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...
Main Authors: | Daisuke Murakami, Mami Kajita, Seiji Kajita |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-09-01
|
Series: | ISPRS International Journal of Geo-Information |
Subjects: | |
Online Access: | https://www.mdpi.com/2220-9964/9/10/577 |
Similar Items
-
Extraction of Continuous and Discrete Spatial Heterogeneities: Fusion Model of Spatially Varying Coefficient Model and Sparse Modelling
by: Ryo Inoue, et al.
Published: (2022-06-01) -
Discovering Spatial-Temporal Indication of Crime Association (STICA)
by: Chao Jiang, et al.
Published: (2021-02-01) -
Robust Variable Selection with Exponential Squared Loss for the Spatial Single-Index Varying-Coefficient Model
by: Yezi Wang, et al.
Published: (2023-01-01) -
Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
by: Joana Paulo Tavares, et al.
Published: (2021-10-01) -
Adaptive estimation for spatially varying coefficient models
by: Heng Liu, et al.
Published: (2023-04-01)