Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis
A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for larg...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/9/10/577 |
| _version_ | 1797552182373908480 |
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| author | Daisuke Murakami Mami Kajita Seiji Kajita |
| author_facet | Daisuke Murakami Mami Kajita Seiji Kajita |
| author_sort | Daisuke Murakami |
| collection | DOAJ |
| description | A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction, dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects a model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran. |
| first_indexed | 2024-03-10T15:56:21Z |
| format | Article |
| id | doaj.art-e05b8be9087d46f3a7b471d2a5d27136 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T15:56:21Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-e05b8be9087d46f3a7b471d2a5d271362023-11-20T15:40:18ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-09-0191057710.3390/ijgi9100577Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime AnalysisDaisuke Murakami0Mami Kajita1Seiji Kajita2Singular Perturbations Co. Ltd., 1-5-6 Risona Kudan Building, Kudanshita, Chiyoda, Tokyo 102–0074, JapanSingular Perturbations Co. Ltd., 1-5-6 Risona Kudan Building, Kudanshita, Chiyoda, Tokyo 102–0074, JapanSingular Perturbations Co. Ltd., 1-5-6 Risona Kudan Building, Kudanshita, Chiyoda, Tokyo 102–0074, JapanA rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction, dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects a model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran.https://www.mdpi.com/2220-9964/9/10/577model selectionspatial regressioncrimefast computationspatially varying coefficient modeling |
| spellingShingle | Daisuke Murakami Mami Kajita Seiji Kajita Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis ISPRS International Journal of Geo-Information model selection spatial regression crime fast computation spatially varying coefficient modeling |
| title | Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis |
| title_full | Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis |
| title_fullStr | Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis |
| title_full_unstemmed | Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis |
| title_short | Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis |
| title_sort | scalable model selection for spatial additive mixed modeling application to crime analysis |
| topic | model selection spatial regression crime fast computation spatially varying coefficient modeling |
| url | https://www.mdpi.com/2220-9964/9/10/577 |
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