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 ontologies for handling absorption and abolishment of municipalities and also confidentiality concerning small area as well as grid square statistics.
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,
economic censuses, and labor force surveys, have been provided as Linked Open Data
(LOD) since 2016 [
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 LODenabled 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, [
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 LODenabled concealed statistical data.
Several statistical LODs have also been published by the national statistics institutes
of Ireland [
Geographic Areas Handled in Statistics This section describes a brief summary of the types of geographic areas used in Japanese 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 prefectural division and the remaining three digits represent a specific municipal division located 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 statistical data such as the census are produced for small areas as well as administrative divisions. Several codelists, which are defined in each statistics and not standardized, exist to uniquely identify each small area. (3) Grid squares:
Grid squares are portions of the country divided into a grid by longitudinal and
latitudinal 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 [
Divide Japan into equal parts measuring 2/3 degree of latitude by 1 degree of longitude. Divide one of the parts into 8 equal parts in latitude 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.
Divide third level grid square in half in latitude and longitude directions.
Divide fourth level grid square in half in latitude and longitude directions.
Other than the world grid square system we adopted, there are several worldwide
grid-square systems such as Open Location Code [
In our statistical LOD, these geographic areas can be referred to as objects of
dimensions in terms of RDF Data Cube Vocabulary [
Fig. 1. RDF expression of the number of 0-year-old girls in a municipality
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 classification and borders, changes with time by passing through events such as: absorption, abolishment, separation, establishment of new municipalities, division into several municipalities, 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.
(11203）
(11203）
（11226） 2001/04/01 shift to
(11203）
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. Standard Area Codes for Statistical Use has included integration and abolition history (absorption, abolishment, new establishment, and so forth) since 1970 for the convenience 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 concept 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 influence 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.
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 temporary 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 temporary standard area code is valid until the next event. In the example above, “Kawaguchi-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 October 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:C1122620111001 sac:C11226 19710401 sacs:previousCode rdfs:label “Hatogaya city” “Hatogaya city” Plain Standard Area Codes
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 LODenabled 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.
sacs:latestCode sacs:previousCode
sacs:previousMunicipality • 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 Area Codes sac:C11203
Temporary Standard Area Codes “2011-10-11” sacs:latest dcterms:issued
Code sac:C1120320111011 rdfs:label
sacs: administrative
Class sacs:past
Code sacs:past Code sacs: previous Code sacs:previous Municipality sac:C1122620111011
rdfs:label “Hatogaya city” “Kawaguchi city” rdfs:label sac:C1120320010401 prseavcios:us admisnaisctsr:ative dcterms“:2is0s0u1e-d04-01” Code Class sacsC:Sitpyecial 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 “Kawaguchi-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 standard 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 area code at the point of survey based on issued and expired date described in each temporary standard area codes. 3.3
We compare our ontology with an existing one, ONS Boundary Change Ontology
[
The two date-related properties, i.e., Operative Date and Terminated Date, are corresponding to dcterms:issued and dcterms:valid, respectively. The main difference between 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 advantage of object property to link their operative date and terminated date to dereferencable URI, e.g., <http://reference.data.gov.uk/id/day/2006-07-21>, 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 information about events causing boundary changes. 4 4.1
Expressing LOD-Enabled Small Areas and Grid Squares
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 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
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 .
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 statistical dataset via dimension properties such as sdmx-dimension:refArea.
Expressed as RDF, both small areas and grid squares are now linkable to other
existing geographic data such as Open Location Code [
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 belonging to specific geographic areas without regard to administrative divisions, which enables us to visualize statistical data as heatmaps. 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 population 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 LODenabled as is. In other words, this method follows the expression method of the statistical table of Table 2 and defines the adding-up destination as a specific area. An example of this method is shown in Fig. 6. X Conceals
prop-obs:concealedBy observation1
sdmx-dimension:refArea observation3 observation5 estat-attribute:obsType sdmx-dimension:refArea estat-attribute:obsType sdmx-dimension:refArea estat-attribute:obsType estat-measure:population 【area: First Block】 sac:S103201189001 【type: Figures kept undisclosed】
estat-attribute-code: obsType-figuresKeptUndisclosed 【area: Third Block】 sac:S103201189003 【type: Figures kept undisclosed】
estat-attribute-code: obsType-figuresKeptUndisclosed 【area: Fifth Block】 sac:S103201189005 【type: Concealing】 estat-attribute-code: obsType-concealing
35 IF ?obstype equals to “concealing” THEN
IF ?obstype does not equal to “concealing” THEN use ?population as the expected result
IF no ?population found THEN
Fig. 7. Data acquisition from LOD-enabled small area codes with concealment
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
performance will deteriorate along with it. Further speedup solutions will be necessary in
the future. We also plan to introduce geometric information based on GeoSPARQL
[
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 statistical LOD, but also provide usage samples and use cases.
When convenience improves with the improvement of speedup solutions and sufficient 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 dataset. 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. Appendix A. List of prefixes and namespaces prefix