Development of Commercial Accumulation Polygon Data Throughout Japan
Based on the Digital Classified Telephone Directory
Yuki AKIYAMA
Researcher
Center for Spatial Information Science
The University of Tokyo
Institute of Industrial Science Cw-503
4-6-1 Komaba Meguro-ku
Tokyo 153-8505 Japan
Tel: (+81) 3 5452 6417
E-mail: aki@iis.u-tokyo.ac.jp

Hiroaki SENGOKU
Ph.D. student
Graduate School of Frontior Science
The University of Tokyo
Institute of Industrial Science Cw-503
4-6-1 Komaba Meguro-ku
Tokyo 153-8505 Japan
Tel: (+81) 3 5452 6417
E-mail: xianshi@csis.u-tokyo.ac.jp

Hiroyuki TAKADA
Master’s degree student
Graduate School of Frontior Science
The University of Tokyo
Institute of Industrial Science Cw-503
4-6-1 Komaba Meguro-ku
Tokyo 153-8505 Japan
Tel: (+81) 3 5452 6417
E-mail: xianshi@csis.u-tokyo.ac.jp

Ryosuke SHIBASAKI
Professor
Centre for Spatial Information Science
The University of Tokyo
5-1-5 Kashiwanoha Kashiwa city
Chiba 277-8568, Japan
Tel: (+81) 4 7136 4290
E-mail: shiba@csis.u-tokyo.ac.jp

Abstract: The polygon data of commercial accumulation (CA) throughout Japan havebeen developed based on the digital classified telephone directory. In recent years,
shopping areas in city center of Japanese local cities are declining. In addition many new
shopping areas are emerging in suburban roadsides. On the other hand, shopping areas
in large cities frequently rise and fall because of severe competitions between shops.
Therefore, the method to monitor the actual state of shopping areas is needed formaintaining of urban vitality. However, there are no data in Japan now that can monitor
not only locations but also shapes of CA. Our data can satisfy this severe demand. First,
the list of business categories they construct CAs was developed based on shop
information collected from website of shopping areas. Second, the telephone directory
was narrowed down by the list. Third, buffer polygons were created from each location
points of shop, company and office through the address geocoding on the selected
telephone directory by the original buffering method. The method gives each tenantunique buffering distances. Finally, polygons of CA were created to integrate withoverlapping buffer polygons. The proposed buffering method in this study can consider
not only distant relationships between near points, but also the average regional distance
between points of each point. Moreover, the method can detect and remove abnormallylarge polygons. To develop the system that can process this method automatically, thelarge-scale data processing was realized. In addition, we have confirmed locations andscales of this polygon data to overlay these polygons on existing statistics data to verify
the reliability of polygons. As a result, it has been shown that our polygon data has highreliability. It has realized to monitor locations and shapes of CA frequently throughout
Japan with the advent of this polygon data.
Keywords: Commercial accumulation, Digital classified telephone directory, Buffering,
Whole land of Japan, Reliability verification

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1. INTRODUCTION
Major parts of attraction and festivity of city are supported by commercial accumulations(Note: it is described “CA” in following text.). In recent years, many new CAs, for example
roadside shop clusters and large scale retail stores emerged in Japan as a result ofmotorization and revolutionizing distribution. They present convenient and attractive
purchasing opportunity for many consumers although there are criticisms that they aredull uniform.
Meanwhile many traditional city centers lose distributive competition with new CAs
because of a delay in taking action to new distributive construction and businesses andinaccessibleness of cars. As a result, commercial vitality and attraction in traditional
shopping areas are losing rapidly. For the problem, many local governments carry out
various policies and infrastructure improvement for aims of regional revitalization andactivation of city centers.
However data are very few that can monitor actual growing, declining and location
changing of CAs in detail across the whole country also in Japan. Because of thislimitation, it is difficult to monitor actual state and evaluate same criteria all over thecountry of CAs and to grasp of effectiveness of policy by governments. Actually, thespatial unit of almost all statistics on spatial distribution of shops, offices and companies(Note: it is described “tenant” in following text.) in Japan, for example the “Establishment
and Enterprise Census” and the “Commercial statistics” in Japan is each cities, towns
and villages. Although the spatial unit of a few statistics is specific size grids, for example
1km and 500m, the spatial resolution is not enough to grasp of actual state andmovement of CAs. It is required now to develop the data of CAs all over Japan.
1.1 Previous studies
There are many previous studies to monitor actual states and changes of CA in Japan.
Utsumi et al. attempted to grasp time-series changes of commercial activities in Osaka
city to observe transitions of total floor space of all buildings using the grid data of floor
spaces in multi years of Osaka city (in 1969, 1985, 1993 and 2001). Tominaga assessed
actual state of CAs in some city centers and presented his recommendations forreactivation of traditional CAs by the census of CAs developed by each prefecture.
On the other hand, other studies collected information of CAs by field surveys andquestionary investigation without relying on existing statistics data. For example,
Takahashi et al. collected locations of tenants around Shimokitazawa station in Tokyo by
the field survey, and developed detailed map of tenant distribution.
In addition, there some studies to monitor CAs and commercial activities in many othercountries same as Japanese studies (e.g., Dimitrova et al. and Neil).
Many studies used existing statistics data to monitor location and actual state of CAs.
However, they are problems that spatial resolution is not so high and collection rate vary.
Moreover, locations and shapes of CA are not opened or they are not contained in

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existing statistics data, we can collect information of CAs in macro-scale unit such aseach prefecture or each city. Furthermore, definitions of CA are different between eachstatistics. On the other hand, some studies tried to collect tenant data and to develop thedata of distribution and shapes of CA. Almost all studies like them are case studies inlimited areas. Therefore, methods in these studies are not realistic for developing data in
broad area because these methods need a large amount of labor and a lot of time.
1.2 Objective
Against such a background, the automatic extractive method of CAs using digital
telephone directory (the “Telpoint data” by ZENRIN CO., LTD.) are developed in thisstudy. Furthermore data processing for large-scale data realizes to develop the
automatic processing system. In addition, we cross check our data with existing statistics
to verify the reliability of our data. The study area is whole land of Japan and we develop
polygon data of CAs all over Japan in 2008 for the verification of versatility of our method.
2. DEVELOPMENT
2.1 Definition of commercial accumulation
The commercial accumulation (CA) in this paper means all kinds of shopping district andstreet, for example shopping areas and streets around train stations, shopping districts atthe core area of cities and cluster of shops in sightseeing areas and so on. These styles
of Japanese commercial district are called “Shotengai” in Japanese. Figure 1 shows anexample of bustling Shotengai street. On the other hand, there are many declining
Shotengai districts especially in local cities like Figure 2.
In addition, road side shops along major roads and large shopping malls with theirtenants that emerge in recent years are also contained. In this study, the method that can
detect distributions of CA is required.

Figure 1 Bustling Shotengai(Shimokitazawa kitaguchi Shotengai,
Tokyo prefecture)

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Figure 2 Declining Shotengai(the center of Kashiwazaki city,
Niigata prefecture)2.2 The detecting method of commercial accumulation
In this study locations and shapes of CA are detected as polygon data using the digital
telephone directory. The digital telephone directory contains address of each tenant.
Each tenant data in telephone directory is converted into point data with longitude andlatitude by the address geocoding. The Japanese address table for geocoding system isdeveloped based on the digital map called the digital residential maps (Zenrin Co. Ltd.).
Almost all addresses are given longitudes and latitudes of the land number or the land
number extension scales. Table 1 shows the result of address geocoding of digital
telephone directory used in this study. Polygon data of CA are developed to create buffer
polygons from each tenant point, and integrate them into one polygon if they overlap.
Many previous studies also adopted this method to create polygons based on data
accumulation in proximity relationship (Eric et al., Konishi et al.). It is needed to decidefollowing two points to develop polygons of CA by our method. The first is the businesscategory that constructs CA. The second is the buffering distance. Figure 3 shows theprocessing flow of our method.
Table 1: Geocoding resolution of digital telephone directory in 2008
Result of address geocoding
The number of telephone
land
numberland number Coarser scale
subscribers (all over Japan)
extension scale
scale
than others
30,749,834
17,079,510
11,268,958
2,401,366
(%)
55.54
36.65
7.81

Figure 3 Processing flow to develop polygon data of CA
2.3 Business category of commercial accumulation in this study
To decide business category that construct CA, we should collect actual information oftenants in CA. Therefore, First tenant information (tenant name and address) werecollected from 28 webpages of actual Shotengai in the Special wards of Tokyo. Secondly,
all tenants were given their own location information, i.e. longitude and latitude by theaddress geocoding. Finally these data were integrated with digital telephone directory byconsidering fluctuation of each locations and tenant names. The method of spatial union
by considering fluctuation is shown in Akiyama et al.

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The digital telephone directory is in 2008. On the other hand tenant information in
Shotengai was collected in October, 2009. Because of this time lag, there are sometenants they could not be integrated with telephone directory. As a result of this spatialintegration, 1761 tenants in 3476 tenants from Shotengai’s homepage were integrated.
About 340 kinds of business categories were selected that construct CAs based on thisresult. Selected business categories contain all kinds of grocery stores, clothing stores,
household and general shops, hair salons, drug stores, all kinds of restaurants,
sporting-goods stores, amusement facilities (i.e. video game arcade and pachinko
parlors), medical clinics, real-estate offices, banks and accommodations and so on.
2.4 Buffering methods of previous study
We introduce 3 kinds of common buffering methods in previous studies. Figure 4, figure 5,
and figure 6 show comparison results of each buffering method with actual distribution of
CA (That was created by the field survey.). Each figure shows around the commercial
area near the city center of Tokyo called “Shimokitazawa”. The Shotengai shown in
Figure 1 is also located in this area. CA polygons of each model are created to integratewith overlapping buffer polygons.
It is one of the most common buffering methods that the buffering distance is fixed andcreates same size circle polygons from each point (e.g., Sheppard et al.). The method
are called “model A” in this paper. In figure 4, the fixed buffering distance is 50m. The
50m buffering can achieve accurate result in suburbs and rural areas. However,polygons of CA are much more widespread than actual in urban area. As shown figure 4,
buffering method by the fixed distance could not achieve the appropriate result because
average distance between tenants changes by area characteristics and function of CA in
the case of creation of CA polygons.
It is also a common buffering method that the buffering distance is defined between eachown point and the nearest point. The method are called “model B” in this paper. Figure 5
shows the result by the model B. By this method, unusual large circle polygons in thisarea were created and integrated with one polygon because tenants far from CAs werealso given buffering distances based on the nearest point of them. On the other hand,
polygons of CA split into some polygons or contain small interspaces in areas where thetenant distance is high, namely distances with tenants are small, because buffering
distances became too small in these areas. This method was not also able to achieve
appropriate results.
It is one of the advanced buffering methods that link information of each CAs are createdusing the Delaunay triangulation net (e.g., Sadahiro). The method are called “model C” inthis paper. Figure 6 shows the result by the model C. In figure 6, buffer polygons werecreated (50m) from overlapped points with the Delaunay triangulation net that maximum
distance of each linking line is 50m. In Figure 7, buffer polygons were created by thesame method as figure 6 in 25m. In the case of figure 6 (50m), polygons of CA are much
more widespread than actual same as model A. On the other hand, polygons of CA are
more compact than model A, model B and model C by 50m. In addition, this method hasan advantage in removal of tenants they locate far from CAs spatially. However almost all
CAs in suburbs and rural areas are not detected because the buffering distance of thismethod is fixed at 25m.

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2.5 The Buffering method of this study
To solve problems in previous method, “variable” buffering distances that can consider
area characteristics should be adopted. The “area characteristic” in this section means
an average distance between tenants of one CA in certain area. For example, it ispossible that the distance is small in Shotengais along the narrow road like Figure 4, on
the other hand it is large in suburban roadside shop clusters.

Figure 4 Polygons of CA (model A)

Figure 5 Polygons of CA (model B)

Figure 6 Polygons of CA(model C with 50m buffer distance)

Figure 7 Polygons of CA(model C with 25m buffer distance)

First, distances from all points to nearest neighbor points of them are calculated. The
nearest neighbor distance is given by (Equation 1).

d min N 

xN  xmin 2   y N  ymin 2(1)

where dminN is the nearest neighbor distance of point N, xN is the longitude of point N, yN
is the latitude of point N, xmin is the longitude of nearest point of point N, and ymin is thelatitude of nearest point of point N.
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Secondly, the buffering distance of point N is defined by (equation 2).
n dE min  d min

DN  k 1

k

N

(2)

n 1

where DN is the buffering distance of point N, dEminn is the nearest neighbor distance ofpoint n located in the circular searching zone RN of point N to consider the effect of
distance from point N and n is the number of points in the circular searching zone RN ofpoint N. The circular searching zone RN of point N is decided that density of points is
calculated in 20m circular zone of point N to 100m every 10m, and the circular zone withhighest dense points is adopted as the circular searching zone RN. In other words, thebuffering distance is the average value of the nearest neighbor distance of points within
the circular searching zone. Figure 8 shows the image how to calculate the buffering
distance of point N. dEminn in (Equation 2) is replaced dminn in Figure 8 for simplicity.

Figure 8 Image how to calculate the buffering distance of point N
In addition, it is important that points without dminN are given weighting. The farer point n
is located from point N, the smaller an effect of point n to point N is even point n is locatedin the circular searching zone of point N. Figure 9 and figure 10 show the method ofweighting. Where dN is the value of point N, dn is the value of point n, en is the impact forpoint n and dEn is the value of point n to be affected by the impact en. Without weighting,in other words, the effect of the value of point n is given to point N completely; dEn equalsdn (figure 9). On the other hand, dEn is given by (Equation 3) in the case that the impact
en is given to point n (figure 10).

dEn  d N  en d n  d N 

Figure 9 Case without weighting (en = 1)
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(3)

Figure 10 Case with weighting (en = 0.5)

In addition en in (Equation 3) is generalized by (Equation 4).

en 

e 1
xn  1RN(4)

where en is the weighting of point n, RN is the circular searching zone of point N, e is theminimum impact (0≦e≦1) and xn is the distance from point N (0≦xn≦RN). Figure 11
shows the relationships between RN with en. The weighting at the completely same
location of point N is 1, meanwhile at the border of the circular searching zone of point N
is the minimum (=e). The weighting (=en) varies along with the distance from point N
according to (equation 4). The nearer point is located from point N, the more effective thenearest neighbor distance of this point is in the average value of the nearest neighbordistance in (equation 2). In the case of e≧0.75, point N are affected by the points in the
searching zone of point N excessively because points located far from point N also havea much impact on point N. On the other hand, in the case of e≦0.3, point N is little
affected by points in the searching zone. As a result, e=0.5 is adopted.

Figure 11 Relationships between RN with en
To integrate with (Equation 2), (Equation 3) and (Equation 4), the buffering distance of acertain point is summarized by (Equation 5).
n

DN 



 e 1 d min   R x  1d min  d min   d min
k 1



N



k

N


n 1

k

N



N

(5)

2.6 Removal of outlier buffer polygons
It is needed to be removed polygons that have much larger buffering distances than
average buffering distances in CAs around them. To solve this problem, the outlier
buffering distance is calculated on buffering distances around point N0 within about 300m
square area like figure 12. Point N0 with large buffering distance is remained. The
buffering polygon of point N0 will be integrated with the CA polygon that will be created at
the northwest of point N0. Therefore the outlier value of buffering distance is calculated
based on the buffering distances of each point in 300m square, and large polygons are
removed by outlier value. The reason of “300m square” is that the maximum searchingradios of each point is 100m (the maximum buffering distance is 100m), and we should
consider the effect of CAs located adjacent to the searching area. The outlier is given toeach point and if the buffering distance is larger than it, the polygon is removed.

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Figure 12 Example of the large buffering distance (point N0)
The outlier is defined generally by (Equation 6).

  x  c

(6)whereτis the outlier, x is the average value of observed values, c is the coefficient valueand σ is the standard deviation of observed values. c is set to the value that can acquire
intended results generally. It is impossible to set c to a certain unambiguous value. Selst
et al. proposed the method to set c based on the number of observed values. Figure 13
shows the decision method of c by Selst et al. This method is very advantage to the casethat the number of observed value changes frequently like our study. To introduce themethod of Selst et al. to our study, the outlier of N0 in figure 12 was calculated. The outlier
is 60.27 (x = 21.45, c = 2.4062 and σ = 16.14). As a result, the buffering distance of point
N0 (= 69.9) is over τ(= 60.27) and it is removed.
Figure 14 shows CA polygons to remove large polygons by the outlier or not. There are
isolated polygons they are not integrated with any buffering polygons and polygons thathave much larger buffering distances than around polygons without removal of polygons
by outliers. On the other hand it is realized that these unusual polygons are removed withremoval of polygons by outliers.
Finally, CA polygons are created to integrate with overlapping buffer polygons and CA
polygons that contain the small number of tenants are removed. The district ofcommercial area in national commercial statistics of Japan is defined that the area isconstructed by about more than 30 tenants. The district of commercial area in somecommercial statistics of local government is defined about more than 20. However, it is
not expected that CAs in suburban roadsides and declining small Shotengai in local cities
contain over 20 or 30 tenants. Therefore if polygons contain over 10 tenants, they areapproved as CA in this study. In other words, polygons are not recognized and areremoved if they contain less than 10 tenants.
Figure 15 shows polygons of CA by our method. Locations and shapes of polygon of CA
in figure 15 agree rather well with actual CAs. In addition, CAs they are not detected by
the model C are also detected. Figure 16 shows the distribution of CAs in broad area.
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Figure 13 Decision method of c by Selst et al.

Figure 14 CA polygons to remove large polygons by the outlier or not

Figure 15 Polygons of CA
using our method
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Figure 16 Distribution map of CAs at thecentral area of Tokyo prefecture

3. RELIABILITY VERIFICATION
3.1 Cross check with the Shotengai directory
The spatial accuracy of our data is verified to cross check our data with the Shotengai
directory of Tokyo prefecture in 2004 (published by Zenkoku Shotengai Shinkougumiai
Rengoukai, Shotengai promotion association of Japan in English). The Shotengai
directory contains almost all addresses of representative organization or representative
person of each Shotengai. All of them are converted into point data by the address
geocoding. Figure 17 shows the distribution of Shotengais in the Shotengai directory inTokyo prefecture. There are 2184 Shotengais in Tokyo prefecture.
On the other hand our data is polygon data. How to cross check point data with polygon
data? The simplest method is to integrate overlapping points on polygons with thesepolygons. If a certain point data contained in the Shotengai library is integrated with ourpolygon data, it means that our polygon data is able to detect the location of thisShotengai. 1390 points of Shotengai contained in the Shotengai library (63.64%) areintegrated with our polygon data by this method.
However there are cases that addresses of representative organization and person are
dwelling houses of representative person and they located in residential area inneighboring their Shotengai. Therefore the number of points is revealed that can be
integrated with our polygons which are increased their area by buffering with small
distances. Table 2 shows the result. About 90% of points of Shotengai are integrated with
our polygons with about 30m buffering. Figure 18 shows even 30m buffering are small inscale compared with our polygons. As a result, it is realized that locations of Shotengai inexisting statistics, namely the Shotengai directory are detected by our data.

Figure 17 Distribution of Shotengai in Tokyo prefecture by the Shotengai directory
Table 2: Result of the cross-check with the Shotengai directory
Buffering distances fromThe number ofRates ofour data
overlapping points overlapping[%]
0m (No buffering)
1,390
63.64
10m
1,566
71.70
20m
1,863
85.30
30m
1,977
90.52

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Figure 18 10m, 20m and 30m buffering from polygons of CA
3.2 Cross check with the commercial statistics
It is shown that our data is able to detect spatial distributions of Shotengai by thecross-check with the Shotengai directory. However CAs included in the directory are only
“Shotengai”; Roadside shop clusters in suburbs, large-scale retail shops and emerging
CAs in recent years are not included. Actually there are 4373 CAs in our polygon data inTokyo prefecture, however the number of Shotengai points in the directory is 2184.
Therefore it is needed to compare the number of CA of our data with the statistics that
contains not only Shotengais but also various CAs. The commercial statistics of Japan
published by Ministry of Economy, Trade and Industry of Japan can satisfy this demand.
The number of CAs in each prefecture (47 prefectures) is contained in this statistics. To
use this information, the number of CAs of our data is compared with the number of CAsof the commercial statistics in 2006. Only CA polygons of our data that contains over 30
tenants are extracted and compared with the commercial statistics, because thedefinition of CA in the statistics is that they contain over 30 tenants. Table 3 and Figure
20 show the comparison result of our data with the statistics. (Figure 19 shows Japaneseregion map.) There is strong correlation between the numbers of CAs of our data with thestatistics. As a result, it is showed that the location and the number of CAs of our dataagree well with existing statistics through cross-checks with statistics.
4. RESULT
Figure 21 shows the distribution of CAs all over Japan in 2008. It is realized to apply themethod to develop CA polygons in this study to digital telephone directory all over Japan.
There are about 44,000 CAs in Japan. Our data can detect not only Shotengais in urban
areas but also roadside shop clusters in suburban areas, small CAs in local small cities
and isolated islands, and shop clusters of souvenir shops and restaurants in tourist area.
This is the first dataset that can monitor locations and shapes of CAs all over Japan.
4.1 Application of the data
This section introduced some examples to utilize our data. Figure 22 shows the attempt
to detect CAs that is growing trend or declining trend by Sengoku et al. The expansion
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and contraction of CAs can be monitored to develop CA polygons in multi years and tocompare them in terms of their locations and shapes. It is expected that such data help
national and local governments to design of policies for revitalization of Shotengai. Figure
23 shows the sample data provided to a private company for the aim of planning to open
new chain stores. The number of tenants with specific business categories or the numberof competing stores in each CAs is clarified. It is possible that our data will become one of
the helpful tools for marketing strategies.
Table 3 The number of CAs of our data and the commercial statistics in each region andsome major prefectures
Region(the numberof prefecutures)

Name ofprefecture

Hokkaido(1)All
All
Miyagi
HukushimaAll
Tokyo
Kanagawa
All
Niigata
Aichi
All
Kyoto
Osaka
All
Hiroshima
All
KagawaAll
HukuokaAll
The sum total

Tohoku(6)Kanto(7)Chubu(10)Kinki(6)Chugoku(5)Shikoku(4)Kyusyu(7)Okinawa(1)Our data
Commercial statistics
The number of CAs:
The number of CAs
The number of Cas (B)over 30 tenants (A)
2039
506
576
3424
1042
1256
803
197
275
725
286
303
11998
3445
3201
4373
1544
928
2334
676
532
9706
2235
2493
1066
256
307
2896
649
607
7178
2040
2156
1071
275
338
3130
964
949
3024
736
768
1262
339
265
1672
370
392
311
86
101
4865
1245
1448
1887
521
487
576
158
117
44482
11577
12407

Figure 19 Japanese regions andprefectures in Table 3

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A/B
0.88
0.83
0.72
0.94
1.08
1.66
1.27
0.90
0.83
1.07
0.95
0.81
1.02
0.96
1.28
0.94
0.85
0.86
1.07
1.35
0.93

Figure 20 Correlation between the numbersof CA in our data with the statistics

Figure 21 Distribution map of CAs all over Japan in 2008

Figure 22 Distribution of growing anddeclining CAs in the city center of Tokyo

Figure 23 Various attribute informationthat can be monitored by our data

5. CONCLUSIONS AND FUTURE WORKIn this study the method to detect locations and shapes of CAs was developed using
digital telephone directory. In addition the original buffering method was developed todetect CAs appropriately. It is realized to develop the dataset easily in a short time that

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can monitor the actual state of locations and shape of CAs in extremely broad area using
our method. The processing of data development is automated and processing ofenormous quantity of data is realized. Our data is the first dataset that can monitor
locations and shapes of CAs and can cover all over Japan with high spatial resolution.
There are some future works. It is an important work to increase the detecting accuracyof CA. Our method is excellent to detect CAs especially in urban area; however there arefew areas in suburban areas with very large distances between shops, i.e. east and north
Hokkaido where our method cannot detect roadside CAs appropriately. It can be
expected that the accuracy will be improved to reconfigure the business category that
construct CAs and to adjust parameters of buffering. Our method is an excellent method
to show CA areas clearly. On the other hand there is a risk to remove tenant information
they are not contained in CA polygons. It may be a method to solve this problem to show
the spatial distribution and density of tenants continuously by the Kernel density
estimation (Sliwinski). However a threshold of the Kernel density that constructs CAs
should be defined in this method. We will improve our method based on cross check
results of statistical data and field survey and some previous studies.
The reliability verification (Table3 and Figure 20) has compared only the number of CAsof our data with commercial statistics. There is no comparison of locations of our CAs
and the statistics. Now location data of CAs in commercial statistics all over Japan arebeing developed to add address to CAs manually and longitude and latitude by address
geocoding. We will cross check this data with our CAs in terms of their locations.
It is also important to check that appropriate CA polygons are developed using telephone
directory in other years and other kind of data by our method. To develop CA polygons
using telephone directory in other years in Tokyo prefecture have already realized by
Sengoku et al. and they obtained optimum results. From now on, we will verify theversatility of our method to conduct the same trial as Sengoku et al. all over Japan andapply our method to foreign telephone directory and statistical data.
In addition, we are now creating the environment to release our data. You can alreadyuse our data for research purposes. In fact, our data are already being used by someuniversities, research institutes, private companies and local governments on a trial basis.
We hope to improve our data to increase users of our data and collect feedbacks fromthem. If you are interested in our data or want to use it, please contact authors (URL:
http://shiba.iis.u-tokyo.ac.jp/member/akiyama/).
Acknowledgements
We are contributed the digital telephone directory (the “Telepoint Pack!”) by ZENRIN CO.,LTD. We would like to thank ZENRIN CO., LTD.
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