Analysis of thefts in Colombia during 2017 using multiple linear regression models and geographically weighted regression
DOI:
https://doi.org/10.47741/17943108.66Keywords:
factors of crime, theft, criminal statistics, crime measuring, statistics, GWR, OLSAbstract
According to information from the crime observatory of the National Police of Colombia, thefts from people and of cell phones have shown an upward trend since 2003 (Norza, Peñalosa and Rodríguez, 2017). This trend motivated the carrying out of the current study to analyze the relationship between socioeconomic factors and theft in the different municipalities of Colombia during 2017, through the use of multiple linear regression models and geographically weighted regression using secondary information sources segregated to municipal level. It was validated that variables enrolled in higher education institutions per thousand people, budget per capita allocated by the general system of participations and the category of the municipality account for 69,5% of the variability of the logarithm of theft from individuals and of cellphones in 532 municipalities using a globally estimated multiple linear regression model and 50,16% using the geographically weighted regression model omitting in the latter the category of the municipality. In this model there were slight variations in the coefficients at the municipal level, reflecting that the social and economic heterogeneity has effects on indicators of theft nationwide.
Downloads
References
Baron, S. W. (2003). Self-Control, Social Consequences, and Criminal Behavior: Street Youth and the General Theory of Crime. Journal of Research in Crime and Delinquency, 40(4): 403-425. https://doi.org/10.1177/0022427803256071.
Breusch, T. S. & Pagan, A. R. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 47(5): 1287. https://doi.org/10.2307/1911963.
Brunsdon, C., Fotheringham, A. S. & Charlton, M. (1996). Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis, 28(4), 281-298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x.
Brunsdon, C., Fotheringham, A. S. & Charlton, M. (1998). Geographically Weighted Regression. Journal of the Royal Statistical Society: Series D (The Statistician), 47(3): 431-443. https://doi.org/10.1111/1467-9884.00145.
Brunsdon, C., Fotheringham, A. S. & Charlton, M. (1999). Some Notes on Parametric Significance Tests for Geographically Weighted Regression. Journal of Regional Science, 39(3): 497-524. https://doi.org/10.1111/0022-4146.00146.
Castillo G, L. C. (2007). Etnicidad y nación: El desafío de la diversidad en Colombia (1. ed.). Colección Libros de investigación. Cali Colombia: Programa Editorial Universidad del Valle. Recuperado de https://books.google.es/books?id=HyYPkuRP89IC
Cahill, M. & Mulligan, G. (2007). Using Geographically Weighted Regression to Explore Local Crime Patterns. Social Science Computer Review, 25(2), 174-193. https://doi.org/10.1177/0894439307298925.
Charlton, M. & Fotheringham, A. S. (2009). Geographically weighted regression (White Paper). Recuperado de https://www.geos.ed.ac.uk/~gisteac/fspat/gwr/gwr_arcgis/GWR_WhitePaper.pdf.
Charlton, M., Fotheringham, A. S. & Brunsdon, C. (2006). Geographically weighted regression: NCRM Methods Review Papers/NCRM/006. Recuperado de http://eprints.ncrm.ac.uk/90/.
Chávez, S. M. (2018). Causas y factores que conllevan a cometer el delito de feminicidio en la provincia de Huánuco 2016-2017. Universidad de Huánuco, Huánuco - Perú.
Chen, X., Thrane, L., Whitbeck, L. B., Johnson, K. D. & Hoyt, D. R. (2007). Onset of conduct disorder, use of delinquent subsistence strategies, and street victimization among homeless and runaway adolescents in the Midwest. Journal of interpersonal violence, 22(9): 1156-1183. https://doi.org/10.1177/0886260507303731.
Dankhe, G. L. (1986). Investigación y comunicación. En C. Fernández-Collado y G. L. Dankhe (Eds.), La comunicación humana: ciencia social (pp. 385-454). México: McGraw-Hill.
Dong, G., Nakaya, T. & Brunsdon, C. (2018). Geographically weighted regression models for ordinal categorical response variables: An application to geo-referenced life satisfaction data. Computers, Environment and Urban Systems, 70, 35-42. https://doi.org/10.1016/j.compenvurbsys.2018.01.012.
Durbin, J. & Watson, G. S. (1950). Testing for Serial Correlation in Least Squares Regression: I . Biometrika, 37(3/4): 409. https://doi.org/10.2307/2332391.
Durbin, J. & Watson, G. S. (1951). Testing for Serial Correlation in Least Squares Regression. II. Biometrika, 38(1/2): 159. https://doi.org/10.2307/2332325.
Durbin, J. & Watson, G. S. (1971). Testing for Serial Correlation in Least Squares Regression. III. Biometrika, 58(1): 1. https://doi.org/10.2307/2334313.
Eisner, M. (2002). Crime, Problem Drinking, and Drug Use: Patterns of Problem Behavior in Cross-National Perspective. The ANNALS of the American Academy of Political and Social Science, 580(1): 201-225. https://doi.org/10.1177/000271620258000109.
Environmental Systems Research Institute (2016). Regresión ponderada geográficamente (GWR)-Ayuda. Recuperado de http://desktop.arcgis.com/es/arcmap/10.3/tools/spatial-statistics-toolbox/geographically-weighted-regression.htm.
Estrada, L. & Durán, C. (2015). Estudio sobre las relaciones espaciales locales entre la pobreza multidimensional, la ruralidad y la capacidad institucional. Recuperado de https://www.researchgate.net/publication/317721631_Estudio_sobre_las_relaciones_espaciales_locales_entre_la_pobreza_multidimensional_la_ruralidad_y_la_capacidad_institucional.
Fajnzylber, P., Lederman, D. & Loayza, N. (2002). Inequality and violent crime. The journal of Law and Economics, 45(1): 1-39.
https://doi.org/10.1086/338347
Fotheringham, A. S., Charlton, M. & Brunsdon, C. (1998). Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis. Environment and Planning A, 30(11), 1905-1927. https://doi.org/10.1068/a301905.
Fotheringham, A. S., Charlton, M. & Brunsdon, C. (2001). Spatial Variations in School Performance: A Local Analysis Using Geographically Weighted Regression. Geographical and Environmental Modelling, 5(1): 43-66. https://doi.org/10.1080/13615930120032617.
Fowler, B. (2013). Understanding Colombian Violence Through Geographic Information Systems and Statistical Approaches (Master Thesis). Western Kentucky University, Bowling Green, Kentucky.
Gottfredson, M. R. & Hirschi, T. (1990). A general theory of crime. Stanford Calif.: Stanford University Press.
Gutiérrez-Puebla, J., García-Palomares, J. C. & Daniel-Cardozo, O. (2012). Regresión Geográficamente Ponderada (GWR) y estimación de la demanda de las estaciones del Metro de Madrid. En XV Congreso Nacional de Tecnologías de la Información Geográfica.Madrid.
Harrison, L. D. (1992). The Drug-Crime Nexus in the USA. Contemporary Drug Problems, 19, 203.
Mancero, X. & Feres, J. C. (2001). El método de las necesidades básicas insatisfechas (NBI) y sus aplicaciones en América Latina. Serie estudios estadísticos y perspectivas: Vol. 7. Santiago: Naciones Unidas, CEPAL. Recuperado de http://hdl.handle.net/11362/4784.
Moran, P. A. P. (1950). Notes on Continuous Stochastic Phenomena. Biometrika, 37(1/2): 17-23. https://doi.org/10.2307/2332142.
Nakaya, T. (2016). GWR4.09 User Manual: GWR4 Windows Application for Geographically Weighted Regression Modelling. Recuperado de https://raw.githubusercontent.com/gwrtools/gwr4/master/GWR4manual_409.pdf.
Norza, E., Peñalosa, M. J. & Rodríguez, J. D. (2017). Exégesis de los registros de criminalidad y actividad operativa de la Policía Nacional en Colombia, año 2016. Revista Criminalidad, 59(3): 9-40.
Öcal, N. & Yildirim, J. (2010). Regional effects of terrorism on economic growth in Turkey: A geographically weighted regression approach. Journal of Peace Research, 47(4): 477-489. https://doi.org/10.1177/0022343310364576
Ortiz-Yusty, C. E., Páez, V. & Zapata, F. A. (2013). Temperature and precipitation as predictors of species richness in northern Andean amphibians from Colombia. Caldasia, 35(1), 65-80.
R Core Team. (2018). Vienna, Austria: R Foundation for Statistical Computing. Recuperado de https://www.R-project.org/.
Ramírez, K. V., López, Y., Castro, J. & Ocampo, J. F. (2017). Factores geográficos que influyeron en la concentración del hurto de motocicletas en diez estaciones policiales del Valle del Cauca, entre el 2010 y el 2015. Revista Criminalidad, 59(2): 9-31.
Rincón-Ruiz, A., Pascual, U. y Flantua, S. (2013). Examining spatially varying relationships between coca crops and associated factors in Colombia, using geographically weight regression. Applied Geography, 37, 23-33. https://doi.org/10.1016/j.apgeog.2012.10.009
Sandoval, L. E. & Barón, D. M. (2008). Una revisión al estudio de la delincuencia y criminalidad. Revista Facultad de Ciencias Económicas: Investigación y Reflexión, 16(1): 105-117. Recuperado de https://dialnet.unirioja.es/descarga/articulo/4237385.pdf.
Shapiro, S. S. & Wilk, M. B. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(3/4): 591. https://doi.org/10.2307/2333709.
Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46(sup1): 234-240.
https://doi.org/10.2307/143141
Wheeler, D. & Tiefelsdorf, M. (2005). Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 7(2), 161-187. https://doi.org/10.1007/s10109-005-0155-6.
Zhao, J., Wang, W. & Cheng, Q. (2014). Application of geographically weighted regression to identify spatially non-stationary relationships between Fe mineralization and its controlling factors in eastern Tianshan, China. Ore Geology Reviews, 57, 628-638. https://doi.org/10.1016/j.oregeorev.2013.08.005.
Downloads
Published
How to Cite
Issue
Section
License
Licencia creative commons CC BY NC ND https://creativecommons.org/licenses/by-nc-nd/2.0/
