Multi-Scale Analysis of Crime and
Incident Patterns in Camden
Dawn Williams
{dawn.williams.10@ucl.ac.uk}
Department of Civil, Environmental & Geomatic Engineering (CEGE), UCL
Progressive Deepening Approach
• Scale effects: the kinds of
relationships or patterns that can
be found are directly affected by
the spatial or temporal granularity
chosen 1.
• A progressive deepening approach
to the STDM 2 shall be applied to
ESTDM
• This will allow analysis through
the continuum from micro to
macro scales
• This methodology can be applied
to different datasets and
processes by changing the tools
and the spatial and temporal
resolutions used.
1 Yao,
Xiaobai. "Research Issues in Spatio-temporal Data Mining." Workshop on Geospatial Visualization and
Knowledge Discovery. Virginia: University Consortium for Geographic Information Science (UCGIS) , 2003.
2 Han,
Jiawei., 2003. "Mining Spatiotemporal Knowledge: Methodologies and Research Issues." Geospatial
Visualization and Knowledge Discovery Workshop. University Consortium for Geographic Information Science,
Virginia.
Planar and Network Kernel Density Estimation
• Kernel Density Estimation is a
commonly applied interpolation
and hotspot technique in spatial
analysis and crime analysis 1.
• A Planar Kernel Density estimate
was conducted using a bandwidth
of 310m and a cell size of 40m.
• Both the planar and network KDE
were useful in identifying the
clusters though the network KDE
algorithm appeared to be less
dependent on input parameters
• Planar and Network KDE revealed
hotspots in the vicinity of
Holborn and Camden Town.
Hagenauer, J., Helbich, M., & Leitner, M., 2011. Visualization of Crime Trajectories with Self-Organizing Maps: A Case Study
on Evaluating the Impact of Hurricanes on Spatio-Temporal Crime Hotspots. In: International Cartographic Conference ICC
2011, International Cartographic Association (ICA), Paris.
1
Spatial Scan Statistics
Overlapping cylinders are obtained.
A crime hotspot or cluster is a cylinder in
which the number of observed crimes is
significantly larger, statistically, than the
expected value.
Clusters were labelled statistically significant when p
≤0.01 for both methods.
The space time permutation algorithm found no
significant clusters for the entire period when tested
against a significance level of 1% i.e. p=0.05.
Spatial Scan Statistics
• The space-time permutation and space-time Poisson models
were used.
• The ST Poisson model found two clusters as expected. Both
clusters had duration of 14 days but the start and end dates
differed.
• The Camden area cluster (red) had a smaller radius and a
greater relative risk, than the wider cluster in the Holborn,
Bloomsbury(orange) area.
• This visualization technique is extremely effective and useful
with the results being clear and easy to interpret.
The Self Organizing Map Method
• The Self Organizing Map (SOM) is an unsupervised, learning neural
network1.
• It allows simple, geometric relationships to be produced from
vector quantization analysis of complex multidimensional
datasets2 through a combination of clustering and dimension
reduction3 while preserving topology2.
Reorderable
Matrix
U Distance Matrix
Map Matrix
Parallel Coordinate
Plot
1
Hagenauer, J., Helbich, M., & Leitner, M., 2011. Visualization of Crime Trajectories with Self-Organizing Maps: A Case
Study on
Evaluating the Impact of Hurricanes on Spatio-Temporal Crime Hotspots. In: International Cartographic
Conference ICC 2011, International Cartographic Association (ICA), Paris.
2 Kohonen, T., Hynninen, J., Kangas, J., & Laaksonen, J., 1996. SOM_PAK: The Self-Organizing Map Program Package.
Helsinki University of Technology, Otaniemi.
3 Guo, D., Chen, J., MacEachren, A. M., & Liao, K., 2006. A Visual Inquiry System for Space-Time and Multivariate
Patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer Graphics , 12 (6), pp. 1461-1474.