Do Fringe Banks Create Fringe Neighborhoods? Examining the Spatial Relationship between Fringe Banking and Neighborhood Crime Rates

Kubrin, Charis E.; Hipp, John R.

doi:10.1080/07418825.2014.959036

Cited by 77 publications

(54 citation statements)

References 36 publications

(33 reference statements)

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“…Future survey research should consider more refined wording than “Have you used a pawnshop?” Not only can one use a pawnshop to pawn items, but also to sell them outright, buy items, and, at some locations, cash checks. Future research also should investigate pawnshop use separately from use of other AFS lenders, as there is ample evidence that pawnshops are different in important ways (Hawkins ; Kubrin and Hipp ; Lee, Gainey, and Triplett 2014).…”

Section: Limitations Discussion and Conclusionmentioning

confidence: 99%

The Influence of Financial Well‐Being on Pawnshop Use

Nicolini¹,

Cude²

2019

Journal of Consumer Affairs

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This research examined the influence of financial well‐being on pawnshop use. Data from the 2015 National Financial Capability Study were used to construct a measure of financial well‐being, based on the Consumer Financial Protection Bureau's definition. A higher financial well‐being reduced the odds of pawnshop use by 17%, even after controlling for having a bank account, having a credit card, being financially overconfident, proximity to local pawnshops, and selected demographic characteristics. The authors make recommendations for future research based on changes in the pawnshop industry and its users.

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Section: Limitations Discussion and Conclusionmentioning

confidence: 99%

The Influence of Financial Well‐Being on Pawnshop Use

Nicolini¹,

Cude²

2019

Journal of Consumer Affairs

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“…Such a project would be hugely ambitious and cost/time prohibitive. We know that organizations have different objectives that promote prosocial or anti-social behavior (Kubrin and Hipp, 2014;Wo, 2014) and organizations like the police are likely to evoke different strategies at different times that significantly impact crime rates. As we mention earlier, crack downs on drug crimes or gang related activities may lead to increases in officially recorded crime data.…”

Section: Discussionmentioning

confidence: 99%

The Spatial and Temporal Dynamics of Neighborhood Informal Social Control and Crime

Wickes

Hipp

2018

Social Forces

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Social disorganization theory is one of the most widely tested theories in criminology, yet few studies consider the temporal and spatial dynamics of neighborhood composition, neighborhood informal social control and crime. To better understand these relationships, we use census data, police data and three survey waves of data from a unique longitudinal dataset with over 4,000 respondents living across 148 neighborhoods in an Australian city undergoing rapid population growth. We employ cross-lagged reciprocal feedback models to test the central tenets of social disorganization theory and its contemporary advances for three crime types: violent crime, property crime and drug crime. Further, we examine the reciprocal relationship between neighborhood composition, three components of informal social control (neighborhood social ties, expectations for informal social control and the exercise of informal social control) and crime and whether socio-demographic changes in nearby neighborhoods shape these relationships over time. We find that changes in the sociodemographic composition in both focal and nearby areas influence neighborhood informal social control, however, in contrast to cross-sectional studies of social disorganization theory, our results reveal little support that neighborhood informal social control significantly decreases crime over time.

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“…The nodes in crime pattern theory are often tied to specific commercial places or different land uses that are predictive of heavy pedestrian use. A (non-exhaustive) list of such factors are bars, liquor, and convenience stores (Wheeler 2018), banks, ATMs, and check cashing stores (Haberman and Ratcliffe 2015;Kubrin and Hipp 2016), apartments and public housing complexes (Haberman, Groff, and Taylor 2013), hotels and motels (Jones and Pridemore 2018), nearby public transit (Bernasco and Block 2011), etc.…”

Section: Explaining Places At High Risk Of Crimementioning

confidence: 99%

Mapping the Risk Terrain for Crime Using Machine Learning

Wheeler

Steenbeek

2020

J Quant Criminol

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Objectives: We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the 'black box' of Random Forests, considerably improving their interpretability. Methods: We generate long-term crime forecasts for robberies in Dallas at 200 by 200 feet grid cells that allow spatially varying associations of crime generators and demographic factors across the study area. We then show how using interpretable model summaries facilitate understanding the model's inner workings. Results: We find that Random Forests greatly outperform Risk Terrain Models and Kernel Density Estimation in terms of forecasting future crimes using different measures of predictive accuracy, but only slightly outperform using prior counts of crime. We find different factors that predict crime are highly non-linear and vary over space. Conclusions: We show how using black-box machine learning models can provide accurate micro placed based crime predictions, but still be interpreted in a manner that fosters understanding of why a place is predicted to be risky.

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Do Fringe Banks Create Fringe Neighborhoods? Examining the Spatial Relationship between Fringe Banking and Neighborhood Crime Rates

Cited by 77 publications

References 36 publications

The Influence of Financial Well‐Being on Pawnshop Use

The Influence of Financial Well‐Being on Pawnshop Use

The Spatial and Temporal Dynamics of Neighborhood Informal Social Control and Crime

Mapping the Risk Terrain for Crime Using Machine Learning

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