How the science of cities can help European policy makers: new analysis and perspectives

Transcription

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 European Commission

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 27 May, 2015

8 GHSL 27 May, 2015

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 1. To find out IF a municipality contains a city 2. To define an 'urban centre' 3. To measure access to transport, green space 4. To measure weighted density instead of average density 5. To measure exposure to air quality

11 Urban centre versus administrative city

12 EU-OECD city and commuting zone definition in three steps 1. Define an urban centre of or more 2. Define a city based on this urban centre (consisting of one or more municipalities) 3. 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 1. Urban centres = contiguous (excluding diagonals) cells with a density of at least 1500 inhab/km 2 and a minimum of inhabitants (after gaps filled with majority rule) 2. Urban clusters = contiguous (including diagonals) cells with a density of at least 300 inhab/km 2 and a minimum of inhabitants (no gap filling) 3. 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 Population per hectare Density drops away from the centre Population density profile of selected mid-sized European capital cities, Stockholm Wien Brussels Lisbon Dublin Budapest Amsterdam Source: Batista e Silva, F. et al. (2012) Distance from city centre (Km)

18 Share of built-up area drops away from the centre 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% % Distance to the city centre

19 Share of built-up area drops away from the centre (cumulative) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% % Maximum distance to the centre

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.

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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 High Medi um Low Null More than ten departures an hour for both medium- and high-speed modes More than ten departures an hour for one mode, but not both Between four and ten departures an hour on one or both modes, but no access to more than ten departures and hour less than four departures an hour for one or both modes, but no access to more than four departures an hour No access within walking distance

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

27 Stockholm: areas and population by access to public transport and its frequency 844,000 1,135,000 1,542,000 2,042,000 inh. inh. inh. inh.

28 Share of popopulation, in % Access to public transport in Brussels Access to a public transport by frequency of Null Distance to the city centre in km Low Medium High Very high

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?

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31 Green spaces in Brussels, % 50% 40% 30% forests agriculture and natural areas sports and leisure green urban areas 20% 10% 0% Distance to city centre in km

32 Share of population Access to green spaces by size 100% 90% 80% 70% 60% 50% 40% 30% 20% Access according to the size of green space extra large large medium small very small (almost) none 10% 0% Maximum distance to the city centre in km

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 m 2 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 ener/focus/2012_01_city.pdf New degree of urbanisation ener/work/2014_01_new_urban.pdf