Abstract
An individual’s decision to commit a crime is influenced, among other things, by his/her whereabouts over time and space. In this chapter, we suggest the use of geographic information systems (GIS), combined with space–time budget techniques, to visualise and track individuals’ daily activities patterns. We first test several GIS-based visualisation techniques for handling spatial and temporal dimensions of activity patterns using a dataset of adolescents in Peterborough, UK. Later, we show how these spatial methods can support the creation of measures of environmental exposure that may help predict group-level offending. Findings indicate that visualisation techniques are effective tools for exploratory analysis of how individuals differ in their patterns of activity across the city. Results also show that tracking groups of individuals by using measures of environmental exposure, in combination with individual characteristics and settings, can help explain differences in their levels of offending.
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Notes
- 1.
The possibility of using real-time location data has, for instance, opened up a number of new research questions and, perhaps, answers to old ones. Examples include work by MIT’s Senseable City Lab, UrbanSense at UCLA, Spatial Information Design Lab at Columbia University and the i-Mobility lab at KTH, Sweden. These projects serve as examples to illustrate the unlimited opportunities mobile communications offer today to understand urban activities and monitor them over time.
- 2.
EDs were later replaced by Output Areas (OA).
- 3.
This procedure can be performed with any other GIS software, such as MapInfo, under ‘create points’ tool, or ArcView 3.x, by ‘add point theme’.
- 4.
These techniques are implemented in CrimeStat 2.0 by Ned Levine & Associates, available at http://www.icpsr.umich.edu/NACJD/crimestat.html#MAPS.
- 5.
- 6.
SQL – Structured Query Language, a language used by relational databases to query, update and manage data.
- 7.
Software available at http://geodacenter.asu.edu/
- 8.
In the subsequent analysis, time spent in neighbourhood with poor collective efficacy was also part of the measure of environmental risk.
- 9.
The Peterborough Study incorporated indicators from a community survey in later analysis. Findings are reported in Wikström et al. 2010.
References
Amir, M. (1971). Patterns in forcible rape. Chicago: University of Chicago Press.
Brantingham, P., & Brantingham, P. (1995). Criminality of place: Crime generators and crime attractors. European Journal on Criminal Policy and Research, 3(3), 1–26.
Cambridgeshire Constabulary. (2005). Police official statistics.
Canter, D., & Larkin, P. (1993). The environmental range of serial rapists. Journal of Environmental Psychology, 13(1), 63–49.
Ceccato, V. (2009). Crime in a city in transition: The case of Tallinn, Estonia. Urban Studies, 46(8), 1611–1638.
Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44(August), 588–605.
Corbett, J. (2001). Torsten Harstrand: Time Geography. Center for spatially integrated social sciences. Available at http://www.csiss.org/classics/content/29. Assessed 10 Nov 2011.
Costello, A., & Wiles, P. (2001). GIS and the journey to crime: An analysis of patterns in South Yorkshire. In A. Hirschfield & K. Bowers (Eds.), Mapping and analysing crime data lessons from research and practice (pp. 27–60). London: Taylor & Francis.
Eagle, N., & Pentland, A. (2006). Reality mining: Sensing complex social systems. Journal Personal and Ubiquitous Computing, 10(4), 255–268.
Ferguson, S. H., Taylor, M. K., Born, E. W., Rosing-Asvid, A., & Messier, F. (1999). Determinants of home range size for polar bears (Ursus maritimus). Ecology Letters, 2(5), 311–318.
Forer, P. C., & Kivell, H. (1981). Space-time budgets, public transport, and spatial choice. Environment and Planning A, 13(4), 497–509.
Fotheringham, A. S., & Wong, D. W. S. (1991). The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A, 23(7), 1025–1044.
Fritzon, K. (2001). An examination of the relationship between distance travelled and motivational aspects of fire setting behaviour. Journal of Environmental Psychology, 21(1), 45–60.
Gahegan, M. (1999). Four barriers to the development of effective exploratory visualisation tools for the geosciences. International Journal of Geographic Information Science, 13(4), 289–309.
Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 4(2), 189–206.
Golledge, R. G., & Stimson, R. J. (1997). Spatial behaviour. New York/London: The Guildford Press.
Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. Nature, 453, 779–782.
Gore, R. Z., & Pattavina, A. (2004). Applications for examining the journey-to-crime using incident-based offender residence probability surfaces. Police Quarterly, 7(4), 457–474.
Gottfredson, D. C., McNeil, R. J., & Gottfredson, G. D. (1991). Social area influences on delinquency: A multilevel analysis. Journal of Research in Crime and Delinquency, 28(2), 197–226.
Griffith, D. A. (1983). The boundary value problem in spatial statistical analysis. Journal of Regional Science, 23(3), 283–307.
Groff, E., Weisburd, D., & Morris, N. A. (2009). Where the action is at places: Examining spatio-temporal patterns of juvenile crime at places using trajectory analysis and GIS. In D. Weisburd, W. Bernasco, & G. Bruinsma (Eds.), Putting crime in its place (pp. 61–86). New York: Springer.
Hägerstrand, T. (1970). What about people in regional science? Papers in Regional Science Association, 24(1), 7–21.
Hooge P. N., & Eichenlaub, B. (2000). Animal movement extension to Arcview, version 2.0. Anchorage, AK: Alaska Science Center – Biological Science Office, U.S. Geological Survey.
Huisman, O., & Forer, P. (1998). Computational agents and urban life spaces: A preliminary realisation of the time-geography of student lifestyles. Computational agents and urban life spaces. http://www.geocomputation.org/1998/68/gc_68a.htm. Assessed 10 Nov 2011.
Janelle, D. G., Goodchild, M. F., & Klinkenberg, B. (1988). Space-time diaries and travel characteristics for different levels of respondent aggregation. Environment and Planning A, 20(7), 891–906.
Kornhauser, R. (1978). Social sources of delinquency. Chicago: University of Chicago Press.
Krak, M. (2003). The space-time cube revisited from a geovisualisation perspective. Proceedings of the 21st International Cartographic Conference (ICC), Durban, South Africa. http://www.itc.nl/personal/kraak/. Assessed 10 Nov 2011.
Kwan, M. P. (1998). Space-time and integral measures of individual accessibility: A comparative analysis using a point-based network. Geographical Analysis, 30, 191–216.
Kwan, M. P. (1999). Gender and individual access to urban opportunities: A study using space-time measures. The Professional Geographer, 51(2), 210–227.
Kwan, M. P. (2000). Interactive geovisualisation of activity-travel patterns using three-dimensional geographical information systems: A methodological exploration with a large data set. Transportation Research Part C, 8(1–6), 185–2003.
LeBeau, J. L. (1987). The journey to rape: Geographic distance and the rapist’s method of approaching the victim. Journal of Police Science and Administration, 15(2), 129–161.
Lenntorp, B. (1976). Paths in space-time environments: A time geographic study of movement possibilities of individuals. Lund Studies in Geography, 44, Royal University of Lund, Sweden.
Lundigran, S., & Canter, D. (2001). A multivariate analysis of serial murderers’ disposal site location choice. Journal of Environmental Psychology, 21(4), 423–432.
Mey, M. G., & Heide, H. (1997). Towards spatiotemporal planning: Practicable analysis of the day-to-day paths through space and time. Environment and Planning B, 24(5), 709–723.
Miller, H. J. (2003). What about people in geographic information science? Computers, Environment and Urban Systems, 27(5), 447–453.
Openshaw, S. (1984). The modifiable areal unit problem. Concepts and Techniques in Modern Geography, 38. Norwich: Geo Books.
Osgood, W. D., Wilson, J. K., O’Malley, P., Bachman, G. J., & Johnston, L. D. (1996). Routine activities and individual deviant behavior. American Sociological Review, 61(4), 635–655.
Pentland, W. E., Harvey, A. S., Lawton, M. P., & McMoll, M. A. (1999). Time use research in the social sciences. New York: Kluwer Academic/Plenum.
Peuquet, D. J. (1994). It’s About time: A conceptual framework for the representation of temporal dynamics in GIS. Annals of the Association of American Geographers, 84(3), 441–461.
Ratcliffe, J. H., & McCullagh, M. J. (1999). Hotbeds of crime and the search for spatial accuracy. Geographical Systems, 1(4), 385–398.
Ratti, C., Sobolevsky, S., Calabrese, F., Andris, C., Reades, J., Martino, M., Claxton, R., & Strogatz, S. H. (2010). Redrawing the map of Great Britain from a network of human interactions. PLoS One, 5(12), e14248. doi:10.1371/journal.pone.0014248.
Raudenbush, S. W., & Sampson, R. J. (1999). Ecometrics: Toward a science of assessing ecological settings, with application to the systematic social observation of neighborhoods. Sociological Methodology, 29(1), 1–41.
Reiss, R. (1961). The distribution of juvenile delinquency in the social class structure. American Sociological Review, 26(5), 720–732.
Rhodes, W. M., & Conly, C. (1981). Crime and mobility: An empirical study. In P. J. Brantingham & P. L. Brantingham (Eds.), Environmental criminology (pp. 11–26). Beverly Hills: Sage.
Robinson, W. S. (1950). Ecological correlations and the behaviour of individuals. American Sociological Review, 15(3), 351–357.
Roncek, D. W., & Maier, P. A. (1991). Bars, blocks, and crimes revisited: Linking the theory of routine activities to the empiricism of hot spots. Criminology, 29(4), 725–755.
Rossmo, D. K. (2000). Geographic profiling. Boca Raton: CRC Press.
Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918–924.
Schönfoelder, S., & Axhausen, K. W. (2003). Activity spaces: Measures of social exclusion? Arbeitsbericht Verkerhrs- und Raumplanung, 140, Institut für Verkehrsplanung und transportsysteme (IVT), ETH Zürich, Zürich. http://ideas.repec.org/a/eee/trapol/v10y2003i4p273–286.html. Assessed 10 Nov 2011.
Shaw, C. R., & Mckay, H. D. (1942). Juvenile delinquency and urban areas. Chicago: University of Chicago Press.
Song, C., Qu, Z., Blumm, N., & Barabasi, A. L. (2010). Limits of predictability in human mobility. Science, 327, 1018–1021.
Takahashi, L. M., Wiebe, D., & Rodriguez, R. (2001). Navigating the time-space context of HIV and AIDS: Daily routines and access to care. Social Science & Medicine, 53(7), 845–863.
Tomlinson, J., Bullock, N., Dickens, P., Steadman, P., & Taylor, E. (1973). A model of student’s daily activity patterns. Environment and Planning, 5(2), 231–266.
Turner, S. (1969). Delinquency and distance. In T. Sellin & M. E. Wolfgang (Eds.), Delinquency: Selected studies (pp. 11–26). New York: Wiley.
White, R. C. (1932). The relations of felonies to environmental factors in Indianapolis. Social Forces, 10(4), 498–509.
Wikström, P. O. (2005). The social origins of pathways in crime: Towards a developmental ecological action theory of crime involvement and its changes. In D. P. Farrington (Ed.), Integrated developmental and life-course theories of offending (pp. 211–246). New Brunswick: Transaction.
Wikström, P. O. (2006). Individuals, settings and acts of crime: Situational mechanisms and the explanation of crime. In P. O. Wikström & R. J. Sampson (Eds.), The explanation of crime: Context, mechanisms and development. Cambridge: Cambridge University Press.
Wikström, P. O. H. (2010). Explaining crime as moral actions. In S. Hitlin, & S. Vaisey (Eds.), Handbook of the Sociology of Morality, Springer Science+Business Media, doi:10.1007/978-1-4419-6896-8_12.
Wikström, P. O., & Loeber, R. (2000). Do disadvantaged neighbourhoods cause well-adjusted children to become individual delinquents? Criminology, 38(4), 1109–1142.
Wikström, P. O., & Loeber, R. (2004). The social origins of pathways in crime: Towards a developmental ecological action theory of crime involvement and its changes. In D. P. Farrington (Ed.), Integrated developmental and life course theories of offending. Advances in criminological theory, 14 (pp. 211–45). New Brunswick: Transaction.
Wikström, P. O., Ceccato, V., Hardie, B., & Treiber, K. (2010). Activity fields and the dynamics of crime: Advancing knowledge about the role of the environment in crime causation. Journal of Quantitative Criminology, 26(1), 55–87.
Wikström, P. O., Treiber, K., & Hardie, B. (2011). Examining the role of the environment in crime causation: Small-area community surveys and space–time budgets. In D. Gadd, S. Karstedt, & S. F. Messner (Eds.), The SAGE handbook of criminological research methods (pp. 111–127). London: Sage.
Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home-range studies. Ecology, 70(1), 164–168.
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Ceccato, V., Wikström, P.O.H. (2011). Tracking Social Life and Crime. In: Ceccato, V. (eds) The Urban Fabric of Crime and Fear. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4210-9_7
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