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The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics
Chen, Jun

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
Fahui WANG
Department of Geography, Northern Illinois University
DeKalb, IL 60115-2854, U.S.A.
Fax: 815-753-6842, E-mail: wang@geog.niu.edu
Keywords: job proximity, job accessibility, GIS, suitable jobs.
This research proposes two indexes, job proximity and accessibility, to measure resident workers’ locational advantage with respect
to their suitable job markets. Job proximity is designed to capture the spatial separation between residents and jobs. Job
accessibility measures one’s ability to overcome such separation that may be affected by transportation means, road network,
congestion, and intensity of competition for jobs among workers. The research suggests that in Cleveland in 1990 the mean wage
rate of $30,000 is a critical turning point: below this level, the higher the mean wage rate in a residential area, the farther the area is
away from the jobs; above the level, the trend is reversed. In other words, below a wage threshold, workers tend to trade better and
more spacious housing (usually farther away from jobs) for more commuting; but above the threshold, workers retreat for saving in
commuting (pertaining to their high opportunity cost of commuting). Although low-wage workers enjoy better job proximity, many of
them (particularly some inner-city residents) have the worst job accessibility because of their limited mobility. Job proximity declines
with distance from the CBD and conforms to the monocentric model, so does job accessibility but to a less degree. Since workers
with various wages respond differently to job access, the distribution of mean wage rates in the metropolitan area is hardly
Many urban policy researchers recognize the profound
impacts of lack of employment opportunities in some areas
in a city ranging from social disorders (Wilson, 1996) to
criminal behavior (Freeman, 1996; Wang and Minor, 2000),
but disagree on its causes. Some attribute to, at least
partially, the spatial separation between residence and
employment such as employment decentralization and
residential segregation (Kain, 1968; Ihlanfeldt and Sjoquist,
1990; Immergluck, 1998). Others suggest that it is caused by
socioeconomic factors including vehicle ownership (Taylor
and Ong, 1995) and racial discrimination (Ellwood, 1986).
Whether it is a spatial or nonspatial problem leads to
different policy remedies.
This research uses two indexes, job proximity and job
accessibility, to distinguish clearly spatial factors from other
factors. Job proximity is designed to capture the spatial
separation between residents and jobs. Job accessibility
measures one’s ability to overcome such a separation that
may be affected by transportation means, routes,
congestion, and intensity of competition for jobs among
workers. The research compares the two measures among
resident workers of various wage groups in attempt to reveal
who have the true advantage with respect to job access.
Although the purpose of this paper is not to explain commute
patterns or residential search behavior, findings from the
research shed light on such issues.
The development of job proximity index follows Wang (2000)
with one important improvement. Wang (2000) does not
consider the segmented labor markets, and assumes all jobs
are equally feasible for resident workers regardless
occupations or wage rates. This research considers only
those jobs within the same wage group of resident workers
as their suitable jobs. The job accessibility index is based on
Shen (1998) with one remedy—accounting for the
congestion effect in areas with higher-density jobs or
residents. Shen (1998) limits his analysis of job accessibility
to low-wage workers in the inner city. This research
evaluates job proximity and accessibility among workers of
all wage groups at all locations in a metropolitan area. By
doing so, the research is interested in the comparison
among workers of various wage groups and their possible
different responses to job access in terms of residential
choice and commute behavior.
While many socioeconomic characteristics may play a role
affecting workers’ job access (Shen, 2000; Wang, 2001), the
research focuses on the wage rates. There are several
reasons for this strategy. First, the classic urban economic
theory considers income (wage as a surrogate) as the most
important factor in determining the residential location. In
order to test the theory, this research analyzes how workers
in various wage groups desire and respond to job access.
Second, wage serves as an indicator to segment the job
markets and identify suitable jobs for workers (Immergluck,
1998; Shen, 1998). Such information (jobs by wage rates) is
available the Census for Transportation Planning Package
(CTPP) data used for this research. Third, wage is perhaps
the most important determinant for vehicle ownership and
thus affects job accessibility.
After a brief discussion of data sources and the study area,
the paper explains the measures of job proximity and
accessibility in separate sections. Then findings are
summarized using the two measures across all locations in
the study area. The paper is concluded with a summary and
discussion of policy implications.
This research uses the 1990 Census for Transportation
Planning Package (CTPP) Urban Element provided by the
Bureau of Transportation Statistics (1996). The Urban
Element of CTPP is aggregated at the level of Traffic
Analysis Zone (TAZ) in Cleveland, Ohio. The CTPP is
composed of three parts. Part 1 is similar to traditional
census data by area of residence, and has information such
as number of resident workers and breakdowns to various
wage groups, mean wage rate, mean commute time, and
household vehicle availability. Part 2 is by area of work
(unique among all census products), and has number of jobs
and breakdowns to wage groups. Part 3 provides very
detailed journey-to-work information, such as number of
commuters from a TAZ to another TAZ by a specific mode
(e.g., drive-alone, car-pool, public bus) and average
commute time between them. The spatial GIS data of the
study area (often referred to as coverage) are from the
Environmental Systems Research Institute, Inc. (2000) web
site, including: (1) the TAZ coverage, (2) the coverage of
urbanized areas, and (3) the road network coverage.
The study area is Cleveland of Ohio, a site selected by the
author for a research project funded by the National Institute
of Justice. The 1990 CTPP data in the Cleveland region are
compiled by the Northeast Ohio Areawide Coordinating
Agency (NOACA) and include five counties: Cuyahoga,
Geauga, Lake, Lorain and Medina (see Figure 1). As the
research focuses on the nonagricultural jobs, the job market