CSIS Discussion Paper No. 57

A GIS-BASED ANALYSIS OF JOBS, WORKERS,
AND JOB ACCESS IN TOKYO

July, 2003

Mizuki Kawabata
Center for Spatial Information Science
University of Tokyo
Bunkyo-ku, Tokyo, JAPAN
Email: mizuki@csis.u-tokyo.ac.jp

Abstract
Understanding urban spatial structures is essential to develop effective planning. This
study presents a GIS-based analysis of Tokyo’s urban spatial structure by examining
spatial distributions of jobs, workers, and job access by travel mode. The study shows
Tokyo’s distinctive monocentric structure with highly centralized jobs and workers.
Jobs are, however, far more centralized than are workers. The computed job-accessmeasures indicate that job accessibility is markedly higher in central-city areas,
particularly around CBDs, than in suburban areas. The measures also indicate that
although Tokyo offers well-developed transit systems, job accessibility is considerably
lower for transit commuters than for auto commuters. Several future directions of thisresearch are discussed.

CSIS Discussion Paper No. 57
July, 2003

1. Introduction
As more and more urban areas exhibit sprawling and auto-dependent urban structures,planning tools that encourage compact development and non-auto travel options havedrawn considerable attention worldwide in recent years (e.g., Kaidou 2001; Kenworthy
et al. 1999; Newman and Kenworthy 1999). Examples are Smart Growth and TransitOriented Development (TOD), which aim to mitigate undesirable consequences ofsprawl and to promote livable, accessible, and sustainable urban development (e.g.,
Benfield et al. 1999; Bernick and Cervero 1997; Boarnet and Chompin 1999; Koizumi
and Nishiura 2003).
Understanding urban spatial structures is essential to develop effective planning.
Examining urban structure from a perspective of job access for U.S. metropolitan areas,
Kawabata (2002) and Shen (1998, 2001) find that job accessibility is much lower fortransit commuters than for auto commuters and that the mode of travel has greater
importance in determining job accessibility than location. This paper extends theirresearch by examining spatial distributions of jobs, workers, and job access for the
Tokyo metropolitan area.
Specifically, this study asks the following two research questions: (1) what are thegeographies of jobs and workers in Tokyo? and (2) to what extent does the level of job
accessibility vary by location and travel mode?
The urban spatial structure differs between Tokyo and U.S. areas. While many U.S.
metropolitan areas have decentralized and auto-oriented development patterns, Tokyo iswell known for its highly centralized employment and well-developed public transit
systems. It is expected that for Tokyo, job accessibility is much higher in central-city
areas than in suburban areas while a difference in job accessibility between auto andtransit users is not that great.
Geographic Information Systems (GIS) are used to visualize the spatial patterns of jobs,
workers, and job access in three dimensions (3D). To show spatially disaggregatedpictures of distributions with great variability, 3D visualization is particularly beneficial.
The examination of those spatial patterns gives a perspective of Tokyo’s urban spatial
structures.
This paper is organized as follows. Section 2 describes the methodological framework,
and Section 3 presents empirical findings. In Section 4, the findings are summarizedwith a discussion of future research directions.

2. Methodology
2.1 Data and Study Areas
Data on jobs and workers are from the 2000 Population Census of Japan. Workers aredefined as persons 15 years of age and over who are in the labor force. The census data

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provide information on the number of employed workers by area of work, which areused as job count. Data on origin-to-destination (OD) average travel time are from the1998 person trip survey conducted by the Tokyo Metropolitan Region Transport
Planning Commission (TMRTPC). The survey includes approximately 883,000
respondents who reside in the Tokyo metropolitan area. The survey data are notpublicly available but minimum data necessary for a study with the purpose of urban
and transportation planning can be borrowed from the Institute of Behavioral Sciences(IBS). The OD travel time borrowed from IBS for this research includes waiting timeand is computed for peak hour (arrival time between 7:00AM and 9:30AM) for auto
commuters and for transit commuters.
The study area is the Tokyo metropolitan area, which is depicted in Figure 1. There areseveral ways to delineate the Tokyo metropolitan area. The study area for this researchis consistent with the Tokyo metropolitan region defined for the 1998 person trip survey,
which covers Tokyo, Kanagawa, Saitama, and Chiba prefectures, and a southern sectionof Ibaragi prefecture. Figure 1 also shows the 23-ku area, which consists of 23 wards inTokyo, and the 3-ku area, which has three wards (Chiyoda-ku, Chuo-ku, and Minatoku). In this study, the 23-ku area is considered the central city, and the 3-ku area theCentral Business Districts (CBDs). The geographic unit of the analysis is the basicplanning zone delineated specially for the person trip survey. The Tokyo metropolitan
region for the 1998 person trip survey contains 595 basic planning zones. Since census
data are not available for this zone, I use GIS to create an area-weighted factor table andconvert city-level census data to the data by basic planning zone.
< Figure 1 >Table 1 shows basic population and transportation characteristics of the Tokyo
metropolitan area. To better understand specific characteristics of Tokyo, Table 1 also
reports the characteristics for the U.S. nation and two U.S. metropolitan areas, Boston
and Los Angeles. The land area of the Tokyo metropolitan area (the study area shown in
Figure 1) is 3,766 square miles, which is roughly the size of Los Angeles PMSA (LosAngeles County). The land area for Tokyo’s central city (the 23-ku area) is 211 square
miles, which is about four times that of the city of Boston and roughly half of that of thecity of Los Angeles. Tokyo has exceedingly large population, accommodating
approximately 35 million metropolitan people.
Tokyo is much less auto dependent than is the U.S. In the Tokyo metropolitan area, theproportion of households without autos is 43%, much higher than 10% for the U.S., andthe proportion of workers who use autos to get to work is 32%, which is considerably
lower than 88% for the U.S. Instead, people in Tokyo are more likely to use public
transportation than to use autos, having 48% transit commuters. The auto use in Tokyo,
however, has been increasing overtime. Between 1988 and 1998, the proportion ofworkers who commute by auto rose from 29% to 32%.

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Commuting in Tokyo is notoriously long. The average travel time is 43 minutes, which
is 17 minutes longer than the U.S. average of 26 minutes. Travel time for suburb-tocentral city commuting, the dominant commuting flow, is particularly lengthy. For thiscommuting flow, the average commuting time is 56 minutes, and the proportion ofworkers whose commuting time is 90 minutes or longer is 16% and is increasing(TMRTPC 1999).

Table 1 Population and transportation characteristics of Tokyo, compared with U.S. areas
Tokyo
Tokyo metro 23-ku area
(study area) (central city)
3,766Land area (sq. mile)

34,860,306
Population
Workers (persons in the
18,559,044
labor force)
43
Household without auto
Means of transportation to work (%)
Auto
32
Public transportation
48(incl. taxicab)
Walked7
Other means
13
Worked at home
Mean travel-time work
Drive alone
Public transportation(incl. taxicab)

211

Boston

U.S.
Total

PMSA

Los Angeles

Boston city

PMSA

L. A. city

3,537,438

2,022

48

8,092,268 1)

281,421,906 3)

3,398,051

589,141 3)

9,519,338 3,694,834 3)

4,456,093 1)
68 2)

138,820,935 3)
10 3)

1,821,120
15

308,395 3)
35 3)

4,312,264 1,690,316 3)
13
17 3)

- 2)
- 2)

88 3)
5 3)

76
14

51 3)
32 3)

86
7

80 3)
10 3)

- 2)
- 2)
-

3 3)
1 3)
3 3)

5
1
3

13 3)
2 3)
2 3)

3
2
4

4 3)
2 3)
4 3)

- 2)
37 2)
56 2)*

26 3)
24 4)
48 4)

29

29 3)
-

29

43
28
60

-

4,061

-

469

30 3)
-

Data source : 1) 2000 Population Census of Japan, 2) 1998 Tokyo person trip data, 2)* 1998 Tokyo person trip data, rail,
3) U.S. Census 2000, 4) Census Transportation Planning Package 2000.

2.2 Measurement of Job Accessibility
The calculation of job accessibility is based on Shen's (1998, 2001) formulas thatincorporate both the supply and demand of the labor market and the mode oftransportation. This study’s job-access formulas are given by:
Ai

auto

=∑
j

E j ( t ) × f (Cij∑{α kWk (t ) × f (Ckj

auto

auto

)

) + (1 − α k )Wk ( t ) × f (C kj

pub

,

(1)

.

(2)

)}

k

Ai

pub

=∑
j

∑{α E
k

E j ( t ) × f (Cijk (t )× f (C kj

auto

pub

)

) + (1 − α k ) Ek ( t ) × f (C kj

pub

)}

k

Aiauto and Aipub are job-access measures for workers living in zone i who are auto
commuters and public transit commuters, respectively. Ej(t) represents the number ofjobs in zone j at time t, and Wk(t) indicates the number of workers living in zone k at timet. Impedance functions for commuters traveling between zone i and zone j for auto users

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CSIS Discussion Paper No. 57
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and for transit users are f(Cijauto) and f(Cijpub), respectively. Likewise, f(Ckjauto) andf(Ckjpub) are impedance functions for auto commuters and transit commuters,
respectively, traveling between zone k and zone j. The proportion of commuters who
use autos in zone k is expressed by αk.1
Equations (1) and (2) provide job-access measures that are more representative than thesimple ratio of jobs to workers in a given zone, since the foregoing equations
incorporate not only opportunities within an area of residence but also opportunities inzones beyond that area, with accessibility values depending on travel costs and travelmodes. Each accessibility value is then interpreted as the number of spatially accessible
jobs per worker in a given zone.
The geographic unit (zone) is the basic planning zone. The impedance function (f(Cij))is specified using two forms. The first form is the simple travel-time threshold function.
The value in the impedance function is set equal to one when travel time between zone iand zone j is less than a threshold travel time; otherwise the value is zero. To examine
whether the measures are sensitive to the threshold time, three different thresholds of 15,30, and 45 minute are used for the job-access calculation. The second form is the
impedance estimated using the gravity model. The estimation method is described inAppendix, where gamma (γ) is the parameter used for the travel impedance function.

2.3 Presenting Spatial Distributions of Jobs, Workers, and Job Access
Section 3.1 presents spatial distributions of jobs, workers, and jobs-to-workers ratios,and Section 3.2 shows distributions of the calculated job-access measures. In each ofthese two sections, I first provide tabulated statistics to show a overall picture of thespatial distributions. With the simple tabulation, however, it is difficult to understand a
spatially disaggregated picture of spatial variations. Geographic Information Systems(GIS) are then used to create maps visualizing the spatial patterns of jobs, workers, andjob access. Because some areas have extremely high concentrations, the maps arepresented in 3D, which can reveal the great variability.

3. Findings
3.1 Spatial Distributions of Jobs and Workers
Table 2 shows workers, jobs, and jobs-to-workers ratios for the entire Tokyo
metropolitan area, central city (the area within 23-ku), and suburbs (the area outside 23ku). As expected, jobs are more centralized than are workers. The proportion of jobs
that are located within the central city is 39%, while the proportion of workers who
reside in the central city is 24%. This result is reflected in the ratios of jobs to workers.
The ratio within 23-ku is greater than one, 1.54, indicating that the central city has more
1

For the sake of simplicity, in equations (1) and (2), the impedance function for transit commuters isused for all non-auto commuters including those who walk to work. Note that in Shen’s (2001) study,
αk is given by the auto-ownership rate in zone k; this information is not provided by the publicly
available Population Census of Japan.

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CSIS Discussion Paper No. 57
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jobs than workers. The ratio outside 23-ku, on the other hand, is 0.75, about half of thatwithin 23-ku, suggesting that the suburbs contain more workers than jobs.

Table 2 Spatial distributions of workers and jobs in Tokyo

Jobs
Workers
Jobs-to-workers ratio

Entire metro(study area)

Within 23-ku(central city)

Outside 23-ku(suburbs)

16,662,000
17,818,000
0.94

6,517,000 (39%)
4,243,000 (24%)
1.54

10,145,000 (61%)
13,575,000 (76%)
0.75

Note: Workers include both employed and unemployed workers.

Figure 2 shows 3D maps for the densities of jobs and workers and the ratios of jobs toworkers, where the length of an elevated feature represents the density or ratio in eachzone. To make the maps comparable, legends for the upper two maps (the densities ofjobs and workers) use the same equal interval classification, and the z value used to
extrude features is made consistent for these two maps.
< Figure 2 >The maps show clearly Tokyo’s distinctive monocentric urban structure. Jobs areheavily concentrated around the central part of Tokyo. Workers are also concentrated
around the city center but are much less centralized than are jobs. The map for the jobsto-workers ratios further indicates the prominent urban core with extremely high ratios
around CBDs (Chiyoda-ku, Chuo-ku, and Minato-ku) and inner wards of the central
city (e.g., Shinjuku-ku and Shibuya-ku). Some suburban areas (e.g., Nishi-ku and Nakaku in Yokohama City) have relatively high ratios but those ratios are much lessconspicuous than those around CBDs.

3.2 Spatial Distributions of Job Access by Travel Mode
Table 3 presents median values of the job-access measures by travel mode and by
location. The access measures are presented for the different impedance specifications,the specification using travel-time thresholds of 15, 30, and 45 minutes and thespecification using gamma calibrated from the gravity model. The median values,
instead of the average values, are used since the access measures’ distributions are
considerably skewed.

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Table 3 Median job-access measures in Tokyo
15-min threshold
Entire Within Outside
metro 23-ku 23-ku
Transit
Auto

0.65
1.14

0.86
2.24

0.62
1.03

30-min threshold

45-min threshold

gamma

Entire Within Outside Entire Within Outside
metro 23-ku 23-ku metro 23-ku 23-ku
0.34
1.92

1.15
4.48

0.30
1.69

0.48
1.44

1.76
2.51

0.41
1.35

Entire Within Outside
metro 23-ku 23-ku
0.66
0.88

0.78
1.38

0.64
0.85

The absolute values of the access measures are highly sensitive to the impedance
specifications. In all specifications, however, consistent patterns are observed. Jobaccessibility is consistently higher in the central city than in the suburbs, as expectedfrom the highly centralized employment. For example, suppose that workers commute
up to 30 minutes. If a typical transit commuter lives in a typical suburban zone, he or
she has an access measure of only 0.30 jobs per worker, but if the same transit
commuter lives in a typical zone in the central city, such a commuter has an accessmeasure of 1.15 jobs per worker.
A large discrepancy between transit and auto users is also discerned. Job accessibility
for transit users is consistently much lower than that of auto users, which is an
unexpected result since Tokyo provides well-developed transit systems. Suppose thatmaximum travel time to work is 30 minutes. While a typical transit commuter in a
typical zone in the central city has an access measure of 1.15 jobs per worker, a typical
auto commuter in that zone has an access measure of 4.48 jobs per worker.
The central city versus suburban dichotomy in the above table provides rather
aggregated pictures of job accessibility. To show more geographically disaggregatedpictures, Figures 3a-d present 3D visualizations of job-access measures. Figures 3a, 3b,
and 3c show the job-access measures calculated using the travel time thresholds of 15,30, and 45 minute, respectively, and Figure 3d displays the measures computed using
gamma calibrated from the gravity model. For comparison, all maps in these four
figures use the same equal interval classification, and the z value to extrude features in
two maps in each figure is set to be equal.
< Figure 3a >< Figure 3b >< Figure 3c >< Figure 3d >Although the spatial variation in job accessibility differs across the different impedance
specifications, similar patterns are found. With the 3D maps, it is apparent that areas
around CBDs have considerably higher job accessibility than do suburban areas. Some
suburban zones (e.g., Nishi-ku and Naka-ku in Yokohama City) have high job-access7

CSIS Discussion Paper No. 57
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levels, but still, those levels are not comparable to exceedingly high job accessibility
around the central zones.
The maps also show a great transit/auto disparity. In all impedance specifications, job
accessibility is much higher for auto commuters than for transit commuters. For auto
commuters, most areas have job-access measures greater than one, whereas for transit
commuters, many suburban areas have access measures less than one, and areas withhigh job accessibility are largely limited to the central zones.
It is interesting to find that some so-called suburban centers have high job accessibility
for auto users but low accessibility for transit users, suggesting high auto dependency inthose areas. For example, when job accessibility is calculated with the 45-minutethreshold, some suburban zones in Machida-shi, Hodogaya-ku, Hachioji, and Tsuzukiku have auto accessibility greater than 2.0 but transit accessibility less than 0.5. These
suburban centers provide developed rail and bus systems, but neighborhoods’ auto
ownership rates are high and people tend to use autos for traveling in and around thesuburban centers.
It is also interesting to see that some zones in the outer suburbs have relatively high job
accessibility, particularly for auto commuters. These areas include some southernmost
zones in Chiba prefecture (e.g., Kyonan), northern parts in Saitama prefecture (e.g.,
Fukaya), and western parts of Kanagawa (e.g., Hakone).
When the spatial variation in job accessibility is compared across the different
impedance specifications, the distributional pattern becomes flatter with longer
commuting time. A closer look at the job-access measures indicates that as the traveltime threshold lengthens, the standard deviation and skewness (a measure of the lack ofsymmetry of a distribution) become smaller. For instance, the access levels around
CBDs are decreased with longer travel time. This result is not surprising given that alarge body of suburban workers commute to the jobs in CBDs. With the 15-minutethreshold, only those who live within or near CBDs can reach the jobs in those districts,
and job-access levels in those CBDs are naturally high. This is particularly true fortransit commuters, whose travel time is longer than that for auto commuters. With the
45-minute threshold, however, many suburban workers can get to CBD jobs, and jobaccess levels in central areas decline. The distributional pattern of job accessibility
calculated using the gravity model (Figure 3d) is somewhat similar to the pattern for the
15-minute threshold (Figure 3a). This is likely related to the fact that the absolute valuesof the calibrated gamma are rather large, indicating a relatively large declining effect ofcommuting time on the number of accessible jobs per worker.

4. Conclusions
Tokyo exhibited a distinctive urban core, having both jobs and workers centered aroundthe central part of the metropolitan area. Jobs were, however, far more centralized thanworkers. The tabulated statistics indicated that in the central city (the area within 23-ku),

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there were more jobs than workers, as suggested by the jobs-to-workers ratio of 1.54,
whereas in the suburbs (the area outside 23-ku), there were more workers than jobs, as
indicated by the jobs-to-workers ratio of 0.75. The 3D maps further revealed Tokyo’s
monocentric urban structure. The spatial distribution of jobs was highly skewed toward
CBDs (the 3-ku area including Chiyoda-ku, Chuo-ku, and Minato-ku), leading to
extremely high jobs-to-workers ratios in those districts.
Job accessibility by travel mode was then calculated using the different impedance
specifications. Although the computed job-access measures were sensitive to theimpedance specifications, some patterns were consistent. One consistent finding is that
central-city areas, particularly areas around CBDs, had much higher job accessibility
than did suburban areas, as expected. Another consistent finding is that job accessibility
for transit commuters was considerably lower than that for auto commuters, although
Tokyo offers well-developed public transit systems. For transit users, most suburbanzones had access measures less than one, and high-accessibility areas were largely
limited to central zones. For auto commuters, on the other hand, most areas had accessmeasures greater than one. The large transit/auto disparity was a rather surprisingfinding since the disparity was expected to be small.
The great transit/auto disparity was also found for the U.S. metropolitan areas byprevious studies (Kawabata 2002; Shen 1998, 2001). Tokyo’s situation, however,differs from the U.S. case. In most U.S. metropolitan areas, jobs are greatly
suburbanized (the suburb-to-suburb flow is the dominant form of commuting), andpublic transit systems in the suburbs are poorly developed. Many suburban jobs arepractically inaccessible by public transportation, and workers who depend on transit
indeed have much fewer accessible job opportunities than do workers who have accessto autos.
In Tokyo, on the other hand, the large transit/auto disparity in job accessibility does notnecessarily mean that transit commuters have much fewer accessible job opportunities
than do auto commuters. Tokyo, where the suburb-to-central city flow dominates
commuting patterns, has one of the most developed public transportation systems. With
the well-extended transit networks, many suburban workers including those who live far
from the central city commute to central-city jobs in spite of long travel. In fact, of
those workers who commute from the suburbs to the central city, approximately 16%
have 90-minute or longer travel time (TMRTPC 1999). Many people endure such longcommutes partly because they put more values on land than on travel cost, and partly
because in general companies subsidize transportation cost. For those transit commuters
who travel between the suburbs and central city, therefore, what matters would not be
the number of job opportunities but the lengthy travel time.
It was interesting to identify suburban centers where auto job accessibility is high but
transit accessibility is low. For such suburban centers, the situation is similar to the U.S.
case. Tokyo’s transit systems are rather radial, and within the suburbs, transit networks
are generally not well connected and only slow buses are available. Traveling between

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suburban locations by public transportation is often burdensome, consuming long travel
time. In terms of suburban job opportunities, having access to autos clearly provides anadvantage to people getting to and from suburban workplaces.
In fact, auto dependency in suburban areas is much higher than that in the central city.
Additionally, while the number of auto trips to or from the central city is slowly
decreasing, the number of intra-suburban auto trips is increasing, particularly in theouter suburbs. Between 1988 and 1998, for example, the number of trips made by autorose by 143% in the northern section of Saitama, by 147% in the southern section of
Ibaragi, and by 149% in the southwestern section of Chiba (TMRTPC 1999).
This paper presented a preliminary analysis of Tokyo’s urban spatial structure from aperspective of residence, workplace, and transportation. Several future directions are
currently planned.
The first direction of future research is to examine longer time for the travel-time
threshold function in the job-access calculation. In this study, the three different
thresholds of 15, 30, and 45 minutes were used. The values of calculated job
accessibility were highly sensitive, although the overall distributional patterns weresimilar. Given that the average commuting time is 43 minutes and given that 16% ofworkers traveling between the suburbs and central city have commuting time of 90
minutes or longer (TMRTPC 1999), examining longer thresholds such as 60, 75, and 90
minutes would improve an understanding of the access measures’ sensitivity to traveltime.
The second research direction is to develop the measurement of job accessibility. In thisresearch, job accessibility was measured in terms of jobs, workers, and transportation.
As for Tokyo, such factors as land price, housing provision, transportation policy arelikely to play significant roles in determining job accessibility. Access measures would
be improved greatly by incorporating these additional factors into the computation of
job accessibility.
The third direction of research is to apply the same analytical framework to the U.S.
metropolitan areas using newly available Census 2000 and compare the results with theTokyo case.
The forth research direction is to compare time periods. Investigating not only thecurrent situation but also the direction of changes is important for developing effectiveplanning. The usability of spatial data has been advanced dramatically since 1990
Census. Examining changes between 1990 and 2000 is feasible and of great interest.
These third and fourth directions together can further clarify similarities and differences
between urban structures in two countries, which would contribute new data useful for a
discussion on sustainable urban form and growth policy.

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Acknowledgement
This research was supported by the Center for Spatial Information Science at the
University of Tokyo through a young scholar research grant. The author wishes to thankAtsu Okabe, Akinori Morimoto, Tetsuo Morita, and Daisuke Shibatani for theirassistance in using transportation data. The author also thanks Tomoyuki Furutani for
his estimating the gravity model and providing helpful comments.

References:
Benfield, F. K., M. D. Raimi, and D. D. T. Chen. 1999. Once there were Greenfields:
How Urban Sprawl is Undermining America’s Environment, Economy, and Social
Fabric. New York: Natural Resources Defense Council.
Bernick, M. and R. Cervero. 1997. Transit Village in the 21st Century. New York:
McGraw-Hill.
Boarnet, M. G. and N. S. Compin. 1999. “Transit-Oriented Development in San Diego
County: The Incremental Implication of a Planning Idea.” Journal of the American
Planning Association 65(1): 80-95.
Kaidou, K. 2001. Compact City: Pursuing Urban Form in Sustainable Society. Kyoto
City, Japan: Gakugei Press. (in Japanese).
Kawabata, M. 2002. Access to Jobs: Transportation Barriers Faced by Low-Skilled
Autoless Workers in U.S. Metropolitan Areas. Ph.D. Dissertation, Department of Urban
Studies and Planning, MIT.
Kenworthy, J. R., F. B. Laube, P. Newman, P. Barter, T. Raad, C. Poboon, and B. Guia.
Jr. 1999. An International Sourcebook of Automobile Dependence in Cities, 1960-1990.
Colorado: University Press of Colorado.
Koizumi, H. and S. Nishiura. eds. 2003. Smart Growth: Sustainable Urban Policy in the
U.S. Kyoto City, Japan: Gakugei Press. (in Japanese).
Newman, P. and J. Kenworthy. 1999. Sustainability and Cities: Overcoming
Automobile Dependence. Washington, DC: Island Press.
Tokyo Metropolitan Region Transport Planning Commission (TMRTPC). 1999. About
the Summary Results of the Comprehensive Investigation of Transportation Conditionsin the Tokyo Metropolitan Region: the 4th Tokyo Person Trip Survey.
http://www.iijnet.or.jp/tokyopt/masmedia5.html, last accessed on June 10, 2003. (inJapanese)

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Shen, Q. 1998. “Location Characteristics of Inner-City Neighborhoods andEmployment Accessibility of Low-Wage Workers.” Environment and Planning B 25:
345–365.
Shen, Q. 2001. “A Spatial Analysis of Job Openings and Access in a U.S. Metropolitan
Area.” Journal of the American Planning Association 67(1): 53-68.

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Appendix: Gravity Model for Impedance Estimation
In this study, the travel impedance is estimated using a gravity model, which is givenby:q mij = exp(α )(O ⋅ D )

β

mi

mj

γ
t mij

,

(a-1)

where qmij and t mij are the number of trips and travel time between zone i and j by modem, respectively. Omi and Dmj are the number of trips originated from zone i by mode mand the number of trips ended in zone j by mode m, respectively. Parameters are
represented by α , β , and γ , which are estimated from a logarithmic transformation of
Equation (a-1). The provided number of OD trips was expanded using an expansion
factor. For the estimation of the parameters, therefore, a generalized least square
technique weighted by the number of sample OD pairs is used. Estimation results ofparameters are shown in Table (a).
Table (a)Mode
Variablesαβγ

Number of OD
pairs
R2

Car
Parameters-2.1620.861-2.758Rail
t-value-35.990331.485-612.295

Parameters
5.252
0.392
-1.922880

t-value
107.753215.735-324.475

66,552

100,378

0.89

0.63

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Figure 1 Geographic boundaries for Tokyo metropolitan areas

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Figure 2 Spatial distributions of workers and jobs in Tokyo metropolitan area

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Figure 3a Spatial variation in job accessibility in Tokyo metropolitanarea(15-min threshold)

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Figure 3b Spatial variation in job accessibility in Tokyo metropolitan area
(30-min threshold)

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Figure 3c Spatial variation in job accessibility in Tokyo metropolitan area
(45-min threshold)

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Figure 3d Spatial variation in job accessibility in Tokyo metropolitan area
(gamma)19