Talk in Birmingham

9609e8b5ecb752618cbb0db56b5c0a46?s=47 alexsingleton
May 24, 2015

Talk in Birmingham



May 24, 2015


  1. @alexsingleton Consumer Data Research Centre An ESRC Data Investment

    Alex Singleton Reader in Geographic Information Science Department of Geography and Planning Future Geodemographics: Network Science and Big Data
  2. Geodemographics and widening participation Critical Geodemographics Topology of cities, networks

    and flows Outreach Visualisation Open Geocomputation and Big Data
  3. All publication titles and abstracts - to Dec 2014 The

    relationship between city structure, population behaviour and life chances
  4. Shevky and Bell, 1951

  5. Lower Scores = Higher “Class” John Edwards (1978) Urban Socio-Spatial

    Change: A Social Area Analysis of Time-Series Data (Birmingham 1961-1966). CURS, University of Birmingham.
  6. None
  7. Geodemographics and widening participation 0% 10% 20% 30% 40% 50%

    60% 70% 80% 90% 100% 2002 2003 2004 2005 2006 2007 2008 Unknown Routine occupations Semi-routine occupations Lower supervisory and technical occupations Small employers and own account workers Intermediate occupations Lower managerial and professional occupations Higher managerial and professional occupations
  8. 0 10 20 30 40 50 60 70 80 1

    2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 HE Progression Rate with 95% Confidence Intervals (%) Geodemographics and widening participation Wealthy Achievers Urban Prosperity Comfortably Off Moderate Means Hard Pressed
  9. 0.0 1.0 2.0 3.0 4.0 5.0 6.0 1A:Wealthy2Executives 1B:Affluent2Greys 1C:Flourishing2Families

    2A:Prosperous2 Professionals 2B:Educated2Urbanites 2C:Aspiring2Singles 3A:Starting2Out 3B:Secure2Families 3C:Settled2Suburbia 3D:Prudent2Pensioners 4A:Asian2Communities 4B:PostLIndustrial2Families 4C:BlueLCollar2Roots 5A:Struggling2Families 5B:Burdened2Singles 5C:HighLRise2Hardship 5D:Inner2City2Adversity Medicine'and'Dentistry 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 1A:Wealthy1Executives 1B:Affluent1Greys 1C:Flourishing1Families 2A:Prosperous1 Professionals 2B:Educated1Urbanites 2C:Aspiring1Singles 3A:Starting1Out 3B:Secure1Families 3C:Settled1Suburbia 3D:Prudent1Pensioners 4A:Asian1Communities 4B:PostLIndustrial1Families 4C:BlueLCollar1Roots 5A:Struggling1Families 5B:Burdened1Singles 5C:HighLRise1Hardship 5D:Inner1City1Adversity Mathematical*and*Computer*Sciences Mathematical and Computer Sciences Medicine and Dentistry Geodemographics and widening participation
  10. Geodemographics and widening participation

  11. OAC 2011 Critical Geodemographics

  12. Critical Geodemographics A:Intermediate Lifestyles B:High Density and High Rise Flats

    C:Settled Asians D:Urban Elites E:City Vibe F:London Life−Cycle G:Multi−Ethnic Suburbs H:Aging City Fringe LOAC 2011
  13. Critical Geodemographics

  14. Critical Geodemographics American Community Survey Aprox - 250k addresses monthly;

    3m per year; 3 year rolling window SAE; tracts around 4k people Concept Domain Population Age Race Education Family Structure Language Environment Stability Housing Density Commuting Economy Occupation Wealth A: Hispanic and Kids B: Wealthy Nuclear Families C: Middle income, single family homes D: Native American E: Wealthy Urbanites F: Low Income and Diverse G: Old, Wealthy White H: Low Income Minority Mix I: African−American Adversity J: Residential Institutions, Young People
  15. Critical Geodemographics

  16. Critical Geodemographics

  17. Postcode Search Propensity by e-Society Types 0" 20" 40" 60"

    80" 100" 120" 140" 160" 180" 200" 220" 240" 260" 280" 300" Index"(Base"100)" Group"A":"E;unengaged" Group"B":"E;marginalised" Group"C":"Becoming"engaged" Group"D":"E"for"entertainment"&" shopping" Group"E":"E;independents" Group"F":"Instrumental"E;users" Group"G":"E;business"users" Group"H":"E;"experts" Critical Geodemographics
  18. 0" 20" 40" 60" 80" 100" 120" 140" 160" 180"

    200" 220" 240" Index"(Base"100)" Group"A":"E:unengaged" Group"B":"E:marginalised" Group"C":"Becoming"engaged" Group"D":"E"for"entertainment"&" shopping" Group"E":"E:independents" Group"F":"Instrumental"E:users" Group"G":"E:business"users" Group"H":"E:"experts" Critical Geodemographics Feedback Origin
  19. 0" 50" 100" 150" 200" 250" 300" 350" 400" 450"

    500" Index"(Base"100)" Group"A":"E:unengaged" Group"B":"E:marginalised" Group"C":"Becoming"engaged" Group"D":"E"for"entertainment"&" shopping" Group"E":"E:independents" Group"F":"Instrumental"E:users" Group"G":"E:business"users" Group"H":"E:"experts" Critical Geodemographics Feedback Destination
  20. School Commuting and CO2 Emissions Open Geocomputation and Big Data

    7m trips
  21. Versus a simple model (straight line, vehicle national averages) +ve

    = simple model overestimating Open Geocomputation and Big Data
  22. Estimate of Average CO2 grams emitted during the journey to

    Secondary school (LSOA) Commuting by Car Bicycle School Commuting and CO2 Emissions Open Geocomputation and Big Data
  23. Topology of cities, networks and flows Outreach Visualisation

  24. Topology of cities, networks and flows Outreach Visualisation 2010 Census

    of Japan Open Atlas Alex Singleton [] Chris Brunsdon, Tomoki Nakaya, Keiji Yano Version 1.0 ! 2011 Census Open Atlas Alex Singleton ( Version 2.0 The ability to code relates to basic programming and database skills that enable students to manipulate large and small geographic data sets, and to analyse them in automated and transparent ways. Although it might seem odd for a geographer to want to learn programming languages, we only have to look at geography curriculums from the 1980s to realise that these skills used to be taught. For example, it wouldn’t have been unusual for an undergraduate geographer to learn how to programme a basic statistical model (for example, regression) from base principles in Fortran (a programming language popular at the time) as part of a methods course. But during the 1990s, the popularisation of graphical user interfaces in software design enabled many statistical, spatial analysis and mapping operations to be wrapped up within visual and menu-driven interfaces, which were designed to lower the barriers of entry for users of these techniques. Gradually, much GIS teaching has transformed into learning how these software package, they increasingly look like advertisements for computer scientists, with expected skills and experience that wouldn’t traditionally be part of an undergraduate geography curriculum. Many of the problems that GIS set out to address can now be addressed with mainstream software or shared online services that are, as such, much easier to use. If I want to determine the most efficient route between two locations, a simple website query can give a response within seconds, accounting for live traffic-volume data. If I want to view the distribution of a census attribute over a given area, there are multiple free services that offer street-level mapping. Such tasks used to be far more complex, involving specialist software and technical skills. There are now far fewer job advertisements for GIS technicians than there were ten years ago. Much traditional GIS-type analysis is now sufficiently non-technical that it requires little specialist skill, or has been automated through software services, with a subscription replacing the employment of a technician. The market has moved on. Geographers shouldn’t become computer scientists; however, we need to reassert our role in the development and critique of existing and new GIS. For example, we need to ask questions such as which type of geographic representation might be most appropriate for a given dataset. Today’s geographers may be able to talk in general terms about such a question, but they need to be able to provide a more effective answer that encapsulates the technologies that are used for display. Understanding what is and isn’t possible in technical terms is as important as understanding the underlying cartographic principles. Such insights will be more available to a geographer who has learnt how to code. Within the area of GIS, technological change has accelerated at an alarming rate in the past decade and geography curriculums need to ensure that they embrace these developments. This does, however, come with challenges. Academics must ensure that they are up to date with market developments and also that there’s sufficient capacity within the system to make up-skilling possible. Prospective geography undergraduates should also consider how the university curriculums have adapted to modern market conditions and whether they offer the opportunity to learn how to code. software systems operate, albeit within a framework of geographic information science (GISc) concerned with the social and ethical considerations of building representations from geographic data. Some Masters degrees in GISc still require students to code, but few undergraduate courses do so. The good news is that it’s never been more exciting to be a geographer. Huge volumes of spatial data about how the world looks and functions are being collected and disseminated. However, translating such data safely into useful information is a complex task. During the past ten years, there has been an explosion in new platforms through which geographic data can be processed and visualised. For example, the advent of services such as Google Maps has made it easier for people to create geographical representations online. However, both the analysis of large volumes of data and the use of these new methods of representation or analysis do require some level of basic programming ability. Furthermore, many of these developments haven’t been led by geographers, and there’s a real danger that our skill set will be seen as superfluous to these activities in the future without some level of intervention. Indeed, it’s a sobering experience to look through the pages of job advertisements for GIS-type roles in the UK and internationally. Whereas these might once have required knowledge of a particular I N M Y O P I N I O N, a geography curriculum should require students to learn how to code, ensuring that they’re equipped for a changed job market that’s increasingly detached from geographic information systems (GIS) as they were originally conceived. January 2014 | 77 Learning to code A L E X S I N G L E T O N is a lecturer in geographic information science at the University of Liverpool P O I N T O F V I E W January 2014 | UK £4.50 M AG A Z I N E O F T H E R OYA L G E O G R A P H I C A L S O C I E T Y ( W I T H I B G ) Geographical HOW INDUSTRIAL FISHING IS EMPTYING THE SEAS AROUND THAILAND Can carbon capture and storage save the world? Deep disposal Manchester is my orchard Turning Moss Side's unwanted fruit into a thriving cider business Net loss "TDFOTJPO*TMBOEq/FQBMq"VSFM4UFJO PLUS
  25. Topology of cities, networks and flows San Francisco–Oakland–Hayward 5,296 vertices

    557,085 edges 1,950,093 commuters Metropolitan Statistical Area Longitudinal Employer-Household Dynamics
  26. Topology of cities, networks and flows

  27. Topology of cities, networks and flows

  28. Topology of cities, networks and flows

  29. Topology of cities, networks and flows

  30. Topology of cities, networks and flows

  31. Geodemographics and widening participation Critical Geodemographics Open Geocomputation and Big

    Data Topology of cities, networks and flows Segmenting Cities Urban Analytics Consumer Data Research Centre An ESRC Data Investment
  32. Planning / Neighbourhoods / Urban Regeneration Lauren Andres Daniel Arribas-Bel

    Geocomputation / Regions City Evolution Austin Barber Transport Infrastructure Env. Change / Resilience Lee Chapman Critical / Qual GIS / Spatial Media Phil Jones Cycling Agnieszka Leszczynski Real Estate Emmanouil Tranos Digital Economy Graham Squires Climate + MANY OTHERS Summary Synergies
  33. Many thanks…