This document summarizes the potential uses of OpenStreetMap data and explores how complete and accurate it is for various purposes. It provides examples of simple visualizations of OSM data on pub density and retail locations. It also describes processes for deriving new data layers from OSM, such as land use polygons and gridded points of interest, and compares OSM to other data sources.

1. Exploring theExploring the
Potential ofPotential of
OpenStreetMap DataOpenStreetMap Data
JerryJerry CloughClough
SK53 on OpenStreetMap
@SK53onOSM
SK53.osm@gmail.com
Maps Matter Blog : www.sk53-osm.blogspot.com

3. OSM (still) in data collection phase
Sep-12 Mar-13 Sep-13 Mar-14 Sep-14 Mar-15
0%
20%
40%
60%
80%
100%
120% Post Boxes Mapped by City on OSM
percentage of current total Pittsburgh
Zurich
Karlsruhe
Krakow
Tallinn
Nantes
Salamanca
Nottingham
Aug-12 Aug-14
0
200
400
600
800
1000
1200
1400
1600
0
20
40
60
80
100
120
140
160
180
200
bookies
NG restaurants
Sep-12 Feb-13 Jul-13 Dec-13 May-14 Oct-14 Mar-15
0%
20%
40%
60%
80%
100%
120%
Restaurants by City on OSM
San Francisco
Pittsburgh
St Louis
Seattle
Oklahoma City
Denver
Zurich
Karlsruhe
Munich
Krakow
Tallinn
Nantes
Salamanca
Nottingham

4. Themes
● How complete is X on OSM?
● Where are the gaps in X?
● Is OSM data usable?
● If so, what's involved?
● Can further information be derived?

5. ToolkitToolkit
● OSM Extracts from Geofabrik
● Overpass
● taginfo & umap
● osmconvert & osmfilter
● osm2pgsql & osmosis
● PostgreSQL
● QGIS
● R
● Possibles
– Routing Packages: graphhopper,
OpenTripPlanner
– RapidMiner
DataData
● Data in PostGIS:
– Static data for GB using
osm2pgsql
● Lose interconnectedness
● Avoid issues in creating
polygons
– Pre-filtered topic-based data
sets
– Limited use of (osmosis)
snapshot schema
● Common processes:
– Geodata » Graph » Geodata
– Gridding of data

6. Looking at the DataLooking at the Data
Counts, Visualisations, ComparisonCounts, Visualisations, Comparison

7. Simple Visualisations:Simple Visualisations:
Pub Density in Great BritainPub Density in Great Britain

8. Basic CountsBasic Counts
Some 'Interesting' Stats for Great Britain
(with apologies to Ordnance Survey)
● Pylons: 75,446 (OSGB: 80,517)
● Post Boxes: 51,023 (93.728)
● Camp sites: 4,347 (8,908)
● Buildings: 3,394,208 (35,397,754)
● Bus Stops: 222,505 (354,099)
● Petrol Stations: 6,575 (7,702)
● Addresses: 892,938 (27,341,262)
● Electricity Poles: 150,209 (183, 987)
● Road length: 522,627 km (407,532
km)
● 11 post boxes with Edward
VIII cypher
● Only 420 War Memorials
● 1,353 Fire Hydrants
● 2,000 Real Ale pubs
– 109 with Real Fires
● 7,845 Cycle Parking
● 300 Wildlife Hides
● 10,968 Stiles
● 2,033 Canal Locks
● 8 Knitting Shops
Ordnance Survey figures: /www.ordnancesurvey.co.uk/blog/2013/04/10-fascinating-facts-from-ordnance-survey/
OSM figures (May '15): /taginfo.openstreetmap.org.uk/

9. Comparisons:Comparisons:
Data CompletenessData Completeness
Food Hygiene Open Data
© 2015OSM contributors

10. Comparisons:Comparisons:
Data AccuracyData Accuracy
Land Registry Inspire parcels
OSM gardens etc, Sutton
Coldfield (B72)
© 2013 OSM Contributors
@2013 Crown Copyright & Database right

11. Comparisons:Comparisons:
Missing DataMissing Data
© 2015 OpenStreetMap contributors
Surrey Rights of Way from rowmaps.org.uk

12. Comparisons:Comparisons:
Internal to OSMInternal to OSM

13. Building a Global DataBuilding a Global Data
SetSet
Retail in OSMRetail in OSM

14. Long-tail of shop tagsLong-tail of shop tags

15. Pubs on OpenStreetMapPubs on OpenStreetMap
© 2015 OpenStreetMap contributors; Country Polygons also OSM

17. Cartograms from Retail POIsCartograms from Retail POIs
© 2015 OpenStreetMap contributors; Country Polygons Natural Earth
Created using Scapetoad

18. Derived & EmergentDerived & Emergent
DataData
Landuse PolygonsLanduse Polygons

19. Deriving Data : Landuse PolygonsDeriving Data : Landuse Polygons
●
●

20. Gridding PolygonsGridding Polygons
• Intersection of all
features on 1km grid
– Reduce polygon size
– Performance
– Avoid joining on
geometries (use key
for grid cell)

21. PostGIS ProcessingPostGIS Processing
OSM
Polygons
OSM
Lines
Painter's
Algorithm
Rules
Clipped
Polygons
Clipped
Lines
Cleaned &
Clipped
Polygons
UA Shape
Polygons
Clean Geometry
Gridded UA
Classes
Filter on Tags & Grid
Gridded &
Buffered
UA Classes
Tag Filter, Grid & Buffer
Clip to Area
Clip to Area
Piecewise Union Union Step 1
Union
Union Step 2
Merge
Class Gridded
Polygons
Merge
Grid
Gridded UA
Polygons
Union
Clipping areas
by UA Class
ClippingRegion
Final
Polygons
Compare
UA/OSM
Union/Intersect/
Difference

22. Emergent Data : Retail land use

23. Classifying Retail
Areas

24. SummarySummary
● Potential future use of OSM data can be tested
by judicious choice of data sets.
● Evaluation of suitability of OSM data for a given
purpose solely using internal criteria is still
hard.
● Deriving rather than consuming data is often
(technically) involved.
● It can be fun in its own right!

25. I'm not alone!I'm not alone!