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Inferring hidden potentials in analytical regions: uncovering crime suspect communities in Medellín

Alejandro Puerta, Andrés Ramírez-Hassan

arXiv 11 Sep 2020 · Econometrics

arXiv:2009.05360 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes a Bayesian approach to perform inference regarding the size of hidden populations at analytical region using reported statistics. To do so, we propose a specification taking into account one-sided error components and spatial effects within a panel data structure. Our simulation exercises suggest good finite sample performance. We analyze rates of crime suspects living per neighborhood in Medell\'in (Colombia) associated with four crime activities. Our proposal seems to identify hot spots or "crime communities", potential neighborhoods where under-reporting is more severe, and also drivers of crime schools. Statistical evidence suggests a high level of interaction between homicides and drug dealing in one hand, and motorcycle and car thefts on the other hand.

Citation extraction

39
references
53
in-text mentions
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distinct cited
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7,550
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Tsionas, E. G. and Kumbhakar, S. C (2014) Firm heterogeneity, persistent and transient technical inefficiency: A generalized true random-effects model0.87452100%
2Li, H., Antonio, P., Desheng, L., and Shiguo, J (2015) Temporal stability of model parameters in crime rate analysis: An empirical examination0.64422100%
3Andresen, M. A (2006) A spatial analysis of crime in vancouver, british columbia: A synthesis of social disorganization and routine activity theory0.64422100%
4Arnio, A. N. and Baumer, E. P (2012) Demography, foreclosure, and crime: Assessing spatial heterogeneity in contemporary models of neighborhood crime rates0.64422100%
5Kakamu, K., Polasek, W., and Wago, H (2008) Spatial interaction of crime incidents in japan0.64422100%
6Kikuchi, G (2010) Neighborhood structures and crime: a spatial analysis0.64422100%
7Olson, J. A., Schmidt, P., and Waldman, D. M (1980) A monte carlo study of estimators of stochastic frontier production functions0.64422100%
8Ramŕez Hassan, A. and Montoya Blandón, S (2017) Welfare gains of the poor: An endogenous bayesian approach with spatial random effects0.64422100%
9Simar, L. and Wilson, P. W (2009) Inferences from cross-sectional, stochastic frontier models0.64422100%
10Duque, J. C., Anselin, L., and Rey, S. J (2012) The max-p-regions problem0.51121100%

Showing the top 10 of 39 scored citations.