Measuring walkable neighbourhoods using new open data - Claudia Baranzelli, EC Joint Research Centre. Workshop on Modernising Statistical Systems, OECD 2018.

1. The European Commission’s
science and knowledge service
Joint Research Centre

2. Measuring Walkable Neighbourhoods
Using New Open Data
Claudia Baranzelli, Mert Kompil, Chris Jacobs-Crisioni
JRC – Territorial Development Unit
OECD Working Party on Territorial Indicators Workshop
Paris, 5th November 2018

3. Content of presentation
 Territorial modelling
 Accessibility to services
 Measures of walkability
 Way forward

4. http://ec.europa.eu/jrc/territorial-policies/
Territorial Modelling

5. Better Knowledge
Knowledge-based Support to European Policy-making
 Evaluation (ex-ante and ex-post) of policies at urban and regional (sub-national) scales (“urban
proofing”).
 Focus on integrated/holistic approaches to territorial and urban development
 Based upon multi-scale modelling (what-if scenarios vs. baseline) and stakeholders
engagement
 Bottom-up initiatives

6. AGEING REPORT
(GDP,EMPL)
ECONOMIC
FACTORS SPATIAL
ITERACTIONS
DEMOGRAPHY
BIOPHYSIC
AL FACTORS
AGRIC. MARKET
OUTLOOK (CAPRI
2016)
Macro socio-economic projections
Regional & Local Dynamics
Territorial Indicators & Analyses
EU POLICIES
INFRASTRUCTUR
ES& SERVICES
Regions
& cities
Activitie
s
Services
Energy
Resource
Efficiency
Accessibil
ity
Populatio
n
Territorial Scenarios:
From macro-level to local indicators

7. http://ec.europa.eu/jrc/territorial-policies/
Accessibility to services

8. Accessibility measures and modelling
Subregional facilities
(e.g. high schools, hospitals, larger
supermarkets etc.)
Regional facilities
(e.g. specialized hospitals, high-tech
services, governmental
organizations etc.)
Local facilities
(e.g. schools, childcare services,
health services, small markets etc.)
Changes in potential accessibility from 2015
to 2030 only from road network improvements
(left) and additionally including changes in
population distribution (right).
Fairness vs. efficiency of service provision

9. http://ec.europa.eu/jrc/territorial-policies/t-board
Measures of walkability

10. Walkability
Walkability index that allows for identifying actionable factors i.e. factors influencing how
walkable a neighbourhood is and which policy intervention can act upon.
Desirable features include the possibility to differentiate the index:
 by type of city dweller and purpose of the trip; and
 by season could be made, taking into account variations in urban micro-climate
conditions.

11. Category Factor
Morphology Surface roughness
Road geometry Curvature
Number of intersections
Vision enclosure
Built environment Greenery
Mix use
Availability of local amenities
Availability of services
Availability of social aggregation places
Urban context Local weather conditions
Air quality
Sound pollution
Vehicular traffic
Safety
Walkability - Factors

12. Service Centres
Identification of service centres as proxy for walkable neighbourhoods
 Input data:
 Open Street Map points of interest (POI)
 Filtered tags include: amenity, leisure, shop and tourism (values excluded if
related to cars and, in general, vehicular mobility)
 Methodology:
 Optimised hotspot analysis (index of agglomeration)

13. OpenStreetMap tags
Tag Condition Values
amenity
DOES NOT MATCH
animal_breeding|animal_shelter|car_wash|charging_station|bench|clock|drinking_water|fountain|grave
_yard|hunting_stand|motorcycle_parking|parking|parking_entrance|parking_space|post_box|table|telep
hone|toilets|vacuum_cleaner|waste_basket|waste_disposal|water_point
DOES NOT CONTAIN abandoned|animal_breeding|animal_shelter|car_wash|charging_station|fire_station|fuel|recycling
leisure DOES NOT CONTAIN
bird_hide|garden|horse_riding|ice_rink|marina|panorama|park|picnic_table|pitch|slipway|stadium|track|
weir
tourism CONTAINS hotel|hostel|guest|motel|bed|museum|gallery|artwork|art
shop DOES NOT CONTAIN abandoned|car|car_parts|car_repair|caravan|edilizia|farm|garden_centre|gas

14. Service Centres
High
Low
Agglomeration
index

15. Service Centres
High
Low
Agglomeration
index

16. Service Centres
High
Low
Agglomeration
index

17. Brussels Wien
Vilnius
Athens
Copenhagen Madrid
Milan
Warsaw
Service Centres

18. Service Centres – Total pop

19. Service Centres – Elderly pop

20. Network distance
Road distance to education and health services:
 Comparison OpenStreetMap Vs Local Data Sources or Google Map
 Identification of target groups:
 Young resident population (< 15 y)
 Elderly resident population (>= 65 y)

21. Education services - Milan
OSM
LOCAL SOURCE

22. Education services - Vilnius
OSM
LOCAL SOURCE

23. Health services - Milan
OSM
GOOGLE MAP

24. Health services - Vilnius
OSM
LOCAL SOURCE

25. Average distance to nearest service
* Only hospitals
A pupil or an elderly person should walk, on average, …km to reach the nearest health service /
education service:
MILAN
Education service
(local sources)
Education service
(OpenStreetMap)
Health service
(Google Map)
Health service
(OpenStreetMap)
SC 0.3 0.7 0.4 0.7
FUA 0.5 2.1 1.3 2.3
VILNIUS
Education service
(local sources)
Education service
(OpenStreetMap)
Health service
(local sources)
Health service
(OpenStreetMap)
SC 0.4 2.4 1.5* 1.4
FUA 1.1 6.3 4.4* 6.7

26. In the next 15 years, how will the age
distribution change?
According to the Reference Scenario, consistent with “Europop2013”
projections:
Young
Population in
FUA
(2015 - 2030)
Elderly
Population in
FUA
(2015 - 2030)
MILAN 14.8% 33.4%
VILNIUS -8.4% 27.1%

27. In the next 15 years, how will the age
distribution change?
According to the Reference Scenario, consistent with “Europop2013”
projections:
Young
Population in SC
SC
(2015 - 2030)
Elderly
Population in SC
SC
(2015 - 2030)
MILAN -22.8% -23.6%
VILNIUS -18.13% -18.06%

28. Population projections by age class

29. Population projections by age class

30. Population projections by age class

31. http://ec.europa.eu/jrc/territorial-policies/t-board
Way forward

32. Next steps
 Refine Service Centres indicator:
 Integrating POIs from different sources
 Including measures of diversity
 Focusing on separate categories of city dwellers
 Include other aspects influencing neighbourhood walkability:
 Urban micro-climate
 Spatio-temporal profiles
 Urban perception:
 Interpretation of street images (sources: Google, Mapillary, Place Pulse)

33. Points of Interest (POIs)
Summary Statistics
Total Points: 4 077
Total Reviews: 736 865
Attractions Points: 705
Attractions Reviews: 272 636
Hotels Points: 679
Hotels Reviews: 219 073
Restaurants Points: 2 640
Restaurants Reviews: 245 140

34. Urban microclimate
Wunderground.com
Evaluate intra-urban variability in local climate, based on:
 surface characteristics
 Geometric characteristics
 Human activities
Dependent variable: air T measured by amateur
monitoring stations

35. Insights on spatiotemporal patterns of
activity
For certain categories of Google POIs, it
is possible to know their degree of activity
24/7 based on how many people frequent
the POI.

36. Urban perception
Data source - Place Pulse 2.0:
 Is an experiment based on 100.000 street images from 56 cities.
 Is running from 2013 - present
 Quantitatively measures urban perception by crowdsourcing visual surveys to users around the globe.
 Measures “safer, livelier, wealthier, beautiful, boring, depressing” perception levels.
Milan

37. Thanks
Any questions?
You can find me claudia.baranzelli@ec.europa.eu

38. The European Commission’s
science and knowledge service
Joint Research Centre