truestar inc. population statistics irregular
Summary
This dataset is for analysis processed and integrated by truestar based on the 1km mesh future estimated population data by the Ministry of Land, Infrastructure, Transport and Tourism’s National Spatial Data Information.
Sample Data
Please select the table you wish to view from the data dictionary in the snowflake marketplace.
Click here for sample data from Japanese Mesh Data
Features/Usages
This is past population data by 5-year age group for 2015 and 2020, and estimated population data for every 5-year interval until 2050, categorized by a 1km mesh. The names of prefectures and municipalities are assigned from the administrative area codes owned by the original data. By filtering by age group, prefecture name, and city/town name, you can easily extract only the necessary information.
By overlaying with POI of the trade area of the company’s stores and planned expansion sites, you can analyze the business impact based on future estimated population in the area, using demand forecasts and other methods.
Please note that this data is very heavy, so we strongly recommend that you first narrow down to one prefecture using the prefecture name (PREF_NAME) when mapping.
Data Fields
| RESEARCH_YEAR |
| PREF_CODE |
| PREF_NAME |
| PREF_NAME_EN |
| PREF_CODE_NAME |
| MESH_CODE |
| CITY_CODE |
| CITY_NAME |
| CITY_CODE_NAME |
| YEAR |
| INCLUDED_IN_AREA_CODE |
| TOTAL_POPULATION |
| AGE_00_04 |
| AGE_05_09 |
| AGE_10_14 |
| AGE_15_19 |
| AGE_20_24 |
| AGE_25_29 |
| AGE_30_34 |
| AGE_35_39 |
| AGE_40_44 |
| AGE_45_49 |
| AGE_50_54 |
| AGE_55_59 |
| AGE_60_64 |
| AGE_65_69 |
| AGE_70_74 |
| AGE_75_79 |
| AGE_80_84 |
| AGE_85_89 |
| AGE_90_AND_OVER |
| MESH_1KM |
References
The data is prepared by Truestar, based on the following data.
Future Estimated Population Data (H30 National Government Office Estimate).
https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-mesh1000h30.html
Special Notes
Note 1: Duplicate MESH_IDs
In the 2018 edition (Heisei 30) of the mesh-based future population projection data, each MESH_ID was unique.
However, in the 2024 edition (Reiwa 6), a MESH_ID is no longer unique on its own.
Uniqueness is ensured only when the following three fields are combined: MESH_ID / CITY_CODE / YEAR (projection year)
Note 2: Cases Where Multiple SHICODE Values Exist for a Single MESH_ID
In the 2024 edition, there are cases where a single MESH_ID is associated with multiple SHICODE values, separated by underscores (“_”).
As a result, it is not possible to clearly identify which SHICODE corresponds to which value.
To address this, we assign the same value to all SHICODEs linked to the MESH_ID, allowing users to select the appropriate SHICODE based on their specific use case.
Note 3: SHICODE Values Not Matching 2024 Administrative Area Codes
In the 2024 edition, some SHICODE values do not correspond to the latest administrative area codes (2024 version).
Therefore, CITY_CODE cannot be assigned directly for affected mesh cells.
To handle this issue, we calculate the municipality polygon (based on the 2024 municipal boundaries) in which the centroid of each mesh cell falls, and assign the corresponding CITY_CODE based on that result.
Tableau Usages
