Using Geospatial datasets for crime analysis: User needs and user requirements

'Geospatial data', can potentially be very useful in understanding crime patterns and in explaining why crime hotspots occur in particular places. However, the extent to which beneficiaries are aware that such data exists is largely unknown, and the added value that such data might bring is largely unexplored.

The GeoCrimeData project investigated current awareness and interest levels of geospatial data for crime analysis (particularly in understanding the distribution of domestic burglary) by engaging with potential end users. These included practitioners (for example police and community safety partners) and academics with research interests in this area. The potential needs and user requirements of geocrime data for crime analysis were examined in two ways

• An online survey of academics and practitioners
• A follow on one day Stakeholder Workshop of academics and practitioners

This blog entry summarises the findings from our user needs analysis. A more detailed report is available as an online document.

The online survey
80 stakeholders (academic and practitioner) who were involved in geospatial data and or burglary analysis were contacted and asked to complete an online survey. 33 respondents completed this questionnaire (40% response rate), and the respondents were almost a 50.50 split in terms of practitioner and academic respondents, about 60% worked in the UK and 40% in the US, 55% were male  and just over 50% were aged 36-50. Respondents were asked to identify three key factors in residential burglary and these are summarised in Table 1 below. A number of these have been developed as products for this project and they are highlighted with an asterisk (*) in the table.

Table 1. Understanding Domestic Burglary Hot Spots

Which of the following do you think are the three most important in understanding domestic burglary hotspots?	Count
Prior burglary	18
Time of day	13
*Type of neighbourhood (affluent, mixed, disadvantaged)?	12
*Accessibility of the street (being able to enter and escape from the street easily)	12
*Street layout/design	9
Design of the property	9
Bordering a neighbourhood that is very different (e.g. a rich area bordering a poor area)	8
*Type of property (flat, bedsit, terrace, semi, detached)?	6
Social cohesion	5
*Visibility of the property from the road?	3
Housing Tenure	2
Being close to a parade of shops	2
Being close to a school	0
Other
Distance/proximity of/number of offenders living near	5
Area Management (burglary watch, estate management etc)	2
Alarms/Physical Security ofproperty	2
Landuse	1

In addition the respondents were presented with a series of six maps which showed examples of the use of different types of geospatial data for burglary analysis and they were asked specific questions about the utilities of each map. The main comments made were:

• The hot spot map of burglary only provides a general overview of crime & needs more context to help understand and respond to crime
• It is important to produce maps that show the timing of burglary offences
• The maps could benefit from animation and hotspot benchmarking
• The maps showing house types are useful but without statistical tables to accompany them they are of limited value
• The complexity of the accessibility analysis was problematic for most crime analysts. The issue here is how this measure might be better communicated
• Cross-referencing house type with accessibility and repeat victimisation was identified as a useful analysis to undertake
• The domestic gardens map was perceived to be the least useful

The Stakeholder Workshop

Following on from the online survey, a one day Stakeholder Workshop was held in Central London with 12 persons who completed the survey (6 practitioners and 6 from academia). The purpose of this was to build on the survey results and to further explore what crime analysts might find useful in analysing and understanding domestic burglary (using geospatial datasets) and how the technical or administrative barriers to using such data can be overcome.

The key findings from the Stakeholders Workshop are summarised in Table 2.