Displaying People with Old Addresses on a Map
Gang Zhang1 and Harumi Murakami1
Graduate School for Creative Cities, Osaka City University,
3-3-138, Sugimoto, Sumiyoshi, Osaka 558-8585 Japan
springsimb@gmail.com

Abstract. This paper proposes a method of converting old addresses
to current addresses for geocoding, with the aim of displaying on a map
people who have such old addresses. Existing geocoding services often
fail to handle old addresses since the names of towns, cities, or prefectures can be diﬀerent from those of current addresses. To solve thisgeocoding problem, we focus on postal codes, extracting them from Web
search result snippets using the query “prefecture name AND important place name AND postal code.” The frequency of postal codes andthe edit distance between the old address and the addresses obtainedusing the postal codes are used to judge the most suitable postal codeand thus the corresponding current address. The eﬀectiveness of the proposed method is evaluated in an experiment using a relative dataset. A
prototype system was implemented in which users could display peopleusing their birthdate and birthplace addresses on a map chronologicallywith an associated history chart.
Keywords: people search, old address, postal code, edit distance, map
display, information extraction.

1Introduction

In our private lives, it is very important to learn about our ancestors and relatives
and organize their information. Marking their positions on a map is a useful andfeasible approach. The aim of this research is to develop a map interface to accesspeople from the past to the present. Geocoding services are available to display
people using their addresses. We want to obtain addresses from documents likecensus registers or other relevant documents. However, existing geocoding services often fail to obtain location information from old addresses. Most of theaddresses contained in documents like family registers follow an older addressing
system out of use today. It is diﬃcult to convert old addresses to currently usedaddresses because house numbers, region names, and even basic town names
change over time.
To solve the geocoding problems caused by old addresses, we focus on postal
codes found on the Web. We assume that useful information for finding current
addresses exists on the Web, and postal codes are good clues since they are easy
to extract and easy to convert to current addresses using data provided by Japan
Post [1].

The frequency of postal codes and the edit distance between old addresses andaddresses obtained from the postal codes are used to identify the most suitablepostal code and thus the corresponding current address. The eﬀectiveness of theproposed method is confirmed in an experiment using a relative dataset that
contains addresses from 1905 to 2006. A prototype system was implemented inwhich users could display people using their birthdate and addresses on a map
chronologically with an associated history chart.
The structure of this paper is as follows. In Section 2 we explain our method.
Experimental results are described in Section 3. The prototype is shown in Section 4. Related work and future work are described in Section 5. The examples
presented in this paper were translated from Japanese into English for publication.

2Approach

2.1

Overview

An overview of the proposed method consists of three stages:(a) postal code collection: collects postal codes relevant to an old addressfrom the Web; (b) best postal code judgment: calculates scores of postal codes
using the frequency of each postal code and the edit distance between the oldaddress and an address associated with the postal code obtained from Japan
Post postal code data [1], and then judges the best one; and (c) address refinement: refines the address using Ministry of Land, Infrastructure, Transport andTourism (MLIT) positioning reference data [2].
Table 1 presents example results from using the proposed method. We arbitrarily set house numbers as “99” in order to protect personal information. In
this paper, we discuss example number 1.
Table 1. Example results of proposed method
No. Address in family register
12
3

2.2

Output address by proposed
method99-banchi, 1-chome, Uchiandoji- 1-chome, Andojimachi, Chuo-ku,machidori, Minami-ku, Osaka-shi Osaka-shi, Osaka-fu
99-banchi, Kitanokofukacho, Kita- 1-chome, Shibata, Kita-ku, Osakaku, Osaka-shishi, Osaka-fu
99-ban-yashiki, Takagi, Kokufu- Takagi-cho, Fuchu-shi, Hiroshimamura, Ashida-gun, Hiroshima-ken ken

Extracting Postal Codes

Addresses change with time. We focus on postal codes since they can be easily
converted to the current addresses. To search for the postal code of an address,

we use Web search engines; in this paper, we use Google. The combination ofimportant place name and prefecture name were assumed to be a good choicefor the query sent to the search engines, and thus the postal codes included inthe snippets of the results were collected.
Extracting Important Place Name We assume the most important typeof place name that “lives long” is town names. To extract a Japanese town
name, we remove the character strings focusing on numerical positions and such
positional keywords as aza, mura, cho (town), gun, ku (ward), shi (city), to, do,fu, or ken (prefecture). For example, “Uchiandojimachidori” is extracted as animportant place name.
Extracting Prefecture Names from the Web To obtain a prefecture
name, the old address without house numbers (e.g. 99-banchi, 99-ban-yashiki)is used for the query to the search engine. When the query “prefecture AND oldaddress without house numbers” is given, our method obtains five snippets ofresults from Google. Prefecture names are extracted by matching a prefecture
dictionary to the snippets and thus obtaining the most frequent prefecture name.
For example, when the query “prefecture 1-chome Uchiandojimajidori Minamiku Osaka-shi” is given, the result “Osaka-fu” is obtained.
Extracting Postal Codes from the Web When the query “prefecturename AND important place name AND postal code” is given to Google, ourmethod obtains one result snippet, and it extracts postal codes by using thefollowing regular expression from the snippet: \d{3}-\d{4}.
When there is no result, our method repeats the above process for the otherfour result snippets. When no result is found even after five snippets, our method
ends and none becomes the answer. For example, when the query “Osaka-fu AND
Uchiandojimachidori AND postal code” is given, such postal codes as 542-0061,542-0067 and 539-0000 are obtained from the snippet of the top page.
2.3

Judging Best Postal CodeWe assume that the more frequently appearing postal codes are related to thecurrent address, and the more similar character strings are related to the current
address. Therefore, we calculate scores of postal codes by using the frequency of
postal codes and the edit distance between addresses to judge the most suitablepostal code. We use Japan Post postal code data to find an address associatedwith a postal code.
Edit distance is a way of quantifying how dissimilar two strings are to one
another by counting the minimum number of operations required to transform
one string into the other. We use Levenshtein distance as the edit distance.
The score is calculated as follows:f(1)dHere, f is the frequency of postal codes and d is the edit distance between anold address string without house numbers and an address associated with thepostal code.
score =We select one postal code having the highest score. For example, the frequency of postal code 542-0061 is 3 and its edit distance is 11 (in Japanese),and thus its score (3/11=0.273) becomes the highest among the collected postalcodes. Consequently, the address associated with the postal code 542-0061 is
obtained as “Andojimachi, Chuo-ku, Osaka-shi, Osaka-fu.”
2.4

Address Refinement

Most of the current oﬃcial Japanese town names consist of a “chome” (districtof a town) and a “town.” The “chome” information is seldom contained in Japan
Post data. Very old addresses do not have the “chome” designation either. We
add a “chome” to the address obtained in section 2.3 to complete the oﬃcial town
name: the available chome information is used when the old address includes it;otherwise, “1-chome” is used tentatively. Next, we check MLIT position reference
data provided by the Ministry of Land, Infrastructure, Transport and Tourism,
which contain current oﬃcial addresses, and if the address exactly matches anentry, it becomes the final answer; if not, the “chome” is deleted. For example,“1-chome, Andojimachi, Chuo-ku, Osaka-shi, Osaka-fu” becomes the answer.
2.5

Geocoding

We obtain latitude/longitude information of the addresses obtained in section
2.4 by using Google Maps Geocoding API v3 [3].

3Experiment

3.1

MethodWe use a relative dataset created in a previous work [4]. The oldest year withan address (birthplace) was 1905, and the newest year with an address (deathplace) was 2006. Since this is a dataset for Japanese relatives from the modernera, many addresses are identical. Thirty-two diﬀerent addresses of birth or death
places are used in the experiment.
The proposed method was compared with a method that gives old addresses
without house numbers directly to Google Geocoding API v3.
We investigated current addresses for the old addresses without house numbers, in other words, town (including chome) levels at each public oﬃce corresponding to the old address. We evaluated the answers (output by the twomethods) at prefecture, city, and town (including chome) levels. Here, traditional precision and recall are used for evaluation:
precision =rn(2)r(3)
cwhere r is the number of obtained correct answers, n is the number of answers
output by the methods, and c is the total number of addresses in the dataset.
recall =3.2

Results

Table 2 shows the results of the experiment. Our method outperformed Google
Geocoding API, particularly in recall.
Table 2. Results of experiment
Proposed method
Google GeocodingPrecision
RecallPrecision
RecallPrefecture 100% (31/31) 97% (31/32) 96% (24/25) 75% (24/32)
City
87% (27/31) 84% (27/32) 80% (20/25) 63% (20/32)
Town
64% (16/25) 64% (16/25) 60% (9/15) 32% (8/25)

4Prototype

We have developed a prototype system that enables users to display people on amap using Google Maps JavaScript API v3 [5] through a timeline. Fig. 1 shows
living people in 1943 on a map with birthplace addresses. This application has
the following functions: (a) users can designate the year to display by inputting
it or by using a slider tool; and (b) users can browse historical events (extractedfrom a history book [6]) before and after the year in a history chart, and they
can also change the year by selecting a historical event in the chart.

Fig. 1. Prototype system

5

Related Work and Discussion

FamilySearch [7] enables users to display people on a map based on manual input of their prefecture-level location. Yamamoto et al. [8] proposed techniques

for achieving the geographical navigation of historical events described in Web
pages as a Virtual History Tour. They extracted historical event sentences fromthe Web and identified one representative place name for each event. The place
names were mainly at the prefecture or city level. However, we converted addresses in family registers to current addresses and focused on the town level.
Many Web sites provide a facility that converts addresses or landmarks into
latitude/longitude, such as geocoding services. In general, however, they cannot handle old addresses. We aim to handle both old and current addresses ingeocoding tasks.
The experimental results here reveal that our method outperformed Google
Geocoding, thus demonstrating our method’s usefulness. In addition, we successfully built a prototype interface that displays relatives on a map.
Future work includes the following. First, we need to improve our method
to work eﬀectively in earlier eras. Second, we need to evaluate other kinds ofplaces like permanent addresses. Third, the map interface still has much room forimprovement, such as adding the ability to display the relationships of relatives.

6Conclusions

We proposed a method of converting old addresses to current addresses in order
to use geocoding to display people on a map. The approach of our method follows three stages: (1) collecting postal codes relevant to an old address from theWeb, (2) judging the best postal code by considering the frequency of each obtained postal code and the edit distance between the old address and an address
associated with a postal code, and (3) refining the address by using location data
provided by the government. The experimental results show that our method issuperior to Google Geocoding API and thus useful for geocoding tasks. Furthermore, a prototype system was implemented as an interface to display people ona map.

References
1. http://www.post.japanpost.jp/zipcode/download.html
2. http://nlftp.mlit.go.jp/isj/
3. https://developers.google.com/maps/documentation/geocoding/
4. Murakami, H., Zheng, N.: Visualizing Family Trees: Development of a Dynamic
Family Retrieval System. CSCW 2012 5th Workshop on Personal Information Management (PIM 2012) (2012)
5. https://developers.google.com/maps/web/
6. Kodansha International Ltd.: Japanese History: 11 Experts Reflect on the Past
Large Print Edition. Kodansha International Ltd., Tokyo (2003)
7. https://familysearch.org/
8. Yamamoto, M., Takahashi, Y., Iwasaki, H., Oyama, S., Ohshima, H., Tanaka, K.,:
Extraction and Geographical Navigation of Important Historical Events in the Web.
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