Presentation on theme: "School of Geography FACULTY OF ENVIRONMENT Spatial Microsimulation and Crime Analysis Mark Birkin Professor of Spatial Analysis and Policy University of."— Presentation transcript:
School of Geography FACULTY OF ENVIRONMENT Spatial Microsimulation and Crime Analysis Mark Birkin Professor of Spatial Analysis and Policy University of Leeds
The Obvious Questions... What is Spatial Microsimulation? Why is it interesting to the audience at a workshop on Crime, Policing and Society?
Microsimulation Introduced to economics in the 1950s by Guy Orcutt – Motivated by a desire to understand the distributional consequences of financial policies (tax, benefits etc) – Represent individual households or population members rather than array-based aggregates – Apply rules e.g. person(s) under 18 then allocate child benefit Idea enthusiastically adopted by geographers – also concerned with (spatial) distributions
The Benefits Easy to generate Easy to aggregate Easy to link Easy to manipulate Easy to scale Easy to implement Easy to project
Benefits (1) Kavroudakis D, Ballas D, Birkin M (2012) Using spatial microsimulation to model social and spatial inequalities in educational attainment, Applied Spatial Analysis and Policy, in press.
Benefits (2) Tomintz M., Clarke G.P., Rigby J. (2008) The geography of smoking in Leeds: estimating individual smoking rates and the implications for the location for stop smoking services, Area 40(3), 341-353
Benefits (3) Spatial microsimulation for rural policy analysis in Ireland: The implications of CAP reforms for the national spatial strategy D. Ballas, G.P. Clarke, E. Wiemers, Journal of Rural Studies 22 (2006) 367–378
Benefits (4) Birkin M, Malleson N, Hudson-Smith A, Gray S, Milton R (2011) Calibration of a Spatial Interaction Model with Volunteered Geographical Information, International Journal of Geographical Information Science, forthcoming.
Benefits (5) population statistics1991200120112021 Households 41,855 47,202 51,074 54,796 Unemployed (as a % of economically active)4.6%2.7%1.8%1.5% LLTI (%)9.5%8.3%5.8%3.9% Elderly (over 64 years as a % of all individuals)34.5%41.9%41.3%45.9% Economically active (%)46.2%49.3%55.1%58.4% Health problems: Anxiety, depression (%)5.7%4.8%3.7%3.8% “Loneliness” (% with no one to talk to in times of need)6.6%7.2%8.8%11.5% Single person households34.4%42.3%43.4%41.8% Cars/Households ratio 0.73 0.88 1.02 1.03 Ballas, D., Clarke G.P., Dorling D. Rossiter D. (2007) Using SimBritain to model the geographical impact of national government policies, Geographical Analysis, 39(1), 44-77
Problems and Issues XLack of standards XLack of software XData is messy and heterogeneous ?Strong applications, weak theory ?Challenging ethics XTend to be mechanistic
From Microsimulation to Individual Based Simulation Need some insights from agent-based modelling to bolster the MSM – Conceptual and behavioural detail – Evidence driven and policy rich Wu B, Birkin M, Rees P (2008) A spatial microsimulation model with student agents, Computers Environment and Urban Systems, 32, 440-453. Jordan, R., Birkin, M., Evans, A. (2011): ‘Agent-based Simulation Modelling of Housing Choice and Urban Regeneration Policy’. In: Bosse, T., Geller, A. and Jonker, C. (eds.), Multi-Agent-Based Simulation XI. Springer, Berlin, 152-166. Malleson and Birkin (2012) Malleson N., Birkin M. (2012) Analysis of crime patterns through the integration of an agent-based model and a population microsimulation, Computers, Environment and Urban Systems, http://dx.doi.org/10.1016/j.compenvurbsys.2012.04.003
MSM-IBM for Crime Policy Analysis Explore relationship between house type and residential burglary Are certain house types more at risk? And do community demographics have an effect? Burglary rates by Housing Type by OAC Super Group Low guardianship? Affluence within disadvantage? Neighbourhood cohesion? ?
MSM-IBM and Crime In recent years, criminologists have become interested in understanding crime variations at progressively finer spatial scales, right down to individual streets or even houses. To model at these fine spatial scales, and to better account for the dynamics of the crime system, agent-based models of crime are emerging. Generally, these have been more successful in representing the behaviour of criminals than their victims. In this paper it is suggested that individual representations of criminal behaviour can be enhanced by combining them with models of the criminal environment which are specified at a similar scale. In the case of burglary this means the identification of individual households as targets. We will show how this can be achieved using the complementary technique of microsimulation. The work is significant because it allows agent-based models of crime to be refined geographically (to allow, for example, individual households with varying wealth or occupancy measures) and leads to the identification of the characteristics of individual victims.
Conclusions Microsimulation is a proven technique in applied economics and spatial analysis Many established applications in housing, health, education, transport... Extension to crime is natural and obvious Strength of the techniques include applied scale and policy relevance Tendency towards rigidity – a more flexible combination of individual based approaches may be the way forward here
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