=Paper=
{{Paper
|id=Vol-1923/article-09
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1923/article-09.pdf
|volume=Vol-1923
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==None==
https://ceur-ws.org/Vol-1923/article-09.pdf
Geographic Area Representations in Statistical Linked
Open Data of Japan
Dan Yamamoto1, Akira Ioku1, Yoko Seki1, Akie Mizutani1 , Junichi Matsuda1,
Hideaki Takeda2 Ikki Ohmukai2, Fumihiro Kato2 ,Seiji Koide2, and Shoki Nishimura3
1 Hitachi, Ltd., Tokyo, Japan
{dan.yamamoto.vx, akira.ioku.dv, yoko.seki.df, akie.mizutani.kj,
junichi.matsuda.ru}@hitachi.com
2 National Institute of Informatics, Tokyo, Japan
{takeda, i2k, fumi, koide}@nii.ac.jp
3 National Statistics Center, Tokyo, Japan
snishimura@nstac.go.jp
Abstract. The Japanese Statistics Center has been provided statistical LOD since
2016 and is still evolving them. This paper focuses on problems and solutions
related to geographic area representations in statistical LOD. We describe ontol-
ogies for handling absorption and abolishment of municipalities and also confi-
dentiality concerning small area as well as grid square statistics.
Keywords: Statistics, Linked Open Data, Area, Grid square, Concealment.
1 Introduction
The Japanese Statistics Bureau and National Statistics Center of Japan have published
approximately 500 government statistics at the Portal Site of Official Statistics in Japan
(e-Stat). The main seven statistics among them, including population censuses, eco-
nomic censuses, and labor force surveys, have been provided as Linked Open Data
(LOD) since 2016 [1]. The statistical LOD site was opened on June 30, 2016 and is still
evolving. Dataset and classification criteria of LOD-based statistical data are now se-
mantically clarified, which not only facilitates data retrieval, but also enables linkage
with other domestic and overseas data. The published LOD consists of approximately
400 million triples and 40 million observations [2].
Throughout its development, we faced two main challenges related to geographic
area representations in statistical LOD: (1) Properly expressing the temporal changes
such as absorption and abolishment of municipal divisions and (2) constructing LOD-
enabled small areas and grid squares with concealed statistical data.
The concept of municipalities is changing by absorption and abolishment. There
have been existing works to handle these temporal changes to geographic data. For
example, [3] proposed a logical model for changing taxonomic concepts. Furthermore,
in Linking Geospatial Data Workshop (March 2014), the common problem of model-
ling and managing temporal changes to geographic datasets was discussed [4] in the
2
context of UK Department for Communities and Local Government (DCLG). In this
paper, we propose a solution to describe the changing municipality concept with the
reason for the change, and also the method for linking statistical data and changing
concept.
Statistical LOD publishes data for not only municipalities but also small areas and
grid squares as geographic areas. We propose a way of constructing these types of LOD.
Furthermore, when publishing small area or grid-square statistics, some data is often
concealed because of privacy protection. We also propose a way of expressing LOD-
enabled concealed statistical data.
Several statistical LODs have also been published by the national statistics institutes
of Ireland [5] and Italy [6] as well as the Scottish government [7] and UK DCLG [8].
Each one of these institutions has its own solutions to geographic area representations
in LOD. For example, the Scottish government uses ONS Boundary Change Ontology
[9] to express boundary changes. Our results described in this paper can be an entry
point to state concrete ontology matching between such existing ontologies and ours,
toward semantic interoperability among international statistical LODs.
2 Geographic Areas Handled in Statistics
This section describes a brief summary of the types of geographic areas used in Japa-
nese statistics, which we aim to represent as LOD. Each statistical data is related to the
following three types of geographic areas:
(1) Administrative divisions:
The Japanese administrative divisions consist of two layers: prefectural divisions
and municipal divisions. Most statistical data is organized per administrative division.
The entire country is divided into 47 prefectural divisions, and each prefectural division
is divided into municipal divisions. Approximately two thousand municipal divisions
exist in Japan.
The Japanese Statistics Bureau has a standardized codelist, the Standard Area Codes
for Statistical Use, which enables us to uniquely identify each division. Every code is
represented as a five-digit number, where the first two digits identify a specific prefec-
tural division and the remaining three digits represent a specific municipal division lo-
cated in the prefectural one. For example, Shinjuku-ward, Tokyo is coded as “13104”
comprising Tokyo, “13”, and Shinjuku-ward, “104.”
(2) Small areas:
Small areas are subdivided portions of a municipal division. Frequently-used statis-
tical data such as the census are produced for small areas as well as administrative di-
visions. Several codelists, which are defined in each statistics and not standardized,
exist to uniquely identify each small area.
(3) Grid squares:
3
Grid squares are portions of the country divided into a grid by longitudinal and lati-
tudinal lines. Frequently-used statistics like the census are also prepared for grid
squares. As the basis of our grid-square statistical data, we use the world grid square
system [10], a compatible extension of the Japanese grid-square coding system (JIS
X0410) to worldwide. Table 1 shows three levels of grid squares used for Japanese
statistics.
Table 1. Grid squares used for Japanese statistics
Third level Divide Japan into equal parts measuring 2/3 degree of latitude by 1
grid squares degree of longitude. Divide one of the parts into 8 equal parts in lat-
itude and longitude directions. Furthermore, divide one of the parts
into 10 equal parts in latitude and longitude directions.
Approximately 1-km square, 386,877 grid squares exist for Japan.
Fourth level Divide third level grid square in half in latitude and longitude direc-
grid squares tions.
Fifth level Divide fourth level grid square in half in latitude and longitude di-
grid squares rections.
Other than the world grid square system we adopted, there are several worldwide
grid-square systems such as Open Location Code [11] or “plus+codes.” Note that both
the world grid square system and Open Location Code are derived from latitude and
longitude coordinates. Hence, we can easily map one from the other, which potentially
enables us to relate our grid-square statistical data with other datasets based on Open
Location Code.
In our statistical LOD, these geographic areas can be referred to as objects of dimen-
sions in terms of RDF Data Cube Vocabulary [12]. Fig. 1 depicts an instance of obser-
vation that refers to a certain administrative division, sac:C11203-20010401, as a di-
mension (in Line 3).
1:
2: a qb:Observation ;
3: sdmx-dimension:refArea sac:C11203-20010401 ;
4: cd-dimension:age cd-code:age-0 ;
5: cd-dimension:nationality cd-code:nationality-japan ;
6: cd-dimension:sex cd-code:sex-female ;
7: cd-dimension:timePeriod "2010" ;
8: estat-attribute:unitMeasure estat-attribute-code:unitMeasure-Person;
9: estat-attribute:unitMult estat-attribute-code:unitMult-0 ;
10: estat-measure:population "2059"^^xs:decimal .
Fig. 1. RDF expression of the number of 0-year-old girls in a municipality
4
3 Expressing the Absorption and Abolishment of Municipal
Divisions
3.1 Brief Description of Absorption and Abolishment of Municipal Divisions
The implication of each geographic area, in regard to attributes including city clas-
sification and borders, changes with time by passing through events such as: absorption,
abolishment, separation, establishment of new municipalities, division into several mu-
nicipalities, name change, boundary change, and shift to a designated city. Fig. 2 shows
the case of Kawaguchi-City (standard area code: 11203) as an example of absorption
and abolishment of city and district municipalities. The city absorbed the neighboring
Hatogaya-City (standard area code: 11226) on October 11, 2011. The implication of
“Kawaguchi-City” changed at the time. Specifically, attributes, such as borders,
changed.
Kawaguchi city Kawaguchi city
(11203） (11203）
Kawaguchi city
(11203）
Hatogaya city
（11226）
2001/04/01 2011/10/11
shift to absorption
Special City
Fig. 2. Example of absorption and abolishment of municipal divisions
The areas referred to in statistical tables are those at a certain temporary point in the
change or during a limited period, namely “areas as snapshots.” The population and the
number of households of "Kawaguchi-City" are included in both the national censuses
in 2010 and 2015. But “Kawaguchi-City” in 2010 and “Kawaguchi-City” in 2015 are
not the same.
3.2 Standard Area Codes with the Notion of Time Period
Standard Area Codes for Statistical Use has included integration and abolition history
(absorption, abolishment, new establishment, and so forth) since 1970 for the conven-
ience of statistics users. We aim to include this historical information into our statistical
LOD to increase its linkability to external LOD resources.
However, because the above standard area codes themselves do not include the con-
cept of time, they are insufficient to express areas as snapshots. In the example above,
using only the code 11203, we cannot distinguish between “Kawaguchi-City” in 2010
and that in 2015 after absorbing “Hatogaya-City.” Such an absorption has a big influ-
ence on a change of population and the number of households. Therefore, being unable
to distinguish between data before and after absorption will pose a problem when we
use statistical data.
5
At first, in view of the above, we defined a system of standard area codes with the
notion of time period (hereafter called “temporary standard area codes”) to identify an
area at a certain period of time by expanding the conventional area codes. The tempo-
rary standard area codes are provided by connecting conventional standard area codes
and the date when events such as absorption were enforced with a hyphen (-). The tem-
porary standard area code is valid until the next event. In the example above, “Kawa-
guchi-City” at the time of the national census in 2010 is expressed as 11203-20010401.
The city at the time of the national census in 2015, after the admission merger on Oc-
tober 11, 2011, is expressed as 11203-20111011.
To achieve our LOD-enabled statistical data format, we adopted the policy in which
we consider the temporary standard area codes as core resources and link them to the
relevant information regarding areas (as snapshots) during the period. Each observation
in our dataset can refer to the temporary standard area code as a value of dimension
such as sdmx-dimension:refArea.
In addition, we made conventional standard area codes without period (hereafter
called “plain standard area codes”) LOD-enabled so that the users can use them as
pointers to the standard data area codes with period. It enables users to refer to each
area by one stop without considering period. Plain standard area codes are useful for
the users who only know the existing standard area codes to obtain information from
our statistical LOD. Fig. 3 shows an example of the LOD-enabled temporary standard
area codes and plain standard area codes, both of which are described with namespace
prefix “sac.”
Temporary Standard Area Codes
rdfs:label
sac:C11203-
“Kawaguchi city”
20111011
Plain Standard sacs:latestCode sacs:previousCode sacs:previousMunicipality
Area Codes
rdfs:label rdfs:label
sacs:pastCode
sac:C11203- sac:C11226-
sac:C11203 “Kawaguchi city” “Hatogaya city”
20010401 20111001
sacs:previousCode sacs:previousCode
rdfs:label rdfs:label
sac:C11203- sac:C11226
“Kawaguchi city” “Hatogaya city”
sacs:pastCode 19740801 19710401
Fig. 3. Example of LOD-enabled temporary standard area code and plain standard area code
Because the causes of events such as absorption and abolishment, which change the
implication of the area, are various and expected to be useful, we made them LOD-
enabled as data expressing the reason of the change. As shown in Fig. 4, an event,
described with namespace prefix “sace”, such as absorption and abolishment has a link
to a change reason, described with namespace prefix “sacr”, and is linked from one or
more temporary standard area codes related to the change event. Examples of links are
as follows.
6
• Municipal organization enforcement from multiple towns to a single city: Link to
each temporary standard area code of the abolished towns and the newly founded
city.
• Change of the boundary line between a city and a town: Link to each temporary
standard area code of the old and new city and town.
Plain Standard Change Events
Temporary Standard Area Codes
Area Codes
“2011-10-11”
dcterms:issued rdfs:label “Kawaguchi city”
sacs:latest
Code sac:C11203- sacs: sacs:Special
sac:C11203 administrative
20111011 City
Class
org:resultedFrom
sacs:previous
sacs:past Municipality
Code sac:C11226-
sace:C5244
sacs: 20111011 org:resultedFrom
previous rdfs:label sacs:reasonForChange
Code
“Hatogaya city” sacr:absorption
“Kawaguchi city” org:changedBy
rdfs:label Kawaguchi absorbed Hatogaya
dcterms:valid
sac:C11203- “2011-10-11”
sacs:past 20010401
Code org:resultedFrom
dcterms:issued
sacs: sace:C4987
sacs: administrative “2001-04-01”
previous Class sacs:reasonForChange
Code sacs:Special
City org:changedBy
sacr:shiftTo
sac:C11203- AnotherKindOfCity
19740801 rdfs:label
“Kawaguchi city” Kawaguchi changed its class
sacs: from City to Special City
administrative
Class sacs:City
Fig. 4. Relations between temporary standard area codes and the change reason
Fig. 4 shows an example of the relations between the temporary standard area code
and the change reason based on city classification and absorption change of “Kawagu-
chi-City.” A temporary standard area code (e.g., sac:C11203-20010401), has links to:
information such as name, type, date of issue and expiry date of the municipal division
that the code identifies during the defined period, previous and subsequent temporary
standard area codes (e.g., sac:C11203-19740801 and sac:C11203-20111011) before
and after the defined period, and the change reason (e.g., sace:C4987) that led to the
generation of the temporary standard area code. In addition, we also have a plain stand-
ard area code (e.g., sac:C11203) as a pointer to all the related temporary standard area
codes via properties of sacs:latestCode and sacs:pastCode.
The average number of change events per municipal division is approximately 3.73
so that the data size of whole temporary standard area codes are about four times larger
than the one of plain standard area codes, which is acceptable increment for our system.
Note that, we do not explicitly define the current or latest standard area codes in our
LOD, instead we can identify the current or latest one according to sacs:latestCode
property from a plain standard area code. We can also point any temporary standard
7
area code at the point of survey based on issued and expired date described in each
temporary standard area codes.
3.3 Comparison with an Existing Statistical LOD
We compare our ontology with an existing one, ONS Boundary Change Ontology
[9], created by the Office for National Statistics (ONS) and used in ONS Geography
Linked Data Portal as well as Scotland’s statistics.gov.scot [7] to express boundary
changes. In statistics.gov.scot, the following four properties defined by ONS Boundary
Change Ontology are actually used:
• http://statistics.data.gov.uk/def/boundary-change/operativedate (Operative Date)
• http://statistics.data.gov.uk/def/boundary-change/terminateddate (Terminated Date)
• http://statistics.data.gov.uk/def/boundary-change/originatingChangeOrder (Origi-
nating Change Order)
• http://statistics.data.gov.uk/def/boundary-change/terminatingChangeOrder (Termi-
nating Change Order)
The two date-related properties, i.e., Operative Date and Terminated Date, are cor-
responding to dcterms:issued and dcterms:valid, respectively. The main difference be-
tween them is the range of properties: the two properties in ONS ontology are object
properties, which have values as resources, whereas the ones in our statistical LOD are
dcterms properties that have values as literals. Scotland’s statistics.gov.scot takes ad-
vantage of object property to link their operative date and terminated date to dereferen-
cable URI, e.g., , from which users
can know further information related to the date from other linked datasets. By contrast,
we adopted datatype properties to keep things simpler, while it is still possible for us to
introduce additional object properties just like ONS ontology when similar rich linkable
dataset of days/months/years is ready for our statistical LOD.
As for the other two properties related to change reasons, the situation is exactly
opposite. The two properties used in statistic.gov.scot, i.e., Originating Change Order
and Terminating Change Order, have literal values to express the order that caused the
boundary change. In contrast with them, we use org:resultedFrom and org:changedBy
to indicate change reasons as resources (e.g., sace:C5244) to represent various infor-
mation about events causing boundary changes.
4 Expressing LOD-Enabled Small Areas and Grid Squares
4.1 Brief Description of Small Areas
Most public statistical information is shown in every municipal division defined as an
administrative unit. However, based on the municipal division, we cannot grasp the
various and detailed statuses of each geographic area, such as where exactly in the area
population and stores are concentrated. To better understand the distribution patterns
of population and households or statuses of private establishments and stores in specific
8
areas such as elementary school districts or city centers, we have to prepare statistical
information in a smaller unit.
Therefore, statistics such as population censuses have been established in smaller
units than municipalities. In particular, we have two smaller units, namely small areas
and grid squares mentioned in Section 2.
4.2 LOD-Enabled Grid Square Statistical Data
Small areas are subdivided portions of municipal divisions so that we can treat them
equally with municipal divisions. However, the grid-square system is different from an
administrative district and may be shared in the international system. Therefore, we
defined additional attribute information such as latitude and longitude as LOD for grid
squares. Fig. 5 shows an example of RDF expression of the grid-square used in our
Statistical LOD.
grid:G2047306771 a gridCode:GridCode3 ;
dcterms:identifier "2047306771" ;
gridCode:lat-NW 34.925000 ;
gridCode:long-NW 136.887500 ;
gridCode:lat-SE 34.916667 ;
gridCode:long-SE 136.900000 ;
gridCode:span-EWN 1.142142 ;
gridCode:span-EWS 1.142258 ;
gridCode:span-NS 0.923454 ;
gridCode:area 1.054769 .
Fig. 5. RDF expression of the grid square
Each grid square can be uniquely identified using the world grid square codes. For
example, “2047306771” shown in Fig. 5 as a value of dcterms:identifier is a code of
the world grid square code system. In addition, “grid:G2047306771” is an URI derived
from the same code, which can be referred to from observed values in grid-square sta-
tistical dataset via dimension properties such as sdmx-dimension:refArea.
Expressed as RDF, both small areas and grid squares are now linkable to other ex-
isting geographic data such as Open Location Code [11] and GeoNames [13].
By describing grid-square statistical data and grid-square codes as LOD, the exact
geographic location and semantics of statistical data are clarified. Furthermore, even
non-statistical data can be easily linked using the grid-square codes defined as URI.
The grid-square codes can also be used as unique keys to acquire statistical data be-
longing to specific geographic areas without regard to administrative divisions, which
enables us to visualize statistical data as heatmaps.
9
4.3 LOD-Enabled Concealed Statistical Data
Target areas include small areas and grid squares with a very small population. If all
the counted results regarding such areas are released, the private information of survey
subjects might be open.
Therefore, concealment is performed in the count process if the private information
of survey subjects can be open. For example, when there is an extremely small popula-
tion or number of households in a certain small area, results should be added into the
results of neighboring small areas and released. The basic idea of concealment is that
items that should be concealed have to be clear in advance, and the results regarding
areas smaller than a certain size should be concealed.
Our proposed LOD-enabled method will make the public statistical table LOD-
enabled as is. In other words, this method follows the expression method of the statis-
tical table of Table 2 and defines the adding-up destination as a specific area. An ex-
ample of this method is shown in Fig. 6.
Table 2. Example of dataset with concealment
Code City Town Block Conceal- Con- Con- Popula-
ment cealed by ceals tion
S103201189 Mori- Suna- First concealed 189005 X
001 oka kozawa
S103201189 Mori- Suna- Second -
002 oka kozawa
S103201189 Mori- Suna- Third concealed 189005 X
003 oka kozawa
S103201189 Mori- Suna- Fourth -
004 oka kozawa
S103201189 Mori- Suna- Fifth conceal- 189001; 35
005 oka kozawa ing 189003
10
【area: First Block】
sdmx-dimension:refArea
observation1 sac:S103201189001
【type: Figures kept undisclosed】
estat-attribute-code:
estat-attribute:obsType obsType-figuresKeptUndisclosed
prop-obs:concealedBy
【area: Third Block】
sdmx-dimension:refArea
observation3 sac:S103201189003
【type: Figures kept undisclosed】
estat-attribute-code:
prop-obs:concealedBy
estat-attribute:obsType obsType-figuresKeptUndisclosed
【area: Fifth Block】
sdmx-dimension:refArea
observation5 sac:S103201189005
estat-attribute:obsType 【type: Concealing】
estat-attribute-code:
obsType-concealing
estat-measure:population 35
Fig. 6. LOD-enabled small area codes with concealment
Fig. 7 shows an example flow of data acquisition using SPARQL from LOD built
using our method.
1. Try to get population in the small area identified by CODE_FOR_SMALL_AREA
select ?population ?obstype IF
where { ?obstype does not equal to
“concealing”
?s estat-measure:population ?population . THEN
?s sdmx-dimension:refArea CODE_FOR_SMALL_AREA . use ?population as
?s estat-attribute:obsType ?obstype . the expected result
}
IF
?obstype
2-1. Find its concealing areas (if necessary) equals to
“concealing”
select ?concealedArea THEN IF
where { no ?population found
?s1 sdmx-dimension:refArea CODE_FOR_SMALL_AREA . THEN
?s2 prop-obs:concealedBy ?s1 .
?s2 sdmx-dimension:refArea ?concealedArea .
}
2-2. Get aggregated population in the concealing area
select ?population ?concealingArea ?concealedArea
where {
?s1 sdmx-dimension:refArea CODE_FOR_SMALL_AREA .
?s1 prop-obs:concealedBy ?s2 .
?s2 estat-measure:population ?population .
?s2 sdmx-dimension:refArea ?concealingArea .
?s3 prop-obs:concealedBy ?s2 .
?s3 sdmx-dimension:refArea ?concealedArea .
}
Fig. 7. Data acquisition from LOD-enabled small area codes with concealment
11
5 Conclusion
In this paper, we described the problems and solutions about an extension method of
statistical LOD regarding geographic areas. The data is increasing sequentially, but per-
formance will deteriorate along with it. Further speedup solutions will be necessary in
the future. We also plan to introduce geometric information based on GeoSPARQL
[14].
At present, general users face a big hurdle in using statistical LOD because they have
to write code in SPARQL. To address this problem, we will not only expand the statis-
tical LOD, but also provide usage samples and use cases.
When convenience improves with the improvement of speedup solutions and suffi-
cient use cases, and LOD-enabled statistical data is aggregated, we will be able to create
indexes from multiple databases and received insights not provided from a single da-
taset. Following the trend of European countries, the number of countries releasing
government statistics as LOD are increasing. We will try to unify the data structure of
LOD so that we can use federated queries more effectively.
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Appendix
A. List of prefixes and namespaces
prefix namespace
qb http://purl.org/linked-data/cube#
sdmx-dimension http://purl.org/linked-data/sdmx/2009/dimension#
dcterms http://purl.org/dc/terms/
geo http://www.opengis.net/ont/geosparql#
sf http://www.opengis.net/ont/sf#
org http://www.w3.org/ns/org#
xs http://www.w3.org/2001/XMLSchema#
sac http://data.e-stat.go.jp/lod/sac/
sacs http://data.e-stat.go.jp/lod/terms/sacs#
sace http://data.e-stat.go.jp/lod/sace/
sacr http://data.e-stat.go.jp/lod/sacr/
estat-attribute http://data.e-stat.go.jp/lod/ontology/attribute/
estat-attribute-code http://data.e-stat.go.jp/lod/ontology/attribute/code/
estat-measure http://data.e-stat.go.jp/lod/ontology/measure/
cd-dimension http://data.e-stat.go.jp/lod/ontology/crossDomain/dimension/
cd-code http://data.e-stat.go.jp/lod/ontology/crossDomain/code/
grid http://data.e-stat.go.jp/lod/gridCode/
gridCode http://data.e-stat.go.jp/lod/terms/gridCode/
prop-obs http://data.e-stat.go.jp/lod/ontology/property/observation/