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Article

Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis

1
Singular Perturbations Co. Ltd., 1-5-6 Risona Kudan Building, Kudanshita, Chiyoda, Tokyo 102–0074, Japan
2
Department of Statistical Data Science, Institute of Statistical Mathematics, 10–3 Midori-cho, Tachikawa, Tokyo 190–8562, Japan
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(10), 577; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100577
Received: 7 August 2020 / Revised: 18 September 2020 / Accepted: 28 September 2020 / Published: 30 September 2020
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. View Full-Text
Keywords: model selection; spatial regression; crime; fast computation; spatially varying coefficient modeling model selection; spatial regression; crime; fast computation; spatially varying coefficient modeling
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MDPI and ACS Style

Murakami, D.; Kajita, M.; Kajita, S. Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis. ISPRS Int. J. Geo-Inf. 2020, 9, 577. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100577

AMA Style

Murakami D, Kajita M, Kajita S. Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis. ISPRS International Journal of Geo-Information. 2020; 9(10):577. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100577

Chicago/Turabian Style

Murakami, Daisuke, Mami Kajita, and Seiji Kajita. 2020. "Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis" ISPRS International Journal of Geo-Information 9, no. 10: 577. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100577

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