The Guardian recently contacted me with a new extract of data related to the riots and was interested in some further analysis looking at the IMD. The data comprised records for 835 people who have appeared in court on charges related to the 2011 riots. The data have been collected from magistrate courts and represent roughly a 42% sample. This is calculated by comparing the Guardian data file record frequency with the ministry of justice counts of people appearing before the courts by midday on 12th October 2011 (1,984 people). Within the Guardian data there were 83 cases with duplicate postcodes; which the Guardian identified as either the same person appearing in court multiple times, or, that these related to multiple people from the same postcode. Duplicates were included for these analyses; however, depending on the distribution between the two groups, this could introduce bias into these findings.
Each record comprised a postcode and enabled identification of the Lower Super Output Area (LSOA) in which the address resided. Of the 835 records there were 767 matches; leaving 68 records without LSOA codes. Typically these records either contained an erroneous postcode, or, a postcode recorded at either Sector or District level. Using this information it was then possible to match the Index of Multiple Deprivation for both 2004 and 2010.
These latest data showed that around 59% of those appearing in court identified their residential location as being within the 20% most deprived areas in England (overall 2010 IMD). Over the different domains of the IMD, these patterns were reproduced with little variation from the overall pattern (See Figure 1), perhaps with the exception of the Education domain which was a little more evenly distributed, albeit still bias towards more deprived areas.
According to the change in IMD decile between 2004 and 2010; for 59% of those addresses appearing in the sample, these areas had not changed decile; however 15% had got worse and 26% better. Ordering into decile bins reduced some detail in these trends; however, if the raw position of change in rank was considered, 37% were worse and 64% better.
These patterns are better explained by Table 1 which shows a cross tabulation between the IMD decile position in 2010 and the movement in position since 2004 for those appearing in the sample data. What this is showing is that for a large proportion of the sample, their addresses are within areas which are very deprived, and have either stayed very deprived since 2004 or become more deprived.
Table 1: Decile Change Cross Tabulated with 2010 IMD Decile
As I drove to work on my usual route last week I was faced with manoeuvring around burnt out cars, vans and melted wheelie bins which littered the road. This was a very odd experience, and one which I hope not to repeat. Many of those who lived in the vicinity of the riots here in Liverpool described it as a terrifying experience to witness. While pundits have begun their usual dissection of such events, I have started to think through the spatial dimensions of why this may have occurred?
The Guardian have today mapped the location of people who have been appearing on riot-related charges in English magistrates’ courts. As part of this exercise they have also been kind enough to release the underlying data.
On the basis of this sample (please see the Guardian website for details about representativeness and caveats), the Guardian note that around 73% were under 25 and 90% were male, however, the residential address can reveal many more characteristics. Simon Rogers at the Guardian very kindly granted me access to a mirror of the public data, however with higher resolution address details which enabled more precise spatial referencing. However, the data were patchy, and indeed many addresses lacked detailed references which seemed to depend on which magistrate court they were attached to. Around 170 of the records could be geocoded at a level enabling higher resolution analysis to be conducted.
If the 2010 Index of Multiple Deprivation is appended to each of the records, this shows that from the sample of those appearing on riot-related charges, around 41% live in the most deprived neighbourhoods (lower super output areas) in England.
The index of multiple deprivation is also available for 2007, and can be used to expand this analysis to measure if these areas have been getting more or less deprived over recent history. The 2007 classification was appended to the same data and the relative rank of the residential areas compared between the two time periods. The results demonstrate that not only are the majority of the areas deprived, but 66% of them have been getting worse between 2007 and 2010.
Thus, these limited data and analysis seem to suggest that those people who have been appearing on riot-related charges (typically young males) live in some of the most deprived areas of our largest cities, and in neighbourhoods where the conditions are getting worse rather than better. Rioting is deplorable, however, if events such as this are to be mitigated in the future, the prevailing conditions and constraints effecting people living in areas must form part of the discussion. A “broken society” happens somewhere, and geography matters!
When the Police.UK website was launched at the beginning of 2011 a reasonable amount of criticism was levied at the choice of representation used for the online mapping of the crime data. I wrote about this briefly over at Floating Sheep, and indeed spoke about it one very early morning on BBC Radio – thus I won’t repeat the issues. I recently received a knowledge exchange voucher from the University of Liverpool Business Gateway, and in collaboration with our new Chair of Human Geography Chris Brunsdon, we have begun to explore some of the representational issues of putting crimes on a map. This project was co-sponsored by the National Police Improvement Agency and this post details results from the pilot project.
A month or so ago my colleague Paul Longley and I ran a course for the National Centre for Research Methods on Geographical Data Visualisation and Geodemographics. The practical labs covered how you can code a set of postcodes by the Output Area Classification and how to generate a set of index score profiles from these data. A further practical also covered how to generate basic maps using the Geocommons services.
I thought it might be a useful set of materials to put in the public domain – as such – here are introduction slides:
The data for both the geocommons and geodemographics tutorials can be found here: GIS Data ; Postcode Data
Step 1: Geodemographic Coding
Downloads: Tutorial 1 is contained within the zip file supplied after downloading the OACoder software located here.
The first task was to visit and download the OACoder software located here; and then follow the tutorial found within the zip file for the example data. This software codes a set of postcodes by OAC. One the tutorial was complete, students were asked to code the data contained in the Postcode Data zip file using the same procedures.
Step 2: Creating Geodemographic Index scores
Downloads: Tutorial Document 2 and Index Score Calculation Excel File
Using the basic functions of Excel, this tutorial demonstrated to students how they could create index scores from two sets of coded postcodes. To speed this task up, an additional Excel file was provided which creates index scores and graphs. This file could be used to generate these content from any list of counts by OAC Sub Groups.
Step 3: Online Mapping using Geocommons
Downloads: Tutorial Document 3
Geocommons is an incredible service and provides a perfect platform for new users of GIS to create some simple, attractive and informative interactive maps with relative ease. This tutorial uses both Shapefile and GeoRSS data.
In a general sense I am concerned with how the social and spatial complexities of individual behaviours can be represented and understood within a framework of quantitative social science and computer modelling.
In particular, my research extends from a geographic tradition of area classification and I have developed a broad critique of the ways in which geodemographic methods can be refined through modern scientific approaches to data mining, geographic information science and quantitative human geography.
Department of Geography and Planning
The University of Liverpool
The Gordon Stephenson Building
74 Bedford Street South
Liverpool L69 7ZQ
Tel: +44 (0)151 794 3108
Fax: +44 (0)151 794 3125
If you would like my full CV, this can be downloaded as a PDF from the following link: Alex D Singleton CV
Peer Reviewed Journals
Feel free to contact me if you want access to a paper.
Riddlesden, D., A.D. Singleton, and T. B. Fischer. 2012. “A Survey of the Use of Geographic Information Systems in English Local Authority Impact Assessments.” Journal of Environmental Assessment Policy and Management
14 (01): 1250006. http://dx.doi.org/10.1142/S1464333212500068
Singleton, A.D. 2012. “The Geodemographics of Access and Participation in Geography.” The Geographical Journal
178 (3): 216–229. http://dx.doi.org/10.1111/j.1475-4959.2012.00467.x
Phillips, Richard, Diane Frost, and A.D. Singleton. 2012. “Researching the Riots.” The Geographical Journal
Singleton, A.D., Alan G Wilson, and Oliver O’Brien. 2012. “Geodemographics and Spatial Interaction: An Integrated Model for Higher Education.” Journal of Geographical Systems
14 (2): 223–241. http://dx.doi.org/10.1007/s10109-010-0141-5
Brunsdon, Chris, Paul Longley, A.D. Singleton, and David Ashby. 2011. “Predicting Participation in Higher Education: a Comparative Evaluation of the Performance of Geodemographic Classifications.” Journal of the Royal Statistical Society: Series A (Statistics in Society)
174 (1): 17–30. http://dx.doi.org/10.1111/j.1467-985X.2010.00641.x
Mateos, Pablo, Michael de Smith, and A.D. Singleton. 2011. “Developments in Quantitative Human Geography, Urban Modelling, and Geographic Information Science.” Transactions in GIS
15 (3): 249–252. http://dx.doi.org/10.1111/j.1467-9671.2011.01258.x
Singleton, A.D., and Daniel J. Lewis. 2011. “Including Accident Information in Automatic Bicycle Route Planning for Urban Areas.” Urban Studies Research
2011: 1–10. http://dx.doi.org/10.1155/2011/362817
Singleton, A.D., P.A. Longley, Rebecca Allen, and Oliver O’Brien. 2011. “Estimating Secondary School Catchment Areas and the Spatial Equity of Access.” Computers, Environment and Urban Systems
35 (3): 241–249. http://dx.doi.org/10.1016/j.compenvurbsys.2010.09.006
Longley, Paul A., A.D. Singleton, Keiji Yano, and Tomoki Nakaya. 2010. “Lost in Translation: Cross-Cultural Experiences in Teaching Geo-Genealogy.” Journal of Geography in Higher Education
34 (1): 21–38. http://dx.doi.org/10.1080/03098260902982476
Cheshire, James A., Paul A. Longley, and A.D. Singleton. 2010. “The Surname Regions of Great Britain.” Journal of Maps
6 (1): 401–409. http://dx.doi.org/10.4113/jom.2010.1103
Harris, Richard, A.D. Singleton, Daniel Grose, Chris Brunsdon, and P.A. Longley. 2010. “Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England.” Transactions in GIS
14 (1): 43–61. http://dx.doi.org/10.1111/j.1467-9671.2009.01181.x
Adnan, M., P.A. Longley, A.D. Singleton, and C. Brunsdon. 2010. “Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases.” Transactions in GIS
14 (3): 283–297. http://dx.doi.org/10.1111/j.1467-9671.2010.01197.x
Singleton, A.D. 2010. “The Geodemographics of Educational Progression and Their Implications for Widening Participation in Higher Education.” Environment and Planning A
42 (11): 2560–2580. http://dx.doi.org/10.1068/a42394
Mateos, Pablo, A.D. Singleton, and Paul Longley. 2009. “Uncertainty in the Analysis of Ethnicity Classifications: Issues of Extent and Aggregation of Ethnic Groups.” Journal of Ethnic and Migration Studies
35 (9): 1437–1460. http://dx.doi.org/10.1080/13691830903125919
Singleton, A.D., and Paul A. Longley. 2009. “Geodemographics, Visualisation, and Social Networks in Applied Geography.” Applied Geography
29 (3): 289–298. http://dx.doi.org/10.1016/j.apgeog.2008.10.006
Singleton, A.D. 2009. “Data Mining Course Choice Sets and Behaviours for Target Marketing of Higher Education.” Journal of Targeting, Measurement and Analysis for Marketing
17 (3): 157–170. http://dx.doi.org/10.1057/jt.2009.13
Longley, P.A., and A.D. Singleton. 2009. “Linking Social Deprivation and Digital Exclusion in England.” Urban Studies
46 (7): 1275–1298. http://dx.doi.org/10.1177/0042098009104566
Longley, P.A., and A.D. Singleton. 2009. “Classification Through Consultation: Public Views Of The Geography Of The E-Society.” International Journal of Geographical Information Science
23 (6): 737–763. http://dx.doi.org/10.1080/13658810701704652
Singleton, A.D., and P.A. Longley. 2009. “Creating Open Source Geodemographics - Refining a National Classification of Census Output Areas for Applications in Higher Education.” Papers in Regional Science
88 (3): 643–666. http://dx.doi.org/10.1111/j.1435-5957.2008.00197.x