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2017 | 88 | 2 | 211-226
Article title

Role of Geospatial Technology in Crime Mapping: A Perspective View of India

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Abstracts
EN
The advancement in computer science technology and development of GIS application softwares and the accessibility of various geographic data through open source data sources make it feasible for police and law enforcement departments to use it effectively.Crime mapping and spatial analysis using GIS tools such as hot spot generation, zonation, navigation, and crime profiling, mobile location identification and web based various application are well recognized and can be scientifically applied for betterment of citizens whereas it can be effectively used for prediction and control of crime. The present study analyzed the temporal crime data (Murder, dacoity, robbery, burglary, theft and riots) of India from the year 2001 to 2015 to understand the temporal trend whereas state wise crime data (IPC crime registered) from the year 2011 to 2015 was utilized to generate crime density map and percent change. We have also used the crime data for 10 citis (highest crime rate) of India including all metro cities for the year 2015 to understand city crime trend towards various crimes types. By analyzing the crime data of 2015 the study reveals that the crime density was in the range of 65.8 to 1140 the lowest in Nagaland whereas highest in Delhi which was found to be roughly 4.5 times than the national average. After the evaluation of crime percent change for the year 2015 with preceding year it was found that 29.6% largest increase in crime in Daman and Diu whereas Kerala and Delhi got second and third position with value 24.3% and 23% respectively. The evaluation of ten cities including the metro cities was done for the year 2015. The various city crime (total cognizable crime under IPC) per lakh population varies from 189.4 to 925.9 was found highest in the city Indore whereas it was found lowest in Chennai city. Murder, dacoity, robbery, burglary, theft, riots and other IPC crime per lakh population were found in the range of (0.9 to 11.3), (0 to 1.7), (0.6 to 31.1), (1.1 to 57.17), (14.8 to 445.6), (0.5 to 35.4) and (147.7 to 576.2) respectively. Patna city leads in Murder and dacoity. Indore leads in the crime like burglary and other IPC crime. Delhi city reported highest in robbery, theft whereas record was found lowest in riots.
Year
Volume
88
Issue
2
Pages
211-226
Physical description
Contributors
author
  • Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh, India
  • Department of Mathematics and MCA, Ranchi University, Ranchi, Jharkhand, India
  • Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh, India
References
  • [1] Ahmad, F. & Goparaju, L. (2016). Analysis of Urban Sprawl Dynamics Using Geospatial Technology in Ranchi City, Jharkhand, India. J. Environ. Geogr. 9 (1–2): 7–13. DOI:10.1515/jengeo-2016-0002
  • [2] Ahmad, F., Goparaju, L. & Abdul Qayum, A. (2017) Agroforestry suitability analysis based upon nutrient availability mapping: a GIS based suitability mapping. AIMS Agriculture and Food. 2(2): 201-220. doi:10.3934/agrfood.2017.2.201
  • [3] Ahmad, F. & Goparaju, L. (2017 a). Long term deforestation assessment in Jharkhand state, India: A grid based geospatial approach. Biological Forum 9(1): 183-188.
  • [4] Ahmad, F. & Goparaju, L. (2017 b). Land Evaluation in terms of Agroforestry Suitability, an Approach to Improve Livelihood and Reduce Poverty: A FAO based Methodology by Geospatial Solution: A case study of Palamu district, Jharkhand, India Ecological Questions 25, 67-84 DOI: http://dx.doi.org/10.12775/EQ.2017.006
  • [5] Khalid, S., Shoaib, F., Qian, T. et al. (2017). Network Constrained Spatio-Temporal Hotspot Mapping of Crimes in Faisalabad. Appl. Spatial Analysis. https://doi.org/10.1007/s12061-017-9230-x
  • [6] Wang, D., Ding, W., Lo, H. et al. (2013). Crime hotspot mapping using the crime related factors—a spatial data mining approach. Appl Intell. 39: 772. https://doi.org/10.1007/s10489-012-0400-x
  • [7] Malvika, P. (2015) Crime Mapping of Rajasthan (2013). A District-level Analysis. Asian Journal of Research in Social Sciences and Humanities 5(6): 139-152. DOI:10.5958/2249-7315.2015.00141.0
  • [8] Karuppannan, J., Shanmugapriya, S., and Balamurugan, V. (2004). Crime Mapping in India: A GIS Implementation in Chennai City Policing. Geographic Information Sciences. 10: 20-34. http://dx.doi.org/10.1080/10824000409480651
  • [9] Brantingham, P.J. and Brantingham, P.L. (1991). Environmental Criminology (eds.). Prospect Heights, IL: Waveland Press. Weisburd, D., & McEwen, T. (1997). Introduction: Crime Mapping & Crime Prevention. In D. Weisburd & T. McEwen (Eds), Crime Mapping & Crime Prevention (Vol. 8, pp. 1-21). Monsey New York: Criminal Justice Press.
  • [10] Eikelboom A., Martini E., Luisa Ruiz L. et al. (2017). Public Crime Mapping in Canada: Interpreting RAIDS Online. Cartographica: The International Journal for Geographic Information and Geovisualization Summer. Vol. 52, No. 2, pp. 108-115. https://doi.org/10.3138/cart.52.2.5101
  • [11] Jefferson, B. J. (2017). Predictable Policing: Predictive Crime Mapping and Geographies of Policing and Race. Annals of the American Association of Geographers, 1-16. DOI:10.1080/24694452.2017.1293500
  • [12] Bunting, R. J., Chang, O. Y., Cowen, C., Hankins, R., Langston, S., Warner, A., Roy, S. S. (2017). Spatial Patterns of Larceny and Aggravated Assault in Miami–Dade County, 2007–2015. Professional Geographer, 1-13. DOI:10.1080/00330124.2017.1310622
  • [13] Curtis-Ham, S.; Walton, D. (2017). Mapping crime harm and priority locations in New Zealand: A comparison of spatial analysis methods. Appl. Geogr. 2017 DOI:10.1016/j.apgeog.2017.06.008
  • [14] Alves, L,G., Ribeiro, H,V., Lenzi, E.K., Mendes, R.S. (2013). Distance to the scaling law: a useful approach for unveiling relationships between crime and urban metrics. PLoS One. 8(8): 1–8. pmid: 23940525
  • [15] Gerber, M.S. (2014). Predicting crime using Twitter and kernel density estimation. Decision Support Systems. 61:115–125.
  • [16] Gorr, W., Olligschlaeger, A. and Thompson, Y. (2003). Short-term forecasting of crime. International Journal of Forecasting. 19(4): 579–594.
  • [17] Liao, R., Wang, X., Li, L.and Qin, Z. (2010). A novel serial crime prediction model based on Bayesian learning theory. In: Proceedings of the 2010 IEEE International Conference on Machine Learning and Cybernetics. vol. 4. p. 1757–1762.
  • [18] Mohler, G,O., Short, M,B., Brantingham, P.J., Schoenberg, F,P. and Tita, G.E. (2011). Self-Exciting Point Process Modeling of Crime. Journal of the American Statistical Association. 106(493): 100–108.
  • [19] Wang, P., Mathieu, R., Ke, J. and Cai, H.J. (2010). Predicting Criminal Recidivism with Support Vector Machine. In: Proceedings of the 2010 IEEE International Conference on Management and Service Science. p. 1–9
  • [20] Anderson, C,A. and Anderson, D,C.( 1984). Ambient temperature and violent crime: Tests of the linear and curvilinear hypotheses. Journal of Personality and Social Psychology. 46(1): 91–97. pmid: 6694060
  • [21] Lawrence, E. and Cohen, M.F. (1979). Social Change and Crime Rate Trends: A Routine Activity Approach. American Sociological Review. 44(4): 588–608.
  • [22] Cotte Poveda A. (2012). Violence and economic development in Colombian cities: a dynamic panel data analysis. Journal of International Development. 24(7): 809–827.
  • [23] Cusimano, M., Marshall, S., Rinner, C., Jiang, D. and Chipman, M. (2010). Patterns of urban violent injury: a spatio-temporal analysis. PLoS One. 5(1): 1–9. pmid: 20084271
  • [24] Hojman, D. E. (2004). Inequality, unemployment and crime in Latin American cities. Crime, Law and Social Change. 41(1): 33–51.
  • [25] Hojman, D. E. (2002). Explaining crime in Buenos Aires: the roles of inequality, unemployment, and structural change. Bulletin of Latin American Research. p. 121–128.
  • [26] Kelly, M. (2000). Inequality and crime. Review of Economics and Statistics. 82(4): 530–539.
  • [27] Levitt, S.D. (2001). Alternative strategies for identifying the link between unemployment and crime. Journal of Quantitative Criminology. 17(4): 377–390.
Document Type
article
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YADDA identifier
bwmeta1.element.psjd-e3379ff9-74aa-421b-b1e7-cd079043833c
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