VGI and formal data. EEO / AGI (Scotland) seminar. David Fairbairn Newcastle University School of Civil Engineering & Geosciences

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1 VGI and formal data EEO / AGI (Scotland) seminar David Fairbairn Newcastle University School of Civil Engineering & Geosciences

2 VGI and formal data Investigating the possible interaction between informal and formal data Informal Volunteer Geographic Information Formal official spatial data Examples of VGI data collection Flickr Panoramio commercial satellite imagery OpenStreetMap

3 Flickr sets

4 Flickr images

5 Panoramio images

6 Using satellite imagery to update spatial databases

7 OpenStreetMap

8 OpenStreetMap

9 OSM data

10 OSM data

11 OSM data

12 Applications for data interaction Photosynth SatNav TomTom Hazard and disaster mapping Sri Lanka Haiti SDI INSPIRE.

13 Photosynth

14 Photosynth pointcloud

15 TomTom Map Share

16 Sri Lanka 2005

17 Haiti OpenStreetMap 2010

18 INSPIRE 2007/2 EU Directive does not mention citizen input BUT can existing structures and shortterm finances meet statutory needs? Whether / how to use citizen-sourced data

19 Applications for data interaction Photosynth SatNav TomTom Hazard and disaster mapping Sri Lanka Haiti SDI INSPIRE more mapping examples later

20 Mapping anew or combining with existing sources? Can VGI initiate new projects? VGI contributing to existing projects updating enhancing / extending integrating Investigating integration

21 Interaction => integration? Need to standardise? Parameters to consider geometry semantics currency completeness collection method nature of collecting organisation Role of metadata

22 Experimental approaches Zielstra and Zipf (2010) Germany accuracy Girres and Touya (2010) France semantics (and other parameters) Haklay (2010) UK completeness

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24 Experimental approaches Zielstra and Zipf (2010) Germany accuracy Girres and Touya (2010) France semantics (and other parameters) Haklay (2010) UK completeness

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26 Experimental approaches Zielstra and Zipf (2010) Germany accuracy Girres and Touya (2010) France semantics (and other parameters) Haklay (2010) UK completeness

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28 Newcastle experiments on geometric comparability Large-scale, not small scale data Comparison for data integration purposes: OpenStreetMap / OS Concentration on determining factors: type of feature / nature of landscape data collection method expertise of data collector

29 Study areas Cramlington, Northumberland Urban area with hard features Clara Vale, Northumberland Rural area with soft features Creation of a reference dataset Comparison of official data (Ordnance Survey) and VGI data (OpenStreetMap)

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31 Cramlington (Google Street View)

32 Clara Vale (Google Street View)

33 Geometrical comparison National Standard for Spatial Data Accuracy (NSSDA) Tested points 20 points A minimum of 20% of the sample points are located in each quadrant Distance between points d/10 RMSE = 1 ( n n n 2 2 δx i + δy i ) i= 1 i= 1 20% 20% d d/10 20% 20%

34 Geometrical comparison

35 A FS / OS data RMSE= m B FS /OSM data RMSE= m

36 Linear displacement

37 Areal comparison Comparative measures of polygon characteristics

38 Regular moments (Hu, 1965) Area moment invariant, invariant to translation, rotation and scaling m pq = N x p y q f ( x, y )dxdy Improved moment invariants (Chen, 1993) Set of invariant functions evaluated on the shape boundary only m = p q pq x y dl FS, OS and OSM C Urban area Rural area

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40 Semantic comparison In effect, comparing specified attributes Equivalent to exercises to combine legends Use of ontologies assessment of semantic similarity of official and VGI categories

41 WordNet Instrumentality Conveyance Mail Housebox Vehicle Public transport Wheeled vehicle Train Bus Car Bicycle Rolling stock establishing synsets and hierarchies semantic connections established testing of relationships using dictionaries

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44 Schema node relationships A1 B1 One-To-One A2 A3 A4 B2 B3 B4 One-To-Many Many-To-One A5 no Target Lacks Data no B5 Source Lacks Data

45 Node relations Rate Single correspondences (One class in Source- To-One class in Target) 0% Single correspondences (One class in Source- To-Many classes in Target) 44% Single correspondences (Many classes in Source-To-One class in Target) 53% Missing correspondence (Source Lacks Data) 47% Missing correspondence (Target Lacks Data) 41% Results of schemas relationships in Cramlington Node relations Rate Single correspondences (One class in Source- To-One class in Target) 0% Single correspondences (One class in Source- To-Many classes in Target) 43% Single correspondences (Many classes in Source-To-One class in Target) 57% Missing correspondence (Source Lacks Data) 43% Missing correspondence (Target Lacks Data) 39% Results of schemas relationships in Clara Vale

46 Similarity measures Semantic similarity Structural similarity Data-type similarity

47 Influences on the accuracy of OSM data Factorial analysis three factors considered number of contributors ( Linus Law ) identifying individuals responsible nature of the area hard or soft features method of data collection field or office data sources? Data source was the major influence

48 Empirical testing in Baghdad

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51 Semantic similarity tests in Baghdad

52 Applications for data interaction Photosynth SatNav TomTom Hazard and disaster mapping Sri Lanka Haiti SDI INSPIRE more mapping examples later

53 Further applications for interaction Environmental monitoring Natural England Mapping of informal settlements example of Kibera, Nairobi Engineering and citizen-based development water / sanitation engineering

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55 OpenStreetMap, Kibera, Kenya

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57 Need for caution Summary Approach to crowdsourcing active data passive crowdsourcing variability of data collection / reality high-quality / low-quality databases? Is integration possible?