This document summarizes a study that analyzed the usability of different geographical data sources and GIS processes for network-based accessibility modeling of services. The study conducted sensitivity analyses to assess how representations of demand, network datasets, supply features, and processes impacted accessibility measurements. Key findings included that choice of network dataset, supply-side location definition, and demand representation all significantly influenced accessibility scores and rankings of areas.

1. Utilising sensitivity analyses of
geospatial processes
Robin Frew
University of South Wales
robin.frew@southwales.ac.uk
Assessing geographic data
usability in analytical contexts
The study reported here forms part of an Ordnance Survey-sponsored PhD research programme. Any
views expressed in this presentation are mine, and do not necessarily represent those of Ordnance
Survey.

2. This study looks at the usability of a range of geographical data
when applied to network-based accessibility modelling.
By conducting sensitivity analysis on a range of demand-side
representations, supply-side features, network datasets and GIS
processes, assessments were made as to their usefulness in such
tasks, using both basic and more sophisticated methods of
measuring accessibility.
Today’s presentation
2

3. How to measure accessibility to services or facilities?
Looking at 3 different approaches:
Euclidean distance
Network distance
Gravity model
3

4. Distance and travel time as measures of accessibility
Location methods and distance measures - Euclidean
! OA population-weighted centroid
School site footprint
^ Site polygon geometric centroid
" Point of Interest point location
#* Access point for pedestrians
Distance to nearest point on boundary
Distance to geometric centroid
Distance to Point of Interest location
Distance to nearest point of access
What is meant by ‘location?’
Where do you measure to?
4
Euclidean distance?

5. Facility centroid
Pedestrian
accesspoint
Mainpedestrian
accesspoint
Mainperimeter
point
0 200 400100
Meters
Distance and travel time as measures of accessibility
Network distance?
5

6. 1/20 = 0.05
Service Point
Transport System
Computed
Catchment
Estimated
Population
Population Model
Availability Score
Floating Catchment Analysis – Step 1

7. 0.05
Availability ScoreFloating Catchment Analysis – Step 1

8. Availability Scores
0.05
0.10
Floating Catchment Analysis – Step 1

9. Availability Scores
0.05
0.10
0.31
Service Demand
Centre
Computed
Catchment
Accessibility
Score
Floating Catchment Analysis – Step 2
+ +

10. 0.46
Accessibility
Scores
Floating Catchment Analysis – Step 2
0.52
0.03

11. FCA tool
11

12. Feature of interest
Network GIS process Output
Population data
Sensitivity Analysis
Simplest approach: change one input factor at a time (OFAT), see how output
is affected.
12

13. Features compared
Primary schools Many features, moderate footprint.
Included in Sites dataset.
Relevant to education, but also active travel agenda.
Secondary schools Fewer in number, large footprint.
Included in Sites dataset.
Again, relevant to active travel initiatives.
GP surgeries Many features, point locations only.
The classic accessibility subject (access to healthcare),
but also relevant to studies of deprivation, etc.
Sports and leisure facilities Few features, depending on interpretation.
Large footprint, but point location data only (at the
present time).
Relevant to health initiatives.
Community hubs More facilities, wide definition (but still open to interpretation).
Point locations only.
Of interest to community cohesion studies.
13

14. Features compared
14
Extracted facility locations from:
• OS Points of Interest
• OS Sites
Several major assumptions had to be taken with respect to each type of
feature, for example:
School potential accessibility assumed pupils can attend their
nearest school, regardless of religious denomination, language,
parental choice or LA boundary.
Other, third-party datasets were used, but found to be (for example):
Out of date (eg WAG, for secondary schools)
Hopelessly inclusive (eg Google, Yell, for schools)

15. Demand datasets used
15
Population data
2011 Output Area polygons and population-weighted centroids

16. Network datasets compared
16
Used a variety of Ordnance Survey, third-party and specially-created network
datasets:
• OS MasterMap Integrated Transport Network™ (ITN) Layer
• OS ITN with Urban Paths
• OpenStreetMap Network dataset from third-party provider
• VectorMap District Network built using ArcGIS
These alternatives cover a range of cost and ‘obtainability’ (also usability
factors).
Compared all the above to Euclidean distances, and to each other.

17. Geographic context
Vale Cardiff
Area (km2) 340 150
Population 126 336 346 090
No. of OAs 412 1077
17

18. 18
Distribution of secondary schools in Vale of
Glamorgan
Includes schools within 8km of the local authority boundary.

19. 19
Distribution of secondary schools in Cardiff
Includes schools within 8km of the local authority boundary.

20. 20
Secondary
Schools
Vale Cardiff
Number of
schools
9 21
County size (sq
km)
339.8 149.5
County
population
126 336 346 090
Coverage 1 school per 37.75 sq km 1 school per 7.12 sq km
Population
covered by each
school
14037 16480
Schools per
100,000
population
7.1 6.07
Feature coverage

21. Vale secondary schools
Euclidean distance, school site centroids
0 21
Kilometers
0 31.5
Kilometers
Detail - Penarth
Detail - Barry

22. Vale secondary schools Best / worst comparison
Euclidean, distance, school boundary VMD network distance, school boundary
Barry
Penarth

23. 23
Quintile map – Vale secondary schools
Network (ITN) 2SFCA, school site centroid

24. 24
Quintile map – Vale secondary schools
Network (OSM) 2SFCA, school site centroid

25. 25
Quintile map – Vale secondary schools
Network (VMD) 2SFCA, school site centroid

26. 26
Cardiff secondary schools
ITN network distance, school site centroid
In lowest distance category: 2.8%
In highest distance category: 17.3%

27. 27
Cardiff secondary schools
ITN network distance, school site access point
In lowest distance category: 5.5%
In highest distance category: 15.3%

28. 28
Cardiff secondary schools
Network distance (VMD), school site boundary
In lowest distance category: 10.2%
In highest distance category: 11.6%

29. 29
Quintile map – Cardiff secondary schools
Network (ITN_UP) 2SFCA, school site centroids
In lowest accessibility category: 20%
In highest accessibility category: 20%

30. 30
Cardiff secondary schools
Network (ITN_UP) 2SFCA, main access points
In lowest accessibility category: 13.3%
In highest accessibility category: 18.2%
Same splits as centroids map

31. 31
Cardiff secondary schools
Network (ITN_UP) 2SFCA, main boundary point
In lowest accessibility category: 3.1%
In highest accessibility category: 25.8%
Same splits as centroid map

32. 32
Euclidean
Centroids
AccPts 86.9
Perim 84.9 92.7
ITN
Centroids 82.0 76.6 74.8
AccPts 76.5 81.2 76.0 92.9
Perim 72.8 73.0 75.5 92.5 88.2
UP
Centroids 89.2 87.7 87.5 90.0 85.7 83.2
AccPts 88.6 87.9 88.0 89.3 89.1 85.5 93.7
Perim 89.0 86.5 90.2 89.0 86.4 85.1 91.6 93.2
OSM
Centroids 71.1 80.3 80.1 88.8 85.0 84.8 84.2 84.0 84.8
AccPts 81.1 76.2 74.5 90.1 96.9 85.7 81.5 84.9 82.2 74.9
Perim 74.9 79.6 81.3 82.0 80.7 80.6 75.7 74.7 77.2 82.0 80.9
VMD
Centroids 57.0 62.7 61.8 71.4 68.7 65.6 64.9 64.3 66.3 66.8 60.3 60.4
AccPts 55.7 64.0 63.0 70.4 71.3 67.3 66.9 68.4 66.6 66.7 58.8 61.7 78.7
Perim 59.1 65.9 67.5 71.3 67.6 66.5 68.4 68.5 70.1 66.9 57.1 69.8 67.7 72.1
Mean dist 1170 1148 1008 1710 1610 1409 1568 1451 1339 1759 1706 1685 2075 1917 1704
Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim
Euclidean ITN UP OSM VMD
Secondary Schools, Cardiff
Destination overlaps (%)

33. 33
Secondary Schools, Cardiff
Differences in distances
Euclidean ITN UP OSM VMD
Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim
Euclidean
Centroids < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
AccPts -6.195 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
Perim -28.428 -27.160 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
ITN
Centroids -28.356 -28.319 -28.428 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 .774 < .001
AccPts -27.210 -27.427 -28.424 -18.836 < .001 .781 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
Perim -21.776 -21.456 -28.295 -28.342 -25.851 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
UP
Centroids -28.322 -28.245 -28.428 -19.400 - .278 -20.808 < .001 < .001 < .001 < .001 < .001 < .001 < .001 .005
AccPts -25.988 -26.245 -28.423 -25.608 -22.045 -9.007 -22.971 < .001 < .001 < .001 < .001 < .001 < .001 < .001
Perim -19.759 -19.851 -28.359 -28.357 -27.513 -12.975 -28.430 -22.636 < .001 < .001 < .001 < .001 < .001 < .001
OSM
Centroids -27.994 -27.798 -28.195 -5.073 -16.922 -27.626 -17.634 -24.265 -27.939 < .001 < .001 < .001 .929 < .001
AccPts -27.638 -28.140 -28.187 -5.916 -15.283 -26.104 -11.256 -25.036 -27.432 -9.813 < .001 < .001 .329 < .001
Perim -27.661 -27.542 -28.180 -5.529 -6.583 -26.030 -6.429 -18.740 -26.199 -8.802 -4.584 < .001 .012 < .001
VMD
Centroids -28.198 -28.119 -28.422 -10.651 -21.565 -28.265 -20.584 -26.166 -28.334 -10.692 -16.921 -13.264 < .001 < .001
AccPts -27.080 -27.456 -28.406 - .287 -4.845 -24.005 -10.448 -20.872 -25.490 - .090 - .976 -2.513 -14.473 < .001
Perim -22.189 -22.781 -28.172 -8.926 -6.790 -3.758 -2.790 -4.717 -14.923 -9.728 -9.591 -7.900 -28.135 -26.509
Below diagonal = Wilcoxon Z scores
Above diagonal = significance (black = sig at < .001 level; green = sig at 1% level; amber = sig at 5% level; red = not significant at 5% level).

34. 34
Euclidean ITN UP OSM VMD
Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim
Euclidean
Centroids .009 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
AccPts -2.605 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
Perim -20.863 -20.832 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
ITN
Centroids -28.335 -28.336 -28.296 .001 < .001 < .001 .008 < .001 .003 < .001 < .001 .006 .018 < .001
AccPts -28.366 -28.366 -28.347 -3.272 < .001 < .001 < .001 < .001 .026 < .001 < .001 .016 < .001 < .001
Perim -28.407 -28.406 -28.405 -11.930 -10.846 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
UP
Centroids -28.371 -28.371 -28.352 -5.628 -3.866 -16.461 .002 < .001 .004 .003 < .001 .946 .701 < .001
AccPts -28.393 -28.392 -28.382 -2.671 -5.314 -15.718 -3.054 < .001 .437 < .001 < .001 .376 .256 < .001
Perim -28.424 -28.424 -28.424 -12.568 -11.515 -5.609 -16.724 -15.867 < .001 < .001 < .001 < .001 < .001 .001
OSM
Centroids -28.367 -28.367 -28.348 -2.929 -2.224 -9.468 -2.841 - .777 -10.873 .104 < .001 < .001 < .001 < .001
AccPts -28.359 -28.360 -28.341 -7.557 -10.001 -8.656 -2.964 -4.650 -10.346 -1.628 < .001 < .001 < .001 < .001
Perim -28.406 -28.406 -28.405 -12.276 -10.887 -7.355 -16.410 -15.768 -5.931 -10.059 -8.288 < .001 < .001 < .001
VMD
Centroids -28.332 -28.334 -28.297 -2.723 -2.417 -6.000 - .068 - .885 -6.620 -4.843 -3.927 -5.810 < .001 < .001
AccPts -28.359 -28.359 -28.340 -2.369 -4.093 -6.019 - .384 -1.135 -5.906 -4.576 -5.591 -5.937 -4.007 < .001
Perim -28.322 -28.322 -28.321 -16.849 -15.231 -9.195 -19.214 -18.061 -3.306 -16.816 -15.940 -9.176 -18.647 -17.882
Secondary Schools, Cardiff
Differences in 2SFCA scores
Below diagonal = Wilcoxon Z scores
Above diagonal = significance (black = sig at < .001 level; green = sig at 1% level; amber = sig at 5% level; red = not significant at 5% level).

35. 35
Euclidean
Centroids
AccPts 87.6
Perim 96.6 86.2
ITN
Centroids 83.3 80.8 80.6
AccPts 84.5 87.9 83.3 84.0
Perim 93.2 83.0 92.7 84.0 87.4
UP
Centroids 86.4 84.0 83.7 94.9 87.4 86.9
AccPts 89.6 92.0 87.4 87.9 93.2 87.9 90.3
Perim 94.7 83.0 93.2 84.0 86.6 97.1 87.9 89.1
OSM
Centroids 81.6 85.7 93.5 93.5 84.0 83.3 90.0 87.1 85.0
AccPts 78.6 89.3 82.8 81.1 97.6 86.7 82.8 93.7 86.4 80.8
Perim 78.4 85.0 92.2 80.6 87.6 96.4 80.6 86.7 94.4 80.7 87.4
VMD
Centroids 83.5 85.9 92.7 99.5 83.5 82.5 94.9 86.9 85.4 93.7 83.5 81.1
AccPts 81.1 89.3 82.5 84.5 95.1 86.4 87.4 92.7 85.9 84.2 95.1 86.7 84.0
Perim 80.8 84.0 92.7 84.0 87.4 98.1 84.0 87.1 95.6 83.5 85.2 94.9 83.0 89.3
Mean dist 1643 1591 1449 2481 2202 2020 2480 2365 2255 2467 2189 1931 2726 2495 2223
Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim
Euclidean ITN UP OSM VMD
Secondary Schools, Vale
Destination overlaps (%)

36. 36
Euclidean ITN UP OSM VMD
Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim
Euclidean
Centroids < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
AccPts -5.882 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
Perim -17.589 -17.589 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
ITN
Centroids -17.551 -17.558 -17.588 < .001 < .001 < .001 < .001 < .001 .715 < .001 < .001 .004 < .001 < .001
AccPts -16.532 -17.583 -17.589 -10.156 < .001 < .001 < .001 < .001 < .001 < .001 .062 < .001 .551 < .001
Perim -13.046 -13.822 -17.488 -17.591 -17.570 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 .008
UP
Centroids -17.553 -17.550 -17.589 -14.638 -4.147 -16.141 < .001 < .001 < .001 .467 < .001 < .001 .390 < .001
AccPts -15.962 -17.589 -17.589 -14.512 -12.893 -10.395 -12.275 < .001 < .001 < .001 < .001 < .001 < .001 < .001
Perim -10.398 -14.062 -17.510 -17.589 -17.550 -8.093 -17.589 -17.591 < .001 < .001 < .001 < .001 < .001 < .001
OSM
Centroids -17.498 -17.524 -17.588 - .365 -8.690 -17.435 -9.601 -13.120 -17.570 < .001 < .001 .043 < .001 < .001
AccPts -17.180 -17.583 -17.589 -7.937 -5.613 -16.907 - .727 -13.096 -17.515 -5.014 < .001 < .001 .116 < .001
Perim -16.519 -17.075 -17.587 -11.246 -1.864 -12.540 -4.923 -5.901 -15.410 -12.074 -5.182 < .001 < .001 < .001
VMD
Centroids -17.559 -17.537 -17.587 -2.917 -10.563 -17.508 -13.037 -14.160 -17.535 -2.023 -9.307 -13.995 < .001 < .001
AccPts -16.400 -17.557 -17.589 -4.960 - .597 -16.631 - .859 -12.256 -17.133 -4.655 -1.571 -4.188 -7.095 < .001
Perim -13.164 -15.479 -17.489 -12.359 -12.076 -2.632 -10.190 -4.575 -11.250 -12.777 -12.562 -7.040 -17.589 -17.594
Secondary Schools, Vale
Differences in distances
Below diagonal = Wilcoxon Z scores
Above diagonal = significance (black = sig at < .001 level; green = sig at 1% level; amber = sig at 5% level; red = not significant at 5% level).
Euclidean ITN UP OSM VMD
Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim
Euclidean
Centroids < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
AccPts -5.882 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
Perim -17.589 -17.589 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
ITN
Centroids -17.551 -17.558 -17.588 < .001 < .001 < .001 < .001 < .001 .715 < .001 < .001 .004 < .001 < .001
AccPts -16.532 -17.583 -17.589 -10.156 < .001 < .001 < .001 < .001 < .001 < .001 .062 < .001 .551 < .001
Perim -13.046 -13.822 -17.488 -17.591 -17.570 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 .008
UP
Centroids -17.553 -17.550 -17.589 -14.638 -4.147 -16.141 < .001 < .001 < .001 .467 < .001 < .001 .390 < .001
AccPts -15.962 -17.589 -17.589 -14.512 -12.893 -10.395 -12.275 < .001 < .001 < .001 < .001 < .001 < .001 < .001
Perim -10.398 -14.062 -17.510 -17.589 -17.550 -8.093 -17.589 -17.591 < .001 < .001 < .001 < .001 < .001 < .001
OSM
Centroids -17.498 -17.524 -17.588 - .365 -8.690 -17.435 -9.601 -13.120 -17.570 < .001 < .001 .043 < .001 < .001
AccPts -17.180 -17.583 -17.589 -7.937 -5.613 -16.907 - .727 -13.096 -17.515 -5.014 < .001 < .001 .116 < .001
Perim -16.519 -17.075 -17.587 -11.246 -1.864 -12.540 -4.923 -5.901 -15.410 -12.074 -5.182 < .001 < .001 < .001
VMD
Centroids -17.559 -17.537 -17.587 -2.917 -10.563 -17.508 -13.037 -14.160 -17.535 -2.023 -9.307 -13.995 < .001 < .001
AccPts -16.400 -17.557 -17.589 -4.960 - .597 -16.631 - .859 -12.256 -17.133 -4.655 -1.571 -4.188 -7.095 < .001
Perim -13.164 -15.479 -17.489 -12.359 -12.076 -2.632 -10.190 -4.575 -11.250 -12.777 -12.562 -7.040 -17.589 -17.594

37. 37
Euclidean ITN UP OSM VMD
Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim Cents AccPt Perim
Euclidean
Centroids .148 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
AccPts -1.445 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
Perim -13.084 -12.683 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001 < .001
ITN
Centroids -13.358 -13.342 -11.001 .874 < .001 < .001 .020 < .001 .452 .167 < .001 .266 .189 < .001
AccPts -13.331 -13.292 -10.872 - .159 < .001 .154 < .001 < .001 .041 .974 < .001 .122 .522 < .001
Perim -15.158 -15.140 -14.682 -9.272 -7.610 < .001 < .001 < .001 < .001 < .001 .935 < .001 < .001 < .001
UP
Centroids -13.564 -13.529 -11.134 -4.202 -1.426 -8.613 .009 < .001 .070 .138 < .001 < .001 < .001 < .001
AccPts -13.189 -13.130 -11.082 -2.333 -3.615 -9.355 -2.611 < .001 .003 < .001 < .001 < .001 < .001 < .001
Perim -15.083 -15.063 -14.656 -8.004 -7.659 -3.871 -10.661 -12.052 < .001 < .001 .581 < .001 < .001 .491
OSM
Centroids -13.131 -13.083 -11.097 - .752 -2.046 -8.272 -1.814 -2.955 -6.980 .027 < .001 .945 .573 < .001
AccPts -13.252 -13.207 -11.040 -1.383 - .033 -8.786 -1.481 - .050 -7.871 -2.213 < .001 .785 .002 < .001
Perim -15.126 -15.111 -14.690 -10.387 -9.698 - .082 -10.225 -11.140 - .551 -12.051 -11.169 < .001 < .001 .089
VMD
Centroids -14.803 -14.762 -13.962 -1.113 -1.546 -7.766 -4.907 -5.197 -6.264 - .069 - .273 -8.590 < .001 < .001
AccPts -14.731 -14.712 -14.083 -1.312 - .595 -8.108 -4.115 -4.158 -7.266 - .564 -3.100 -9.338 -5.138 < .001
Perim -15.547 -15.529 -15.318 -10.528 -9.935 -8.111 -10.312 -10.995 - .689 -8.956 -10.796 -1.702 -6.486 -7.448
Secondary Schools, Vale
Differences in 2SFCA results
Below diagonal = Wilcoxon Z scores
Above diagonal = significance (black = sig at < .001 level; green = sig at 1% level; amber = sig at 5% level; red = not significant at 5% level).

38. Summary of results
Network
In most cases the choice of network made statistically significant differences to the
accessibility outcomes , whatever the network used.
38
0
400
800
1200
1600
2000
ITN UP OSM VMD
km
Total network lengths - Cardiff
ITN 82% of ITN_UP
OSM 71% of ITN_UP
VMD 64% of ITN_UP
0
200
400
600
800
1000
1200
1400
ITN UP OSM VMD
km
Total network lengths - Vale
ITN 91% of ITN_UP
OSM 72% of ITN_UP
VMD 70% of ITN_UP

39. 39
Process
The tools used in calculating 2SFCA results were not suited to the more detailed dataset (ie
Sites).
If full use of these datasets is to be made in accessibility studies, new toolset algorithms will be
required. These new tools will be more complex and sophisticated than those currently
available.
The increased complexity may not be matched in increases in accuracy, due to different
usability issues with the other data used in the calculations (ie network quality and/or demand
feature attributes and location).
Summary (continued)
Location method
Changes to the method of locating supply features (in this case using different methods to
represent the location of secondary schools) also made statistically significant differences to
accessibility results, in most cases.

40. Summary (continued)
Analysis
The majority of results for distance and 2SFCA combinations had significant
statistical differences, whether Vale or Cardiff.
The Vale of Glamorgan had higher levels of Destination Overlap and higher
correlation levels than Cardiff, reflecting fewer Supply features and less complex
networks, which restricted options for destination choice.
40

41. 41
Supply
Third-party datasets were generally poor in terms of selection and classification criteria,
being overly inclusive (good for classifications such as “doctor,” poor for classifications
such as “school,” though this has improved recently).
Free and open sources were not useful for these studies in the geographical areas
chosen (eg very little data available from OSM).
Demand
More detailed population representations offer greater geographical accuracy, but are
challenging in terms of presentation, visualisation and interpretation (eg the ability to
remove non-domestic post-codes from accessibility calculations).
Brief observations from other parts of the study

42. 42
Conclusions
Although analysis using other supply features is, at present, incomplete, the results
so far indicate general similarity to those shown here.
No patterns have emerged. As yet.
Limitations with the tools and GIS processes currently used in calculating FCA-
type accessibility measures mean the potential usefulness of new, more detailed
datasets is being neglected.
The levels of assumptions and adjustments required to operate these tools with
the more detailed datasets reduces the utility of these datasets, thus losing the
opportunity to obtain higher levels of precision and accuracy in similar
accessibility studies.
Choice of network and/or choice of location method can have a significant effect on
accessibility outcomes.
Some combinations (as detailed earlier) produce highly correlated results with no
statistical difference.
These can be accounted for providing users (producers and customers) are aware
of the implications of such choices, and of the effects that may be caused.

43. 43
Further work
Identify patterns between different services / supply features (primary schools, GP
surgeries, sports facilities, community hubs) in order to make wider assessments
of usability and/or usefulness of the geographical data involved.
Examine more detailed demand representations (post code and/or address-level
population points), while addressing their visualisation issues.
Assess usability of the new OS Open Roads dataset.
Parallels may be made with outcomes from data quality studies, but it is becoming
evident that data assessed as high quality may not necessarily be the most useful
in a particular context.
Exploration and analysis will continue, with the ultimate aim of identifying pre-
procurement usability criteria, whether context specific or (ideally) more general.

44. Thank you
The study reported here forms part of an Ordnance Survey-
sponsored PhD research programme. However, any views expressed
in this presentation are mine, and do not necessarily represent
those of Ordnance Survey.
robin.frew@southwales.ac.uk

45. 45
Supplementary slides

46. 46
Vale secondary schools
Euclidean 2SFCA, centroids

47. Vale secondary schools
Network distance (ITN), school site centroids

48. Vale secondary schools
Network distance (OSM), school site centroids

49. 49
Comparison of Demand representations
Euclidean distance using Post Codes for Demand, school centroids for Supply

50. 50
Cartogram visualisation
Cardiff, ITN with UP, site centroid

51. 51
Cartogram visualisation
Euclidean distance using Post Codes for Demand, school centroids for Supply
Cartogram approach

52. Post Code representation – usability issues
Example: GP surgeries in Cardiff, using Euclidean distance
Distance to
nearest facility
• Large number of non-
domestic post codes.
• No population allocated
to those PC polygons.
• Population instead
distributed through the
other polygons.
• More accurate?
• Visualisation issues?
• Not a match for OAs, nor
LA areas.
52