2.50 11.53 6.99 7.10

POR 451734122585400 Chehalem Creek at Newberg, ORPOR_cheha 76.36 29.38 8.60 2.21 29.31 9.22 8.98

POR 452149123194900 North Yamhill Creek nr 

Yamhill, OR

POR_nyamh 1.46 1.38 0.63 1.76 7.61 3.41 4.39

POR 452231122200000 Deep Creek nr Sandy, OR POR_deep 57.55 18.96 4.38 1.84 16.72 5.86 6.36

POR 452337122243500 North Fork Deep Creek at 

Barton, OR

POR_nfdep 115.67 40.80 27.06 2.63 39.15 17.78 15.76

POR 452414122213200 Tickle Creek nr Boring, 

OR

POR_tickl 207.93 69.51 19.03 3.16 32.11 18.32 16.12

POR 452526122364400 Kellogg Creek at Milwaukie, ORPOR_kello 1,125.14 445.28 81.00 7.24 88.06 72.93 59.55

POR 452912122291200 Johnson Creek at Circle 

Ave, OR

POR_johns 553.03 212.80 41.94 4.97 58.71 39.85 33.19

POR 453506123125700 Iler Creek nr Forest Grove, 

OR

POR_iler 3.97 1.71 0.41 0.86 4.35 0.00 1.86

POR 454321122352300 Curtin Creek nr Vancouver, WAPOR_curti 646.50 228.54 72.42 5.63 69.02 53.73 44.17

POR 454510122424900 Whipple Creek nr Salmon 

Creek, WA

POR_whipp 264.72 95.01 37.56 3.22 48.55 26.24 22.49
36 Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas of the US

Table 3. Urban intensity indices and variables used to calculate the national urban intensity indices (MA-NUII, NUII) for nine 

metropolitan areas. — Continued

[POPDEN00, 2000 population density; HUDEN, housing unit density; P_NLCD1_2, percentage of basin areas in developed land; ROADDEN, road density; 

MA-UII, metropolitan area urban intensity index; MA-NUII, metropolitan area national urban intensity index; NUII, national urban intensity index; BOS, Boston, 

Massachusetts; RAL, Raleigh, North Carolina; ATL, Atlanta, Georgia; BIR, Birmingham, Alabama; MGB, Milwaukee-Green Bay, Wisconsin; DEN, Denver, Colorado; DFW, Dallas-Fort Worth, Texas; SLC, Salt Lake City, Utah; POR, Portland, Oregon; R, river; nr, near; Rd, road; abv, above; Cr, creek; SR, secondary road; 

Blvd, boulevard; Br, branch; N, north; blw, below; Trib, tributary; ab, above; St, street; Pkwy, parkway; WY, way; Dr, drive; CR, county road; Fm, farm-to-market 

road; Ih, interstate highway; WWTP, wastewater-treatment plant; L, little; S Fk, south fork; SF, south fork; Ave, avenue]

Metropolitan 

area

USGS 

station number USGS Station name Station abbreviation POPDEN00 HUDEN P_NLCD1_2 ROADDEN MA-UII MA-NUII NUII

POR 454543122524900 South Scappose Creek at 

Scappose, OR

POR_sscap 38.55 16.33 3.25 1.76 7.79 5.03 5.71

POR 454549122295800 Salmon Creek nr Battleground, WAPOR_salmo 75.44 23.27 13.21 2.48 19.88 11.57 10.79

POR 455122122310600 Rock Creek nr Battleground, WAPOR_rocwa 55.79 15.38 7.51 2.73 22.87 10.15 9.59
Prepared by:

USGS Enterprise Publishing Network

Raleigh Publishing Service Center

3916 Sunset Ridge Road

Raleigh, NC 27607

For additional information regarding this publication, contact:

Thomas F. Cuffney

U.S. Geological Survey

3916 Sunset Ridge Road

Raleigh, NC 27607

phone: 1-919-571-4019

email: tcuffney@usgs.gov

Or visit the Effects of Urbanization on Stream Ecosystems (EUSE) NAWQA Project Web site at:

http://water.usgs.gov/nawqa/urban/
Cuffney and Falcone—Nationally Consistent Indices Representing Urban Intensity within and across Nine Metropolitan Areas—Scientific Investigations Report 2008–5095