Group 11 Overview

Abstract

Based on UN’s Survey of Crime Trends published in 2006, England and Wales have one of the highest crime rates among OECD countries. We have developed CrimeModeler, a geospatially modelling tool to investigate the spatial variation of crime across different districts in England and Wales, and the relationship between crime and socio-economic characteristics for each district. As it is common for neighbouring areas to have correlation in their crime rate, we compare the use of geographically weighted regression (GWR) and conventional (or global) multiple regression model to see whether a better result can be obtained from GWR. We will also investigate whether there are certain variables that have an impact on crime rate in one area but not in another. Local governments may use this information to come up with better policies to tackle crime.

Introduction

Based on UN’s Survey of Crime Trends published in 2006, comparisons were made for OECD countries by CIVITAS Institute for the Study of Civil Society. Comparisons are based on six of the most serious crimes: intentional homicide, rape, robbery, assault causing serious bodily harm, burglary and vehicle theft.

The main datasets of interest are:

•   Stop and search dataset
•   Crime Data
•   Outcome Data
•   Shape of police force boundaries
•   Shape of neighbourhood boundaries

Secondary dataset includes people, population and community data from Office for National Statistics.

•   Health and socialcare
•   Housing
•   Income and wealth
•   Shape of police force boundaries
•   Population

Visualisation/Aanlysis

• Choropleth map (Temporal and Spatial)
• Clustering
• Parallel plot
• Time series analysis

Statistical and visualisationTools

• R
• R Shiny
• Bootstrap (Update from version 3 in shiny to version 4)
• D3 (If alternatives in R is not available or not as rich/interactive)
• Angular Js (For single page application routing and databinding)