Category: Geodemographics

Geodemographic Software: OAC Coding and Description

Last week saw the launch of a plethora of new tools related to the open geodemographic classification OAC.

I am just about to step down as chair of the Output Area Classification User Group which represents OAC and last week convened my final annual conference. One of my long standing irritations about the Output Area Classification has been the lack of simple tools that firstly enable you to append the classification to your own postcode level data, and secondly; the availability of descriptive material about things end users are actually interested in. However, in the past few months I have pushed hard to rectify these issues through our user group.

OACoder – Postcodes to OAC Conversion

Through one of my side projects with researchers at UCL (Public Profiler) we have released a simple coding tool called OACoder that uses the newly released ONS Open NSPD dataset. This can be downloaded here and takes a CSV list of postcodes and simply appends the OAC codes.

OAC Grand Index

A grand index is a basic descriptive tool which disaggregates index scores by geodemographic clusters for multiple responses to survey variables, administrative and transactional data. This is a resource which the user group will add to over time. This can be downloaded here.

OAC Colours

Ongoing debate over the years has surrounded the standardisation of colours used in the generation of OAC maps and graphs. These are by no means ‘official’ colours, however are those commonly used by members of the OACUG committee. The colour codes can be found here.

NYC Cab Flow Data

NYC Yellow Cabs have GPS fitted to them which have been tracked for the past year or so… anyway, there is an excellent article on this in the New York Times with obligatory map visualization:

http://www.nytimes.com/2010/04/03/nyregion/03icab.html

map (http://www.nytimes.com/interactive/2010/04/02/nyregion/taxi-map.html?ref=nyregion)

If we assume that taxi are the generally present in areas where people
are, this could be a useful source of population mobility and certainly the sort of data which may be useful in real-time geodemographics.

Areaprofiles

I have just finished creating and adding some content to my new blog Areaprofiles which will provide discussion on the use and creation of geodemographic and neighborhood classifications. You will note an American spelling of neighbourhood in the last sentence and the blog will also have an international flavour. Although area classification feature on other geomarketing blogs, these are predominantly focused on commercial applications. Thus, the main thrust of this blog will be about how area classification have utility for investigation of the form and function of cities rather than how they can be used to sell more units of product X. I also hope to add some tutorials on how classifications can be created from data sources, normalisation procedures and aggregation methods.

MSN Local a Crime?

Somehow this passed me by but http://local.uk.msn.com/ now includes some socio-economic data at a national level.

The crime information is worth a look and appears created from Experian data using the British Crime Survey rather than actual crime occurrences – then interpolated some how into a surface. Additionally, when you click the map, the data returned includes a series of the long descriptive profiles for the Mosaic Types or Groups within the “area” (however defined).

I really do worry about these types of commercial representation, specifically given the lack of detail over the methods used and the potential consequences of their erroneous interpretation. The information reported on this page about the crime data: http://money.uk.msn.com/MSN-Local/help.aspx#C appears to be all about perceptions of crime, which is very different from actual crime – as specified on the map. I am guessing that the crime map is created by taking the British Crime Survey, appending Mosaic, modeling weighted values for “perception” / fear of crime – then taking these values at postcode level and interpolating between them, probably with IDW into high / low scores.

The potential for creating spurious values is HUGE given so many uncertainties – at some point in this operation, a categorical value from a black box geodemographic is used to model a continuous point score, not to mention the fact that further values are then interpolated between these points in the conversion to a raster surface. To me, this really doesn’t sound like a set of plausible operations – why not just plot the crime domain of the IMD like we do on LondonProfiler?

Moving to real time segmentation: efficient computation of geodemographic classification

Two prizes came from the UCL camp at this years GISRUK. The previously mentioned mashup, and also the prize for the best young research paper by my student Adnan - titled: “Moving to real time segmentation: efficient computation of geodemographic classification -
M Adnan, A D Singleton, C Brunsdon and P A Longley”.

Moving to real time segmentation: efficient computation of geodemographic classification

Ordnance Survey Geospatial Mashup Challenge

Today was a jolly nice day with Ollie and I winning the Ordnance Survey Geospatial Mashup Challenge at GISRUK 2009. There were some really good entries and I was quite surprised with the result. One site which really impressed me was: “User Adaptive Trip Planner -Ramya Venkateswaran, Pia Bereuter, University of Zurich – hopefully this will be online soon”.

Our winning site is was titled “Contextualising Educational Careers for Widening Participation in Higher Education”, however is really a nice educational atlas. This should be online soon with luck, but here are a few slides to view for now:

Contextualising Educational Careers for Widening Participation in Higher Education
View more presentations from Alex Singleton.

And a short youtube:

Virtual Geodemographics: Repositioning Area Classification for Online and Offline Spaces

I have just published a new working paper:

Abstract:

Computer mediated communication and the Internet has fundamentally changed how consumers and producers connect and interact across both real space, and has also opened up new opportunities in virtual spaces. This paper describes how technologies capable of locating and sorting networked communities of geographically disparate individuals within virtual communities present a sea change in the conception, representation and analysis of socioeconomic distributions through geodemographic analysis. We argue that through virtual communities, social networks between individuals may subsume the role of neighbourhood areas as the most appropriate units of analysis, and as such, geodemographics needs to be repositioned in order to accommodate social similarities in virtual, as well as geographical, space. We end the paper by proposing a new model for geodemographics which spans both real and virtual geographies.

Download the full paper from the CASA website.