Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model
Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced to addr...
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doaj-e2ea8d3d546e4daea997f08f26d6e60a2020-11-25T01:12:58ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-01-01916010.3390/ijgi9010060ijgi9010060Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial ModelJianguo Chen0Lin Liu1Luzi Xiao2Chong Xu3Dongping Long4Center of GeoInformatics for Public Security, School of Geographic Sciences, Guangzhou University, Guangzhou 510006, ChinaCenter of GeoInformatics for Public Security, School of Geographic Sciences, Guangzhou University, Guangzhou 510006, ChinaCenter of GeoInformatics for Public Security, School of Geographic Sciences, Guangzhou University, Guangzhou 510006, ChinaCenter of GeoInformatics for Public Security, School of Geographic Sciences, Guangzhou University, Guangzhou 510006, ChinaCenter of GeoInformatics for Public Security, School of Geographic Sciences, Guangzhou University, Guangzhou 510006, ChinaNegative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced to address spatial heterogeneity in crime analysis. However, GWPR could not account for overdispersion, which is commonly observed in crime data. The geographically weighted negative binomial model (GWNBR) was adopted to address spatial heterogeneity and overdispersion simultaneously in crime analysis, based on a 3-year data set collected from ZG city, China, in this study. The count of residential burglaries was used as the dependent variable to calibrate the above models, and the results revealed that the GWPR and GWNBR models performed better than NB for reducing spatial dependency in the model residuals. GWNBR outperformed GWPR for incorporating overdispersion. Therefore, GWNBR was proven to be a promising tool for crime modeling.https://www.mdpi.com/2220-9964/9/1/60residential burglaryspatial heterogeneityoverdispersiongeographically weighted poisson regressiongeographically weighted negative binomial regression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jianguo Chen Lin Liu Luzi Xiao Chong Xu Dongping Long |
spellingShingle |
Jianguo Chen Lin Liu Luzi Xiao Chong Xu Dongping Long Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model ISPRS International Journal of Geo-Information residential burglary spatial heterogeneity overdispersion geographically weighted poisson regression geographically weighted negative binomial regression |
author_facet |
Jianguo Chen Lin Liu Luzi Xiao Chong Xu Dongping Long |
author_sort |
Jianguo Chen |
title |
Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model |
title_short |
Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model |
title_full |
Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model |
title_fullStr |
Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model |
title_full_unstemmed |
Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model |
title_sort |
integrative analysis of spatial heterogeneity and overdispersion of crime with a geographically weighted negative binomial model |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2020-01-01 |
description |
Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced to address spatial heterogeneity in crime analysis. However, GWPR could not account for overdispersion, which is commonly observed in crime data. The geographically weighted negative binomial model (GWNBR) was adopted to address spatial heterogeneity and overdispersion simultaneously in crime analysis, based on a 3-year data set collected from ZG city, China, in this study. The count of residential burglaries was used as the dependent variable to calibrate the above models, and the results revealed that the GWPR and GWNBR models performed better than NB for reducing spatial dependency in the model residuals. GWNBR outperformed GWPR for incorporating overdispersion. Therefore, GWNBR was proven to be a promising tool for crime modeling. |
topic |
residential burglary spatial heterogeneity overdispersion geographically weighted poisson regression geographically weighted negative binomial regression |
url |
https://www.mdpi.com/2220-9964/9/1/60 |
work_keys_str_mv |
AT jianguochen integrativeanalysisofspatialheterogeneityandoverdispersionofcrimewithageographicallyweightednegativebinomialmodel AT linliu integrativeanalysisofspatialheterogeneityandoverdispersionofcrimewithageographicallyweightednegativebinomialmodel AT luzixiao integrativeanalysisofspatialheterogeneityandoverdispersionofcrimewithageographicallyweightednegativebinomialmodel AT chongxu integrativeanalysisofspatialheterogeneityandoverdispersionofcrimewithageographicallyweightednegativebinomialmodel AT dongpinglong integrativeanalysisofspatialheterogeneityandoverdispersionofcrimewithageographicallyweightednegativebinomialmodel |
_version_ |
1725163967430524928 |