Research Article of American Journal of Geographical Research and Reviews
Role of Geospatial technology in Crime Mapping: A case study of Jharkhand state of India
Firoz Ahmad 1*, Md Meraj Uddin2, Laxmi Goparaju1
1Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh, India.
2University Department of Mathematics, MCA, Ranchi University, Ranchi, Jharkhand, India
Crime is a social stigma which needs to be addressed beyond talks. In developed country Geospatial technology has become well established within the criminology and forensic fields in recent past. In order to achieve this proper database of various crimes (state/ district level) should be available for decision making. The present study was an attempt made to study the district wise crime data (IPC crime registered) for murder, rape, kidnapping, dacoity, burglary, theft and riots of state of Jharkhand for the year 2013 to understand the crime trend. We have generated various maps including crime density map of Jharkhand based on crime types using ARC/ GIS Software and MS EXCEL. The crime density such as murder, rape, kidnaping and riots were found in the range of (2.2 to 17.8), (1.6 to 12.6), (2.3 to 10.4) and (1.0 to 17.5) respectively. Murder crime density was highest in Gumla district whereas it was found to be lowest in Gridih district. Sahebganj district has high crime density for rape and kidnapping. Palamu district had low crime density in rape, whereas Ranchi district recorded low crime density in kidnapping. Crime density for riots was found lowest for district Simdega whereas highest for Koderma. The Indian police and law enforcement departments has not yet exploited the GIS aspect which will fetch better result as far as crime control is considered.
Keywords: Crime Analysis, Geographical Information Systems, Crime Mapping, Jharkhand
How to cite this article:
Firoz Ahmad, Md Meraj Uddin, Laxmi Goparaju. Role of Geospatial technology in Crime Mapping: A case study of Jharkhand state of India. American Journal of Geographical Research and Reviews, 2018; 1:5. DOI:10.28933/AJGRR-2018-01-1801
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., Meraj Uddin, M & Goparaju, L. (2017b) Role of Geospatial technology in Crime Mapping: a perspective view of India. World Scientific News. 88(2):221-226
3 Ahmad, F., Goparaju, L. & Abdul Qayum, A. (2017a) 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
4 Ahmad, F. & Goparaju, L. (2017a) Long term deforestation assessment in Jharkhand state, India: A grid based geospatial approach. Biological Forum 9(1):183-188.
5 Ahmad, F. & Goparaju, L. (2017b) 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
6 Ahmad, F. & Goparaju, L. (2017c) Soil and Water Conservation Prioritization Using Geospatial Technology – a Case Study of Part of Subarnarekha Basin, Jharkhand, India. AIMS Geosciences, 2017, 3(3): 375-395. doi: 10.3934/geosci.2017.3.375.
7 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
8 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
9 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
10 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.
11 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
12 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
13 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
14 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
15 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
16 Gerber, M.S. (2014) Predicting crime using Twitter and kernel density estimation. Decision Support Systems. 61:115–125.
17 Gorr, W., Olligschlaeger, A. and Thompson, Y. (2003) Short-term forecasting of crime. International Journal of Forecasting. 19(4):579–594.
18 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.
19 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.
20 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
21 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
22 Lawrence, E. and Cohen, M,F. (1979) Social Change and Crime Rate Trends: A Routine Activity Approach. American Sociological Review.44(4):588–608.
23 Cotte Poveda, A.( 2012) Violence and economic development in Colombian cities: a dynamic panel data analysis. Journal of International Development. 24(7):809–827.
24 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
25 Hojman, D,E. (2004) Inequality, unemployment and crime in Latin American cities. Crime, Law and Social Change.41(1):33–51.
26 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.
27 Kelly, M.(2000) Inequality and crime. Review of Economics and Statistics.82(4):530–539.
28 Levitt, S,D. (2001) Alternative strategies for identifying the link between unemployment and crime. Journal of Quantitative Criminology.17(4):377–390.
29 Gupta, R. , Rajitha, K., Basu, S. and Mittal, S.K. (2012) Application of GIS in Crime Analysis: A Gateway to Safe City. 14th Annual International Conference and Exhibition on Geospatial Information Technology and Application. Indian Geospatial Forum 7th to 9th February 2012.http://www.indiageospatialforum.org/2012/proceedings/ppt/Prof%20Gupta,%20Development.pdf
30 Thangavelu, A. , Sathyaraj S.R. and Balasubramanian S. (2013) Assessment of Spatial Distribution of Rural Crime Mapping in India: A GIS Perspective. International Journal of Advanced Remote Sensing and GIS, 2( 1): 70-85
31 Shahebaz, M. A. and Kale, K. V. (2014) Mapping and Analysis of Crime in Aurangabad City using GIS Journal of Computer Engineering, 16(4): 67-76
32 McKinley J., Ruffell A., Harrison M, Meier W, Kemp H, Graham C. Barry L (2008) Spatial thinking in search methodology: a case study of the “No body murder enquiry”. West of Ireland. In:Ritz K, Dawson L, Miller D (eds) Criminal and environmental soil forensics. Springer.New York, pp 285-302
33 Noond, J., Schoficld, D., March, J. (2002) Visualising the scene: computer graphics and evidence presentation. Sci Justice 42(2):89-95
34 Wolff, M., Asche, H. (2009) Towards geovisual analysis of crime scenes – a 3D crime mapping approach. Bi HH. Chen. 11C (2003) Collaborative workflow management for interagency crime analysis. In: Intelligence and security informatics, proceedings. Lecture notes in computer science, vol 2665. Springer, Berlin, pp 266-280
35 Longley, P., Goodchild, M.F., Maguire, D.J., Rhind, D.W. (2005) Geographical information systems and science, 2nd edn. Wiley, New York
36 Groff, E.R., Vigne. N.G.L. (1998) The use of geographic information systems (GIS) for state and local crime analysis. Paper presented at the conference of European statisticians. Ottawa, 5-7 Oct 1998
37 La Vigne, N.G., GrolT ER (2001) The evolution of crime mapping in the United Slates: from the descript tive to the analytic. In: Hirschfield A. Bowers K (eds) Mapping and analysing crime data: lessons from research and practice. Taylor & Francis, New York, pp 203—222
38 Singh. K. M., Singh, R. K. P., Meena, M. S., Kumar, A., Jha, A. K. and Kumar, Anjani. (2012). Rural poverty in Jharkhand: an empirical exploration of socio-economic determinants. MPRA Paper No.44811. ICAR Research Complex for Eastern Region, Patna.
39 Mamalian, C.A., LaVigne, N.G. (1999) The use of computerized crime mapping by law enforcement: survey results. National Institute of Justice Journal Research Preview, January, Washington, DC
40 Markovic, J., Bueermann, J., Smith, K. (2006) Coming to terms with geographical information systems.Police Chief 73(6):60–73
41 Nelson, L. (1999) GIS. A powerful weapon for law enforcement. ArcUser, Jan–Mar 1999
42 Wang, F. (2005) Geographic information systems and crime analysis. Idea Group Publishing, Hershey, p 345
43 Wartell, J. (2003) Crime mapping and data sharing. In: Leipnik MR, Albert DP (eds) GIS in law
44 enforcement: implementation issues and case studies. Taylor & Francis, New York
45 Asano CH, Rogers T, McGuire G, Juck D, Jacobs J (2012) Application of infrared photography for the detection of hidden evidence at the crime scene: passive and active thermography techniques through drywall. [Studies/research reports]. Identif Can 35(2):40–55
46 Davenport, G.C. (2001) Archaeologists as forensic investigators: defi ning the role. Hist Archaeol 35(1):87–100
47 Schreiber, S. (2009) Seeing through to the hot spots: amid falling costs and improved features, law enforcement may be able to fi nd a few more reasons to pick up a thermal imaging camera.
48 Law Enforc Technol 36(7):35–40
49 Schultz, P.D. (2008) The future is here: technology in police departments. Police Chief 75(6):19–27
50 Schultz, J.J. (2012) Detecting buried remains using ground penetrating radar. Department of
51 Anthropology, University of Central Florida, p 235
52 Canter, D., Hammond., L. (2007) Prioritizing burglars: comparing the effectiveness of geographical profiling methods. Police Pract Res 8(4):371–384. doi: 10.1080/15614260701615086
53 Canter, D. (2009) Developments in geographical offender profiling: commentary on Bayesian
54 journey- to-crime modelling. J Invest Psychol Offender Profi ling 6(3):161–166