  Violent crime in London is an area of increasing interest following policing
and community budget cuts in recent years. Understanding the locally-varying
demographic factors that drive distribution of violent crime rate in London
could be a means to more effective policy making for effective action. Using a
visual analytics approach combined with Statsitical Methods, demographic
features which are traditionally related to Violent Crime Rate (VCR) are
identified and OLS Univariate and Multivariate Regression are used as a
precursor to GWR. VIF and pearson correlation statistics show strong
colinearity in many of the traditionally used features and so human reasoning
is used to rectify this. Bandwidth kernel smoothing size of 67 with a Bi-Square
type is best for GWR. GWR and OLS regression shows that there is local
variation in VCR and K-Means clustering using 5 clusters provides an effective
way of seperating violent crime in London into 5 coherent groups.
