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
The “Big Society” features as a key part of the coalition government’s legislative programme, aiming to decentralise control of public services and to empower local communities to manage and deliver services that better meet local needs. Over the past twelve months I have been involved in an AHRC Funded Connected Communities project involving researchers at the universities of Glasgow, Edinburgh, Liverpool and Portsmouth, where we have been examining the various ways in the “Big Society” concept can be defined, measured, and more importantly mapped.
As part of this ongoing work we have created the following website (http://measuringbigsociety.org/) containing a short animated film about the Big Society and a survey where you can tell us how well you think Big Society will develop in your area?
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?
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 (again, trying to catch up here!) I spoke at the annual PLUG conference in London. As always, lots of very interesting talks about National Pupil Database applications and developments. All the talks are available on the PLUG website – link. My slides were as follows…
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.
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.
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.
I have fallen a bit behind on my blog as teaching activities have ramped up. However, over the next couple of days I will sort this out and post a couple of updates. The first of these is a talk I did at University of Oxford in April for one of their Retail Location Analysis courses. My daughter was sick and in hospital so I recorded this the night before I was supposed to appear. It was also the first time I have used screen capture software – this is actually very good, and no doubt I will use this again for my lectures. I did however find out that this takes a much longer time to record than doing a lecture – the temptation to re-record segments is far too tempting!
the Lower Super Output Area E01021988 located near to Tendring in the East of England. This output area was ranked the third most deprived area in 2007. The most deprived area in 2007 was located in Liverpool (E01006755), which in the 2010 classification is now ranked sixth. I would claim this is due to my recent move to the city, however unfortunately the data pre-date my arrival. Deprivation in seaside locations is explored in this DCLG report from 2007. View Larger Map
The Index of Multiple Deprivation 2010 is available to download here.
Last week I started creating some data extraction code for the new England and Wales crime maps website using the R software / language. Although there is an API, a more efficient way of accessing all of the data (and without causing stress to their API server) is to download the CSV files located here for each police force. To download these manually, extract the data and process in R would take a very long time, not to mention be very dull. BUT….
With some R magic, all is not lost, and the data can even be easily imported into a MYSQL database with ease using a relatively small amount of code.
You can use the code to download data by street, or by “neighbourhood” (I am still not sure what these are?). And, with luck, if the server / naming conventions do not change, the code should be re-usable each time new data is released.
You need both R and MYSQL installed – see here and here.
The only things which you need to specify in the code are:
con <- dbConnect(MySQL(), user="root", password="password", dbname="Police", host="localhost")#and
level <-'street'#'street or neighbourhood'
downloaddir <-'/home/alex/Desktop/'#where you will download the files
I have been waiting for this software for an age it seems – actually… only since about last October! Previously Zotero has integrated into Firefox, however, with a plethora of other (and often) quicker browsers this has always annoyed me (but not that much as it is free!). Anyway, I may be going wholesale Chrome in a couple of months now that this new version of Zotero is out which runs standalone: http://www.zotero.org/support/standalone. Initial tests seem that this is again a very nippy bit of software. I maintain a public list on Zotero with geodemographic references – this can be found here.