Using Geographically Weighted Regression to Explore Local Crime Patterns

Cahill, Meagan; Mulligan, Gordon F.

doi:10.1177/0894439307298925

Cited by 170 publications

(116 citation statements)

References 15 publications

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“…In addition, GWR considers spatial autocorrelation, which is difficult to deal with in traditional statistical models (Brown et al, 2012). As an emerging technique, GWR has recently been applied in several disciplines, such as identification of high crime areas (Cahill and Mulligan, 2007), forest damage evaluations (Pineda et al, 2010), human health and disease analysis (Carrel et al, 2011), and atmospheric pollutant assessment (Song et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Chen

Mei

Dahlgren

et al. 2016

Science of The Total Environment

162

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• The modified GWR models had higher R 2 and reflected the actual spatial features.• A manual variable excluding-selecting method is explored to avoid multicollinearity.• Influences of the dominant indicator on water quality varied with space and seasons.• Protection policies need consider sitespecific conditions and seasonal variations. G R A P H I C A L A B S T R A C Ta b s t r a c t a r t i c l e i n f o As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2~5 9.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use Contents lists available at ScienceDirectScience of the Total Environment j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s c i t o t e n v conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations.

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Section: Introductionmentioning

confidence: 99%

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Chen

Mei

Dahlgren

et al. 2016

Science of The Total Environment

162

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“…Other methods that use population information in crime analysis are the thematic mapping of geographic areas as a hot spot method that can be linked with population to calculate crime rates (Chainey, Tompson, & Uhlig, 2008), and prospective predictive techniques such as the Geographically Weighted Regression (GWR) that use population as a parameter estimate of the prediction model (Cahill & Mulligan, 2007). …”

Section: Background: Population Crime Time and Placementioning

confidence: 99%

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Kounadi

Ristea

Leitner

et al. 2017

Cartography and Geographic Information Science

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Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public.

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“…Research examining the role of population density in predicting crime rates has produced mixed results with some researchers finding a significantly negative relationship between population density and crime patterns (Cahill & Mulligan, 2003;Cahill & Mulligan, 2007) while others have found a significantly positive relationship (Byrne & Sampson, 1986;Dahlbäck, 1998). These mixed results reflect the fact that while population density can increase the number of potential offenders (and targets) in an area; it can also increase the amount of capable guardians against crime.…”

Section: Inferential Resultsmentioning

confidence: 99%

“…Unlike OLS and spatial regression, in GWR the relationships between the independent and dependent variables are not assumed to be the same at all locations (Gao & Li, 2011). One of the common problems with estimating global regression models, like OLS and spatial regression, for spatial data are that 14 variations over space that might exist in the data are suppressed (Cahill & Mulligan, 2007). In contrast, GWR searches the data for systematic spatial regularities while attempting to preserve and model the complexity that nonrandomly diverges from average global patterns (Graif & Sampson, 2009).…”

Section: Modelling Frameworkmentioning

confidence: 99%

The effect of altitude and slope on the spatial patterning of burglary

Breetzke

2012

Applied Geography

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Physical geography is significant for crime, and its presence or absence, yet no studies have investigated the relationship between crime and certain broader features of physical geography such as altitude, and slope. In this study I attempt to fill this gap by using OLS and geographically weighted regression to gauge the effect of altitude and slope on burglary patterns in Tshwane, South Africa. In the analysis I found considerable evidence that residing at a greater altitude reduces your risk of burglary victimization, although residing on steeper slopes had no effect. In the discussion I argue that the underlying relief and terrain on which neighborhoods are built should form an essential "physical" component of the environmental backcloth that surrounds offenders and influences their spatial decision making processes.

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Using Geographically Weighted Regression to Explore Local Crime Patterns

Cited by 170 publications

References 15 publications

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

The effect of altitude and slope on the spatial patterning of burglary

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