Table of Contents
Crime analysts and researchers in related fields could benefit substantially by using existing geospatial data (e.g. Open Street Map and Ordnance Survey MasterMap) but rarely make use of these datasets at present. This is due largely to the technical expertise required to obtain the data and analyse them spatially using complex geographical routines. This project has explored many novel data sources and manipulated them using geographical routines in order to generate new forms of spatial intelligence that can help to add value to the interpretation of recorded crime data. This post will summarise the methodology and results of the project as a whole. The following table provides a brief overview of our project outputs – full details about outputs are available here.
2. A Review of Geospatial Data
The first stage in the project was to review the range of geospatial data that could be used by the project. The full review is available here. We looked at a range of data sources, including those available as part of the data.gov scheme (a national programme of releasing public data to make government more transparent), those produced by LandMap and the numerous Ordnance Survey (OS) datasets (some of which are publicly available and others which have restrictive licenses). Once we had determined what data were available we were able to conduct a user needs analysis in order to identify particular sources that could be useful to crime analysts.
3. User Needs Analysis
The 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 spatial data for crime analysis were examined in two ways:
Following the results of the user needs analayis, we were able to focus on a smaller number of data sources which are best suited to providing the information in which the crime analysts were interested. The data we identified were two OS MasterMap products called the 'Integrated Transport Network' (a road network) and the 'Topography Layer' (which contains land and building features). In addition, we identified OpenStreetMap (OSM) data as an alternative source of road network data (see Figure 2). OSM has the advantage of being publicly available.
4. Geospatial Methods
Having identified the spatial attributes (i.e. data items) that crime analysts were interested in and the data that could be used to generate the new intelligence, we developed geospatial methods to analyse the data. The methods were implemented in a software package that is open source and freely available. For more information about the methods themselves, refer to the Geospatial Methods document.
In terms of the road network data, vehicle or pedestrian traffic volume was identified as a factor that could influence crime patterns and hence the project estimated how accessible each street segment was (i.e. the stretches of road between junctions) by calculating each road's integration value. Integration is a mathematical measure that can be used to estimate how well connected a road is to the remainder of the road network which, interestingly, this has also been found to correlate with traffic volume.
With the building data, there were a number of features identified by the crime analysts that could increase or decrease a house’s risk of becoming a target of burglary. Although many factors must be left for future work, the project was able to calculate the density of buildings, the type of the house (detached, semi-detached, terraced or corner-terrace) and the distance to the nearest road or footpath. Each of these attributes can have an influence on crime risk.
5. Data Outputs
The outputs from the project have been uploaded into repositories and are available for download. The Data Outputs document has more details and also acts as a source of technical information about what the data comprise. Although the OpenStreetMap road network is publicly available, the buildings data can only be accessed by users of the Digimap Service who hold the appropriate Ordnance Survey licenses.
6. The Added Value of GeoSpatial Data
The final stage in the project was to identify how the data can be used to generate new information about what underpins crime patterns and inform crime analysis. For the full analysis, see the Added Value of Geospatial Data document. In terms of the building data, we calculated the number of burglaries in each property type and then examined how the property-specific burglary rates varied across different types of residential neighbourhood. These were identified with a commonly used and freely available socio-demographic classification called the Output Area Classification. Interestingly, the risk of burglary in each property type varied substantially by the type of neighbourhood that the building was located in. For example, there were exceptionally low rates of burglary among terraced properties in Blue-Collar Communities and Prospering Suburbs all of which call for further investigation and, significantly, none of which would have been revealed using conventional burglary rates that fail to identify crime by house type and instead use a rate such as the number of burglaries per thousand houses.
To investigate how the road integration data might be used, we aggregated the integration values for all roads in an area and divided that by the number of roads to calculate a mean integration value (i.e. an average accessibility score). We then examined how burglary rates by house type varied across neighbourhoods with different levels of accessibility. We found a strong link between increasing levels of accessibility and burglary rates within detached houses (the busier the streets, the higher the burglary rate) which suggests that detached properties were much more vulnerable if they were in more accessible streets. For example, detached houses were over three times more likely to be burgled in the most accessible 10% of OAs compared with the least accessible 10%; a major difference indeed! Once again, these findings would not have emerged without bringing together different geospatial datasets.
Other Information
The team
- Introduction
- Review of geospatial data
- User Needs Analysis
- Geospatial Methods
- Data Outputs
- The Added Value of GeoSpatial Data
Crime analysts and researchers in related fields could benefit substantially by using existing geospatial data (e.g. Open Street Map and Ordnance Survey MasterMap) but rarely make use of these datasets at present. This is due largely to the technical expertise required to obtain the data and analyse them spatially using complex geographical routines. This project has explored many novel data sources and manipulated them using geographical routines in order to generate new forms of spatial intelligence that can help to add value to the interpretation of recorded crime data. This post will summarise the methodology and results of the project as a whole. The following table provides a brief overview of our project outputs – full details about outputs are available here.
| Name | Description | License | Download Location |
| GeoCrimeData Buildings - Leeds (version 2) | OS MasterMap data showing buildings and some new information about them (distance from footpath, house type etc). | Ordnance Survey Data Sub-Licence Agreement | http://hdl.handle.net/10389/210 |
| GeoCrimeData Road Accessibility - Leeds (version2) | OS ITN road network data for Leeds with measures of integration. | Ordnance Survey Data Sub-Licence Agreement | http://hdl.handle.net/10389/209 |
| GeoCrimeData Road Accessibility - GB (version 1) | OpenStreetMap road network data for GB with measures of integration. | Creative Commons Attribution-ShareAlike 2.0 | http://www.sharegeo.ac.uk/handle/10672/295 |
2. A Review of Geospatial Data
The first stage in the project was to review the range of geospatial data that could be used by the project. The full review is available here. We looked at a range of data sources, including those available as part of the data.gov scheme (a national programme of releasing public data to make government more transparent), those produced by LandMap and the numerous Ordnance Survey (OS) datasets (some of which are publicly available and others which have restrictive licenses). Once we had determined what data were available we were able to conduct a user needs analysis in order to identify particular sources that could be useful to crime analysts.
3. User Needs Analysis
The 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 spatial data for crime analysis were examined in two ways:
- Through an online survey of academics and practitioners
- Through a follow on one day Stakeholder Workshop of academics and practitioners
Following the results of the user needs analayis, we were able to focus on a smaller number of data sources which are best suited to providing the information in which the crime analysts were interested. The data we identified were two OS MasterMap products called the 'Integrated Transport Network' (a road network) and the 'Topography Layer' (which contains land and building features). In addition, we identified OpenStreetMap (OSM) data as an alternative source of road network data (see Figure 2). OSM has the advantage of being publicly available.
4. Geospatial Methods
 |
| Estimates of road 'business' in Scotland |
 |
| Estimates of building type in Leeds |
With the building data, there were a number of features identified by the crime analysts that could increase or decrease a house’s risk of becoming a target of burglary. Although many factors must be left for future work, the project was able to calculate the density of buildings, the type of the house (detached, semi-detached, terraced or corner-terrace) and the distance to the nearest road or footpath. Each of these attributes can have an influence on crime risk.
5. Data Outputs
The outputs from the project have been uploaded into repositories and are available for download. The Data Outputs document has more details and also acts as a source of technical information about what the data comprise. Although the OpenStreetMap road network is publicly available, the buildings data can only be accessed by users of the Digimap Service who hold the appropriate Ordnance Survey licenses.
6. The Added Value of GeoSpatial Data
The final stage in the project was to identify how the data can be used to generate new information about what underpins crime patterns and inform crime analysis. For the full analysis, see the Added Value of Geospatial Data document. In terms of the building data, we calculated the number of burglaries in each property type and then examined how the property-specific burglary rates varied across different types of residential neighbourhood. These were identified with a commonly used and freely available socio-demographic classification called the Output Area Classification. Interestingly, the risk of burglary in each property type varied substantially by the type of neighbourhood that the building was located in. For example, there were exceptionally low rates of burglary among terraced properties in Blue-Collar Communities and Prospering Suburbs all of which call for further investigation and, significantly, none of which would have been revealed using conventional burglary rates that fail to identify crime by house type and instead use a rate such as the number of burglaries per thousand houses.
To investigate how the road integration data might be used, we aggregated the integration values for all roads in an area and divided that by the number of roads to calculate a mean integration value (i.e. an average accessibility score). We then examined how burglary rates by house type varied across neighbourhoods with different levels of accessibility. We found a strong link between increasing levels of accessibility and burglary rates within detached houses (the busier the streets, the higher the burglary rate) which suggests that detached properties were much more vulnerable if they were in more accessible streets. For example, detached houses were over three times more likely to be burgled in the most accessible 10% of OAs compared with the least accessible 10%; a major difference indeed! Once again, these findings would not have emerged without bringing together different geospatial datasets.
Other Information
The team
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