1. How the science of cities can help European policy makers: new analysis and perspectives
By Lewis Dijkstra, PhD
Deputy Head of the Economic Analysis Unit,
DG Regional and Urban Policy
European Commission 1

2. Overview Data revolution Defining cities
New analysis and links to the urban sustainable development goal indicators
Density
Land use changes
Green space
Public transport

3. A data revolution
More micro data (population register, business register, patents)
More geo-coded data (building register, census…)
More remote sensing data (water, green, imperviousness, buildings…)
More big data (smart phones, geotagged pictures, messages…)

4. Copernicus Urban Atlas
Thematic classes based on CORINE Land Cover nomenclature
But more specific for built-up areas, and less specific outside urban areas
Geometric resolution of 1:10,000
Minimum mapping unit of 0.25 ha in urban areas, 1 ha in other areas
Imagery reference year: 2006 and 2012

5. CORINE Land Cover

6. Urban Atlas

7. Sealed Surface Layer
22 November, 2018

8. GHSL
22 November, 2018

9. What makes a city? Buildings, mass, proximity People, density, size
Exchange, intensity, distance
Functions, specialised, variety
Political
Economically linked
Labour market, commuting zone

10. Population distribution within a city
To find out IF a municipality contains a city
To define an 'urban centre'
To measure access to transport, green space…
To measure weighted density instead of average density
To measure exposure to air quality

11. Urban centre versus administrative city

12. EU-OECD city and commuting zone definition in three steps
Define an urban centre of or more
Define a city based on this urban centre (consisting of one or more municipalities)
Define a commuting zone based on this city (including check for polycentric cities)
IMPORTANT! Cities are selected based on the population of their centre, not total population

13. One, two, three

14. Three grid concepts
Urban centres = contiguous (excluding diagonals) cells with a density of at least inhab/km2 and a minimum of inhabitants (after gaps filled with majority rule)
Urban clusters = contiguous (including diagonals) cells with a density of at least 300 inhab/km2 and a minimum of inhabitants (no gap filling)
Rural grid cells = cells outside urban clusters

15. Three degrees of urbanisation
Three grid concepts (Cork, IE)
Three types of municipalities

16. Three types of municipalities
Cities
> 50% pop. in urban centres
Towns and suburbs
> 50% pop. in urban clusters
< 50% pop. in urban centres
Rural area
> 50% pop. in rural grid cells
Urban areas = Cities + Towns and Suburbs

17. Density drops away from the centre

18. Share of built-up area drops away from the centre

19. Share of built-up area drops away from the centre (cumulative)

20. Density or land use indicators
Target 11.b holistic disaster risk management
Population density measured over continuous urban footprint
Target 11.3 sustainable urbanization (& 11.a)
Ratio of land consumption rate to population growth rate at comparable scale
Problems
Urban footprint or building footprint
What is land consumption?
What scale?

21. Proposal land use efficiency indicator
Measure built-up area (building footprint) per inhabitant based on GHSL for
Cities following the EU-OECD definition and
Commuting zone (if commuting is available) or
Suburbs following degree of urbanisation or
A buffer based on population size of a city
Monitor the changes in built-up area per capita over time (land use efficiency)
Cities with a high efficiency can reduce it, cities with low efficiency should increase it.

23. Measuring access to public transport: input data
Location of all public transport stops
Timetables of services: 2 groups:
bus and tram
train and metro
Population per building block based on:
detailed population grids
census tracts
neighbourhood statistics
plus disaggregation using land use data and/or imperviousness if needed

24. Spatial distribution of population matters
No location awareness: assuming uniform
population density throughout the city
High-resolution spatial distribution of population:
Opportunities for new indicators

25. Frequency of departures
Average stops an hour from 6:00 to 20:00 on a normal week day
Very high
More than ten departures an hour for both medium- and high-speed modes
High
More than ten departures an hour for one mode, but not both
Medium
Between four and ten departures an hour on one or both modes, but no access to more than ten departures and hour
Low
less than four departures an hour for one or both modes, but no access to more than four departures an hour
Null
No access within walking distance

26. Typology of frequency classes
Very high
Access to more than ten departures an hour for both medium- and high-speed modes
High
Access to more than ten departures an hour for one mode, but not both
Medium
Access to between four and ten departures an hour on one or both modes, but no access to more than ten departures and hour
Low
less than four departures an hour for one or both modes, but no access to more than four departures an hour
Null
No access within walking distance

27. Stockholm: areas and population by access to public transport and its frequency
844, ,135, ,542, ,042,000
inh inh inh inh.

28. Access to public transport in Brussels

29. Target 11.2 Public transport
Share of people living within 0.5 km of public transit [running at least every 20 minutes] in cities with more than 500,000 inhabitants
Specify the city definition to be used
Km of high capacity (BRT, light rail, metro) public transport per person for cities with more than 500,000 inhabitants
Why not measure access to high capacity public transport?

31. Green spaces in Brussels, 2012

32. Access to green spaces by size

33. Target 11.7 Green and public space
Area of public space as a % of total city space
Share of residents within 0.5 km of accessible green and public space
Accessible is extremely difficult to determine
Public space: roads, sidewalks, squares?
Proposal: share of residents with
Almost no open space in a buffer of 0.5 km
No green space of at least x m2 within 0.5 km
This avoids the problem of measuring access, but it will be a subset of the population with no access

34. Conclusion Data revolution is in full swing, but we need
a universe of cities using a single methodology
Understand population distribution within cities
Be aware of the modifiable area unit problem:
Use uniform building blocks (like grid cells)
Use population with access rather than area share
When using area shares, use a grid definition, not an administrative one
Take full advantage of new continuous, high resolution data sets (vs coarse and binary data)

35. More information EU-OECD City definition
New degree of urbanisation