School of Geography FACULTY OF ENVIRONMENT Spatial
Microsimulation and Crime Analysis Mark Birkin Professor of Spatial
Analysis and Policy University of Leeds Slide 2 The Obvious
Questions... What is Spatial Microsimulation? Why is it interesting
to the audience at a workshop on Crime, Policing and Society? Slide
3 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 Slide 4 Represent Individuals Slide 5
Distributional Consequences Slide 6 Policy Rules Slide 7 And then
the clever stuff... Slide 8 The Benefits Easy to generate Easy to
aggregate Easy to link Easy to manipulate Easy to scale Easy to
implement Easy to project Slide 9 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. Slide 10 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
Slide 11 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) 367378 Slide 12 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. Slide 13 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 Slide 14 Problems and
Issues XLack of standards XLack of software XData is messy and
heterogeneous ?Strong applications, weak theory ?Challenging ethics
XTend to be mechanistic Slide 15 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
Slide 16 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? ? Slide 17
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.
Slide 18 MSM-IBM and Crime Slide 19 Slide 20 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
