This slide is used for OpenSym2018 held at Paris, 22nd-24th, Aug. 2018. The use of geographic information systems (GISs) has become widespread in data-driven industries, and they are utilized to visualize various kinds of spatial data using mappings. In addition to a large amount of available open-source GIS software, various types of data (e.g., boundary data for administrative regions, numerical data for individual areas, and data representing the objects on a map) are provided by local governments as open data. However, in many cases, the data have been inadequately maintained. Thus, advance preparation is required to utilize the data effectively. This paper discusses the work required to utilize open data by considering the case studies of Hachioji-city, Tokyo, Japan.

1. Lessons Learned from
Data Preparation for
Geographic Information
Systems using Open Data
OpenSym2018
Jun Iio
Chuo University, Tokyo JAPAN

2. Copyright © Jun Iio 2
Chuo University
742-1, Higashi-nakano,
Hachioji-shi, Tokyo

3. Copyright © Jun Iio 3
Background
●
Data Visualization
– The data are aggregated within administrative
districts and scattered on the map of Hachioji-
city
●
Target data (as of FY2016)
– The number of registered citizens
– The number of visitors, who are checked out
more than one book a year
– The number of times of checking out
– The number of books which are checked out

4. Copyright © Jun Iio 4
Hachioji-city Libraries
Chuo Univ.

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Key Index
● The density of visitors di
for the district i
– di
＝ Tot. # of visitors / Tot. # of residents
● The ratio on the density of visitors Di
– Di
＝ di
/ maxj
｛ dj
｝
– Characteristics of Di
● Di
tends to be 1.0 for the closest district i to the
library
●
The longer the distance from the library is, the
lower the value is.

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Data Visualization with the Graph
Y-axis：The ratio on the density of visitors
X-axis：The distance from library to gravity center

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Data Visualization on the Map
● Visualizing the value Di
for each library
Central Library Lifelong Learning Center Library
Kawaguchi Library Minamiosawa Library

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The areas whose ratio is more than 20%

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Data Preparation
●
We have to prepare two kinds of (open-)
data
– Geographical data
●
Boundary data (city, town, administrative area)
●
Road, Rail, River, Mountain, etc.
●
Building, other objects…
– Various data to be analyzed

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Open-data from Hachioji-city

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Geographical Open-data (1)

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Geographical Open-data (2)

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Problems
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Representational mismatch
●
Too much complicated
●
Not suitable for some analysis

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Solving Problems before Analysis

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Mismatch in Two Datasets

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Too Much Complicated
Rock reef
Seawall

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Merging Several Regions

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Merging Two Adjacent Regions

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Appropriate Boundary

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Conclusions and Future Work
●
Some ministries (MLIT and MIC) have published
boundary data for defining administrative regions on
the internet.
●
However, the raw data provided are inappropriate
for use in spatial analyses using GIS tools.
●
This paper reported procedures required to clean
the boundary data provided by the Japanese
government as open-data.
●
The methods explained here, including the name
aggregation and data cleansing, are operated by
applying some scripts manually.
●
Therefore, a trial to implement a system to
automatically realize these procedures remains in the
scope of future work.