Skype
Task 183_3
City typology
Intermediate Report D1.0
Prepared by:
Mirko Gregor (GeoVille),
Christoph Schröder, Ece Aksoy (UMA)
Jaume Fons, Miquel Sainz (UAB)
Alexander Storch, Pia Thielen, Wolfgang Schieder
(UBA-V)
Date:
15.07.2014
Project Manager:
Geertrui Louwagie
Universidad de Malaga
ETCSIA
PTA - Technological Park of Andalusia
c/ Marie Curie, 22 (Edificio Habitec)
Campanillas
29590 - Malaga
Spain
Telephone: +34 952 02 05 48
Fax: +34 952 02 05 59
Contact: etc-sia@uma.es
TABLE OF CONTENTS
1
Key messages ......................................................................... 1
2
Introduction............................................................................ 2
2.1
2.2
2.3
2.4
3
Urban domains ...................................................................................... 5
Data overview ....................................................................................... 6
Applicable projects ................................................................................. 7
Methodological definition ........................................................ 5
4.1
4.2
4.3
5
2
2
3
4
Conceptualisation and data overview...................................... 5
3.1
3.2
3.3
4
Context and objectives ...........................................................................
Relevant policies ....................................................................................
Project tasks .........................................................................................
Definitions ............................................................................................
Selection of indicators ............................................................................ 5
Prioritisation of indicators and basic key figures ........................................ 11
Methodological approach........................................................................ 16
References ............................................................................ 21
European Topic Centre Spatial Information and Analysis
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1 KEY MESSAGES
The key messages will be completed for the final report.
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2 INTRODUCTION
2.1 CONTEXT AND OBJECTIVES
Cities and urban areas in Europe are very different because they are located in different
geographic situations (littoral, mountain, island, fluvial corridor, etc.), have different
climate, heritage (morphology, size, age of housing, spatial segregation, etc.) and
trajectory (shrinking cities, sprawl, etc.), activities (industry, tourism, etc.), urban
management, population (demography, ageing), etc. For all these differences, it is
impossible to compare cities and to take into account all the complexity of urban system.
Some environmental data concerning the urban system exist, such as noise, air quality,
waste water management, soil sealing. For certain others cases, proxy data can be used
or computed. As it is a cross-cutting issue, the system of information on urban
sustainability must be conceived in synergy and cross-fertilisation with many other topics
(air, water, waste management, biodiversity, land, soil, etc.). The development of a set
of indicators on urban sustainability can be done by developing specific indicators (green
infrastructure, urban sprawl, etc.), by re-using the indicators already existing (in
particular for air quality, noise, water and vulnerability), by gathering scattered data. The
work will consist more in giving sense to all the dispersed information than create new
information.
The aim is to provide synthetized information in an easy-to-understand way in order to
facilitate the communication of the key messages. A typology of cities based on an
integrated analysis and taking into accounts the huge diversity of cities (size,
development, demography, environmental profile, etc.), will be developed. This typology
of urban areas could be based on statistical approach such as for example the ESPON
study on the European Sea typologies in the ESaTDOR project
http://www.espon.eu/main/Menu_Projects/Menu_AppliedResearch/ESaTDOR.html). Each
class of the typology, composed of a group of urban areas will be followed in the future in
order to analyse the trajectory of the group and not the individual trajectory of each
urban areas. The expected final outcome should be a typology to support the assessment
of urban sustainability.
2.2 RELEVANT POLICIES
The following major policies are of relevance for the city typology activity:

7th Environmental Action Programme (COM(2012) 710 final), http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2012:0710:FIN:EN:PDF

Thematic Strategy on the Urban Environment (SEC(2006) 16), http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2005:0718:FIN:EN:HTML

Roadmap to a resource-efficient Europe (COM(2011) 571 final),
http://ec.europa.eu/environment/resource_efficiency/pdf/com2011_571.pdf

Soil Thematic Strategy COM(2006)231 final, http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2006:0231:FIN:EN:PDF

DG ENV Soil sealing guidelines,
http://ec.europa.eu/environment/soil/pdf/guidelines/EN%20%20Sealing%20Guidelines.pdf

Thematic Strategy on the sustainable use of natural resources (COM(2005) 670
final), http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52005DC0670:EN:NOT
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
EU2020 Biodiversity Strategy (COM(2011) 244 final), http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2011:0244:FIN:EN:PDF

Upcoming EC Communication on Land as a Resource in 2015

UNCCD “Zero net land degradation”,
http://www.unccd.int/Lists/SiteDocumentLibrary/Rio+20/UNCCD_PolicyBrief_Zero
NetLandDegradation.pdf

Rio+20 outcome document “The Future We Want” (A/RES/66/288) mentioning ‘a
land degradation neutral world in the context of sustainable development’
(reflected in the 7th EAP proposal, Preamble (17)),
http://www.uncsd2012.org/content/documents/727The%20Future%20We%20Wa
nt%2019%20June%201230pm.pdf
2.3 PROJECT TASKS
The task is divided into 4 sub-tasks that can be directly linked to a milestone or
deliverable.
Sub-task
Conceptualisation
Activities
Identification of the main domains to be
considered (e.g. energy, water, land use)
Assessment of data availability related to
main domains identified above, time series
and reporting unit(s)
Overview of past/existing projects (in
particular ESPON) and developments in the
IUME community (in particular Regio GIS)
Milestone: inception report
Definition of typologies
One single typology vs multiple typologies
and clustered system (reality/applicability
vs ideal system)
Linking typologies to policy objectives:
objective of the typology (e.g. cohesion,
resource efficiency –including land,…), in
particular to urban vulnerability to climate
change.
Milestone: proposal of typologies
(methodologies)
Implementation
First milestone: intermediate report
Second milestone/key deliverable: final
report
Development of map book
Finalisation of the map book related to
urban vulnerability (started in 2013)
Prepare a concept of map book for urban
typologies
Milestone/ key deliverable: final report
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The four project tasks are also reflected in the structure of the report.
2.4 DEFINITIONS
Typology
Basic definitions from the www:

The study or systematic classification of types that have characteristics or traits in
common. (http://www.thefreedictionary.com/typology)

A classification according to general type, especially in archaeology, psychology,
or the social sciences
(http://www.oxforddictionaries.com/definition/english/typology)
In our case the city typology could be understood as “a both quantitative and qualitative
characterisation of cities, which should be structured in hierarchical systems providing a
broad view on cities, their situation and basic functions, their individual performance and
main activities, their threats and their most important changes (i.e. potential pressures
and development paths)”.
City
This definition more refers to the reference unit that is to be applied in the context of the
city typology:

Urban Audit core city and/or Urban Audit Larger Urban Zone as administrative
reference units that have a direct relation to the statistical data collected and
provided on European level by Eurostat

New degree of urbanisation as new harmonised definition of Urban Audit cities

UMZ as morphological approximation of the “real” city (boundary); either derived
from CLC or Urban Atlas

A composite reference unit, such as using the core city with an additional buffer of
a fixed width to take account of the periphery
This issue will be an important decision to be made, being closely related to data
availability.
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3 CONCEPTUALISATION AND DATA OVERVIEW
The aim of the first sub-task was the identification of the main urban thematic domains
to be considered (e.g. energy, water, land use,..), an assessment of data availability
related to the main thematic domains identified above, time series and reporting unit(s),
and to provide an overview of past/existing projects (in particular ESPON) and
developments in the IUME community (in particular Regio GIS).
3.1 URBAN DOMAINS
The starting point of the conceptualisation was the identifica
tion of a number of thematic urban domains which are considered to be relevant for the
analysis of urban sustainability. Figure 1 below presents a mind map sketch of the major
urban thematic domains and their inter-linkages.
Figure 1: Mind map providing a sketch overview of the thematic urban domains
and their potential inter-linkages
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3.2 DATA OVERVIEW
For each of the domains it was attempted to identify a number of data sets that could be
used in the framework of the city typologies activity. The list so far contains 44 data sets
with a relatively unequal distribution across domains. The majority of the data come from
the land use and socio-economic domain while other domains are represented by only a
small number of available data (cf. Figure 2). The proportions of tabular, raster and
vector data, respectively, are almost identical (cf. Figure 3). The data are to a large and
again almost equally distributed extent either reference data, variables or indicators, to a
lesser extent already prepared typologies (cf. Figure 4).
The entire list of data sets is delivered in a separate excel sheet 1. It is understood to be a
living document that can be extended at any time during the task duration until the
implementation of the typology commences. Afterwards, it will become more difficult to
take additional data sets into account, but they can still be added to the list for later
consideration.
Figure 2: Number of data sets per urban domain
1
http://forum.eionet.europa.eu/etc-sia-consortium/library/2014-subvention/183_3-citiestypology/milestones/183_3-data-list
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Figure 3: Proportion of tabular, raster and vector data sets, respectively
Figure 4: Proportion of information type
3.3 APPLICABLE PROJECTS
The following table contains a list of relevant projects from which either concepts or data
could be considered for being integrated into the current activity.
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BESECURE Best practice
Enhancers for
Security in
Urban
Environments
BRIDGE Sustainable
urban planning
decision
support
accounting for
urban
metabolism
CHANCE2SUSTAI
NUrban Chances:
City growth
and the
sustainability
challenge;
Comparing fast
growing cities
in growing
economies
CityBench ESPON
CityBench for
benchmarking
European
Urban Zones
FP7
The BESECURE project aims to support local
policymakers in the creation, enhancement and
implementation of security policies in urban
zones. Urban security is a critical subject within
the EU, but dealt with in widely different manners.
Building a comprehensive and
pragmatic set of indicators, and a
pragmatic risk assessment model that
can provide cues about the
development of certain scenarios.
On-going
(03/2015)
FP 7
The BRIDGE project aims at bridging the gap
between bio-physical sciences and urban
planners and at introducing innovative planning
strategies for urban planning and design in
Europe.
2008-2011
FP 7
This research programme examines how
governments and citizens in cities with differing
patterns of urban economic growth make use of
participatory (or integrated) spatial
knowledge management to direct urban
governance towards more sustainable development.
Development of a Decision Support
System
Methods and data to:
Quantitatively estimate energy,
water, carbon and pollutants fluxes
at local scale.
Quantitatively estimate the
environmental impacts of the above
components.
Translate the above environmental
impacts to socio-economic benefits.
Development of a participatory
spatial knowledge models of
metropolitan governance networks.
ESPON
CityBench project will deliver the CityBench
webtool. This webtool will help policymakers,
practitioners and public and private investors to put
economic, social and environmental
sustainability of cities at the core of decisions.
The conceptualisation of the webtool,
including the selection/development
of feasible urban indicators.
The design, testing and establishment
of a ready-to-use practical webtool to
benchmark and monitor European
cities.
On-going
(02/2014)
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On-going
(-03/2014)
CORFU Collaborative
research on
flood resilience
in urban areas
FOCI - Future
Orientation for
Cities
FP7
Interdisciplinary international project that will look
at advanced and novel strategies and provide
adequate measures for improved flood
management in cities.
Evaluation of future impacts of urban
growth and climate change on flood
probability through scenario studies.
On-going
(03/2014)
ESPON
FOCI analyses the current state, trends and
development perspectives for the largest
cities and urban agglomerations within the
European territory. It shall identify the driving
forces of urban development which are the most
relevant for understanding urban evolutions and
offer scenarios for the development of
Europe’s cities leading to alternative policy
options.
Finished
(2010)
GREEN SURGE Green
Infrastructure
and Urban
Biodiversity for
Sustainable
Urban
Development
and the Green
Economy
FP 7
GREEN SURGE will identify, develop and test ways
of connecting green spaces, biodiversity,
people and the green economy, in order to meet
the major urban challenges related to land use
conflicts, climate change adaptation, demographic
changes, and human health and wellbeing.
Indicators on the functional
specification of FUA/LUZ and new
complex indicators of cities´
development opportunities,
competitiveness, socio-economic
and environmental situation.
Typologies of the urban system of
Europe according to the functional
specialisation of the cities and their
competitiveness.
Development of tools to:
Develop urban green infrastructure
as a planning concept for both
integration and promotion of
biodiversity and ecosystem services,
and adapt it to local contexts.
Explore how valuation and real
market integration of biodiversity
and ecosystem services can facilitate
choices in favour of the development of
multifunctional green spaces in urban
areas.
MOLAND Monitoring
Land use/cover
Dynamics
JRC
The aim of MOLAND is to provide a spatial
planning tool that can be used for assessing,
monitoring and modeling the development of urban
and regional environments.
Definition and computation of
territorially-based indicators to
allow integrated approaches
Development of scenarios for longterm strategies of sustainable
development.
On-going
programm
e
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On-going
(-10/2017)
OpenNESS Operationalisat
ion of Natural
Capital and
Ecosystem
Services
FP 7
Phenotype Positive health
effects on the
natural outdoor
environment
in typical
populations of
different
regions in
Europe.
PRIMUS Policies and
research for an
integrated
management of
urban
sustainability
FP7
SGPTD Secondary
Growth Poles
and Territorial
Development in
Europe;
Performance,
Policies and
Prospects
ESPON
OpenNESS aims to translate the concepts of
Natural Capital (NC) and Ecosystem Services
(ES) into operational frameworks that provide
tested, practical and tailored solutions for
integrating ES into land, water and urban
management and decision-making. It examines
how the concepts link to, and support, wider EU
economic, social and environmental policy
initiatives and scrutinizes the potential and
limitations of the concepts of ES and NC
PHENOTYPE is intended to provide a better
understanding of the potential mechanisms, and
better integration of human health needs into
land use planning and green space
management.
5 case studies on “Sustainable urban
management” (ES in urban land use
planning, GI Strategy in urban
planning, Sustainable urban planning)
On-going
(05/2017)
Elaboration of core indicators to
assess and monitor different types
of natural environment
Data collection of the natural
environment (green spaces)
On-going
(-12/2015)
The PRIMUS project has been designed to bridge
the gap between research on the European level on
one hand, and policy-making at (and for) the local
level on the other hand.
Development of set of advanced
sustainability indicators for local
governments to measure their
performance in response to the
renewed EU Sustainable
Development Strategy, the Urban
Thematic Strategy and the Aalborg
Commitments,
serving as a basis for developing
measurable targets and timeframes for
the mid-term.
Typology of secondary cities
Review of policy towards secondary
cities at European Union level and in
individual Member States
2009-2012
SGPTD develops a common understanding of the
opportunities and prospects for the territorial
development of secondary cities. The key
objective hence is to produce clear policy
recommendations about the challenges and
opportunities facing secondary cities in Europe
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Finished
(2012)
SUME Sustainable
urban
metabolism for
Europe
FP 7
TOWN – Small
and MediumSized Towns
ESPON
TURAS Transitioning
Towards Urban
Resilience and
Sustainability
FP7
Urban Nexus
The SUME project analysed the impacts of
existing urban forms on resource use and
estimate the future potential to transform
urban building and spatial structures in order
to signifi-cantly reduce resource and energy
consumption, thereby taking into account
differences in urban development dynamics.
The main objective of this project is to create a
methodology to identify small and mediumsized towns. This methodology should be
compatible with the recently revised urbanisation
classification at EU scale identifying all small and
medium-sized towns as places having an urban
centre with a population between 5 000 and 50 000
inhabitants.
The TURAS Project brings together decision makers
in local authorities with SMEs and academics to
respond to the efforts of city communities. Eleven
local authorities or local development agencies are
involved as project partners and they will orient
research and development from the outset
towards the most significant sustainability
and resilience challenges facing their cities.
URBAN-NEXUS will develop a Strategic Dialogue &
Partnership Framework to organise a long-term
collaboration with stakeholders in relation to
the key dimensions of sustainable urban
development:
Urban Climate Resilience
Health and Quality of Life
Integrated Urban Management
Integrated Data and Monitoring
Competing for Urban Land.
European Topic Centre Spatial Information and Analysis
Identification and analysis of urban
form in its relation to urban
metabolism => urban form typology.
Development of an urban metabolic
rate typology.
2008-2011
Town typologies
A methodological framework for
studying small and medium-sized
towns in their functional area
contexts.
Analysis and empirical evidence on the
development and territorial potentials
of small and medium sized towns in
their respective functional area
contexts at both European and regional
level.
GIS database and tools for urban
resilience
Report on limiting urban sprawl.
On-going
(12/2014)
Synthesis Report "Integrated Data and
Monitoring"
Synthesis Report “Competing for Urban
Land.
On-going
(-08/2014)
3
On-going
(-09/2016)
URBES – Urban
Biodiversity
and Ecosystem
Services
Biodivers
a
URBES is a three-year research project funded by
BiodivERsA that aims to bridge the knowledge
gap on the links between urbanization,
ecosystem services and biodiversity.
URGENCHE Urban
Reduction of
GHG Emissions
in China and
Europe
FP 7
URGENCHE develops and applies a methodological
framework for the assessment of the overall
risks and benefits of alternative greenhouse
gas (GHG) emission reduction policies for health
and well-being.
VITRUV Vulnerability
Identification
Tools for
Resilience
Enhancements
of Urban
Environments
FP 7
The objective of VITRUV is the development of
tools to support urban planners to consistently
integrate security issues into the considerations
made in the long and complex process which makes
up urban planning.
European Topic Centre Spatial Information and Analysis
Analysis of linkages between urban
biodiversity, ecosystem services
and land-use.
Pan-European study on ecosystem
service provision by urban areas
Scenarios of future urban land-use
Develop a modelling platform and a
related database for urban impact
assessment, including topics:
Urban energy generation and use,
and GHG and other pollution release
Urban spatial data including the
urban spatial plan, building stock,
transportation and population
Socio-economic, demographic,
exposure, health and well-being of the
population
Development of planning tools at
local/micro level
4
On-going
(- 2014)
On-going
(08/2014)
On-going
(04/2014)
4 METHODOLOGICAL DEFINITION
Sub-task 2 of the project aims at selecting a number of indicators to be used for the
computation of the city typology; moreover, the processing approach should be
developed, tested and evaluated.
Indicators are useful tools to quantify and summarise a certain amount of
data/information related to a topic of interest. Moreover, indicators provide the
opportunity to identify groups of individuals that share similar properties and, therefore,
possible parallel trends, pressures and development paths.
In the context of the conceptual development of the cities typology the following
questions appear to be highly relevant:

How to select a set of cities to illustrate an indicator?

How to identify groups of cities that share similar properties? Is it possible to
establish a typology of cities connecting some city characteristics to come up with
a fundamental characterisation of European cities for environmental reporting and
statistics?
4.1 SELECTION OF INDICATORS
First drafts and ideas have been elaborated and provided by UBA-V and are in the
following linked to the first sub-task and the list of domains and data available.
In their document the UBA-V colleagues have proposed an approach that is based on a
quantitative characterisation of cities by both static and dynamic indicators and key
figures supported by a qualitative characterisation to categorise cities according to their
functions and main activities, which could be amended by information about geographic
location.
4.1.1 Quantitative characterisation: static indicators
A quantitative characterisation of cities by static information about the structure (aligned
to a date, a year or as an average value of a defined period – within an adequate time
range for comparing cities) provides a description of structure state. Examples are to set
a number of classes on indicators focussing on specific topics and containing a certain
number of sub-indicators. This indicator classes could for instance be determined by the
domains identified in the first sub-task:
S1 Urban dimension and land use

Administrative area

Cover area with buildings and infrastructure (based on Urban Atlas)

Green urban spaces (based on Urban Atlas)

Urban sprawl

Land use mix

Degree of soil sealing
S2 Urban form and distribution

Compactness
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
Share of high density area (based on Urban Atlas)

Share of low density area (based on Urban Atlas)

What is high- and what is low-density? Could be very different for countries.
S3 Climate

Average air temperature

Average wind speed

Average total annual precipitation

Use data from water accounts; issue of insulation/no. of sunny days
S4 Soil

Degree of soil sealing

Soil functions

Soil degradation

Agricultural use

Land recycling is missing
S5 Socio-economics

Population:
o
Inhabitants
o
Density
o
Age structure
o
Migration and segregation

Housing

Economy

Employment

Tourism:
o
number of visitors
o
number of people working in touristic sector

issue of income, poverty, GDP

housing: age of building

tourism: % of second house possible?
S6 Water

Supply networks

Consumption

Fresh water

Possible to define the type of catchment in which the city is located? E.g. droughtprone area?
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
Water accounts data

Flood areas missing
S7 Energy

Consumption

Supply networks

Conservative/fossil vs. renewables
S8 Waste

Production

Collection

Deposition

Recycling

Energy recovery?
S9 Air quality

Pollution by source

Fresh air
S10 Transport and mobility

Transport networks (length of roads, railways)

Modal split, e.g.
o
Modal split of public transport performance/ways
o
Modal split of biking transport performance/ways

Pollution

Cars per citizen

Public parking Area for cars per citizen

Capacity of public transport networks per inhabitant

length of bicycle ways including mixed use with pedestrian for each direction per
inhabitant
S11 Noise

Pollution

Traffic

Airport
S12 Governance

Local government revenue

Voters participation
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
Civic associations

Government effectiveness index (WorldBank, national level)

City committed to fight climate change (Urban Audit Perception Survey 2013)

City administrative services: support efficiency, trust (Urban Audit Perception
Survey 2013)
4.1.2 Quantitative characterisation: dynamic indicators
A quantitative characterisation of dynamics of change provides information about
pressures or trends (aligned to a year or as an average value of a defined short period
over a defined longer period to have a good view on a possible mid-term change).
Ideally, there would be dynamic indicators based on time series of the static indicators.
In reality this will not be the case. But still
D1 Urban dimension and land use

Changes of Cover area with buildings and infrastructure (based on Urban Atlas,
2012 not available yet)

Green urban spaces (based on Urban Atlas, 2012 not available yet)

Urban sprawl

Changes in degree of soil sealing (based on Imperviousness layer, available for
2006, 2009 and soon for 2012; cf. urban vulnerability factsheet on soil sealing)
D2 Urban form and distribution

Compactness

Share of high density area (based on Urban Atlas, 2012 not available yet)

Share of low density area (based on Urban Atlas, 2012 not available yet)
D3 Climate
Changes in

Average air temperature

Average wind speed

Average total annual precipitation
D4 Soil

Changes in degree of soil sealing (based on Imperviousness layer, available for
2006, 2009 and soon for 2012; cf. urban vulnerability factsheet on soil sealing)

Change in agricultural use  maybe quantitatively not relevant
D5 Socio-economics

Population
o
Inhabitants: growth and shrinkage of number of inhabitants
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o
Density
o
Age structure: change in age structure (are cities getting older?)
o
Migration and segregation

Housing

Economy: Regional gross value per inhabitant

Employment:

o
growth or shrinkage of number of unemployed
o
In-commuter per employee within the city
o
Out-commuter per citizen
Tourism:
o
Changes in number of visitors
o
Changes in number of people working in touristic sector
D6 Water

Change in consumption
D7 Energy

Change in consumption

Conservative/fossil vs. renewables: change in split
D8 Waste
Changes in

Production

Collection

Deposition

Recycling
D9 Air quality

Changes in pollution by source
D10 Transport and mobility

Transport networks (length of roads, railways)

Modal split, e.g.

Modal split of public transport performance/ways

Modal split of biking transport performance/ways

Pollution

Cars per citizen

Public parking Area for cars per citizen
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D11 Noise

Change in noise levels
D12 Governance

Change in Local government revenue

Change in Voters participation

Change in Civic associations

Change in Government effectiveness index (World Bank, national level)
4.1.3 Qualitative characterisation
A qualitative characterisation can be given by defined criteria to categorize cities
according to their function or main activities etc.. Examples are cities of

Private Services

Banking and assurance

Industry

Public services and administration

Higher education

Culture and art

Health and recreation

Tourism and conferences

Families (high share of young citizens)

Retiree (high share of elder citizens)

Immigration

Emigration
This categorisation per main function/activity could also be combined with basic
information about the geographic location of a city, such as coastal, mountainous, in a
fluvial corridor, etc.
Moreover a general perspective
(A) on quality of live for citizens, and
(B) on the local business condition
seems to be very useful to characterize cities according to the smart city and sustainable
development approach (cf. UBA-V project on SmartCities,
http://www.klimafonds.gv.at/foerderungen/aktuelle-foerderungen/2011/smart-energydemo-fit-for-set/).
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4.2 PRIORITISATION OF INDICATORS AND BASIC KEY FIGURES
To restrict the number of indicators and basic key figures for the computation of the
typology an iterative prioritisation exercise was carried out based on an excel sheet in
which all partners gave their scoring for each indicator twice with respect to a number of
criteria:

Data availability

Data completeness

Methodology

Processing

Policy relevance

Message
Finally, each partner had to give a “yes” or “no” indication if the indicator should be
prioritised. While governance indicators were missing in the first round, they have been
added to the list for the second vote. Unfortunately, not everybody replied to the second
vote request, so there are some indicators which have a lower number of votes even
though this might not mean that they should be removed from the priority list. To create
consistency between the votes, the old votes have been used where no new votes had
been provided.
For the final selection, the number of “yes” scores was counted, leading to a number of
indicators (except the ones on governance) with the maximum of 5 times “yes”, 4 or 3
“yes” (yellow) and 2,1 or 0 “yes” (red).
Below is the preliminary list of the 36 “green” static and dynamic indicators and key
figures (as of 30/06/2014). As there are still discussions on-going the list is not final yet.
The full list of all indicators and the related scorings will be uploaded to the Forum as an
amendment to this report2.
2
http://forum.eionet.europa.eu/etc-sia-consortium/library/2014-subvention/183_3-citiestypology/milestones/city-typology-t2-indicator-prioritisationlist/download/2/City_typology_indicator_prioritisation_D2.1.xlsx
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Indicator
ID
Domain
S1.3
S1.4
Urban dimension and land use
S1.5
Urban dimension and land use
S1.6
S2.1
Urban dimension and land use
S2.2
Urban form and distribution
S2.3
Urban form and distribution
S3.3
Climate
S4.1
Soil
S5.7
Socio-economics
Employment: rate of unemployed
S6.2
Water
Consumption
S6.4
Water
Flood areas [km²]
S7.2
Energy
Consumption
S7.3
Energy
Fossil vs. Renewables
S8.1
Waste
Production
S8.4
Waste
Rate of recycling
S10.1
Transport and mobility
Length of transport networks (roads, rail lines)
S10.2
Transport and mobility
Modal split
Green urban spaces (based on Urban Atlas)
Urban dimension and land use
Urban sprawl
Land use mix
Degree of soil sealing
Urban form and distribution
Compactness
Share of high density area (based on Urban Atlas)
Share of low density area (based on Urban Atlas)
Average total annual precipitation
Degree of soil sealing
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Change in green urban spaces (based on Urban Atlas,
2012 not available yet)
Urban sprawl
D1.2
Urban dimension and land use
D1.3
Urban dimension and land use
D1.4
Urban dimension and land use
D2.2
Urban form and distribution
D2.3
Urban form and distribution
D4.1
Soil
D5.1
Socio-economics
No. of inhabitants, growth and shrinkage
D5.2
Socio-economics
Population density
D5.3
Socio-economics
Age structure
D5.4
Socio-economics
Migration/segregation
D5.6
Socio-economics
Economy: change in GDP, regional gross value per
inhabitant
D5.7
Socio-economics
Employment: rate of unemployed
D6.1
Water
Consumption
D7.1
Energy
Consumption
D7.2
Energy
Change in split of fossil vs. renewables
D8.4
Waste
change in Rate of recycling
D9.1
Air quality
change in Pollution by source
D10.2
Transport and mobility
change in Modal split
Change in degree of soil sealing
Change in share of high density area (based on Urban
Atlas, 2012 not available yet)
Change in share of low density area (based on Urban
Atlas, 2012 not available yet)
Change in degree of soil sealing
When further listing the indicators and key figures that have scored with 4 times “yes”,
another 24 parameters are added.
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ID
Domain
S1.1
Urban dimension and land use
Indicator
Administrative area
Cover area with buildings and infrastructure (based on
Urban Atlas)
S1.2
Urban dimension and land use
S3.1
Climate
S4.5
Soil
Land recycling
S5.1
Socio-economics
No. of inhabitants
S5.2
Socio-economics
Population density
S5.3
Socio-economics
Age structure
S5.6
Socio-economics
Economy: GDP
S5.8
S5.10
Socio-economics
Tourism: no. of visitors
Socio-economics
Tourism: % of second houses
S8.2
Waste
Collection
S8.3
Waste
Deposition
S9.1
Air quality
Pollution by source
S10.4
Transport and mobility
Cars per citizens
S11.1
Noise
Pollution
S11.2
Noise
Traffic
S11.3
Noise
Airport
Average air temperature
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D1.1
D2.1
Urban dimension and land use
Change in cover area with buildings and infrastructure
(based on Urban Atlas, 2012 not available yet)
Urban form and distribution
Compactness
D5.10 Socio-economics
Tourism: change in % of second houses
D10.1 Transport and mobility
change in Production
change in Length of transport networks (roads, rail
lines)
D10.4 Transport and mobility
change in Cars per citizens
D11.1 Noise
Change in noise levels
D8.1
Waste
Concerning the governance indicators, four out of the proposed six static indicators were
prioritised by all partners who undertook the prioritisation; that is, voters’ participation,
WGI, city committed to climate change and city administrative services. Looking at the
dynamic indicators, three of the proposed four scored high; that is, change in local
government revenue (the change information was considered more important than the
status information as it would allow for an assessment of the financial situation and the
potential financial capability of a city to invest in environmental sustainability projects),
change in voters’ participation, and change in the WGI.
S12.1
Governance
Local government revenue
S12.2
Governance
Voters participation
S12.3
Governance
Civic associations
S12.4
Governance
Government effectiveness index (WGI)
S12.5
Governance
City committed to fight climate change
S12.6
Governance
City administrative services (support efficiency, trust)
D12.1
Governance
change in local government revenue
D12.2
Governance
change in Voters participation
D12.3
Governance
change in Civic associations
D12.4
Governance
change in Government effectiveness index (WGI)
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Lastly, UBA-V colleagues proposed four additional indicators for the mobility domain, two
static and two dynamic ones, where the latter describe the change in the situation
between two points in time.
S10.6
Transport and mobility
S10.7
Transport and mobility
D10.6
Transport and mobility
D10.7
Transport and mobility
Capacity of public transport networks per inhabitant
length of bicycle ways including mixed use with
pedestrian for each direction per inhabitant
Capacity of public transport networks per inhabitant
length of bicycle ways including mixed use with
pedestrian for each direction per inhabitant
All in all, this means that the set of indicators/key figures that is now being prioritised
sums up to a total of more than 40 (when taking only the ones with a score of “5 yes”)
and almost 70, respectively (when also including the ones with 4 “yes”). This seems to
be a lot for a meaningful and balanced typology, taking into account that the typology
should also contain a number of qualitative indicators, such as some information on the
function of a city (e.g., industrial city, service-oriented city) or its geographic location,
e.g. at the coast, in a fluvial corridor or in the mountains.
4.3 METHODOLOGICAL APPROACH
4.3.1 Reference unit
As already indicated in chapter 2 one important step will be to decide about the reference
unit(s) to be selected and employed in the data processing. By and large, 5 different
types of potential reference units are available:

Urban Audit Core City (CC)

Urban Audit Larger Urban Zone (LUZ)

EC/OECD new degree of urbanisation as future basis for urban Audit
(complemented for urban sprawl assessments by the urban-rural typology of
NUTS-3 regions)

Urban Morphological Zone

o
Corine-based
o
Urban Atlas-based
Core city or UMZ with a buffer of a defined width, created e.g. in accordance with
the MOLAND approach, i.e., in dependence of the size of the city (or another
sound method)
It was therefore decided to collect pros and cons of all delineations and list them in a
table.
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Reference unit
Pros
Cons
Urban Audit core city
Available Urban Audit
Statistics
Somewhat arbitrary
delineation with national
specificities
Link to available
national/subnational
statistics possible (=> gap
filling)
Urban Audit LUZ
Available Urban Audit
Statistics
some major gaps for certain
countries and years
Reflects functional urban
zone BUT
… includes wide areas of
non-urban land uses
Includes many different
administrative areas
Very large in some
countries
EC-OECD New degree of
urbanisation
New delineation of the
Urban Audit, relation to
core city and LUZ
UMZ
Reflects urban continuity
and changes in land cover
If based on Urban Atlas
delineation is pretty precise
in the reflection of the city
morphology
Revision leads to changes in
number of cities and extent
of some cities  socioeconomic indicators not
consistent for all cities
Defines all built-up areas
(=> not only cities)  could
be overcome by
intersecting UMZ and core
city
Does not reflect
administrative units => no
link to statistics
If based on CLC substantial
urban areas are missing
due to MMU issue
Core city or UMZ with buffer
zone
Includes surrounding areas
=> allows sprawl and
compactness analysis
Not directly a con, but
determining the extent of
the periphery (i.e. the
buffer) is critical.
Although it seems to be mandatory on a first view to establish the basic reference unit
for the typology, internal discussions led to the question whether there really is this need
for actually defining strictly one reference unit. It needs to be taken into account that the
basic way of storing the data is the form of a database and, probably, a point map, which
will refer generically to the "city" (not the city extent). So it would make sense to use the
administrative boundary for all statistical information (Urban Audit) available, with the
possibility to use member states’ local statistics for gap filling (waste, energy, water,
etc.). For land cover aspects (urban sprawl, soil sealing) a morphological approach of
delineating the city could be used (e.g., extract the mean degree of soil sealing from the
grid data based on one of the reference units), linking the indicator to the related core
city. Additionally, the chosen approach has to be very data-driven and also addressed to
the "user" of the typology. If the idea is to support local decision-making and
comparability of local actions (in its administrative borders), then the approach should
rely on these borders.
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4.3.2 Processing approach
Next to the appropriate urban reference unit (core city, UMZ) and how to potentially
integrate more than one reference unit into the processing, the identification of the most
suited methodology as such (e.g. statistical approach like k-means clustering) is the
main challenge.
In that context, it must always be kept in mind that the aim is the creation of a city
typology based on a number of urban sustainability indicators and key figures. For this
objective, it is required to integrate as much as possible parameters into the process to
best capture the entire picture of the city. Moreover, the link of the typology to the key
policy questions or policy support must be identified.
Cluster analysis or clustering is “a statistical procedure that starts with a data set
containing information about a sample of entities and attempts to reorganise these
entities into relatively homogeneous groups.” (Aldenderfer and Blashfield, 1989) The
reorganisation of the elements is based on similarity, so clustering can be understood as
the task of grouping a set of objects in such a way that objects in the same group (called
cluster) are more similar (in some sense or another) to each other than to those in other
groups (clusters). (Diansheng, 2002)
Similarity is mostly determined by distances, based either on single or multi-dimensions.
The simplest way of computing distances in a multi-dimensional feature space is the
application of Euclidean distances, which simply are the geometric distances in the multidimensional space. (Aksoy, 2009)
Figure 5: Examples of cluster analysis methods
The targeted clustering (i.e. typology) of European cities will be based on a large number
of parameters (indicators) and, thus, multiple dimensions. As a consequence, a large
number of cluster analysis methods are available, such as hierarchical clustering, kmeans algorithms, etc. Some discussion led to the proposition to use k-means clustering
algorithms, despite them also having some drawbacks. Pros and cons of the k-means
approach together with some concrete steps how to tackle the typology are presented in
the chapters below.
In data mining, k-means clustering is a method of cluster analysis which aims to partition
n observations into k clusters in which each observation belongs to the cluster with the
nearest mean (Aksoy, 2009). k-means clustering is in general rather easy to implement
and apply even on large data sets. As such, it has been successfully used in various
topics, ranging from market segmentation, computer vision, geo-statistics and
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astronomy to agriculture. It is often used as a pre-processing step for other algorithms,
for example to find a starting configuration (e.g. feature learning step for supervised
classifications).
Limitations3 are related to

Differing sizes

Differing densities

Having non-globular shapes
It has also problems with outliers and empty clusters.
Figure 6: Limitation differing densities
3
http://www.cs.uvm.edu/~xwu/kdd/Slides/Kmeans-ICDM06.pdf
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Figure 7: Limitation non-globular shapes
Considering that the majority of the partners agreed upon using k-means clustering, the
question remained whether a pre-grouping of the indicators would be required, e.g., by
having a Principal Component Analysis reducing the amount of data. There was no
consensus about this, so the first proposition is to use a three-tiered approach on all
indicators at once and compare/cross-evaluate them/the outcomes of the three methods:

Pure PCA

K-means with preceding PCA

Pure k-means
It was argued that clustering all indicators in one step would make the interpretation
difficult as the respective influence of the single indicators becomes rather unclear.
Therefore, another option would be to create a set of composite indicators per urban
domain (e.g., by applying a PCA on the domain-specific set of indicators and variables)
upon which the cluster analysis is applied.
The cross-comparison should allow identifying if one of the methods is best suited or if
further research has to be done.
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5 REFERENCES
Aksoy, E. (2009): “Classifying Turkish District Data With K-Means and SOM Algorithms”
Book, VDM Publication, ISBN: 978-3-639-13560-2 Germany, 2009
Aldenderfer, M.S. and Blashfield, R.K. (1989): Cluster Analysis. Sage Universty
Publications, Sixth printing, USA
Diansheng, G. (2002): Spatiasl Cluster Ordering and Encoding for High-Dimensional
Geographic Knowledge Discovery, UCGIS2, Summer, 2002
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