All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

ESA, in collaboration with European Space Imaging, has collected this WorldView-2 dataset covering the most populated areas in Europe at 40 cm resolution. The products have been acquired between July 2010 and July 2015. Spatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service.

In this paper, the authors construct a unique data set of Internet offer prices for flats in 48 large European cities from 24 countries. The data are collected between January and May 2012 from 33 websites, where the advertisements of flats for sale are placed. Using the resulting sample of 750,000 announcements the authors compute the average city-specific house prices. Based on this information they investigate the determinants of the apartment prices. Four factors are found to be relevant for the flats’ price level: income per capita, population density, unemployment rate, and income inequality. The results are robust both to excluding variables and to applying two alternative estimation techniques: OLS and quantile regression. Based on their estimation results the authors are able to identify the cities, where the prices are overvalued. This is a useful indication of a build-up of house price bubbles.

Medieval European urbanization presents a line of continuity between earlier cities and modern European urban systems. Yet, many of the spatial, political and economic features of medieval European cities were particular to the Middle Ages, and subsequently changed over the Early Modern Period and Industrial Revolution. There is a long tradition of demographic studies estimating the population sizes of medieval European cities, and comparative analyses of these data have shed much light on the long-term evolution of urban systems. However, the next step—to systematically relate the population size of these cities to their spatial and socioeconomic characteristics—has seldom been taken. This raises a series of interesting questions, as both modern and ancient cities have been observed to obey area-population relationships predicted by settlement scaling theory. To address these questions, we analyze a new dataset for the settled area and population of 173 European cities from the early fourteenth century to determine the relationship between population and settled area. To interpret this data, we develop two related models that lead to differing predictions regarding the quantitative form of the population-area relationship, depending on the level of social mixing present in these cities. Our empirical estimates of model parameters show a strong densification of cities with city population size, consistent with patterns in contemporary cities. Although social life in medieval Europe was orchestrated by hierarchical institutions (e.g., guilds, church, municipal organizations), our results show no statistically significant influence of these institutions on agglomeration effects. The similarities between the empirical patterns of settlement relating area to population observed here support the hypothesis that cities throughout history share common principles of organization that self-consistently relate their socioeconomic networks to structured urban spaces.

The Global Covenant of Mayors for Climate and Energy (GCoM) is the largest dedicated international initiative to promote climate action at city level, covering globally over 10,000 cities and in the European Union almost half the population by end of March 2020. The present dataset refers to a harmonised, complete and verified dataset of GHG inventories for 6,200 cities, signatories of the GCoM initiative as of end of 2019, in the: European Union, EFTA countries and UK, Western Balkans, Eastern and Southern EU neighbourhoods countries. The methodology and the general approach for the data collection can be found in Bertoldi et. al. 2018. Guidebook: How to develop a Sustainable Energy Climate Action Plan (SECAP). (2018) doi:10.2760/223399.

Dataset underlying the analysis in: Nina Schwarz, Urban form revisited—Selecting indicators for characterising European cities, Landscape and Urban Planning, Volume 96, Issue 1, 15 May 2010, Pages 29-47, ISSN 0169-2046, http://dx.doi.org/10.1016/j.landurbplan.2010.01.007. It is a combination of two data sources for 231 European cities:- CORINE land cover for computing city size based on land use and landscape metrics for urban form.- Urban Audit for socio-economic indicators describing urban form.Copyrights for the underlying datasets:CORINE: ©EEA, Copenhagen, 2007Urban Audit: Eurostat

Our Europe Zip Code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas for numerous European countries. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Analysis of ‘A dataset of GHG emissions for 6,200 cities in Europe and the Southern Mediterranean countries’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/57a615eb-cfbc-435a-a8c5-553bd40f76c9 on 07 January 2022.

--- Dataset description provided by original source is as follows ---

The Global Covenant of Mayors for Climate and Energy (GCoM) is the largest dedicated international initiative to promote climate action at city level, covering globally over 10,000 cities and in the European Union almost half the population by end of March 2020. The present dataset refers to a harmonised, complete and verified dataset of GHG inventories for 6,200 cities, signatories of the GCoM initiative as of end of 2019, in the: European Union, EFTA countries and UK, Western Balkans, Eastern and Southern EU neighbourhoods countries. The methodology and the general approach for the data collection can be found in Bertoldi et. al. 2018. Guidebook: How to develop a Sustainable Energy Climate Action Plan (SECAP). (2018) doi:10.2760/223399.

--- Original source retains full ownership of the source dataset ---

Airbnb Price Determinants in Europe

Characteristics and Effects

By [source]

About this dataset

This dataset contains Airbnb rental data for European cities, including characteristics and their effects on price. The dataset includes several features such as the total price of the listing, room type, host status (superhost or not), amenities, and location information which can be used to analyze the factors that affect Airbnb prices. This data can help travelers find an accommodation that satisfies their needs without spending more than necessary. It can also provide business owners valuable insights on how to set competitive prices and optimize their listings for increased bookings. Furthermore, this data is useful for property investors who want to understand pricing trends in different cities across Europe and make informed decisions about investing in real estate

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For more datasets, click here.

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How to use the dataset

This dataset contains Airbnb rental data for multiple European cities, including price, room type, host status, amenities and location information. This data can be used to better understand the factors that influence Airbnb rental prices in Europe.

The columns of the dataset include: - realSum (total price of the listing) - room_type (type of room offered such as private/shared/entire home/apt)
- room_shared (whether or not the room is shared) - person_capacity (maximum number of people allowed in the property)
- host_is_superhost(whether or not the host is a superhost) (boolean value so either true or false)
- multi (whether it’s for multiple rooms or not)
- biz(whether it’s for business use or family use ) .
dist(the distance from city center )
metro dist (the distance from nearest metro station ) lng(longitude value ) lat(latitude value ) guest satisfaction overall () Cleanliness rating () Bedrooms () and Real sum -Total Price.

First step would be to select features that are important and relevant to you according to your purpose. You can start by selecting the features like realSum ,room type ,host etc which will give you an understanding on how potential customers best fits your requirements i.e how many people will fit into a particular property when renting out a single bedroom versus renting out an entire home/apartment. After that review associated values; this could help you decide on pricing strategies such as offering discounts or raising prices according to needs and demands in different neighbourhoods depending on demand levels, availability and seasonality etc.. The next step would be to plot distance variables with respect to latitude & longitude which will indicate geographical locations where businesses could benefit from having higher occupancy rates by leveraging neighbourhood proximityi n order tackle seasonal variations . And lastly using correlation matrix between all other variables one can correlating parameters which display strong correlations thereby helping establish relationships across other variables relative towards each other as well as decide what set parameters should come into play when based upon one parameter . This dataset however does not provide dates

Research Ideas

• Price forecasting - Analyzing previous data about Airbnb listings, such as pricing, room type and amenities, could help predict potential rental prices in the future.

• Business or tourist rental hotspots - By looking at each listing’s location in relation to business and tourism centers and correlating this with pricing can help determine areas where Airbnb rentals will be most profitable.

• Customer sentiment analysis - Analyzing customer comments and satisfaction ratings to measure the effectiveness of a specific listing on their overall customer experience could be an useful tool for...

Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment.

This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.

Temporal extent
The acquisition dates of the different data sources vary to some degree:
- Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017.
- Dependent variables: the 3D building models are from 2012-2020 depending on data provider.
- Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016.
Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015.

Data format
The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.

Further information
For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication
Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: https://doi.org/10.1016/j.rse.2020.112128

Acknowledgements
The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission. The European Settlement Mask was obtained from the European Commission. 3D building models were obtained from Berlin Partner für Wirtschaft und Technologie GmbH, Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung, Landeshauptstadt Potsdam, Bezirksregierung Köln / Geobasis NRW, and Kompetenzzentrum Geodateninfrastruktur Thüringen. This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This list contains the government API cases collected, cleaned and analysed in the APIs4DGov study "Web API landscape: relevant general purpose ICT standards, technical specifications and terms".

The list does not represent a complete list of all government cases in Europe, as it is built to support the goals of the study and is limited to the analysis and data gathered from the following sources:

• The EU open data portal

• The European data portal

• The INSPIRE catalogue

• JoinUp: The API cases collected from the European Commission JoinUp platform

• Literature-document review: the API cases gathered from the research activities of the study performed till the end of 2019

• ProgrammableWeb: the ProgrammableWeb API directory

• Smart 2015/0041: the database of 395 cases created by the study ‘The project Towards faster implementation and uptake of open government’ (SMART 2015/0041).

• Workshops/meetings/interviews: a list of API cases collected in the workshops, surveys and interviews organised within the APIs4DGov

Each API case is classified accordingly to the following rationale:

• Unique id: a unique key of each case, obtained by concatenating the following fields: (Country Code) + (Governmental level) + (Name Id) + (Type of API)

• API Country or type of provider: the country in which the API case has been published

• API provider: the specific provider that published and maintain the API case

• Name Id: an acronym of the name of the API case (it can be not unique)

• Short description

• Type of API: (i) API registry, a set, catalogue, registry or directory of APIs; (ii) API platform: a platform that supports the use of APIs; (iii) API tool: a tool used to manage APIs; (iv) API standard: a set of standards related to government APIs; (v) Data catalogue, an API published to access metadata of datasets, normally published by a data catalogue; (vi) Specific API, a unique (can have many endpoints) API built for a specific purpose

• Number of APIs: normally only one, in the case of API registry, the number of APIs published by the registry at the 31/12/2019

• Theme: list of domains related to the API case (controlled vocabulary)

• Governmental level: the geographical scope of the API (city, regional, national or international)

• Country code: the country two letters internal code

• Source: the source (among the ones listed in the previous) from where the API case has been gathered

Based on a wide variety of categories, the top major global smart cities were ranked using an index score, where a top index score of ** was possible. Scores were based on various different categories including transport and mobility, sustainability, governance, innovation economy, digitalization, living standard, and expert perception. In more detail, the index also includes provision of smart parking and mobility, recycling rates, and blockchain ecosystem among other factors that can improve the standard of living. In 2019, Zurich, Switzerland was ranked first, achieving an overall index score of ****. Spending on smart city technology is projected to increase in the future.

Smart city applications Smart cities use data and digital technology to improve the quality of life, while changing the nature and economics of infrastructure. However, the definition of smart cities can vary widely and is based on the dynamic needs of a cities’ citizens. Mobility seems to be the most important smart city application for many cities, especially in European cities. For example, e-hailing services are available in most leading smart cities. The deployment of smart technologies that will incorporate mobility, utilities, health, security, and housing and community engagement will be important priorities in the future of smart cities.

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The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

This project has received funding from the European Union’s Horizon 2020 programme

Description of the containing files inside the Dataset.

The dataset was divided at European country level, except the Adriatic area which was extracted as a region and not on a country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the abovementioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layers includes information fro the whole Europe and the second layer has only the information regaridng the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standars. Below there are tables which present the dataset.

Copernicus Land Monitoring Service	Resolution	Comment
Coastal LU/LC	1:10.000	A Copernicus hotspot product to monitor landscape dynamics in coastal zones
EU-Hydro - Coastline	1:30.000	EU-Hydro is a dataset for all European countries providing the coastline
Natura 2000	1: 100000	A Copernicus hotspot product to monitor important areas for nature conservation
European Settlement Map	10m	A spatial raster dataset that is mapping human settlements in Europe
Imperviousness Density	10m	The percentage of sealed area
Impervious Built-up	10m	The part of the sealed surfaces where buildings can be found
Grassland 2018	10m	A binary grassland/non-grassland product
Tree Cover Density 2018	10m	Level of tree cover density in a range from 0-100%

Joint Research Center	Resolution	Comment
Global Human Settlement Population Grid GHS-POP)	250m	Residential population estimates for target year 2015
GHS settlement model layer (GHS-SMOD)	1km	The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities
GHS-BUILT	10m	Built-up grid derived from Sentinel-2 global image composite for reference year 2018
ENACT 2011 Population Grid (ENACT-POP R2020A)	1km	The ENACT is a population density for the European Union that take into account major daily and monthly population variations
JRC Open Power Plants Database (JRC-PPDB-OPEN)	-	Europe’s open power plant database
GHS functional urban areas (GHS-FUA R2019A)	1km	City and its commuting zone (area of influence of the city in terms of labour market flows)
GHS Urban Centre Database (GHS-UCDB R2019A)	1km	Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data	Resolution	Comment
Open Street Map (OSM)	-	BF, Transportation Network, Utilities Network, Places of Interest
CEMS	-	Data from Rapid Mapping activations in Europe
GeoNames	-	Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.
Global Administrative Areas	-	Administrative areas of all countries, at all levels of sub-division
NUTS3 Population Age/Sex Group	-	Eurostat population by age ansd sex statistics interesected with the NUTS3 Units
FLOPROS		A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

The Cities of Making pattern language card set was developed as an interdisciplinary planning instrument used in co-creation settings. The instrument is developed for different stakeholders concerned with urban development, who aim for integrating manufacturing in European cities. The instrument includes relevant (design) solutions from the fields of spatial design, governance and materials and technology.

This repository contains supplementary materials for the article:

Determinants of Airbnb prices in European cities: A spatial econometrics approach

(DOI: https://doi.org/10.1016/j.tourman.2021.104319)

The materials include the used datasets and Python scripts for spatial regression models.

Datasets

For each city two files are provided: data for weekday and weekend offers

The columns are as following:

• realSum: the full price of accommodation for two people and two nights in EUR
• room_type: the type of the accommodation
• room_shared: dummy variable for shared rooms
• room_private: dummy variable for private rooms
• person_capacity: the maximum number of guests
• host_is_superhost: dummy variable for superhost status
• multi: dummy variable if the listing belongs to hosts with 2-4 offers
• biz: dummy variable if the listing belongs to hosts with more than 4 offers
• cleanliness_rating: cleanliness rating
• guest_satisfaction_overall: overall rating of the listing
• bedrooms: number of bedrooms (0 for studios)
• dist: distance from city centre in km
• metro_dist: distance from nearest metro station in km
• attr_index: attraction index of the listing location
• attr_index_norm: normalised attraction index (0-100)
• rest_index: restaurant index of the listing location
• attr_index_norm: normalised restaurant index (0-100)
• lng: longitude of the listing location
• lat: latitude of the listing location

Programming Scripts

In this repository you will find a script for spatial regressions in Python using PySAL (models_robust.py).

The codes cover the following regression models:

• OLS
• SLX (lagged_x)
• SAR (lagged_y)
• SDM (lagged_x_y)
• SEM (lagged_e)
• SDEM (lagged_e_x)

Main parameters:

• cities - list of cities from the dataset to be included in the analysis
• Robust=False: calculate the OLS, SLX, SAR and SDM regressions with W (weight matrix) based on 10 closest neighbours
• Robust=True: calculate all regression models with different specifications of W
• direct_indirect=True: calculate the direct and indirect effects (based on Golgher, A. B., & Voss, P. R. (2016). How to Interpret the Coefficients of Spatial Models: Spillovers, Direct and Indirect Effects. Spatial Demography (Vol. 4). https://doi.org/10.1007/s40980-015-0016-y)

Key functions:

• create_weights - defines the W specification
• write_stats - calculates's Moran's I and Geary's C
• direct - calculates the direct effect of the variable
• indirect - calculates the indirect effect
• coord - sets the coordinate refence system (CRS) appropriate to the analysed city
• total_results calculates the regressions
• the coordinates are projected from GPS (epsg:4326) to the local CRS (km_lat, km_lon)
• all regressions are saved as formatted txt table
• the results can be also saved as csv table

This layer was created by Duncan Smith and based on work by the European Commission JRC and CIESIN. A description from his website follows:--------------------A brilliant new dataset produced by the European Commission JRC and CIESIN Columbia University was recently released- the Global Human Settlement Layer (GHSL). This is the first time that detailed and comprehensive population density and built-up area for the world has been available as open data. As usual, my first thought was to make an interactive map, now online at- http://luminocity3d.org/WorldPopDen/The World Population Density map is exploratory, as the dataset is very rich and new, and I am also testing out new methods for navigating statistics at both national and city scales on this site. There are clearly many applications of this data in understanding urban geographies at different scales, urban development, sustainability and change over time.

"The BBC's Great Debate" was broadcasted live in the UK by the BBC on Tuesday 21 June 2016 between 20:00 and 22:00 BST. It saw activity on Twitter with the #BBCDebate hashtag. I collected some of the Tweets tagged with #BBCDebate using a Google Spreadsheet.The raw data was downloaded as an Excel spreadsheet file containing an archive of 38,166 Tweets (38,066 Unique Tweets) publicly published with the queried hashtag (#BBCDebate) between 14/06/2016 22:03:18 and 22/06/2016 09:12:32 BST. Due to the expected high volume of Tweets only users with at least 10 followers were included in the archive. The Tweets contained in the Archive sheet were collected using Martin Hawksey’s TAGS 6.0. Given the relatively large volume of activity expected around #BBCDebate and the public and political nature of the hashtag, I have only shared indicative data. No full tweets nor any other associated metadata have been shared. The dataset contains a metrics summary as well as a table with column headings labeled created_at, time,
geo_coordinates (anonymised; if there was data YES has been indicated; if no data was present the corresponding cell has been left blank), user_lang and user_followers_count data corresponding to each Tweet. Timestamps should suffice to prove the existence of the Tweets and could be useful to run analyses of activity on Twitter around a real-time media event.No Personally identifiable information (PII), nor Sensitive Personal Information (SPI) was collected nor was contained in the dataset.Some basic deduplication and refining of the collected data performed.I have shared the anonymised dataset including the extra tables as a sample and as an act of citizen scholarship in order to archive, document and encourage open educational and historical research and analysis. It is hoped that by sharing the data someone else might be able to run different analyses and ideally discover different or more significant insights.For more information including methodological and limitation issues etc. please click on the references listed below.

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Attention: This “transposed” dataset was created with the aim of realising the dataviz “Election results”. The structure of this file is therefore different than usual, to meet the needs of visualisation. You are free to reuse this dataset, but the “full” results in the usual format can be found here: Results of the 2019 European elections _ Detailed results for Rennes for the first round of the 2019 European election, by polling station, in CSV format. A polling station belongs to a polling place, itself registered in a canton that belongs to a constituency. At the end of the file, totals are made by canton and by place of vote. Attention: voting locations may vary from one election to another, as well as the division of townships and constituencies. These are the results of Rennes exclusively: cantons and constituencies often concern territories larger than the only city of Rennes.Attention: This “transposed” dataset was created with the aim of realising the dataviz “Election results”. The structure of this file is therefore different than usual, to meet the needs of visualisation.You are free to reuse this dataset, but the “full” results in the usual format can be found here: Results of the 2019 European elections _ Detailed results for Rennes for the first round of the 2019 European election, by polling station, in CSV format. A polling station belongs to a polling place, itself registered in a canton that belongs to a constituency. At the end of the file, totals are made by canton and by place of vote. Attention: voting locations may vary from one election to another, as well as the division of townships and constituencies. These are the results of Rennes exclusively: cantons and constituencies often concern territories larger than the only city of Rennes.