University of Tsukuba
Graduate School of Life and Environmental Sciences
Division for Spatial Information Science (SIS)

Mobility and Urban Structure:
A Case Study of Four Asian Capital Cities
Konstantin GREGER, Yuji MURAYAMA
http://www.konstantingreger.net

greger@geoenv.tsukuba.ac.jp

This study uses four movement data sets, provided by the Center for Spatial Information Science (CSIS) at the University of Tokyo. The data sets contain information about the movements of sample populations. These include theorigin and destination of all trips, both regarding time and location, as well as the purpose of the trip and the modes of transportation per subtrip. In addition, the data sets also contain several socio-demographic attributes about theindividuals, such as sex, age, and occupation.
Each data set in itself poses an immeasurable depth of information about the individual movements, the resulting mobility patterns, and their connection to the urban spatial structure of the city they occurred in. The comparison over
all four these data sets promises to reveal even more interesting insights into the commonalities and differences among these four asian capital cities.

Dhaka
Data capture:
1.-2. October 2009
Individual persons: 42,114
Database size:
27.05 GBAt under 50,000 sample individuals the Dhaka
data set is the smallest of the four. The
distribution of occupations is very unbalancedwith only 31 blue-collar workers and ca. 2%
unemployed and retired. In contrast, white-collar
workers, students, and housewives andhousehusbands form the majority at 36%, 33%,and 25%, respectively.
The data set from Hanoi contains data of ca.
60,000 people. The group of unemployed andretired is the most strongly represented in thesample at 28%, closely followed by white-collar
workers at 27%. Students and blue-collar
workers are also represented well in the sample
at 21% and 15%, respectively.
The Jakarta data set is the largest in this study
at almost 300,000 people. More than half of thepeople did not disclose their occupation. The
remaining population consists of twice as many
white-collar workers than blue-collar workers,
only 3% students and no housewives andhousehusbands.
The data set for Manila, at ca. 200,000 people
the second biggest, shows a strong presence ofstudents (32%), followed by white-collar workers(28%), housewives and househus-bands (16%)
and blue-collar workers (10%).
An analysis of the average total distance traveledover the 24-hour sampling period showed thatyounger people in Dhaka cover a rather shortdistance, often under 10 km, while a signifcant
number of elderly people travel distances of 30km or more per day. Almost half of these trips(43%) are homeward trips. At 26% trips to theworking place are the most common, followed bytrips to education institutions at 13%. In Hanoi
and Jakarta the distributions of total trip distanceshows a similar overall distribution, but nodifferences between the age groups. The data for
Manila show a large number of very short trips.
The cities also show differences in the averagecommuting times. When separated for men andwomen as well as the morning and the evening
commute the data also reveals interesting
patterns:

Hanoi
Data capture:
1.-2. October 2004
Individual persons: 58,018
Database size:
36.66 GB

Mean
Mean
Dhaka
duration subtrips
Allmorning 34 min.
1.6evening
40 min.
1.6
Menmorning 36 min.
1.6
evening
42 min.
1.6
Women morning 30 min.
1.4
evening
37 min.
1.5
Mean
Mean
Jakartaduration subtrips
Allmorning 34 min.
1.5
evening
37 min.
1.6
Menmorning 35 min.
1.5
evening
38 min.
1.5
Women morning 32 min.
1.5
evening
36 min.
1.6

Mean
Mean
duration subtrips
morning 19 min.
1.0
evening
20 min.
1.0
Menmorning 20 min.
1.0
evening
21 min.
1.0
Women morning 19 min.
1.0
evening
20 min.
1.0
Hanoi
All

Mean
Mean
Metro Manila
duration subtrips
Allmorning 31 min.
1.8
evening
38 min.
1.8Menmorning 33 min.
1.8
evening
40 min.
1.8
Women morning 29 min.
1.8
evening
36 min.
1.8

The data from Dhaka and Manila show that theevening commute takes in average 6-7 minutes
longer than the morning commute. In Hanoi andJakarta the differences amount to only 2-4
minutes. Furthermore, Hanoi has not only theoverall shortest commuting times (~20 minutes),
but also the lowest number of subtrips thesecommuting trips consist of. This can be explainedby the prevalence of motorcycles (40%), while inall other cities walking is the most common modeof transportation. In contrast taxis in Dhaka(36%), and buses in Jakarta and Manila (31%and 34%, respectively) represent the mostcommon modes of transportation in their respective cities.
A closer look at the number and purpose of
subtrips started at every hour during the day also
provides interesting insights into the commuting
behavior in these asian capital cities. Dhaka
shows a clear commuting trend before 12pm,
both for work and education. A small peak ofpeople returning home between 3am and 6am
can be identifed, most likely a result of shiftwork. Hanoi, Jakarta, and Manila all show clear
signs of a shift system, both for work andeducation, albeit more pronounced in Hanoi, andmostly for students in Manila. All three havesignifcant numbers of people returning home justbefore noon, only to be followed by a second
wave of work- and education-bound trips a few
hours later.
Finally we analyzed the spatial distribution ofpeople over the course of the day in respect totheir distance from the city center. For thispurpose we created concentric ring buffers in
5 km distances and counted the absolute
numbers of distinct people in each ring at eachhour. Again the two-shift systems in Hanoi andJakarta stand out with peaks of the numbers ofpeople in the innermost buffers, while the data for
Dhaka show only one pronounced commuting
peak in the morning and another less pronounced peak in the evening, and also a large
number of people in the city center over thecourse of the whole day.

Jakarta
Data capture:
1.-2. October 2002
Individual persons: 297,043
Database size:
190.55 GB

ManilaData capture:
1.-2. October 1996Individual persons: 189,335
Database size:
66.68 GB

Follow this project on GitHub:

https://github.com/kogreger/capital-cities