Alex Singleton

Reader in Geographic Information Science at the University of Liverpool

© 2014. Alex Singleton All rights reserved. About my blog code

Transport Map Book

Transport Map Book The Transport Map Books are available for each local authority district in England and present a series of maps related to commuting behaviour. The data are derived from multiple sources including: the 2011 Census, Department for Transport estimates and the results of a research project looking at carbon dioxide emissions linked to the school commute.

All the maps are available to download HERE; and the R code used to create them and the emissions model is on Github.

Travel to work flows

Travel to work flows These data relate to Middle Layer Super Output Area (MSOA) level estimates of travel to work flows by transport mode. The raw data are available from the ONS. For the maps, the flows have been limited to those both originating and terminating within each local authority district.

Accessibility to Services

Accessibility to Services The Department of Transport provide a range of statistics at Lower Layer Super Output Area level about accessibility and connectivity to a series of key services. A subset of variables were mapped.

Emissions associated with the school commute

Emissions These data were generated as part of an ESRC funded project investigating emissions associated with the school commute. The model provides an estimate of the carbon dioxide emitted at Lower Layer Super Output Area level. For full details of the methodology, see the open access paper:

Singleton, A. (2013) A GIS Approach to Modelling CO2 Emissions Associated with the Pupil-School Commute. International Journal of Geographical Information Science, 28(2):256–273.

Car availability and travel to work mode choice

Car These attributes were extracted from the 2011 census data provided by Nomis at Output Area level.

Distance and mode of travel to work

Distance Workplace zones are a new geography for the 2011 census for the dissemination of daytime population statistics. A number of attributes were selected related to transport, and also were downloaded from Nomis.

Geodemographics and Code

Talk given at NUI Maynooth - Code and the City, 3-4 September 2014.

Temporal OAC

As part of an ESRC Secondary Data Analysis Initiative grant Michail Pavlis, Paul Longley and I have been working on developing methods by which temporal patterns of geodemographic change can be modelled.

Much of this work has been focused on census based classifications, such as the 2001 Output Area Classification (OAC), and the 2011 OAC released today. We have been particularly interested in examining methods by which secondary data might be used to create measures enabling the screening of small areas over time as uncertainty builds as a result of residential structure change. The writeup of this work is currently out for review, however, we have placed the census based classification created for the years 2001 - 2011 on the new website, along with a change measure.

Some findings

  • 8 Clusters were found to be of greatest utility for the description of OA change between 2001 and 2011 and included
    • Cluster 1- "Suburban Diversity"
    • Cluster 2- "Ethnicity Central"
    • Cluster 3- "Intermediate Areas"
    • Cluster 4- "Students and Aspiring Professionals"
    • Cluster 5- "County Living and Retirement"
    • Cluster 6- "Blue-collar Suburbanites"
    • Cluster 7- "Professional Prosperity"
    • Cluster 8 – "Hard-up Households"

A map of the clusters in 2001 and 2011 for Leeds are as follows:

  • The changing cluster assignment between 2001 and 2011 reflected
    • Developing "Suburban Diversity"
    • Gentrification of central areas, leading to growing "Students and Aspiring Professionals"
    • Regional variations
      • "Ethnicity Central" more stable between 2001 and 2011 in the South East and London, than in the North West and North East, perhaps reflecting differing structural changes in central areas (e.g. gentrification)
      • "Hard-up Households” are more stable in the North West and North East than the South East or London; South East, and acutely so in London, flows were predominantly towards “Suburban Diversity”

Advances in the geodemographic study of population and place

A talk given at the Oxford Institute for Population and Ageing, University of Oxford 4/6/14.

What is so big about big data?

Talk given at National Centre for Geocomputation: Home - NUI Maynooth 21/5/14.

Census Open Atlas Project Version Two

CensusAtlasThis time last year I published the first version of the 2011 Census Open Atlas which comprised Output Area Level census maps for each local authority district. This turned out to be quite a popular project, and I have also extended this to Japan.

The methods used to construct the atlases have now been refined, so each atlas is built from a series of PDF pairs comprising a map and a legend. These are generated for each of the census variable (where appropriate), with the layout handled by Latex. As with demonstrated in the Japan atlas, this also gives the advantage of enabling a table of contents and better description for each map.

Some other changes in version two include:

  • Labels added to the legends
  • Scale bars added
  • Addition of the Welsh only census variables
  • Removal of overly dense labels

When the original project was picked up by the Guardian I made an estimate of the actual number of maps created, however, for this run, I counted them. In total 134,567 maps were created.

Download the maps

The maps can be downloaded from github; and again, the code used to create the maps is here (feel free to fix my code!).

Automated Savings

A manual map might typically take 5 minutes to create - thus:

  • 5 minutes X 134,567 maps = 672,835 minutes
  • 672,835 minutes / 60 = 11,213.9 hours
  • 11,213.9 hours / 24 = 467.2 days (no breaks!)

So, if you take a 35 hour working week for 46 weeks of a year (6 weeks holiday), this equates to 1,610 hours of map making time per year. As such, finishing 134,567 maps would take 6.9 years (11,213.9 / 1,610).

This would obviously be a very boring job; however, it would also be expensive. If we take the median wages of a GIS Technician at £20,030 then the "cost" of all these maps would be 6.9 X £20,030 = £138,207. This toy example does illustrate how learning to code can help save significant money, and indeed what a useful tool R is for spatial analysis.