Neighbourhood Analysis: Crime and Security

Overview

This one-day, hands-on GIS-based course on neighbourhood analysis explores:

• analytical techniques for identifying priority neighbourhoods

• the use of geodemographic lifestyle datasets

• the signal crimes approach and mapping of visual audits and surveys

• the data to consider in a Neighbourhood Profile

The course is run by UCL's Jill Dando Institute of Security and Crime Science. It's held at our offices in London, but can also be delivered at your site for a minimum of six staff.

Who this course is for

This GIS-based course is for users of MapInfo and ArcGIS (ArcMAP).

It's aimed at:

• police

• neighbourhood and CSP analysts

• researchers and information officers

Course content

The course includes the following topics:

The vulnerable localities index (VLI)

You'll be taken through a step-by-step approach to calculating the VLI, including a discussion of the merits of each indicator used in its calculation, and how the VLI can be best used to help identify priority neighbourhoods.

Using geodemographics for neighbourhood policing

Geodemographic lifestyle data has been popular in the marketing field for a number of years for helping to target advertising campaigns and profile customer populations.

In recent years this data has been linked with results from the British Crime Survey to provide another dimension to its use, which includes identifying community differences between fears, worries and perceptions of crime.

We'll explore the qualities of this type of data, how it can be analysed and mapped, and its use in supporting neighbourhood policing requirements.

Neighbourhood profiles

We'll discuss the contents that should go into a summary neighbourhood profile.

Entry requirements

You'll need at a minimum of good/intermediate Excel skills. The course is only suitable for users of Mapinfo, ArcView, ArcGIS, or Cadcorp SIS.

Cost and concessions

There's a 10% reduction for bookings of two or more people - all group delegates must be booked at the same time.