Monthly Labor Review • March 2007 51

A Geospatial Distribution Geospatial Distribution

Sheryl Konigsberg

The geospatial distribution 

of employment: a new visual asset

By combining geographic information with data 

from the Quarterly Census of Employment and Wages program, 

BLS provides analysts with a tool that will 

offer new insights into data that were previously unobserved 

Sheryl Konigsberg is an 

economist in the Office of Employment 

and Unemployment 

Statistics, Bureau of Labor Statistics. E-mail:

Konigsberg.Sheryl@

bls.gov

The advent of powerful computing capabilities and mapping software now allows more sophisticated analysis of 

new and existing problems through the visual display of information. The center point 

of these new features is the ability to provide 

pinpoint locations for geographic features; 

defined by precise latitude and longitude coordinates, called “geocodes.” In any geocoding 

system involving businesses, the key is to have 

accurate physical location addresses.

1

This article discusses the background of the 

Quarterly Census of Employment and Wages 

(QCEW) program, the definition of geocoding, 

and its current and potential uses. It provides 

examples of existing applications using labor 

market information and new ways of presenting these data. The article highlights an earlier 

pilot project that obtained and used geocodes 

from the Bureau of Labor Statistics business 

establishment list. Finally, the article profiles 

future uses, and explains how BLS plans to 

continue its efforts of geocoding business establishments from the QCEW.

The QCEW

The QCEW program is a by-product of the 

Unemployment­­ Insurance (UI) system and 

managed in a Federal/State cooperative environment. This program releases comprehensive tabulations of employment and wage information for workers covered by State UI

laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. BLS provides policies, standards, and funding, whereas States 

and the District of Columbia collect, edit, 

tabulate, and publish the data. 

The QCEW program serves as a near business census and constitutes the only set of 

monthly employment and quarterly wage information. The QCEW program already provides economic data by the six-digit North 

American Industry Classification System 

(NAICS) at the national, State, Consolidated 

Metropolitan Statistical Area (CMSA), Metropolitan Statistical Area (MSA), and county levels in the Federal statistical system. This 

quarterly census is published within 6 months 

after each calendar quarter.

Every quarter, under the laws of each State, 

businesses are required to report the number of employees for all 3 months, total wages, taxable wages, UI taxes, and administrative 

data, such as physical location addresses. After these UI reports are collected and entered 

by the State UI department, they are passed to 

the State QCEW program for the reviewing, 

editing, and publishing stages. These data also 

are used for the QCEW business register.

In addition to the UI reports, BLS funds 

two other collections to support the needs of 

its users. The first is the Annual Refilling Survey (ARS) that, over a 3-year period, contacts 

all businesses to update or complete industry information (NAICS codes) and addresses. This is the primary method for updating 
Geospatial Distribution

52 Monthly Labor Review • March 2007

physical location addresses within the QCEW business 

register. The second is the quarterly Multiple Worksite 

Report (MWR) that collects data for each individual establishment of a multi-unit business. The combination of 

information from these three sources makes up the resulting QCEW program. The program’s comprehensiveness results in precise business and employment data with substantial industry and geographic detail.

Data from the QCEW serve as an important input to 

many BLS programs as well as other Federal and State 

programs. These data are used as a benchmark for the 

Current Employment Statistics and Occupational Employment Statistics. The QCEW also is used by the Bureau 

of Economic Analysis for gross domestic product (GDP) 

and personal income estimates. 

Geocoding

Geocoding is the process of adding geographic information, such as latitude and longitude, to a file or database 

for use in a geographic information system (GIS). AGIS

is a set of activities that involve the use of computer pro- of activities that involve the use of computer programs and staff to capture, store, update, manipulate, analyze, and display spatial information; often in the form of

maps. 

Geocoding uses either a point or polygon approach. In 

a point-based approach, business establishment information is linked to latitude and longitude coordinates. This 

information allows a user to plot locations on a map. In 

a polygon-based approach, business establishment information is linked to the center of a polygon that represents a reference layer such as census block group, census 

tract, ZIP Code or county. This information allows a user 

to identify and use all types of data that may be collected or available from other sources. The QCEW microdata 

file contains a rich set of geographic information, such as 

physical location address, city, State, ZIP Code, and county, that can be geocoded and applied to answer questions 

about the labor market.

There are two types of geocodes: address geocodes and 

ZIP Code centroid geocodes. The most precise is the address geocode. Address geocodes are derived using the 

physical location address. Addresses are geocoded by BLS

using commercial software that accesses U.S. postal data 

files. This software estimates the location of each address 

record from an input file and standardizes the address. 

These standardized addresses are then matched against a 

Geographic Base File (GBF), which contains directories 

of street segment records. The second type, ZIP Code centroid geocodes, assigns the geographic center of each ZIP

Code to an address. If the geocoding software is unable to 

match against an address, it will attempt to geocode to the 

ZIP Code centroid. These matching processes assign geographic codes to address records, establishing their spatial location.

Potential range of geospatial data

Geocoded data are used extensively in government, business, and research for a wide range of applications including environmental resource analysis, land-use planning, 

locational analysis, tax appraisal, utility and infrastructure 

planning, real estate analysis, marketing and demographic analysis, and habitat studies. At the most detailed levels, 

geocoded business addresses are valuable to transportation planning where approximate locations or higher level 

county aggregations are inadequate. For this purpose, the 

side of the street, the location along the block, and the exact corner of an intersection are critical to optimal planning of bus lines and other public transportation. 

Geocoding QCEW data allows labor market information to be presented in a new dimension. Demands for 

more local data give BLS an incentive to provide data for 

cities, towns, and even smaller areas. With the availability of geocoded data, BLS potentially can develop lower 

levels of aggregations, including cities, postal ZIP Codes, 

census tract, census block, and natural boundaries such as 

floodplains. 

Data presentation

The conventional way of presenting economic data is twodimensional, through tables and graphs. If tabular data are 

geocoded, they can be used to create a drawing illustrating 

the relationship among three data items. With the rise of 

Internet usage and improving technology, GIS has made it 

possible to plot economic data to create illustrations and 

publish in the form of maps. This can be done by using 

geographic information, computers and geographic software to read the information and create spatial data visually. 

As an example, the QCEW program produces an annual bulletin with tabular data aggregated by State. Table 

1, which shows establishment counts, employment, and 

wages by State, is a section from the 2002 QCEW publication. The data in table 1 are a standard way of presenting labor market information that has been in practice for 

many years. With this traditional way of displaying data, 

the lowest level of aggregation by boundaries is by county. This table can be challenging for an analyst to interpret 
Monthly Labor Review • March 2007 53

Total United States ....................................... 	 150,107 3,364,485 $

	Alabama............................................................ 	 1,782 34,206 

	Alaska ............................................................... 365 7,07

	Arizona.............................................................. 	 2,369 51,875 

	Arkansas ........................................................... 	 1,202 20,36

	California........................................................... 	 22,265 499,681 

	Colorado............................................................ 3,877 93,397 

	Connecticut ....................................................... 	 1,871 41,145 

	Delaware........................................................... 334 7,

	District of Columbia........................................... 	 1,129 25,448 

	Florida ............................................................... 	 8,751 177,973 

	Georgia ............................................................. 	 4,492 132,432 

	Hawaii ............................................................... 691 11,701 

	Idaho	 ................................................................ 713 9,162 

	Illinois ................................................................ 	 6,454 	 145,409 7,667,8

	Indiana .............................................................. 2,178 42,528 

	Iowa		 ................................................................. 	 1,743 35,193 

	Kansas .............................................................. 	 1,485 50,745 

	Kentucky ........................................................... 	 1,767 31,745 

	Louisiana........................................................... 	 1,659 29,018 

	Maine ................................................................ 736 11,546 

	Maryland ........................................................... 	 2,914 53,449 

	Massachusetts .................................................. 	 4,521 99,989 

	Michigan............................................................ 	 3,977 73,480 

	Minnesota.......................................................... 	 3,036 67,161 

	Mississippi......................................................... 	 1,047 16,070 

	Missouri............................................................. 	 3,174 70,899 

	Montana ............................................................ 	 830 7,78

	Nebraska........................................................... 	 1,013 24,690 

	Nevada.............................................................. 921 16,967 

	New Hampshire................................................. 	 917 12,821 

	New Jersey ....................................................... 	 4,058 112,163 

	New Mexico....................................................... 971 16,864 

	New York........................................................... 	 11,713 295,415 

	North Carolina................................................... 	 3,736 78,955 

	North Dakota..................................................... 	 420 7,92

	Ohio		 ................................................................. 	 4,202 101,279 

	Oklahoma.......................................................... 	 1,694 35,496 

	Oregon .............................................................. 2,244 36,211 

	Pennsylvania..................................................... 5,980 128,315 

	Rhode Island..................................................... 616 11,132 

	South Carolina .................................................. 1,466 28,154 

	South Dakota .................................................... 	 475 6,79

	Tennessee......................................................... 	 2,198 51,639 

	Texas	................................................................. 	 9,626 248,879 

	Utah		 ................................................................. 	 1,562 29,808 

	Vermont............................................................. (

1

) (

1

) (

1

) (

1

) (

1

)

	Virginia .............................................................. 4,011 105,816 

	Washington ....................................................... 	 3,182 92,714 

	West Virginia ..................................................... 	 774 13,306 

	Wisconsin.......................................................... 	 2,096 51,123 

	Wyoming ........................................................... (

1

) (

1

) (

1

) (

1

) (

1

)

	Puerto Rico ....................................................... 	 508 21,273 

	Virgin Islands..................................................... 	 45 

Area

Establishments, employment, and wages in the private industry information sector, by State and 6-digit 

NAICS industry, 2002 annual averages

Average Annual average Annual wages Average

establishment employment per employee weekly wage

Table 1.

Total annual

wages (in

thousands)

1 Data do not meet BLS or State agency disclosure standards.
Geospatial Distribution

54 Monthly Labor Review • March 2007

what is being conveyed.

By contrast, table 2 is an example of tabular data that 

presents details on the number of establishments, average 

monthly employment, and total quarterly wage, by industry sector for the city of Cleveland, Ohio. 

Table 2 demonstrates how geocoded data can be displayed at the subcounty level. Without the latitude and 

longitude information, these data could not have been aggregated at this level of fine detail. In addition, such data 

can be used in even richer applications, which this article 

illustrates later.

In September 2003, Hurricane Isabel, a category 5 

hurricane in the Atlantic Ocean, made landfall on the 

east coast of North Carolina. Table 3 displays establishments within industries in the floodplain areas of Brunswick and New Hanover counties. North Carolina was 

able to show that approximately 11 percent of units or 

establishments and 10 percent of employment in Brunswick and New Hanover Counties are located in a floodplain. Some industries with a higher percentage of units 

and employment in affected areas of the hurricane might 

not be surprising. For example, some units might be in areas where boat rentals or other water recreational activities take place. These data help users determine the potential impact of this disaster. 

These examples illustrate the traditional method of displaying data in a tabular format. A GISbased presentation also provides a visual display that 

was previously unavailable. The following examples 

demonstrate the power of a GIS and how it conveys 

information visually. 

The position of the business establishments (dots 

on the map) in map 1 conveys an immediate visual 

impression. (See page 57.) The dots on the map describe whether an establishment is within or outside 

a floodplain area. Most of the establishments lie outside of the floodplain. When this map is combined 

with the data from table 3, a user can see the distribution of the 11 percent of units located in the floodplain. The use of geocoding and mapping the QCEW

data can help users understand the spatial distribution 

of employment, which can lead to better informed decisions about the local economy. 

The hurricanes that hit central Florida in 2004—

Charley, Frances, and Jeanne—are shown in map 2. (See 

page 58.) The State of Florida was able to track the path 

of each hurricane with a 20-mile radius to show the potential impact on employment within the affected areas. 

This map shows that all three hurricanes crossed through 

Polk County, Florida, where the density is 1 to 75 employers per square mile. 

The impact of the October 2003 fires in San Diego, California, is shown in map 3. (See page 59.) The 

State of California was able to combine geographic 

information with QCEW data from the second quarter of 2003 to show establishments that were located within the fire areas and within a half mile of the 

fire areas. By looking at this map, one is able to see 

the areas where clusters of employment potentially 

were affected.

The State of Minnesota was able to display employment around major highways by using a thermal density 

map as shown in map 4. (See page 60.) With this type of 

map, States can show areas with a high concentration of 

employment without displaying confidential information.

It is apparent that maps show how “a picture paints a 

thousand words.” Information displayed in a graphic format can allow a reader to process information more quickly, therefore, allowing for more timely conclusions about a 

particular set of information, such as employment density 

within a particular distance of a floodplain or fire area as 

shown in the previous maps.

Geocoding pilot project

In March 2003, the QCEW program completed a geocoding 

pilot project with the following 14 States and the District 

of Columbia: California, Connecticut, Florida, Hawaii, 

Maine, Maryland, Minnesota, Missouri, North Carolina, 

Ohio, Oregon, South Carolina, Texas, and West Virginia. These States published data based on the geocodes derived from the QCEW data. This study was used to help refine plans for implementing geocoding in all States. 

The most important investment in the geocoding pilot 

project was the time State workers spent to improve the 

vast number of physical location addresses. Traditionally 

the States’ primary resource for locating addresses was Internet sites such as company Web sites, online phonebook 

services, and online maps. They also used other sources 

such as telephone books and phone calls to employers to 

obtain addresses. These last two sources proved to be less 

reliable and more time consuming for most of the States 

in the pilot study.

Obtaining government physical location addresses was 

a major obstacle for all States that participated in the pilot project. Governments tend to provide county-wide reports and finding a geocodeable address can be difficult.

Lastly, nondisclosure is an issue. Many States were unsure if they could publish subcounty data and to what extent. Some questions that arose during the project were: 
Monthly Labor Review • March 2007 55

Does a point on a map disclose confidential data about a 

business establishment based on address, employment, or 

industry? Some States concluded that they could publish 

this type of information, whereas other States could not 

because nondisclosure laws vary from State to State.

Future uses

Since 2004, the Bureau’s geocoding effort has provided insight into the techniques for improving the accuracy of 

QCEW physical location addresses. These techniques have 

involved extensive work, researching and updating the 

Bureau’s existing business establishment list. With geocoded data, BLS is able to provide new economic information such as subcounty estimates, including city, census 

tract, or census block group for future research. There also 

is the potential to standardize addresses and reduce mailing costs for sample users. 

Another use of geocoded QCEW data is to improve the 

Business Employment Dynamics (BED), a set of statis- a set of statistics generated from the QCEW. These quarterly data series 

consist of gross job gains and gross job losses statistics 

Industrial sector Total wages (in

thousands of dollars)

Table 2.

		

	Total covered under Ohio unemployment compensation

		 Law 1 ........................................................................................ 	 9,365	 279,396	 $2,958,645

	Private sector .............................................................................. 	 9,273	 230,658	 2,441,135

	Agriculture, forestry, fishing and hunting ..................................... 4	 21	 94

	Mining	......................................................................................... 7	 231	 3,295

	Utilities......................................................................................... 14	 1,042	 15,761

	Construction................................................................................ 511	 6,198	 73,192

	Manufacturing ............................................................................. 	 1,138	 31,964	 333,537

	Wholesale trade .......................................................................... 678	 12,229	 159,430

	Retail trade.................................................................................. 	 1,247	 13,458	 73,763

	Transportation and warehousing................................................. 243	 4,116	 34,241

	Information .................................................................................. 163	 7,151	 99,972

	Finance and insurance................................................................ 500	 23,046	 420,040

	Real estate and rental and leasing.............................................. 311	 2,696	 20,157

	Professional and technical services............................................ 	 1,162	 21,367	 312,719

	Management of companies and enterprises............................... 53	 6,418	 93,517

	Administrative and waste services.............................................. 446	 15,624	 99,983

	Educational services ................................................................... 73	 9,960	 92,572

	Health care and social assistance............................................... 768	 46,598	 445,038

	Arts, entertainment, and recreation............................................. 108	 5,150	 56,995

	Accommodation and food services ............................................. 898	 14,112	 47,337

	Other services, except public administration............................... 949	 9,276	 59,492

	State and local government ........................................................ 92	 48,738	 517,510

	State government........................................................................ 21	 3,940	 46,608

	Local government........................................................................ 71	 44,798	 470,902

	Federal Government 2 ................................................................. 18	 8,213	 103,766

	 	

City of Cleveland geocoded data on establishments, average employment and total wages paid by industrial sector, as covered under the Ohio and Federal unemployment compensation laws, first quarter 

2002

Average monthly

employment

Number of

establishments

1

The first quarter 2002 covered employment and wage data for the

city of Cleveland were developed as part of a special project conducted

in cooperation with the U.S. Bureau of Labor Statistics. For this project,

approximately 38,000 establishment records covering almost 764,000

employees in Cuyahoga County were processed for Geocoding using

Geostan software by Sagent Technologies. A geocodable record

contains a physical location address that can be assigned a longitude,

latitude, and place code. In all, 87 percent of establishments, covering

97 percent of employment, were able to be geocoded at the subcounty

level. The information presented in this table were those records

identified as having the place code for the City of Cleveland (16,000)

and are based upon employers’ reports for first quarter 2002 received in

the Bureau of Labor Market Information through January 1, 2003.

2

Includes only Federal Government agencies.

NOTE: Summed totals and subtotals may not equal the sum of

industrial divisions because of the exclusion of those industries with

fewer than three employers or because of rounding. Includes the Private

Sector andState Government entities, but excludes Federal Government

agencies.

SOURCE: Ohio Department of Job and Family Services Office of

Research, Assessment and Accountability Bureau of Labor Market

Information Columbus 43266 03/28/03
Geospatial Distribution

56 Monthly Labor Review • March 2007

from 1992 forward. These data help to provide a picture of the dynamic state of the labor market. Most data 

in these series are linked across time, using a process that 

matches establishments by a unique number—the State Employment Security Agency identification numbers. Records that are not linked by this process are linked by various other means, one of which is a weighted 

match. The weighted match involves creating blocks such 

as name, address, and telephone number to match data in 

the current quarter with data in the previous quarter.With 

geocoded data, longitude and latitude information can be 

used in these blocks to create more accurate matches, thus 

allowing for better gross job gains and job losses data.

Not only does geocoded data improve the existing 

QCEW, but it enhances the uses of the data by improving 

the inputs to other programs within the Federal statistics system. Within BLS, these improvements benefit the 

Current Employment Statistics (CES), Occupational Employment Statistics (OES), and Local Area Unemployment Statistics, (LAUS) by creating more precise county 

and MSA data. The Bureau of Economic Analysis (BEA) 

and Bureau of Census (BOC) also benefit, as BLS maintains ongoing data-sharing agreements with these agencies that rely on the QCEW as primary inputs into key statistical products. 

Lastly, GIS technology and spatial data play an important role in emergency response and preparedness. Large 

scale emergencies that have an impact on humans and land 

are unpredictable and hard to envision. Two types of hazards are natural disasters and human-induced disasters. 

Natural disasters include events such as hurricanes, earthquakes, volcanoes, landslides, wildfires, and floods. Human-induced disasters include events such as man-made 

fires, toxic spills, war, and bioterrorism.2 A GIS saves a great 

deal of time in decisionmaking and in evaluating the impact of a disaster before and after it occurs.3

Getting the QCEW fully geocoded

The QCEW database contains approximately 8.8 million 

establishments with an employment level of approximately 135 million. By the third quarter of 2006, 83 percent of 

the QCEW records and 93 percent of the employment data 

had been geocoded. BLS considers this rate extremely good 

Sector

Percent of 

employment

in floodplain

Table 3.

		

Total........................................................................................ 	 8,478	 1

	Agriculture, forestry, fishing and hunting ..................................... 36	 11	 2

	Mining	......................................................................................... (

1

)	 (

1

)	 (

1

)

	Utilities......................................................................................... (

1

)	 (

1

)	 (

1

)

	Construction................................................................................ 	 1,287	 12	 11	 	

	Manufacturing ............................................................................. 288	 5	 12

	Wholesale trade .......................................................................... 420	 8	 7

	Retail trade.................................................................................. 	 1,313	 9	 6

	Transportation and warehousing................................................. 217	 11	 4

	Information .................................................................................. 121	 9	 2

	Finance and insurance................................................................ 406	 5	 5

	Real estate and rental and leasing.............................................. 374	 18	 29

	Professional, scientific, and technical services ........................... 841	 11	 12

	Management of companies and enterprises............................... 31	 11	 1

	Administrative and support and waste management

		 and remediation services ......................................................... 545	 9	 4

	Educational services ................................................................... 124	 10	 17

	Health care and social assistance............................................... 655	 5	 2

	Arts, entertainment, and recreation............................................. 166	 18	 10

	Accommodation and food services ............................................. 698	 20	 18

	Other services (except public administration) ............................. 692	 8	 7

	Public administration................................................................... 116	 29	 7

	Unclassified................................................................................. 148	 0	 0

	 	

Geocoded industries and employment in North Carolina, Brunswick and New Hanover Counties 

floodplain, first quarter 2003

Percent of units in 

floodplain Units

1

This is a suppressed value, which is usually very small. SOURCE: Labor Market Information Division, Employment Securi

Commission of North Carolina.	
Monthly Labor Review • March 2007 57

 Map 1. Establishments in the Brunswick and New Hanover Counties, North Carolina floodplains, 

first quarter 2003

Establishments in floodplain

Establishments not in floodplain

Floodplain

Wilmington, NC

Brunswick and New Hanover

Counties

SC

Atlantic

Brunswick Ocean

County

New

Hanover

County

SOURCE: Labor Market Information Division, North Carolina Employment Security Commissio

and sufficient to proceed with developing a range of products such as maps and subcounty research data. The remainder of the units is mostly new small firms or Federal, State and local government units that do not provide 

QCEW data by worksite. A small number of large units 

also do not provide QCEW data by worksite. BLS continues to work with these firms to obtain accurate data by 

county and industry to allow for geocoding these areas.

GEOGRAPHIC DATA ARE AN ASSET in data analysis, especially with the QCEW. Since BLS has implemented this 

new feature, the original tabular data can be combined 

or used to create an in-depth way of viewing data. Using a geographic information system such as geocoding 

and mapping software, many datasets can be combined 

into one picture, thus saving time in reviewing data results and providing new insights that previously were unobserved. This article has provided just a few examples of 

how data users can benefit from the use of QCEW geocoded data. 

0 2

Miles
Geospatial Distribution

58 Monthly Labor Review • March 2007

 Map 2. Employer density and hurricane tracks in Central Florida, 2004

Hurricane

Jeanne

September

25, 2004

Escambia

Santa

Rosa

Okaloosa

Walton

Holmes

Washington

Bay Calhoun

Jackson

Gulf

Liberty

Gadsden

Wakulla

Leon

Franklin

Jefferson Madison

Taylor

Hamilton

Suwannee

Lafayette

Dixie

Columbia

Union

Baker

Bradford

Gilchrist Alachua

Duval

Marion

Nassau

Levy

Clay St.

Johns

Putnam

Flagler

Sumter

Citrus Lake

Orange

Volusia

Seminole

Osceola

Hardee

Brevard

Polk

Manatee

Hillsborough

Pasco

Hernando

Pinellas

Okeechobee

Highlands

St.

Lucie

Indian

River

DeSoto Sarasota Martin

Glades

Lake

Okeechobee

Palm Beach

Charlotte

Broward

Lee Hendry

Miami-Dade Monroe

Collier

0

1–75

76–150

151–225

226–255

20-mile radius and track of hurricane

SOURCE: Agency for Workforce Innovation, Florida Labor Market Statistics; Quarterly Census of Employme

and Wages; and Florida Department of Environmental Protection.

Hurricane Charley

August 13, 2004

Hurricane Frances

September 5, 2004

0 25 50 100 Miles

Employer density per square mile
Monthly Labor Review • March 2007 59

 Map 3. Employment within fire affected areas, Southern California, 2003

Piru

Fire

Simi Fire

Grand Prix

Fire

Old Fire

Paradise 

Fire

Cedar

Fire

San Diego

Riverside

San Bernardino

Los Angeles

Ventura

Orange

Employers

Fire areas

Employers within fire areas:

Fire area Employers Employment Wages

Total 1,274	 24,775	 	 	$224,46

Cedar and Paradise	 790	 13,831	 	 	120,724,115

Grand Prix and Old Fire	 300	 4,648	 41,796,288

Piru and Simi	 184	 6,297	 61,945,386

Employers within fire areas plus

those within 1/2 mile

of fire perimeter:

Fire area Employers Employment Wages

Total	 5,654	 91,991	 $867,76

Cedar and Paradise	 2,948	 52,447	 514,078,146

Grand Prix and Old Fire	 976	 10,358	 80,496,437

Piru and Simi	 1,730	 29,186	 273,194,503

NOTE: Employment is from June 2003. Wages are the total paid for the second quarter of 200

SOURCE: California Quarterly Census of Employment and Wages, second quarter 2003; fire perimeters from Geospati

Multi-Agency Coordination Group (GeoMAC), Nov. 3, 2003; cartography from Current Economic Statistics Group, Labor

Market Division, California Employment Development Department, November 2003, on the Internet at www.calmis.

ca.gov.

0 
Geospatial Distribution

60 Monthly Labor Review • March 2007

Notes

1 Richard Clayton, “Geocoding the Business Register at the Bureau 

of Labor Statistics,” Paper presented at 15th International Rountable 

on Business Survey Frames, Washington, DC, Oct. 22–25, 2001.

2 “Challenges for GIS in Emergency Preparedness and Response,” An 

ESRI White Paper, Environmental System Research Institute, 2000, on the 

Internet at www.esri.com/library/whitepapers/pdfs/challenges.pdf.

3

 “GIS Aids Emergency Response,” ArcUser, July–September 2001, 

on the Internet at www.esri.com/news/arcuser/0701/umbrella15.

html.

 Map 4. Employment and housing density, Minnesota, Twin Cities Metropolitan Area, 2000

Housing unit density

1 dot = 50 units

Interstate and other major highways

Employment density (jobs per acre)

(Less than 1 not shown)

SOURCE: Metropolitan Council, based on Quarterly Census of Employment and Wages (QCEW) data from the Minnesota Department 

Employment and Economic Development.

1 0.5 0 

1–10

10–50

50–100

100 or more