Multi-Scale Analysis of Crime and Incident Patterns in Camden Dawn Williams 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), Han, Jiawei., "Mining Spatiotemporal Knowledge: Methodologies and Research Issues." Geospatial Visualization and Knowledge Discovery Workshop. University Consortium for Geographic Information Science, Virginia.

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. Planar and Network Kernel Density Estimation 1 Hagenauer, J., Helbich, M., & Leitner, M., 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.

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.

U Distance MatrixMap MatrixReorderable Matrix Parallel Coordinate Plot The Self Organizing Map (SOM) is an unsupervised, learning neural network 1. It allows simple, geometric relationships to be produced from vector quantization analysis of complex multidimensional datasets 2 through a combination of clustering and dimension reduction 3 while preserving topology 2. The Self Organizing Map Method 1 Hagenauer, J., Helbich, M., & Leitner, M., 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., SOM_PAK: The Self-Organizing Map Program Package. Helsinki University of Technology, Otaniemi. 3 Guo, D., Chen, J., MacEachren, A. M., & Liao, K., A Visual Inquiry System for Space-Time and Multivariate Patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer Graphics, 12 (6), pp