Using Google data to measure access to amenities in cities - Talia Kaufmann, OECD. Workshop on Modernising Statistical Systems, OECD 2018.

1. Talia Kaufmann, School of Public Policy and Urban Affairs, Northeastern University
with the Center for Entrepreneurship, SMEs, Local Development and Tourism and the International Transport Forum, OECD
Measuring Accessibility to Services across cities
the case of French cities

2. Data-Driven metrics to assess Accessibility::
Potential Barriers

3. Data Sources
Open Street Maps
80% full globally
Global Human
Settlement
Globally full &
consistent
Google Places
96 categories
Globally consistent

4. Data Sources
Open Street Maps
Global Human
Settlement
81 French Cities
Open Street Maps
Google Places
1.5M points
Walking:: 15 categories
Driving + Public
Transport: 42 categories

5. Data Validation
Total Points
Street-facing listings
(60%)
Points Of Interest (POI)
(40%)
validatable
(84%, CI. 82% - 86%)
unvalidatable
(16%, CI. 14% - 18%)
new
(7%, CI. 5% - 9%)
falsified
(20%, CI. 18% - 23%)
verified
(80%, CI. 77% - 82%)

6. Accessibility Indicators

7. Indicator Mode Parameters
Closest amenity by walking distance Walking Closest amenity (list);
Walking duration measured in minutes, median
weighted by population
Share of population with walking accessibility Walking Percentage of FUA’s population;
Time thresholds (5,10,15,20 minutes)
Diversity of opportunities:
Share of population by accessibility levels
Walking, Driving, Public
transport
Amenity types (list); number of amenities
walking(0,<1, <5, >5); driving(10,20,30), public
transport(15,30,45)
Accessibility indicators

8. point Closest park
Closest restaurant
Closest school
Closest grocery store
Closest bank
Closest amenity by walking distance

9. Closest amenity by walking distanceAccess to Restaurants

10. Walking duration to closest
restaurant (minutes)
Closest amenity by walking distance

11. Closest amenity by walking distanceAccess to Parks

12. Walking duration to closest
park (minutes)
Closest amenity by walking distance

13. Some data visualization:: initial results
Closest amenity by walking distance

14. Closest amenity by walking distance

15. Share of Population with walking accessibilityAccess to Restaurants

16. Share of Population with walking accessibilityAccess to Supermarkets

17. Share of Population with walking accessibilityAccess to Parks

18. Diversity of opportunities
point
No access
Low (1)
Medium (1 to 5)
High (> 5)

19. Diversity of opportunitiesAccess to Restaurants

20. Diversity of opportunitiesAccess to Supermarkets

21. Diversity of opportunitiesAccess to Pharmacies

22. Diversity of opportunitiesAccess to Parks

23. Diversity of opportunities
pointpoint
Public transportation Driving

24. Diversity of opportunitiesAccess to shops
> 50 shops
< 50 shops
No access
> 50 shops
< 50 shops
No access
> 50 shops
< 50 shops
No access
> 50 shops
< 50 shops
No access
> 50 shops
< 50 shops
No access
> 50 shops
< 50 shops
No access
15 Minutes 30 Minutes 45 Minutes 10 Minutes 20 Minutes 30 Minutes
Public transportation Driving

25. Diversity of opportunitiesAccess to Entertainment & Recreation
> 50 amenities
< 50 amenities
No access
> 50 amenities
< 50 amenities
No access
> 50 amenities
< 50 amenities
No access
15 Minutes 30 Minutes 45 Minutes 10 Minutes 20 Minutes 30 Minutes
Public transportation Driving
> 50 amenities
< 50 amenities
No access
> 50 amenities
< 50 amenities
No access
> 50 amenities
< 50 amenities
No access

26. Accessibility Metrics

27. ➢ expand:: Globally consistent - All OECD countries
➢ measure:: Demand - how people use available
amenities
➢ implement:: Decision-making platforms
Next Steps
Talia Kaufmann, School of Public Policy and Urban Affairs, Northeastern University
with the Center for Entrepreneurship, SMEs, Local Development and Tourism and the International Transport Forum, OECD