Award winning poster at SocInfo'17 with Robin Bader, Cristina Kadar and Irena Pletikosa

1. Partners
The offender behavior performance is measured using the ratio of crime
locations passed vs average distance traveled by each offender
(inspired by the Predictive Accuracy Index [3]) - the larger the result, the
better the performance of the index.
The various simulation scenarios show a similar performance of the
different search space selection strategies (the uniform strategy
performs slightly better), while the target selection algorithm selecting
popular POI’s as movement target performs best. Combined, the
uniform distance selection strategy selecting popular POI’s as targets
performs best, followed by the algorithm selecting random POI’s. This
confirms H2 and partly H3, stating that offenders travel to POI and
popular POI, but does not confirm that offenders’ movement distances
follow a Lévy-flight (H1). In contrast, the uniform distribution seems to
better capture offender mobility.
Towards simulating criminal offender
movement based on insights from
human dynamics and location-based
social networks
Raquel Rosés Brünnger, Robin Bader, Cristina Kadar, Irena Pletikosa
Chair of Information Management, D-MTEC, ETH Zurich
SocInfo’17
4 Results
Fig. 2. Agent mobility performance
5 Conclusion
Novel data from social media and human mobility insights, can inform
more realistic offender mobility. We derive that POI can be used as
targets for offender movement and will include additional factors in
the model to refine unknown offender mobility. Future models will include
better defined offender starting points, limited number of trips before
returning to starting location, additional POI’s and a more elaborate
performance measurement. The results shown above are the basis for
further development of generalizable offender mobility with the goal to
inform more realistic crime simulations.
6 References
[1] Malleson, N., See, L., Evans, A., and Heppenstall, A. 2012. Implementing
comprehensive offender behaviour in a realistic agent-based model of burglary.
SIMULATION 88, 1, 50–71.
[2] Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. 2008. Understanding
individual human mobility patterns. Nature 453, 7196, 779–782.
[3] Chainey, S., Tompson, L., and Uhlig, S. 2008. The Utility of Hotspot Mapping
for Predicting Spatial Patterns of Crime. Security Journal 21, 1-2, 4–28.
3 Simulation
The ABM is built on a geographic environment with agents moving along
the road network and counting the historic crimes they pass along their
way. We study 9 possible offender mobility options, combining search
distances strategies (see table 1) and target selection algorithms (see
table 2). The model is run for each agent type, with 25 agents and 50
steps. The agents start at a random point of the road network.
1 Introduction
Interest in data-driven crime simulations has been growing in recent
years, confirming its potential to advance crime prevention and
prediction. Previous work on agent-based models (ABM) for crime
simulate offender behavior in a geographical environment, relying
exclusively on a small sample of offender homes and crime locations.
The complex dynamics of crime and the lack of information on criminal
offender’s movement patterns [1] challenge the design of offender
movement in simulations. We propose an ABM with offender agents
driven by human mobility information and location–based social
networks data to simulate unknown offender movement within a
city.
Road alg. POI alg. Popular POI alg.
Static strat. 0.324 0.439 0.347
Uniform strat. 0.382 0.519 0.523
Lévy strat. 0.267 0.425 0.283
0
0.2
0.4
0.6
The hypotheses to be resolved question if (H1) offenders follow
human mobility patterns identified for the general population, (H2)
offenders move between Points of Interest (POI), and (H3) offenders are
drawn to more popular POI.
The data used to build this model contains: (1) road network for
New York City (NYC) from NYC open data portal - 117,321 street
segments, (2) criminal offence data for 2015 from NYC open data
portal - 69,731 reported crime incidents, (3) Foursquare venues with
check-in counts for May and June 2016 from Foursquare - 273,149
venues and over 122 million check-ins, (4) human mobility information
such as average trip length traveled by residents from New York State
population statistics.
2 Hypotheses and Data
Static search distance Uniform search distance Lévy-flight search
distance
Fixed distance movement
(average NYC trip length)
r= 40’000 feet
Distance drawn from
uniform distribution (max
distance is 2x NYC): r[0,
80’000] feet
Distance drawn from power
law distribution β=0.6 for
humans [2]
Travel between
intersections
Travel between POI Travel between popular
POI
Random intersections
within search radius on
street network
Random POI from
Foursquare within search
radius
POI from Foursquare
weighted by venue
popularity (total check-ins)
Tab. 1. Search distance strategies
Tab. 2. Target selection algorithm
Fig. 1. Performance measure