Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul
Accurate demographic data are essential for evaluating flood exposure in urban areas, where heterogeneous environment and localized risks complicate modeling efforts. Gridded population datasets serve as valuable resources for such assessments; however, differences in spatial resolution and methodol...
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MDPI AG
2025-07-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/7/262 |
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| author | Julieber T. Bersabe Byong-Woon Jun |
| author_facet | Julieber T. Bersabe Byong-Woon Jun |
| author_sort | Julieber T. Bersabe |
| collection | DOAJ |
| description | Accurate demographic data are essential for evaluating flood exposure in urban areas, where heterogeneous environment and localized risks complicate modeling efforts. Gridded population datasets serve as valuable resources for such assessments; however, differences in spatial resolution and methodology can significantly affect flood-exposed population estimates. This study evaluates how various gridded population datasets influence the sensitivity and accuracy of flood exposure estimates in Gangnam District, Seoul. Seven datasets from Statistical Geographic Information Service (SGIS), National Geographic Information Institute (NGII), and Intelligent Dasymetric Mapping (IDM), ranging from 30 m to 1 km in resolution, were evaluated against census data to assess their accuracy and variability in flood exposure estimates. The results indicate that multi-source gridded population datasets with different spatial resolutions and modeling approaches strongly affect both the accuracy and variability of flood-exposed population estimates. IDM 30 m outperformed other datasets, showing the lowest variability (CV = 0.310) and the highest agreement with census data (RMSE = 193.51; <i>R</i><sup>2</sup> = 0.9998). Coarser datasets showed greater estimation errors and variability. These findings demonstrate that fine-resolution IDM population dataset yields reliable results for flood exposure estimation in Gangnam, Seoul. They also highlight the need for further comparative evaluations across different hazard and spatial contexts. |
| format | Article |
| id | doaj-art-c2a89b2ce9144e918a9675cf8bafdf47 |
| institution | Matheson Library |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-c2a89b2ce9144e918a9675cf8bafdf472025-07-25T13:24:55ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-07-0114726210.3390/ijgi14070262Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, SeoulJulieber T. Bersabe0Byong-Woon Jun1Department of Geography, Kyungpook National University, Daegu 41566, Republic of KoreaDepartment of Geography, Kyungpook National University, Daegu 41566, Republic of KoreaAccurate demographic data are essential for evaluating flood exposure in urban areas, where heterogeneous environment and localized risks complicate modeling efforts. Gridded population datasets serve as valuable resources for such assessments; however, differences in spatial resolution and methodology can significantly affect flood-exposed population estimates. This study evaluates how various gridded population datasets influence the sensitivity and accuracy of flood exposure estimates in Gangnam District, Seoul. Seven datasets from Statistical Geographic Information Service (SGIS), National Geographic Information Institute (NGII), and Intelligent Dasymetric Mapping (IDM), ranging from 30 m to 1 km in resolution, were evaluated against census data to assess their accuracy and variability in flood exposure estimates. The results indicate that multi-source gridded population datasets with different spatial resolutions and modeling approaches strongly affect both the accuracy and variability of flood-exposed population estimates. IDM 30 m outperformed other datasets, showing the lowest variability (CV = 0.310) and the highest agreement with census data (RMSE = 193.51; <i>R</i><sup>2</sup> = 0.9998). Coarser datasets showed greater estimation errors and variability. These findings demonstrate that fine-resolution IDM population dataset yields reliable results for flood exposure estimation in Gangnam, Seoul. They also highlight the need for further comparative evaluations across different hazard and spatial contexts.https://www.mdpi.com/2220-9964/14/7/262gridded population datasetsintelligent dasymetric mappingflood exposure assessmentpopulation estimation |
| spellingShingle | Julieber T. Bersabe Byong-Woon Jun Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul ISPRS International Journal of Geo-Information gridded population datasets intelligent dasymetric mapping flood exposure assessment population estimation |
| title | Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul |
| title_full | Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul |
| title_fullStr | Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul |
| title_full_unstemmed | Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul |
| title_short | Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul |
| title_sort | exploring the impact of multi source gridded population datasets on flood exposed population estimates in gangnam seoul |
| topic | gridded population datasets intelligent dasymetric mapping flood exposure assessment population estimation |
| url | https://www.mdpi.com/2220-9964/14/7/262 |
| work_keys_str_mv | AT juliebertbersabe exploringtheimpactofmultisourcegriddedpopulationdatasetsonfloodexposedpopulationestimatesingangnamseoul AT byongwoonjun exploringtheimpactofmultisourcegriddedpopulationdatasetsonfloodexposedpopulationestimatesingangnamseoul |