Techniques and Methods 11–C2

U S S G 

Geographic Information System Software 

to Remodel Population Data Using 

Dasymetric Mapping Methods

U.S. Department of the Interior

U.S. Geological Survey
This page intentionally left blank
Geographic Information System Software to Remodel 

Population Data Using Dasymetric Mapping Methods

By Rachel Sleeter and Michael Gould

Techniques and Methods 11–C2

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 2007

This report and any updates to it are available online at: 

http://pubs.usgs.gov/tm/tm11c2 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS

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

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS

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

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted material contained within this report. 

Cataloging-in-publication data are on file with the Library of Congress (http://www.loc.gov/).

Produced in the Western Region, Menlo Park, California

Manuscript approved for publication, February 19, 2008

Text edited by James W. Hendley II

Layout and design by Judy Weathers
iii

Contents

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

Purpose and Scope ..............................................................................................................................1

Review of Areal-Interpolation and Dasymetric-Mapping Approaches.......................................1

Case Study—Clatsop County, Oregon......................................................................................2

Dasymetric-Mapping Extension..................................................................................................................4

Overview.................................................................................................................................................4

Installation..............................................................................................................................................4

Enabling and Operating the Dasymetric-Mapping Extension .......................................................6

Input Data and Parameters........................................................................................................6

Output Files and Symbolization .................................................................................................9

Output Maps ...............................................................................................................................10

Summary........................................................................................................................................................10

Acknowledgments.......................................................................................................................................11

References Cited..........................................................................................................................................14

Appendix—Equations for Dasymetric-Mapping Extension (Mennis, 2003).......................................15

Figures

1. Diagram showing how dasymetric mapping redistributes ancillary information to better 

represent population distribution.......................................................................................................2

2. Diagram showing three fundamental approaches to mapping population distribution..............3

3. Screen image of “Welcome to the Dasymetric-Mapping-Tool Setup Wizard” 

 dialog box................................................................................................................................................5

4. Screen image of “Select Installation Folder” dialog box .................................................................5

5. Screen image of “Confirm Installation” dialog box ...........................................................................6

6. Screen image of “Installation Complete” dialog box ........................................................................6

7. Screen image of the “Customize” dialog box to enable ArcMap extension.................................7

8. Screen image of the “Dasymetric-Mapping Extension” user form ................................................8

9. Screen image of the “Processing Complete” dialog box .................................................................9

10. Screen images showing four steps to symbolizing dasymetric-mapping output data ..............11

11. Choropleth map (top) and dasymetric map (bottom) of Astoria, Oregon .....................................12

12. Choropleth map (left) and dasymetric map (right) of Seaside, Oregon ........................................13

Tables

1. Dasymetric-Mapping Extension output file names with descriptions. ........................................10
Geographic Information System Software to Remodel 

Population Data Using Dasymetric Mapping Methods

By Rachel Sleeter and Michael Gould

Introduction 

The U.S. Census Bureau provides decadal demographic 

data collected at the household level and aggregated to larger 

enumeration units for anonymity purposes. Although this 

system is appropriate for the dissemination of large amounts of 

national demographic data, often the boundaries of the enumeration units do not reflect the distribution of the underlying 

statistical phenomena. Conventional mapping methods such as 

choropleth mapping, are primarily employed due to their ease of 

use. However, the analytical drawbacks of choropleth methods 

are well known ranging from (1) the artificial transition of population at the boundaries of mapping units to (2) the assumption 

that the phenomena is evenly distributed across the enumeration 

unit (when in actuality there can be significant variation).

Many methods to map population distribution have 

been practiced in geographic information systems (GIS) and 

remote sensing fields. Many cartographers prefer dasymetric 

mapping to map population because of its ability to more 

accurately distribute data over geographic space. Similar to

"choropleth maps", a dasymetric map utilizes standardized 

data (for example, census data). However, rather than using 

arbitrary enumeration zones to symbolize population distribution, a dasymetric approach introduces ancillary information 

to redistribute the standardized data into zones relative to land 

use and land cover (LULC), taking into consideration actual 

changing densities within the boundaries of the enumeration 

unit (Langford and Unwin, 1994) (fig. 1). Thus, new zones are 

created that correlate to the function of the map, capturing spatial variations in population density. The transfer of data from 

census enumeration units to ancillary-driven homogenous 

zones is performed by a process called areal interpolation.

Purpose and Scope

An extension module to add specialized functionality to 

a GIS was created to automate the process of transferring data 

from source population zones (census enumeration districts) 

to overlaying target zones (LULC). Here, the dasymetric mapping technique explores an areal interpolation scheme adopted 

and developed from Mennis (2003) that practices empirical sampling and areal weighting. This report reviews the 

extension installation, the dasymetric mapping methodologies 

behind each point-and-click feature, and the benefits of using 

this method and associated extension by examining the output 

files. The ArcMap extension module developed and modified 

by Michael Gould of the U.S. Geological Survey (USGS), 

utilizes at its core, original Visual Basic for Applications 

(VBA) programming written by Jeremy Mennis and Torrin 

Hultgren in 2004 at the University of Colorado, Boulder, 

Geography Department.

Even though the nature of a population distribution is more 

realistically represented with a dasymetric map and adequate 

research has been done on the utility and function of dasymetric mapping, the complexity of this method compared to the 

choropleth method often deters cartographers from using the 

dasymetric mapping method (Charpentier, 1997). The ArcMap 

extension discussed in this report is designed to facilitate the 

use of dasymetric mapping by automating the areal interpolation process within a GIS framework. Creating an extension that 

overcomes the obstacles inherent in dasymetric mapping, the 

method becomes transferable to a wider spectrum of users.

Review of Areal-Interpolation and DasymetricMapping Approaches

Spatial analysis often requires the transferring of data from 

one set of geographic zones to another set of overlapping zones. 

For example, data may exist on the number of people living 

within census enumeration units, but there is a need to estimate 

the number of people living within watershed boundaries. Areal 

interpolation is the process of transforming spatial-data values 

from a set of source zones (the original unit of data aggregation) 

to target zones (the intersecting zone). Areal-based functions 

practice volume-preserving properties, what Tobler (1979) 

refers to as the pycnophylactic property, that is, no data is lost or 

created during the transformation. All areal-interpolation methods are based upon the geometric intersection of the source and 

target zones, where some may or may not use ancillary information to add more intelligence to the redistribution. Simple areal 

weighting does not use ancillary information and bases interpolation on the areal geometric properties of the source and target 
2 Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

Figure 1. Diagram showing how dasymetric mapping redistributes ancillary information to better represent population distribution. 

Data may be collected for individual households, as shown by the four blocks on the left. In this example, assume that every male/

female pair represents a household of five persons. However, due to confidentiality, data is often aggregated into larger units of 

analysis, as shown by the four blocks on the right. Dasymetric mapping attempts to disaggregate the four figures on the right into 

their original state on the left.

zones. The dasymetric mapping method uses ancillary information to generate parameters used in areal interpolation, while 

maintaining volume-preserving properties. The most common 

form of ancillary data used for population estimation is LULC 

classifications derived from remotely sensed imagery, topographic maps, or land parcel databases (Wright, 1936; Yuan and 

others, 1997; Mennis, 2003; Hawley and Mollering, 2005). The 

areal-interpolation performance has a direct correlation with the 

quality and accuracy of the LULC-derived data. 

The most straightforward dasymetric mapping approach 

using LULC data is the binary division approach in which 

LULC is classified as “populated” or “unpopulated” and 

census populations are simply redistributed to “populated” 

areas (Wright, 1936; Langford and Unwin, 1994; Fisher and 

Langford, 1996; Holt and others, 2004; and Wu and others, 

2005). Holloway and others (1997) combined multiple ancillary 

datasets to detect and remove uninhabited lands from the area of 

analysis. Four types of LULC were ruled out, including census 

blocks with zero population, lands owned by local, State, or 

Federal government, corporate timberlands, water and wetlands, 

and open and wooded areas with terrain slopes greater than 15 

percent. Holloway and others (1997) assigned a fixed proportion 

of the total population to each specific LULC (Urban 80 percent, Open 10 percent, Agriculture and Forested 5 percent, and 

Uninhabited 0 percent). These proportions are determined subjectively with prior knowledge of the area and field observation. 

One critical drawback to assigning fixed proportions to designated LULC is that the spatial area of each LULC zone within 

each source zone is not accounted for, so if there is a source 

zone with a very small spatial area that is classified urban, that 

urban zone still receives 80 percent of the source population.

Mennis (2003) used a combination of areal weighting 

and a three-tier, urban-classification method, using satellitederived thematic data to break down census tracts into 

homogenous residential zones. To account for the relative 

densities in each urban class, empirical sampling provides a 

proportional density fraction used as a weighted value representative of each urban class. To deduce the necessary urban 

class population density values, source zones that are entirely 

occupied by a single inhabited land-cover class are isolated 

and the population density is calculated by dividing total 

population by total area. These density ratios are extrapolated 

to all source zones that have a combination of inhabited LULC 

classes. Area ratios are then calculated to adjust the population density fraction by the percentage of that block group’s 

total area that the urban class occupies. The combination 

of an empirical sampling and areal-based weighting makes 

this particular dasymetric method a good choice because the 

method addresses the weaknesses noted in Holloway and others (1997). A simple graphic, figure 2, shows the urban 3-class 

method with concepts of areal weighting and the relative difference in density between LULC classes.

Case Study—Clatsop County, Oregon

Local communities potentially affected by natural disasters 

are taking strides to minimize loss in the event of a catastrophic 

event. Scientific analysis and technological advances have led to 
Introduction 3

the growth of geographic tools that have the capacity to aid local 

jurisdictions in emergency planning. The use of a GIS allows users 

to visually and statistically characterize populations at risk within 

hazard zones. The goal of this case study is to develop a more 

accurate representation of population distribution along the Oregon 

Coast, estimating daytime verses nighttime fluctuations for use 

in emergency, preparedness planning for a Cascadia Subduction 

Zone (CSZ) tsunami. The implementation of a micro-scale map of 

population density in the daytime and nighttime provides a realistic snapshot of who is on the ground when a tsunami event occurs, 

highlighting higher risk areas during response and recovery efforts. 

By using a dasymetric mapping technique to manipulate U.S. Census Bureau enumeration units, population values are distributed to 

10-m grid cells with the input of a tax parcel dataset acting as the 

primary ancillary indicator of inhabited LULC. 

A critical first step in managing these threats is to understand who is exposed to these hazards. In the United States, 

Figure 2. Diagram showing three fundamental approaches to mapping population distribution. A, Total number of people, aggregated 

by census delineated unit; B, represents inhabited (red) verses uninhabited (green) with populations evenly distributed within the 

inhabited land use; and C, represents an urban 3-class method, where populations are distributed in high (red), medium (pink), and low 

(yellow) population class based on LULC-class code and areal weighting.

emergency managers have access to decadal census data that 

summarizes residential populations. However, in many coastal 

communities, there are typically greater numbers of nonresidents (for example, employees, tourists) in high tsunami hazard zones (Wood and Good, 2004). Emergency managers need 

to estimate the number of residents, employees, and visitors 

who could potentially be exposed to possible hazards if they 

are to develop realistic risk reduction and preparedness strategies. Datasets like LandScan, developed as part of the Oak 

Ridge National Laboratory (ORNL) Global Population Project 

(Oak Ridge National Laboratory, 2005), focus on ambient 

population and have been used for post-disaster assessments at 

the global scale. Ambient population is defined as the number of 

people likely to be in an area as opposed to residents alone, with 

population counts for each 1-km2 cell being based on probability coefficients. Verified to be accurate to within 10 percent of 

the given values, these coefficients are based on road proximity, 

A B

C
4 Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

slope, land cover, and nighttime lights. However, with a 1-km2

cell size, Landscan is considered too coarse for local planning. 

The research conducted at the USGS focused on the dasymetric 

mapping methods for mapping daytime and nighttime population density at the local level with intentions of applying these 

methods to other local communities involved with hazard planning and response. For demonstration purposes in this report, 

we only focus on nighttime population values.

The quality of the interpolation estimates relies heavily on 

(1) how source zone and target zone boundaries are defined, (2) 

the LULC classification accuracy, (3) the degree of generalization in the interpolation process, and (4) the characteristics of 

the partitioned surface (Lam, 1983). We use the census-block 

group as the source zones of known population values and a 

tax-parcel database as the target zones. The parcel database 

has many attributes that allow for hierarchical density weighting relative to building type, land-use, and description. For 

example, an apartment complex indicates high density, a single 

family home indicates medium density, and a large acreage 

estate indicates low density. The parcel database was divided 

into four density classes (high, medium, low, and exclusion) to 

act as a hierarchical measure within the interpolation process.There were many incentives for using parcel data for the 

Clatsop County application. The creation of a methodology that 

can be transferred to other hazard-prone regions was necessary 

for future work, which required the data inputs to be accessible, 

affordable, and uniformly structured. All local governments 

have land-parcel information for their areas, and about 70 percent of all national parcels have been converted to a geospatial 

format (National Academy of Sciences, 2007). Also, the scale, 

resolution, and accuracy derived from parcel data are significantly better than most remote-sensing-derived LULC products, 

such as the National Land Cover Dataset, created at a 30-m grid 

resolution (Vogelmann and others, 2001). To capture land-cover 

classifications at such a high resolution, the more common 

approach would be to use remotely sensed imagery with high 

spatial resolution (for example, IKONOS–4-m multispectral 

imagery). However, due to the spatial and spectral heterogeneity of the urban environment, inconsistencies become prevalent 

when trying to characterize built-up structures mixed in with 

vegetation cover (Herold and others, 2002). Consequently, 

parcel data becomes a more practical option for delineating 

residential neighborhoods into density classes.

The approach we used to map nighttime population distribution for Clatsop County, Oregon, draws from a combination of previous methods (Mennis 2003; Holloway and others, 

1997) leading to the use of four total density classes [three 

urban-residential classes and one exclusion class (representing all uninhabited areas)]. The parcel attributes describe and 

designate the land-uses, building types and density parameters 

present on each parcel of land, which are used as primary indicators to develop four density classes: high density, medium 

density, low density, and uninhabited. The newly created 

LULC layer is converted to raster format at a 10-m resolution acting as the target zones for the interpolation of censusblock groups. The ArcGIS extension module automates the 

areal interpolation using the principles of areal weighting and 

empirical sampling of relative densities which result in a threetier distribution of population density at a 10-m pixel resolution. The output maps are shown after the demonstration of the 

ArcGIS extension (see figs.11 and 12 later in this report).

Dasymetric-Mapping Extension (DME)

Overview

The GIS extension we developed is summarized in this 

report to assist potential users with conceptual background of 

dasymetric mapping relative to the functionality of the extension. The Dasymetric-Mapping Extension (DME) operates in 

ArcGIS 9.1 or 9.2 as a point-and-click dialog box prompted by a 

button on the main toolbar. The main objective of this extension 

is to automate the process of taking population data from census 

enumeration units and transferring the data values to overlaying homogenous zones while (1) maintaining volume preserving properties and (2) using an empirical sampling method for 

determining relative densities for each homogenous zone. The 

outcome is a raster representation of population per pixel. 

The input data required for the operability of the DME is 

twofold. First, the user is required to obtain U.S. Census Bureau 

data in a shapefile format, at any geographic level (block, blockgroup, tract, county). Second, the ancillary data needs to represent LULC density parameters in a raster ArcGRID format. 

For example, the ancillary data used, whether one or multiple 

datasets, must be recoded into a singe ESRI ArcGRID format 

with four density classes representing high, medium, and low 

population density, as well as an uninhabited class where zero 

population will be assigned. Refer to the section “Input Data 

and Parameters” for detailed guidelines. 

Installation

Before installing the DME, the following programs must 

be loaded on the host computer in order for the extension to 

work properly:

•	 ArcGIS 9.1 or 9.2 (must have the Spatial Analyst 

Extension installed and enabled).

•	 ArcObjects InterOp libraries [If installed, these 

libraries would be located in a folder called “DotNet” 

in the ArcGIS install directory (that is, C:\Program 

Files\ArcGIS\DotNet). If the folder is missing, run 

the ArcGIS installation disc in modify mode to install 

the libraries for .NET.]; and, 

•	 Microsoft .NET Framework 1.1 (To check if your 

system has this, go to your desktop taskbar and click 

Start->Control Panel->Add/Remove Programs and 

look for an entry for the .NET Framework 1.1.).

Note: If you see that Microsoft .NET Framework 

2.0 has been installed, the application will still run 

properly, as long as Microsoft .NET Framework 1.1 

is also installed.
Dasymetric-Mapping Extension (DME) 5

The installation package for the DME is loaded into a 

WinZip file that can be accessed at http://geography.wr.usgs.

gov/science/dasymetric/data.htm. The WinZip file includes a 

setup.exe, a setup.ini and a setup.msi file. To guarantee that all 

Figure 3. Screen image of the 

“Welcome to the DasymetricMapping-Extension Setup Wizard” 

dialog box. Clicking the “Next” 

button in this screen brings up the 

“Select Installation Folder” dialog 

box (fig.4). The user should install 

the folder to the default directory, 

which is “C:\Program Files\,” to 

ensure proper installation and 

functioning. The user is also able 

to determine here whether the 

extension is accessible to “just 

me” or “everyone” who uses the 

computer.

Figure 4. Screen image of the 

“Select Installation Folder” dialog 

box. Clicking the “Next” button in 

the “Select Installation Folder” 

dialog brings up a “Confirm 

Installation” dialog box (fig. 5). 

The user clicks “Next” to begin 

the extension installation.

necessary files and linkages are installed, users should install 

the extension by opening the “setup.exe” file. On opening the 

“setup.exe” file, the Dasymetric-Mapping-Extension Setup 

Wizard will be launched (figs. 3-6).
6 Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

Figure 5. Screen 

image of the “Confirm 

Installation” dialog 

box. If the DasymetricMapping Extension is 

successfully installed, 

an “Installation Complete” dialog box will 

appear (fig. 6). The 

user should then click 

the “Close” button to 

close the installation 

dialog box.

Figure 6. Screen 

image of the 

“Installation Complete” 

dialog box. Note that 

if the “setup.exe” fails 

to install the extension, 

the user must ensure 

that the host computer 

has all the necessary 

permissions and 

required configurations 

described earlier in this 

section.
Dasymetric-Mapping Extension (DME) 7

Enabling and Operating the DasymetricMapping Extension

Once the downloadable extension has been properly 

installed, the user can enable the extension within ArcGIS 9.1 

or 9.2. To do this, the extension will register under the Tools/

Customize/Commands menus in ArcMap (fig.7). Simply 

drag and drop the tool titled, “DasymetricMapping VB.NET” 

onto an open space in the ArcMap toolbar and the graphical 

icon will appear as the operating button. In order to open the 

DME’s main dialog box via the map button on the main toolbar, the user must add their input data files into the ArcMap 

document by using the “Add Data” function. 

Input Data and Parameters

With the intention of attracting a wide range of users and 

applications, the DME attempts to simplify the dasymetric 

mapping process to a point-and-click interface. There are two 

primary datasets necessary for the DME to function properly; a U.S. Census Bureau population-data shapefile, and a 

LULC-derived raster layer developed into four data classes 

representing the inhabited/uninhabited areas as a density 

stratification (for example, (1) high-density residential, (2) 

low-density residential, (3) non-urban inhabited, (4) uninhabited). The parameter settings relate to the data fields needed to 

run the DME and the principles used in the areal interpolation. 

The numbered steps in the text give a detailed explanation to 

operating the DME; however, the appendix contains simple 

mathematical equations derived from Mennis (2003) used as 

the programming backbone.

(1) Enter an output path of folder: User must browse 

to choose output destination folder. Output destination folder must be empty when running the software 

application. Program will not overwrite or add to 

existing files in output folder, resulting in an error.

Figure 7. Screen image of the “Customize” dialog box to enable ArcMap extension. By navigating in ArcMap, Tools/Customize/

Commands, the Dasymetric-Mapping Extension becomes available. Drag and drop onto your main ArcMap toolbar to enable the extension.
8 Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

(2) Select population layer: Any demographic data in 

a geospatial format that has one population value 

representing each areal unit (polygon) (example, U.S. 

Census Bureau Data by block, block-group, tract, 

county.) User must select the population file available 

in the drop-down list. Population data must be loaded 

into the ArcMAP project in order for the data to be 

available in the drop-down list 2 of the user-form.

(3) Select ancillary layer: A land use or land coverderived raster layer that has been reclassified into four 

classes representing inhabited/uninhabited areas into 

density stratification (for example, (1) high-density 

residential, (2) low-density residential, (3) non-urban 

inhabited, (4) uninhabited). User must select the ancillary layer from drop-down list 3 on the user-form. 

The grid cell size can be determined by the user and 

is dependent on the scale and the input data available. 

There is no limit to how small the grid cell size can 

be, however computation time may be affected.

Figure 8. Screen image of the “Dasymetric-Mapping Extension” user form. Numbers 1 through 7 relate to the different steps 

needed to operate the extension correctly. The steps are explained in the text.

(4) Select field in your population dataset that contains 

population count data: Drop-down list will be populated with all of the fields present in the population layer. 

(5) Select percent of source unit that must be covered 

by the ancillary class: To model the relative difference in population density between the “inhabited 

classes” we use an empirical sampling approach. 

The grid cell with the higher inhabited class has a 

higher population density than a grid cell with a 

low or non inhabited class code based the empirical 

measurement. In order to determine this relative 

difference between classes, the population density 

values are sampled for each “inhabited class”. The 

sampling process selects all block-groups that have 

a specified “percent cover” (this threshold is set by 

the user within the tool. for example, 80 percent) by 

a single “inhabited class” that is equal to or exceeds 

that threshold. The total population and area are 

calculated to find the aggregated density of the 
Dasymetric-Mapping Extension (DME) 9

samples. This sampling technique provides a value 

that can be used as a “population density fraction,” 

which is the percentage of a block-group’s total 

population that should be assigned to each “inhabited class” within the block-group.

(6) Select preset ancillary class to zero: If you have an 

ancillary class that you know is “uninhabited” (for 

example, water, non-residential developed, uninhabited 

wetlands, or forest) you may “preset” these classes to 

“0” so no data may be distributed to these zones. 

(7) Click run

Output Files and Symbolization

After successful completion of the DME, the “Processing Complete” dialog box will appear indicating no errors 

in computation performance (fig. 9.). The DME outputs an 

ESRI ArcGRID named “Dasymetric Result” representing 

population/grid cell. The actual data output files have been 

written to the output directory specified by the user and have 

file names different than what appears in the “Layers” view 

with the completion of the DME (table 1). The “Dasymetric 

Result” file appearing in the view following the compleFigure 9. Screen image of the “Processing Complete” dialog box. The output data file will look skewed until the user adjusts the 

symbolization of the results.

tion of the DME is the result of a tabular join between the 

“dasy_rast” (ArcGRID) and the “dasytable” (*.dbf). The join 

is performed by appending all of the data in the “dasytable.

dbf” to the “dasy_rast” based on the “Value” fields in both 

files. We have automated this join to facilitate manipulation of 

the output data; however, the join will not be saved once the 

ArcMap document has been closed. 

In most GIS applications, the final result is a map that 

needs to convey information to the user in a manner that can 

be easily interpreted and related to reality. The effectiveness 

and use of GIS results can be compromised if the incorrect 

cartographic symbolization is chosen (Ramroop, 1998). Upon 

completion of the DME, the symbolization of the output data 

must be adjusted in order to demonstrate meaningful results. 

The user may symbolize the output data in any representation 

desired; however, we recommend the following to maximize 

data comprehension. In figure 10 we outline four steps within 

the ArcGIS “Layer Properties” and “Classification” dialogs to 

help achieve meaningful presentation of the spatial information. 

The ArcGRID named “Dasymetric Result” has a field (“NEW 

DENSITY”) that contains all of the new density values produced by the DME and must be modified for proper visualization of the results:
10 Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

(1) Right click on the layer named “Dasymetric Result” 

and go to “Properties”. This will bring up “Layer 

Properties.” Click the “Symbolization” tab. The recommended symbolization is the “Classified” data range 

with the desired amount of classes within a natural 

breaks classification method. Under “Fields - Value,” 

choose the field named “DasyTable.NEWDENSITY.” 

Before selecting a color ramp, the user must adjust 

the histogram to exclude all zeros from the data range. 

Under “Classification,” click “Classify.”

(2) In the “Classification” dialog, click “Exclusion” to 

bring up “Data Exclusion Properties.” Enter “0” into 

the excluded values field. 

(3) Click on the “Legend” tab. Choose legend properties 

for all zero values by checking the box “Show color 

for excluded data” (for example, choose white to 

symbolize all “0” values). Click OK until returning 

back to “Layer Properties.”

(4) After adjusting the histogram of data values to exclude 

all pixels with zero population, the user should notice 

that the minimum value is now greater than zero. The 

final recommended step is to choose a color ramp 

that signifies population density. Under Color Ramp, 

there is a pull down menu of choices. Cartographically, density on a color ramp should be symbolized 

using either dark to light or warm to cold hues. We 

have chosen the progression from red to green, a 

warm to cold transition indicating high density in 

reds, gradually shifting to medium density in yellows, 

and low density in greens. The zero population values 

are symbolized in white to indicate the absence of 

the phenomena. Black would be another wise choice 

for the zero population values; however, black also 

implies “no data” within raster environments. 

Output Maps

The following maps of Astoria and Seaside (figs. 11 and 

12), two coastal cities in Clatsop County, Oregon, are output 

representations of the DME. The dasymetric maps have been 

symbolized using the steps outlined in the section above. In 

these figures, choropleth maps have been made to show the contrast in visualization between the two methods. The choropleth 

map assumes that population is distributed evenly throughout 

the census block-group, when in actuality there are many areas 

that are vacant or uninhabited. Due to the ease of use, choropleth maps have been the chosen approach for mapping population density, regardless of some of the inaccuracies associated 

with aggregated data. For the case study of Clatsop County, 

where there is a significant tsunami hazard risk, the dasymetric 

mapping approach provides detailed information indicating 

which areas are most vulnerable. In comparison to the choropleth maps shown in figures 11 and 12, the emergency responder 

is able to take the dasymetric map and focus on preparedness 

strategies down to the parcel level. 

In a GIS, there are other functions that can be used to 

manipulate the output data from the DME. The ArcGRID is 

a seamless raster file enabling the user to select an area of 

interest, or a cluster of pixels, and quantify the population 

of that particular area of interest, regardless of source zone 

boundaries. This capability is available because the DME creates a surface representation of population density, rather than 

discrete polygons where one aggregated value represents the 

entire polygon. The output structure of the DME provides a 

superior dataset to the choropleth map for overlaying population density with other relative datasets. The areas that have 

zero population can be symbolized with no color and transparency can be used for the populated pixels to model a much 

more realistic view as to where people reside. 

Summary

The objectives of this GIS software extension are (1) to 

automate the 3-class dasymetric-mapping method within a 

GIS environment, (2) to present the point-and click functionality of the DME, (3) highlight the options for optimal 

symbolization of the results, and (4) relate this software 

package to a real world application demonstrated in Clatsop 

County, Oregon. The software extension available here could 

increase the productivity, ease, and efficiency of researchers/

analysts who work with U.S. Census Bureau data and spatial 

analysis. We have used this methodology to map the coastal 

DME Output File Names Format Description

dasy_rast ESRI GRID GRID file of intersection of inputs (LULC and CENSUS) w/ 

pixel counts and unique ID

popraster ESRI GRID conversion of vector census file to raster GRID file

info ESRI GRID- support files support file for ESRI GRID - user must keep this file in the same location as GRID file

dasytable *.dbf summary table of all results per grid cell (populaton/grid cell)

Dasymetric_Stats *.dbf run-time summary for DME

Table 1. Dasymetric-Mapping Extension output file names with descriptions. 
Acknowledgments 11

Figure 10. Screen images showing four steps to symbolizing dasymetric-mapping output data. 

areas of Clatsop County, Oregon in an effort to reveal (1) 

a superior visualization of the population distribution, (2) 

areas that have the highest density in relation to the tsunami 

hazard zones, and (3) what areas need the most focus during 

a catastrophic event. 

There has been significant research conducted on arealinterpolation and dasymetric-mapping methodologies. In the 

past, some major drawbacks for the use of dasymetric mapping methods were the time-intensive preparation of multiple datasets and transferring the methodology to function 

smoothly within a GIS environment. With these obstacles 

in mind, many researchers resort to using standardized U.S. 

Census Bureau data. By creating geospatial tool or extension that streamlines the areal interpolation process, the 

user has a cutting edge utility for remodeling population 

data with very easy point-and-click features. We continue 

to research the input data and its overall accuracy for the 

dasymetric-mapping technique, with the intention to help 

local communities prone to natural disasters. By generating 

a downloadable tool with affordable data layers as inputs, 

this method will be an attractive alternative to the standard 

aggregated census data that limit geospatial research for 

emergency management and response. 

Acknowledgments

We thank the USGS Geographic Analysis and Monitoring Program for funding this effort. Richard Taketa, Amy 

Mathie, Mara Tongue, and Susan Benjamin provided insightful comments and technical edits of this manuscript. Michael 

Gould (mgould@usgs.gov) is the chief software developer 

for the Dasymetric-Mapping Extension and is the primary 

contact for questions and comments on the extension software functionality. We also thank Jeremy Mennis and Torrin 

Hultgren for developing the original ArcObjects script based 

on methods of Mennis (2003). 
12 Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

Figure 11. Choropleth map (top) and dasymetric map (bottom) of Astoria, Oregon. 

Astoria, Oregon

Census Block

Group outline

Persons/ Km2

1.0 - 75.0

75.0 - 200.0

200.0 - 500.0

500.0 -1000.0

1000.0 - 2000.0

0 0.5 1 2 KILOMETERS

Oreg

Astoria, Oregon

Persons/10 m pixel

0.004 - 0.01

0.010 - 0.1

0.100 - 0.5

0.500 - 1.5

1.500 - 6

Zero Persons

Oreg

Census Block

Group outline

N

N

0 20 KM

0 0.5 1 2 KILOMETERS

0 20 KM
Acknowledgments 13

0 0.5 1 2 KILOMETERS 0 0.5 1 2 KILOMETERS

Seaside, Oregon Census Block

Group outline

Persons/ Km2

1.0 - 75.0

75.0 - 200.0

200.0 - 500.0

500.0 -1000.0

1000.0 - 2900.0

N N

Seaside, Oregon Census Block

Group outline

Persons/10 m pixel

0.004 - 0.01

0.010 - 0.1

0.100 - 0.5

0.500 - 1.5

1.500 - 6

Zero Persons

Oreg Oreg

0 10 20 KM 0 10 20 KM

Figure 12. Choropleth map (left) and dasymetric map (right) of Seaside, Oregon.
14 Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

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Appendix 15

Appendix—Equations for Dasymetric-Mapping Extension (Mennis, 2003)

(A) duc = Puc/(Pbc + Plc + Pnc) , (1) 

where duc = population-density fraction of urbanization class u in county c, Puc = population density 

(persons/900 m2

) of urbanization class u in county c, 

Pbc = population density (persons/900 m2

) of urbanization class h (high) in county c, 

Plc = population density (persons/900 m2

) of urbanization class l (low) in county c, and 

Pnc

 = population density (persons/900 m2) of urbanization class n (nonurban) in county c. 

(B) baub = (nub/nb

)/0.33 , (2) 

where aub = area ratio of urbanization class u in block-group b, 

nub = number of grid cells of urbanization class u in block-group b, and 

nb

 = number of grid cells in block-group b. 

(C) fubc = (duc*aub)/ [(dhc*ahb) + (dlc*alb) + (dnc*anb)] , (3) 

where fubc = total fraction of urbanization class u in block-group b and in county c, 

duc = population-density fraction of urbanization class u in county c, aub = area ratio of urbanization class u 

in block-group b, 

dhc = population-density fraction of urbanization class h (high) in county c, dlc = population-density fraction of 

urbanization class l (low) in county c, dnc = population-density fraction of urbanization class n (nonurban) in 

county c, 

ahb = area ratio of urbanization class h (high) in block-group b, 

alb = area ratio of urbanization class l (low) in block-group b, and 

anb = area ratio of urbanization class n (nonurban) in block-group b. 

(D) popubc = (fubc*popb

)/nub (4) 

where popubc = population assigned to one grid cell of urbanization class u in block-group b and in county c, 

f

ubc = total fraction for urbanization class u in block-group b and in county c, popb

 = population of blockgroup b, and nub = number of grid cells of urbanization class u in block-group b. 
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Sleeter and Gould—GIS Software to Remodel Population Data Using Dasymetric Mapping Methods—U.S. Geological Survey Techniques and Methods 11–C2 G U S S