Estimating ambient population density using physical features from GIS and machine learning: a study based on Japanese neighborhood
This research aimed to develop an alternative method for estimating ambient population density at the neighborhood scale by utilizing a simple and universally available dataset. Physical environment data from GIS & OSM databases, basic statistics, and population density data from Mobile Spatial...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-11-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2024.2428262 |
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| _version_ | 1846124647494451200 |
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| author | Pasit Rojradtanasiri Junko Tamura Masami Kobayashi |
| author_facet | Pasit Rojradtanasiri Junko Tamura Masami Kobayashi |
| author_sort | Pasit Rojradtanasiri |
| collection | DOAJ |
| description | This research aimed to develop an alternative method for estimating ambient population density at the neighborhood scale by utilizing a simple and universally available dataset. Physical environment data from GIS & OSM databases, basic statistics, and population density data from Mobile Spatial Statistics were combined to train tree-based Machine Learning models. The experiment resulted in an XGBoost model using 16 features capable of estimating ambient population density across three classes of outcome with 75.9% accuracy. The trained models were analyzed and visualized using SHAP and Partial Dependence Plot techniques to reveal feature importance and their threshold values. The study concludes that physical features can be effectively used as predictors of ambient population density and highlights areas for further investigation. |
| format | Article |
| id | doaj-art-51ebaeba32744b8eb5d13d1e50b9c8f2 |
| institution | Kabale University |
| issn | 1347-2852 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-51ebaeba32744b8eb5d13d1e50b9c8f22024-12-13T15:19:01ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522024-11-010011610.1080/13467581.2024.24282622428262Estimating ambient population density using physical features from GIS and machine learning: a study based on Japanese neighborhoodPasit Rojradtanasiri0Junko Tamura1Masami Kobayashi2Meiji UniversityMeiji UniversityMeiji UniversityThis research aimed to develop an alternative method for estimating ambient population density at the neighborhood scale by utilizing a simple and universally available dataset. Physical environment data from GIS & OSM databases, basic statistics, and population density data from Mobile Spatial Statistics were combined to train tree-based Machine Learning models. The experiment resulted in an XGBoost model using 16 features capable of estimating ambient population density across three classes of outcome with 75.9% accuracy. The trained models were analyzed and visualized using SHAP and Partial Dependence Plot techniques to reveal feature importance and their threshold values. The study concludes that physical features can be effectively used as predictors of ambient population density and highlights areas for further investigation.http://dx.doi.org/10.1080/13467581.2024.2428262neighborhood planningmachine learningcorrelation analysisambient populationgis |
| spellingShingle | Pasit Rojradtanasiri Junko Tamura Masami Kobayashi Estimating ambient population density using physical features from GIS and machine learning: a study based on Japanese neighborhood Journal of Asian Architecture and Building Engineering neighborhood planning machine learning correlation analysis ambient population gis |
| title | Estimating ambient population density using physical features from GIS and machine learning: a study based on Japanese neighborhood |
| title_full | Estimating ambient population density using physical features from GIS and machine learning: a study based on Japanese neighborhood |
| title_fullStr | Estimating ambient population density using physical features from GIS and machine learning: a study based on Japanese neighborhood |
| title_full_unstemmed | Estimating ambient population density using physical features from GIS and machine learning: a study based on Japanese neighborhood |
| title_short | Estimating ambient population density using physical features from GIS and machine learning: a study based on Japanese neighborhood |
| title_sort | estimating ambient population density using physical features from gis and machine learning a study based on japanese neighborhood |
| topic | neighborhood planning machine learning correlation analysis ambient population gis |
| url | http://dx.doi.org/10.1080/13467581.2024.2428262 |
| work_keys_str_mv | AT pasitrojradtanasiri estimatingambientpopulationdensityusingphysicalfeaturesfromgisandmachinelearningastudybasedonjapaneseneighborhood AT junkotamura estimatingambientpopulationdensityusingphysicalfeaturesfromgisandmachinelearningastudybasedonjapaneseneighborhood AT masamikobayashi estimatingambientpopulationdensityusingphysicalfeaturesfromgisandmachinelearningastudybasedonjapaneseneighborhood |