Demography, foreclosure, and crime:

Arnio, Ashley N.; Baumer, Eric P.

doi:10.4054/demres.2012.26.18

Cited by 46 publications

(8 citation statements)

References 65 publications

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“…Geographically weighted regression (GWR) is a modeling technique proposed by Fotheringham et al [49] which allows some or all regression parameters to vary spatially, and can be used to address this issue. Recent studies have demonstrated the applicability of the GWR model to the varying relationship between crime and related factors [50][51][52][53]. Geographically weighted Poisson regression (GWPR) is an extension of GWR for count data which takes the following form:…”

Section: Statistical Model Designmentioning

confidence: 99%

Spatial Variation Relationship between Floating Population and Residential Burglary: A Case Study from ZG, China

Chen

Liu

Zhou

et al. 2017

IJGI

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With the rapid development of China's economy, the demand for labor in the coastal cities continues to grow. Due to restrictions imposed by China's household registration system, a large number of floating populations have subsequently appeared. The relationship between floating populations and crime, however, is not well understood. This paper investigates the impact of a floating population on residential burglary on a fine spatial scale. The floating population was divided into the floating population from other provinces (FPFOP) and the floating population from the same province as ZG city (FPFSP), because of the high heterogeneity. Univariate spatial patterns in residential burglary and the floating population in ZG were explored using Moran's I and LISA (local indicators of spatial association) models. Furthermore, a geographically weighted Poisson regression model, which addressed the spatial effects in the data, was employed to explore the relationship between the floating population and residential burglary. The results revealed that the impact of the floating population on residential burglary is complex. The floating population from the same province did not have a significant impact on residential burglary in most parts of the city, while the floating population from other provinces had a significantly positive impact on residential burglary in most of the study areas and the magnitude of this impact varied across the study area.

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Section: Statistical Model Designmentioning

confidence: 99%

Spatial Variation Relationship between Floating Population and Residential Burglary: A Case Study from ZG, China

Chen

Liu

Zhou

et al. 2017

IJGI

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“…Geographically weighted regression (GWR) [7] is used to estimate spatially varying coefficients (SVCs) on covariates [8]. Among spatial effects, we especially focus on SVC modeling that allows for estimating the local determinants of crimes [2,9,10]. For instance, [2] found that affluence reduces crime in a suburban area of Portland, Oregon, whereas it increases crime in the city center.…”

Section: Introductionmentioning

confidence: 99%

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

Murakami

Kajita²,

Kajita³

2020

IJGI

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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.

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“…These and other findings have implications for researchers and policymakers seeking to understand non-metropolitan teenage birth rates. Arnio and Baumer (2012) utilized GWR, among other methods, to look at the interplay of demography and crime (specifically homicide, robbery, and burglary crimes), in the context of housing foreclosure in Chicago, Illinois, U.S.A., during the late-2000s. The authors provided a theoretical justification for expecting spatial heterogeneity in neighborhood crime.…”

Section: Spatial Econometric Papersmentioning

confidence: 99%

Progress in Spatial Demography

Matthews¹,

Parker²

2013

DemRes

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BACKGROUNDDemography is an inherently spatial science, yet the application of spatial data and methods to demographic research has tended to lag that of other disciplines. In recent years, there has been a surge in interest in adding a spatial perspective to demography. This sharp rise in interest has been driven in part by rapid advances in geospatial data, new technologies, and methods of analysis. OBJECTIVESWe offer a brief introduction to four of the advanced spatial analytic methods: spatial econometrics, geographically weighted regression, multilevel modeling, and spatial pattern analysis. We look at both the methods used and the insights that can be gained by applying a spatial perspective to demographic processes and outcomes. To help illustrate these substantive insights, we introduce six papers that are included in a Special Collection on Spatial Demography. We close with some predictions for the future, as we anticipate that spatial thinking and the use of geospatial data, technology, and analytical methods will change how many demographers address important demographic research questions.

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Demography, foreclosure, and crime:

Cited by 46 publications

References 65 publications

Spatial Variation Relationship between Floating Population and Residential Burglary: A Case Study from ZG, China

Spatial Variation Relationship between Floating Population and Residential Burglary: A Case Study from ZG, China

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

Progress in Spatial Demography

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