Understanding crime patterns cannot be achieved just by looking at recorded crime data held by the police. You need information about all of the other influences on crime patterns that might have an affect. The good news is that much of the data about the geographical context of the crime or what we call the 'crime environment' is already available in the form of geospatial datasets. When we use these as well as crime data we can begin to see what's happening on the ground and the links between crime patterns and what shapes them.
The GeoCrimeData project has generated datasets about places produced from what we call the primary geospatial components of 'place'. These are referred to as polygons (e.g. areas/neighbourhoods), lines (e.g. roads) and points (e.g. houses). Our full analysis is available as an
online document, and this post is a summary of the analysis.
House Type Analysis
The house type information has been produced by software created specifically for our project that examines the shapes of buildings as they appear on Ordnance Survey MasterMap and calculates whether they are detached, semi-detached, a terraced house or a new category of house type, a corner property. (For more information about the data and how you can download it, see
this document). Once we were able to estimate the type of each house, it is possible to combine this with burglary data to work out how many burglaries occurred in the different types of properties. This would not be possible without the geospatial data because normally data of this type been aggregated. For example, we might know that there were 20 burglaries in a particular area, and there were 100 terraced houses and 50 detached houses in the same area, but we would not know how many burglaries occurred in each house type!
To determine some of the benefits of using our new geospatial data, we examined house type, burglary rates and information about community demographics from the Output Area Classification (OAC). City Living areas had consistently higher burglary rates than any other Super Group for each of the different property types. A detached property in City Living neighbourhoods was over four times more likely to be burgled than a detached property in parts of Leeds classified as Countryside and over three and a half times more likely to be burgled than a detached house in the Prosperous Suburbs.
City Living areas have a lot of privately rented property in them and younger single residents which often means that there are fewer people around during the day to keep an eye on things. This is what criminologists 'poor guardianship' and neighbourhoods like this are known to have higher levels of crime. These were clearly the types of area in Leeds most vulnerable to burglary.
Most property types in the Constrained by Circumstances social group had burglary rates that were more or less in line with the average for Leeds except for detached houses which were over two and a half times more likely to be burgled in these areas than in Leeds generally. There were also exceptionally low rates of burglary among terraced properties in Blue-Collar Communities and Prospering Suburbs all of which call for further investigation and, significantly, all of which would have been masked using conventional burglary rates.
Street Accessibility Analysis
The accessibility of each street segment (that is stretches of road between road junctions) to each other street has also been calculated from street network data (see the
methods description for information about this method or
this document to download street accessibility data). A measure of accessibility can be useful in crime analysis because it has been found to correlate with levels of vehicle or pedestrian traffic - highly accessible roads can expect higher volumes of traffic. Just as it was possible to look at differences in burglary rates by property type across the Super Groups, the GeoCrimeData Project has also made it possible to examine such differences in parts of Leeds that had been grouped together according to how busy the streets are.
Each street segment has been given a busyness score or what is technically called an 'Integration Value'. These integration values for all of the streets occupying each Output Area have then been added up and divided by the number of streets in each OA to give an average integration value. Once these have been calculated for each OA it is fairly straightforward to rank the OAs in decreasing order of their integration values and to identify the top 10% of OAs with the highest values (i.e. those having the busiest streets in Leeds), the next 10% of areas and so on until all OAs have been grouped together. We have defined 10 groups or deciles each containing 10% of OAs ranging from the top 10% (the busiest streets) through to the bottom 10% (OAs with the least busy/ most isolated streets) in Leeds.
This has enabled us to answer the question 'how do burglary rates in the most accessible areas compare with those in the least accessible areas?' and to go on to answer the more precise question 'how do burglary rates within each property type vary by street accessibility in Leeds?'.
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| Figure 1: Burglary rates by accessibility for detached houses |
There appears to be a strong link between increasing levels of accessibility and burglary rates within detached houses. This is a positive relationship which means that the busier the streets, the higher the burglary rate (see Figure 1). With just two exceptions (Deciles 7 and 8) , the higher the decile the higher the burglary rate for detached properties. This 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!
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| Figure 2: Burglary rates by accessibility for terraced houses |
Burglary levels of terraced properties were not quite as susceptible to increasing levels of accessibility compared with other house types. Burglary rates in the most accessible decile (10) were just under twice those in the least accessible decile (1). This can be seen in Figure 2.
Once again, these findings would not have emerged without bringing together different geospatial datasets.
