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Seeing the light

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Slides from talk at SotM13 in Birmingham, abstract etc. on Lanyrd.

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Seeing the light

  1. 1. Seeing the LightSeeing the Light Local Government Open DataLocal Government Open Data Jerry Clough – SK53 Maps Matter (www.sk53-osm.blogspot.com)
  2. 2. NaPTAN : Bus StopsNaPTAN : Bus Stops • Data imported 2009 • Never cross-checked systematically • Duplicate stops (survey & NaPTAN) • Name Changes – Pub => Other landmark • Mainly used for adding street names • Not updated
  3. 3. • Grit Bins • Disabled Parking • Motorcycle Parking • Leisure Centres • Libraries • Local Nature Reserves • Planning Applications • Polling Stations • School Crossing Patrols • CCTV • Places of Worship NottinghamOpenNottinghamOpen DataData • Streetlights • Schools • Public Rights of Way • Tramroutes • Bus Stops • Childcare • Pedestrian Crossings • Food Hygiene Scheme • Licensed Premises • Illuminated Road Signs
  4. 4. CCTVCCTV
  5. 5. Licensed PremisesLicensed Premises • Not just Pubs & RestaurantsNot just Pubs & Restaurants – at least 2 Floristsat least 2 Florists • Licenses forLicenses for – Alcohol (on and off site)Alcohol (on and off site) – DancingDancing – Music (live & recorded)Music (live & recorded) – Boxing & WrestlingBoxing & Wrestling
  6. 6. Licensed Premises : Data-drivenLicensed Premises : Data-driven SurveySurvey
  7. 7. Food Hygiene RatingsFood Hygiene Ratings • Addresses • Partial geolocation – postcode • Business Types – Pub/Bar/Nightclub – Supermarket – Café/Restaurant – Other Retail • Covers at least 50-60% of retail outlets • Usually current – Typical inspection interval 6-12 months
  8. 8. Streetlights : OSMAccuracyStreetlights : OSMAccuracy Many streets traced from unaligned Yahoo imagery, provides quick recognition of them.
  9. 9. Streetlights : Unadopted RoadsStreetlights : Unadopted Roads
  10. 10. Streetlights : PathsStreetlights : Paths
  11. 11. Streetlights : Named StreetsStreetlights : Named Streets
  12. 12. Streetlights : AddressesStreetlights : Addresses
  13. 13. Achievements (so far)Achievements (so far) • Tram line construction tracked closely – Allows better tracking of: • Road closures • Construction areas • Licensed Premises – 94% reconciled • Up from ~40% in March • Food Hygiene – 72% reconciled (1759/2433) • Retail Premises – 95% of all shops in city now mapped • Postcodes mapped – 500+ added – 100% increase • Addresses – Several ‘000 added • Images – 8000 collected for mapping
  14. 14. ErrorRates : Licensed PremisesErrorRates : Licensed Premises Mapped Total Not Mapped Total Not applicable Total Grand Total PC Outer Data Y X G I N (blank) ? D N/a NG1 No. 347 5 49 3 404 12 9 21 4 11 15 440 Pct 78.86% 1.14% 11.14% 0.68% 91.82% 0.00% 2.73% 2.05% 4.77% 0.91% 2.50% 3.41% 100.00% NG11 No. 36 1 2 39 7 6 1 14 1 1 54 Pct 66.67% 1.85% 3.70% 0.00% 72.22% 12.96% 11.11% 1.85% 25.93% 1.85% 0.00% 1.85% 100.00% NG2 No. 57 6 63 2 3 5 2 8 10 78 Pct 73.08% 0.00% 7.69% 0.00% 80.77% 2.56% 3.85% 0.00% 6.41% 2.56% 10.26% 12.82% 100.00% NG3 No. 62 2 7 71 5 3 8 1 1 80 Pct 77.50% 2.50% 8.75% 0.00% 88.75% 6.25% 0.00% 3.75% 10.00% 0.00% 1.25% 1.25% 100.00% NG5 No. 104 1 2 107 4 1 5 1 1 113 Pct 92.04% 0.88% 1.77% 0.00% 94.69% 3.54% 0.88% 0.00% 4.42% 0.88% 0.00% 0.88% 100.00% NG6 No. 70 3 1 74 4 3 1 8 82 Pct 85.37% 3.66% 1.22% 0.00% 90.24% 4.88% 3.66% 1.22% 9.76% 0.00% 0.00% 0.00% 100.00% NG7 No. 233 2 27 262 3 4 2 9 9 9 280 Pct 83.21% 0.71% 9.64% 0.00% 93.57% 1.07% 1.43% 0.71% 3.21% 0.00% 3.21% 3.21% 100.00% NG8 No. 118 5 1 124 2 2 4 1 1 129 Pct 91.47% 0.00% 3.88% 0.78% 96.12% 1.55% 1.55% 0.00% 3.10% 0.00% 0.78% 0.78% 100.00% NG9 No. 4 4 2 1 3 7 Pct 57.14% 0.00% 0.00% 0.00% 57.14% 28.57% 14.29% 0.00% 42.86% 0.00% 0.00% 0.00% 100.00% Total No. 1031 14 99 4 1148 29 32 16 77 8 30 38 1263 Total Pct 81.63% 1.11% 7.84% 0.32% 90.89% 2.30% 2.53% 1.27% 6.10% 0.63% 2.38% 3.01% 100.00% Y = On OSM, X= Surveyed, not added, G = Surveyed, gone (no longer present), I = Imaginary (surveyed, never present) N = Known to exist, not surveyed yet, (blank) = status not known, not surveyed ? = Surveyed, existence hard to determine : D= duplicate data, N/a = Open spaces and other non-addressed POIs
  15. 15. ConclusionsConclusions • Non-import approaches to Open Data can be highly productive – Smaller focussed data sets are easier to cope with: • Pubs, Places of Worship, not Bus Stops or Streetlights – Side benefits considerable • On-the-ground surveys extended to many parts of the city – Many additional images to assist interpretation of aerial imagery • Addresses already available for POIs, shorter surveys needed (= increased overall coverage) • Postcode coverage • House numbers can be collected on small scale – Valuable because additional numbers can be interpolated from OD sources • Open Data requires interpretation – Original purpose often at odds with mapping – Error rates ~ 5% • Good quality ancillary information really helps – Aerial imagery – Postcode centroids (open data) give approximate location • Ordnance Survey OGL is major barrier for some data sets • Conflation and Change Detection not easily automated – Necessary for data maintenance – Necessary for large data sets
  16. 16. Data Matching : what’s needed forData Matching : what’s needed for ConflationConflation • Point sources initially • Multiple (weighted) matching criteria – Geographical co-ordinates • Precise • Fuzzy (postcode centroids) • Areas – POI Type • Fuzzy – Bar/pub/restaurant – Name • Fuzzy Matching of names – Levenstein distances inadequate – “Sycamore Primary School” vs “Sycamore Academy” – “Robin Hood” vs “Robin Hood and Little John” – Tokenise ? • Building Blocks – Nominatim – OSL Musical Chairs (ris) – Library of Congress (chippy, schuyler)

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