Linking Neighborhood Characteristics and Drug-Related Police Interventions: A Bayesian Spatial Analysis

Marco, Miriam; Gracia, Enrique; López‐Quílez, Antonio

doi:10.3390/ijgi6030065

Cited by 21 publications

(12 citation statements)

References 59 publications

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“…Police officers completed an index based on their perceptions and experience (objective records were not available), which comprised five items on a 5‐interval scale (where 0 = very low, and 4 = very high) including interventions such as drug‐related crimes, public drunkenness and fights, vandalism, homeless people, and truancy. Senior police officers with a thorough knowledge of the area were selected to score each item in each census block group (Marco, Gracia, & López‐Quílez, ). Cronbach's alpha was .74.…”

Section: Methodsmentioning

confidence: 99%

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The university campus environment as a protective factor for intimate partner violence against women: An exploratory study

Marco

Gracia

López‐Quílez

2018

Journal Community Psychology

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Some neighborhood characteristics linked to social disorganization theory have been related to intimate partner violence against women (IPVAW). The study of other neighborhood‐level factors that may influence IPVAW risk, however, has received less attention. The aim of this study is to analyze the influence of university campuses on IPVAW risk. To conduct the study, IPVAW cases from 2011 to 2013 in the city of Valencia, Spain, were geocoded (n = 1,623). Census block groups were used as the neighborhood analysis unit. Distance between each census block group and the nearest university campus was measured. A Bayesian spatial model adjusted for census block group‐level characteristics was performed. Results showed that the distance from a university campus was associated with an approximate 7% increase in IPVAW risk per kilometer. These results suggest that university campuses integrated in the city are related to IPVAW risk. Further research is needed to explain the mechanisms involved.

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Section: Methodsmentioning

confidence: 99%

“…Cronbach's alpha was .74. In addition, this measure showed adequate validity properties because it was associated with neighborhood‐level variables related to concentrated disadvantage and disorder (Marco, Gracia, et al., ; Marco, Martín‐Fernández, Gracia, & López‐Quílez, ).…”

Section: Methodsmentioning

confidence: 99%

The university campus environment as a protective factor for intimate partner violence against women: An exploratory study

Marco

Gracia

López‐Quílez

2018

Journal Community Psychology

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“…The posterior probability is considered to be a Bayesian counterpart of the p-value which is commonly used for evaluating the statistical significance of parameters in frequentist models [32,53]. To indicate parameter significance in this study, we followed previous research which have utilized the posterior probability of the parameter being different from zero (i.e., the posterior probability of the parameter being either positive or negative is greater than 90%) [54,55]. We found that the probability of being positive for EBF and THI was both higher than 90% and thus relevant to the response variable, specifically, p(β EBF > 0 O it ) = 91.6% and p(β THI > 0 O it ) = 99.7%.…”

Section: Spatio-temporal Analysismentioning

confidence: 99%

Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013

Persad¹

2019

Medical Sciences

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Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space–time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010–2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010–2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74–0.91) to 1.26 (95% CI 1.13–1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region’s average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.

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“…There are also two kinds of spatial factors, both designed based on a perpetrator's tendency to commit crimes in a familiar environment: (1) We account for the time and space factors of neighboring grids and make use of the neighborhood characteristics to make predictions [38]. In other words, given the diffuse nature of crime, there is a high likelihood that police activity in one crime-ridden area will push criminals into neighboring areas.…”

Section: Features Constructionmentioning

confidence: 99%

Grid-Based Crime Prediction Using Geographical Features

Lin

Yen

2018

IJGI

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Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design's baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

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Linking Neighborhood Characteristics and Drug-Related Police Interventions: A Bayesian Spatial Analysis

Cited by 21 publications

References 59 publications

The university campus environment as a protective factor for intimate partner violence against women: An exploratory study

The university campus environment as a protective factor for intimate partner violence against women: An exploratory study

Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013

Grid-Based Crime Prediction Using Geographical Features

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