Pro Yearly is on sale from $80 to $50! »

Mapping Data: Beyond the Choropleth

F62c2ae37434caf1f893b9aaa5600084?s=47 oobrien
May 24, 2016

Mapping Data: Beyond the Choropleth

Contents: Technology Summary for Web Mapping, Choropleth Maps: The Good and the Bad, Moving Beyond the Choropleth, Example: CDRC Maps, Example: named – KDE “heatmap”, Case example: Country of Birth Map - concerns of the data scientist & digital cartographer.

F62c2ae37434caf1f893b9aaa5600084?s=128

oobrien

May 24, 2016
Tweet

Transcript

  1. Mapping Data: Beyond the Choropleth Oliver O’Brien Senior Research Associate

    UCL Dept of Geography Twitter: @oobr Research blog: http://oobrien.com/ ADRC-E Training Course: Introduction to Data Visualisation 16-17 May 2016, Farr Institute, UCL
  2. Contents •  Technology Summary for Web Mapping •  Choropleth Maps:

    The Good and the Bad •  Moving Beyond the Choropleth •  Example: CDRC Maps •  Example: named – KDE “heatmap” •  Case example: Country of Birth Map –  Concerns of the data scientist & digital cartographer 2
  3. 1. Technology Summary for Web Mapping 3

  4. Managed Wrappers •  MapBox Studio •  CartoDB •  ESRI ArcGIS

    Online •  Tableau •  Google Fusion Tables •  Google Maps Embed API •  Google Static Maps API 4 Example: MapBox Studio style editor
  5. JavaScript APIs •  OpenLayers •  Leaflet •  D3 •  Google

    Maps JS API 5 http://earth.nullschool.net/ http://osm.org/
  6. Programming/Scripting –  Typically to produce raster imagery which then can

    be combined with vector data in a Javascript API (or other) map –  R –  Java –  Mapnik 6 http://twitter.mappinglondon.co.uk/
  7. WMS/WFS •  “Non-slippy” webmap servers –  MapServer (C++) –  GeoServer

    (Java) 7 http://worldnames.publicprofiler.org/
  8. 2. Choropleth Maps: The Good and the Bad x  Treat

    unpopulated and populated areas similarly x  Can be hard to see the “story” – the interesting results x  Prone to M.A.U.P. 8 ü  Easy to make ü  Computationally quick to make ü  Retain a geographic familiarity if done carefully ü  Good for comparing areas quickly http://maps.cdrc.ac.uk/
  9. A Basic Choropleth Map Map of household income in the

    US by census tract. Source: Campus Activism blog.
  10. 10 London Mayoral Election 2016 – Result. Source: BBC News.

    http://www.bbc.co.uk/news/uk-politics-36303157 A Basic Choropleth Map
  11. Adding Geographical Contextual Features US Census: ACS Survey results

  12. Adding Geographical Contextual Features Map of Housing Affordability, 2014. Source:

    The Guardian.
  13. Adding Geographical Contextual Features Map of Philadelphia housing prices per

    square foot using 2014 property assessment data. Source: Campus Activism blog.
  14. Adding Geographical Contextual Features Change in Socio-Economic Status, 2011-2011. Source:

    Neal Hudson at Savills
  15. 3. Moving Beyond the Choropleth •  Adapting Choropleths –  Limiting

    Display to Populated Places •  Dot Maps •  Cartograms •  Grid Maps 15
  16. Source: http://jamesjgleeson.wordpress.com/

  17. Adapted Choropleth •  Limiting Data Display to Populated Places • 

    Adding Contextual Information Source: Neal Hudson at Savills
  18. Dot Density •  Fairer for population fluctuations –  Although layering

    of dots is crucial •  Hard to read data in high-density areas •  Assumption of random distribution across areas –  Unless areas are restricted to buildings Ethnicity across the New York City metropolitan area. 1 dot = 20 people. Source: Eric Fischer.
  19. 19 Map of ethnicity supergroups based on ONS Census (2001)

    data. 1 dot = 50 people. Source: @geographyjim Dot Density
  20. EthniCity from London: The Information Capital (James Cheshire, Oliver Uberti)

    http://theinformationcapital.com/ Dot Density
  21. Single Dot in Area Centroid 21 Wards - London’s Political

    Colour http://vis.oobrien.com/london/ Map: OSM CC-By & OS OGL
  22. 22 http://vis.oobrien.com/tube/#metric=wardwords Data: ONS & OS, OGL.

  23. Cartograms •  Fairer display of data •  Harder to relate

    geography unless carefully done Maps from Worldmapper: http://www.worldpopulationatlas.org - © SASI Research Group, University of Sheffield
  24. Cartograms Created by Ben Hennig – http://viewsoftheworld.net/

  25. Square Cartograms •  Aftertheflood Squares (Boroughs) 25 Map/Concept: Aftertheflood.co (L).

    GLA version (R).
  26. Half Way Between Choropleth & Cartogram •  Rentonomy (Postcode Prefixes)

    26 Source: Rentonomy. http://www.rentonomy.com/london-rental-map
  27. 27 Gridded Choropleth Map data: ONS OGL. Source: Duncan Smith,

    UCL CASA. http://luminocity3d.org/
  28. 28 Gridded Choropleth Map data: ONS OGL. Source: Duncan Smith,

    UCL CASA. http://luminocity3d.org/
  29. 29 Gridded Choropleth Map data: ONS OGL. Source: Duncan Smith,

    UCL CASA. http://luminocity3d.org/
  30. 4. Example: CDRC Maps •  CDRC needs maps of population/consumer

    data which are quick to interpret and effective •  The technology is simple –  We want to maintain the simplicity of creating choropleth mapping •  The key innovations are to: –  Put some contextual information above the choropleth –  Constrain the choropleth display to areas of population 30 http://maps.cdrc.ac.uk/
  31. 31 Map data: Ordnance Survey and ONS OGL. Source: http://maps.cdrc.ac.uk/

  32. 32 Map data: Ordnance Survey and ONS OGL. Source: http://maps.cdrc.ac.uk/

  33. 33 Map data: Ordnance Survey and ONS OGL. Source: http://maps.cdrc.ac.uk/

  34. Map data: Ordnance Survey and ONS OGL. Source: http://maps.cdrc.ac.uk/

  35. Map Layers •  Also Postcode Pin Layer (Vector) •  KML

    Drag-and-Drop Display Layer (Vector)
  36. Tabular Data > Choropleth > Real-World Map

  37. UI Evolution: “New Booth” > DataShine > CDRC Maps 37

    Map data: OS & ONS OGL. Sources: http://vis.oobrien.com/booth/ + http://datashine.org.uk/ + http://maps.cdrc.ac.uk/
  38. Geodemographics & Indices on CDRC Maps •  OAC, LOAC, TOAC

    •  COWZ-EW, IUC •  IMD 2010 & 2015, 2010-15 Change, Components Diff •  SIMD 2012 •  Retail – Rental Value, Value Change by Sector Map data: Ordnance Survey and ONS OGL. Source: http://maps.cdrc.ac.uk/
  39. Map data: Ordnance Survey and ONS OGL. Source: http://maps.cdrc.ac.uk/

  40. Software (for CDRC Maps) •  Simple (static image “tiles”) –

    no “map server” •  Apache web server •  Mapnik (pre-renders the images) –  & python-mapnik (to slice them up) •  OpenLayers (3) •  JQuery and JQueryUI (for the non-map UI) •  PostgreSQL database –  PostGIS spatial extensions
  41. 5. Examples: named 41 Source: Adapted from named. http://named.publicprofiler.org/

  42. Source: named. http://named.publicprofiler.org/

  43. Replacing the “Worldnames” Choropleths Source: http://worldnames.publicprofiler.org/

  44. KDE Mapping: “Sinclair”

  45. KDE Mapping Graphs: Wikipedia. Background Map: OpenStreetMap.

  46. •  “Where your name is unusually popular” •  “Where we

    think you might have met” The Website Source: named. http://named.publicprofiler.org/
  47. The Response… Sources: Daily Mail and Mirror websites.

  48. Software (for named) •  Java service which retrieves the data,

    grids it, creates a corresponding KDE grid and converts it to a PNG •  Apache web server •  PHP to glue the two together •  OpenLayers (3) •  JQuery and JQueryUI •  PostgreSQL database –  No need for a spatial database as just points
  49. 6. Case Example: Country of Birth Map •  “Top Metric”

    maps are pseudo-geodemographic maps –  showing a single value for an area that represents the most significant part of the population there 49
  50. 6. Case Example: Country of Birth Map •  Need three

    kinds of skills –  Data Scientist •  to manage the data and discover the story –  Demographic Geographer •  to make it a representative map –  Digital Cartographer •  to communicate the story effectively 50
  51. •  Top Metric Maps require care and curation to produce

    a map with: –  fair groupings – wary of aggregation bias –  a sensible threshold – maximise signal-to-noise ratio but don’t lose the story –  appropriate removal of spatially overwhelming majorities –  appropriate colours – use hues to show categorization and hierarchies (HSL) –  curated emphasis with colours - emphasise/fade certain data to tell the story of the data effectively - use brighter hues for more unusual results, and more modest ones for results that would otherwise dominate, while retaining balance Country of Birth Map
  52. Country of Birth Map: Data Scientist

  53. •  An all-UK map of Census 2011 data, combining the

    equivalent (but subtly varying) tables from the 3 National Statistics bodies – ONS, NRS & NISRA. •  English excluded from all UK, & natives from their country –  Internal national land borders included to show these rule transitions •  8% threshold –  Balance between “exaggeration” and showing an interesting story Country of Birth Map: Demographic Geographer Map data: Ordnance Survey and ONS OGL. Source: http://maps.cdrc.ac.uk/
  54. Country of Birth Map: Digital Cartographer Map data: Ordnance Survey

    and ONS OGL. Source: http://maps.cdrc.ac.uk/
  55. Country of Birth Map: Digital Cartographer Map data: Ordnance Survey

    and ONS OGL. Source: http://maps.cdrc.ac.uk/
  56. Country of Birth Map Map data: Ordnance Survey and ONS

    OGL. Source: http://maps.cdrc.ac.uk/
  57. Paper on CDRC Maps Mapping •  O’Brien O, Cheshire J

    (2015) Interactive mapping for large, open demographic data sets using familiar geographical features –  Journal of Maps (T&F) –  Published online –  Online, PDF download –  Open access (CC-By) DOI: 10.1080/17445647.2015.1060183 57
  58. Links http://maps.cdrc.ac.uk/ http://maps.cdrc.ac.uk/#/metrics/countryofbirth/ http://named.publicprofiler.org/ 58 Map data: Ordnance Survey and

    ONS OGL. Source: http://maps.cdrc.ac.uk/
  59. Thanks! •  Research blog: http://oobrien.com/ •  Twitter: @oobr 59 Source:

    http://vis.oobrien.com/tube/