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The European Workshop on Urban Climate Indicators, https://easychair.org/cfp/ewuci2021, tackles the design of relevant and computable urban climate indicators to study and adapt to climate change, specifically indicators that can be scaled in space and in time throughout Europe thanks to a better access to and better sharing of data. It is organised with the support of EuroSDR, of the national mapping agencies, statistical institutes and meteorological institutes of France and of Finland (IGN, INSEE, MétéoFrance, Statistics Finland, FMI, NLS), of the finnish environment institute (SYKE) and of the european project ERA4CS URCLIM on urban climate services. Targeted audience is scientists and experts who specify, prototype or produce indicators relevant to urban climate study, scientists and experts who advance the field of information infrastructure to support this application.

The workshop will include paper sessions (see below) and a challenge track (see below for challenges description). In both cases, you are invited to submit a short paper (5-6 pages) describing your contribution on https://easychair.org/conferences/?conf=ewuci2021. All accepted submissions will be archived in an electronic proceedings published by CEUR-WS.org.

Papers track : (deadline February 1st 2021)

Paper contributions are welcome about: specific indicators design to study climate change and adapt to it, more generally applications illustrating the benefits of better exploiting such information, technical challenges to find and combine different data sources, indicate quality and suitability of results and store results in an accessible way, and indicate quality / suitabilityinstitutional and economic prerequisites and barriers regarding GIS information access, sharing, merging, quality management, various facilitation roles that different agencies could play to further access and sharing of such GIS, as well as quality assurance.

Challenges tracks : (deadline March 31st)

Within the general scope of the workshop, three specific challenges have been identified for which we welcome contributions in the challenge track. They are described below. Through these challenges, we hope to encourage either scientists who may be new to climate studies but skilled in data integration and harmonization, or climate scientists who may ignore the availability of data to make a step and engage in fruitful exchanges during the workshop based on concrete proposals.  The challengers may use any data or classification that might prove useful, at the European, national or local level, preferably open data. They will describe the difficulties they will have faced,  whether it be in terms of lack of data, quality of data, metadata, interoperability, scalabilty (comparability in space and in time), licensing and aggregation or disaggregation  methodology. To help them, the chairs draw their attention to the existing datasets or to the existing classifications. If you wish to use a dataset that is not in this list yet, please send the description of this dataset to ewuci2021 at easychair.org so that we can add it to the list and other participants can more easily discover it. If you wish to register to one or more challenges, or if you have any question, please send an email to ewuci2021 at easychair.org so that we can contact you further on.


Challenge 1 : Adding environmental indicators to the European Grid LAEA

Summary : The challenge consists in adding  environmental indicators to the already existing grid and to their socio-economic data. The indicators may be linked to urban climate, pollution, geospatial information, or meteolorogy. The quality and the sensibility of each indicators will be assessed as well.

Context : This challenge is in line with the UN Global Statistical Geospatial Framework (GSGF). In order to foster cross domain analysis, Principle 3 of the GSGF recommends using “common geographies” for the dissemination of information by different national or global bodies. To achieve this goal,  the European Grid (ETRS 89, LAEA) used by Eurostat and by many European countries (France, Finland, …) seems to be a suitable territorial classification. This grid complies, for example, with the Inspire recommendations. Insee already provides data (population, dwelling, incomes) using the 200m version of the grid, while Eurostat aims to release some of the data of the next 2021 census round using the 1km version.

Data of possible relevance for challenge 1 especially : see https://easychair.org/cfp/ewuci2021


Challenge 2 : Urban concepts and definitions for urban climate change studies

Summary : The challenge consists in highlighting various key concepts and practical stakes in the context of research and studies questioning current urban concepts and definitions applicability for urban climate change studies.

Context : Common terms allow us to define common concepts. Cross-domain topics are dependent on well-defined and described data. Standardized concepts are one of the key elements fostering cooperation, joint development and allowing data to be obtained from various sources and to be utilised by information users and analysts in various domains. Are the current definitions of urban areas, mainly urban centres and urban delineation recalled below, also applicable for urban climate change studies? What restrictions on use, advantages or deficiencies have been identified? Are there inconsistencies in the definitions? How are they aligned with other definitions used in the urban climate change domain or how can they be used in combination with other definitions? Are the corresponding geographical delineations of alternative definitions available?

As a reminder, the definition of urban clusters and urban centres has been developed by EU (Eurostat, DG REGIO and JRC in cooperation with the national statistical institutes). The unified definitions across Europe, and the delineation of urban clusters and centres made accordingly, allow to examine urban areas in a consistent manner. Both concepts are part of the “degree of urbanisation” framework of concepts, that has been recommended by the UN Statistical Commission in March 2020 for international statistical comparisons and has received support from ILO, FAO, OECD UN-Habitat and the World Bank. Urban centres are defined as groups of contiguous raster cells of 1 sqkm size, having a population density of at least 1500 inhabitants/km² and a total population of  at least 50000 inhabitants. These groups act as a raster-based representation of cities. Smaller urban areas are called “urban clusters” are defined as groups of contiguous raster cells of 1 sqkm size, having a population density of at least 300 inhabitants/sqkm and a total population of at least 5000. These groups represent towns and suburbs. The definition of urban centres and urban clusters underpins the urban/rural typology of NUTS3 regions and the degree of urbanisation classification of local administrative (LAU) units. With respect to delineations, the Urban Clusters and Urban Centres datasets contains urban clusters and centres, based on local population data of 2011. The data are derived from the population grid 2011 produced by the GEOSTAT project (Eurostat), combined with data on the share of land area by grid cell (JRC).

Classifications and data of possible relevance to challenge 2 especially : https://easychair.org/cfp/ewuci2021


Challenge 3 : Urban adaptation indicators

Summary : The challenge can be addressed in several ways, as indicated by the following questions:

– provide theoretical and methodological underpinning, as well as an outline of an urban adaptation indicator set which has the features outlined below (see context)

– present alternative combinations of data sources to produce selected indicators and succinctly describe strengths and weaknesses (e.g. resolution – uncertainty trade-offs; adaptability; portability (applicability in many places); ….)

– propose a method to adapt LCZ classifications or parameter values as means to represent adaptation interventions in the urban environment (e.g. raising vegetation factors without reducing building density)

Context : The resilience of urban areas with respect to climate change depends on the local climate conditions and the natural environment, on the degree of exposure steered by human activity, land use and urban morphology, and on the variations in vulnerability across space and social strata. Urban adaptation indicators somehow have to represent these conditions and the associated risk levels. Furthermore, for the purpose of giving guidance in decision making and policy monitoring, urban adaptation indicators should be able to show development of the urban climate resilience over time, and, ideally, enable options to disclose the components of change for selected indicators. Last but not least indicators should be relatively easy to use, interpret, and update for practitioners, whereas the total number of indicators must remain manageable, and the indicator set should be applicable across a range of different cities (even though occasionally some indicators may be only relevant for subsets of cities). Obviously, the indicators should allow for spatial representation as well. Although achieving resilient cities is in the scope of the UN 17 Sustainable Development Goals (see Goals 11 and 13), related indicators are designed for national levels and are not enough to meet the above objectives.

These at least mildly conflicting demands make it very challenging to develop a comprehensive set of urban adaptation indicators. It is probably wise to regard such an indicator system not as a once completed system, but rather as evolving. Due to new experiences, policy changes, and technical development, new indicators may be added, while other indicators may be merged, redefined, or abolished. Cities and urban regions are not equally equipped when starting to develop and use such indicator sets. Even though the practical applicability of the indicators has been emphasized above, insufficient data availability at early stages should not lead to neglect of certain adaptation risk domains. Under such circumstances the design and stagewise development of the indicator set should closely coordinated with the improvement of the necessary data sets.

Classifications and data of possible relevance to challenge 3 especially : https://easychair.org/cfp/ewuci2021

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