France FR: Population in Largest City data was reported at 10,844,847.000 Person in 2017. This records an increase from the previous number of 10,789,031.000 Person for 2016. France FR: Population in Largest City data is updated yearly, averaging 9,226,364.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 10,844,847.000 Person in 2017 and a record low of 7,410,735.000 Person in 1960. France FR: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

A new multi-sensor dataset (MSC-France) of aerial and satellite imagery over France, with three different sensors (Sentinel-2, Landsat-8, SPOT-6) and a subset with several high-resolution pairs (SPOT-6, BDORTHO).

The purported spatial segregation of Muslim populations in Western Europe constitutes a longstanding policy and scholarly concern, yet little spatial data exists for purposes of empirical study. The MAPISLAM dataset is a research effort aimed at bridging this gap for the French empirical context. MAPISLAM is a spatial dataset built from publicly available, online repositories of addresses for places of interest destined to the Muslim communities of France’s major cities.

The catchment area of a city is a group of municipalities, of a single enclave and enclave, which defines the extent of the influence of a cluster of population and employment on the surrounding municipalities, this influence being measured by the intensity of commuting to work. Urban area zoning follows the zoning into urban areas in 2010. An area consists of a pole and a crown. — Poles are determined mainly on the basis of density and total population criteria, using a methodology consistent with that of the municipal density grid. A threshold of jobs is added in order to prevent essentially residential municipalities with few jobs from being considered poles. Within the pole, the most populous commune is called the center commune. If a pole sends at least 15 % of its assets to work in another pole of the same level, the two poles are associated and together form the heart of a catchment area. — Municipalities that send at least 15 % of their assets to work in the pole are the crown of the area. The definition of the largest catchment areas of cities is consistent with the definition of “cities” and “functional urban areas” used by Eurostat and the OECD to analyse the functioning of cities. Zoning into catchment areas thus facilitates international comparisons and makes it possible to visualise the influence in France of major foreign cities. For example, seven areas have a town located abroad (Bâle, Charleroi, Geneva, Lausanne, Luxembourg, Monaco and Saarbrücken). The areas are classified according to the total number of inhabitants of the area in 2017. The main thresholds selected are: Paris, 700,000 inhabitants, 200,000 inhabitants and 50,000 inhabitants. Areas whose pole is located abroad are classified in the category corresponding to their total population (French and foreign). Urban catchment areas, dated 2020, were constructed with reference to commuting known in the 2016 Census. Downloadable files provide the characteristics of the city’s catchment areas (size slice, number of municipalities) and the municipal composition of the city’s catchment areas.

This data set is composed by 5 geojson files, that can be used to generate maps of mainland France :

motifs_all.geojson : pattern about transport extracted from contributions of the French Great National Debate (Grand Débat National). Original dataset : https://granddebat.fr/pages/donnees-ouvertes

bikeway_fr.geojson and railroad_fr.geojson : cycleways and railways of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html

trainstations.geojson : train stations and halts of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html

au2010_carto.geojson : categorized urban areas of mainland France. Original dataset : https://www.insee.fr/fr/information/2115011

communesimportantes.geojson : the main cities of mainland France

The data set is in French.

Ce jeu de données est composé de 5 fichiers geojson qui peuvent être utilisés pour générer des cartes en France métropolitaine :

motifs_all.geojson : motifs à propos du transport extraient des contributions en ligne au Grand Débat National. Jeu de données d'origine : https://granddebat.fr/pages/donnees-ouvertes

bikeway_fr.geojson and railroad_fr.geojson : pistes cyclables et voies ferrées en France métropolitaine, venant d'Open Street Map. Jeu de données d'origine : https://download.geofabrik.de/europe/france.html

trainstations.geojson : gares et petites gares en France métropolitaine, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html

au2010_carto.geojson : aires urbaines catégorisées en France métropolitaine, définies par l'INSEE. Jeu de données d'origine : https://www.insee.fr/fr/information/2115011

communesimportantes.geojson : principales villes de France métropolitaine

This upload contains two Geopackage files of raw data used for urban analysis in the outskirts of Lille and Nice, France. 
The data include building footprints (layer "building"), roads (layer "road"), and administrative boundaries (layer "adm_boundaries")
extracted from version 3.3 of the French dataset BD TOPO®3 (IGN, 2023) for the municipalities of Santes, Hallennes-lez-Haubourdin,
Haubourdin, and Emmerin in northern France (Geopackage "DPC_59.gpkg") and Drap, Cantaron and La Trinité in southern France 
(Geopackage "DPC_06.gpkg").

Metadata for these layers is available here: https://geoservices.ign.fr/sites/default/files/2023-01/DC_BDTOPO_3-3.pdf

Additionally, this upload contains the results of the following algorithms available in GitHub (https://github.com/perezjoan/emc2-WP2?tab=readme-ov-file)

1. The identification of main streets using the QGIS plugin Morpheo (layers "road_morpheo" and "buffer_morpheo") 
https://plugins.qgis.org/plugins/morpheo/ 

2. The identification of main streets in local contexts – connectivity locally weighted (layer "road_LocRelCon")

3. Basic morphometry of buildings (layer "building_morpho")

4. Evaluation of the number of dwellings within inhabited buildings (layer "building_dwellings")

5. Projecting population potential accessible from main streets (layer "road_pop_results")

Project website: http://emc2-dut.org/

Publications using this sample data: 
Perez, J. and Fusco, G., 2024. Potential of the 15-Minute Peripheral City: Identifying Main Streets and Population Within Walking Distance. In: O. Gervasi, B. Murgante, C. Garau, D. Taniar, A.M.A.C. Rocha and M.N. Faginas Lago, eds. Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14817. Cham: Springer, pp.50-60. https://doi.org/10.1007/978-3-031-65238-7_4.

Acknowledgement. This work is part of the emc2 project, which received the grant ANR-23-DUTP-0003-01 from the French National Research Agency (ANR) within the DUT Partnership.

Introduction

Welcome to the Algerian Arabic Scripted Monologue Speech Dataset for the Travel domain, a carefully constructed resource created to support the development of Arabic speech recognition technologies, particularly for applications in travel, tourism, and customer service automation.

Speech Data

This training dataset features 6,000+ high-quality scripted prompt recordings in Algerian Arabic, crafted to simulate real-world Travel industry conversations. It’s ideal for building robust ASR systems, virtual assistants, and customer interaction tools.

Topic Coverage

The dataset includes a wide spectrum of travel-related interactions to reflect diverse real-world scenarios:

Context Elements

To boost contextual realism, the scripted prompts integrate frequently encountered travel terms and variables:

Transcription

Every audio file is paired with a verbatim transcription in .TXT format.

Metadata

Each audio file is enriched with detailed metadata to support advanced analytics and filtering:

Spatial dataset related to urban areas, street network and some central poles of cities and towns in northern France identified on the maps of the Etat-Major.

This is the dataset for the article 'Projecting the World. The Mediated Geography of the Projection Lantern in Belgium c.1900-c.1920' with Thomas Smits.

The dataset contains announcements and reviews of lantern lectures published in Belgian newspapers. The studied newspapers provide an accurate overview of the Belgian newspaper landscape in three sample periods (1902-1904, 1914-1918, and 1922-1924). We considered the different ideological backgrounds (Catholic, liberal, and socialist) and the language of the publication (French and Dutch) and focussed on the two largest cities of Belgium at that time: Antwerp and Brussels. Using different search strings, we identified 8,230 announcements and reviews for lantern lectures, published in 45 different newspaper titles. We included all types of performances (travel, economic, religious, etc). Next to the geographic references, we also recorded: the newspaper and its production date, the date of the performance, the speaker and their profession, the location of the performance, and, when available, information on which images were shown and the reaction of the audience. Taking into account that some lectures were announced and discussed multiple times, our final dataset consists of 5,673 unique lectures of which 2,570 have a spatial reference (45%). We identified three levels of spatial references: continent, country, and city. If the announcements mentioned a place in one of the last two levels (country or city), we completed the parent levels. For instance, we filled in Europe and France for lectures that mentioned Paris. We recorded place names as they were mentioned in the newspapers but also normalized the spatial references to modern-day cities and countries (see methodology).

This is an abbreviated version of the dataset collected for my doctoral dissertation 'From 'Magic' to 'the Masses' Mapping the Lantern Lecture Circuit in Antwerp and Brussels, c.1900-c.1920' (University of Antwerp). You can e-mail me for the larger dataset.

In the article, we analysed the dataset using Jupyter Notebooks which can be found in the GitHub repository: https://github.com/tpsmi/projectingtheworld

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in France. Power Plant emissions from all power plants in France were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information

The catchment area of a city is a group of municipalities, of a single enclave and enclave, which defines the extent of the influence of a cluster of population and employment on the surrounding municipalities, this influence being measured by the intensity of commuting to work. Urban area zoning follows the zoning into urban areas in 2010. An area consists of a pole and a crown. — Poles are determined mainly on the basis of density and total population criteria, using a methodology consistent with that of the municipal density grid. A threshold of jobs is added in order to prevent essentially residential municipalities with few jobs from being considered poles. Within the pole, the most populous commune is called the center commune. If a pole sends at least 15 % of its assets to work in another pole of the same level, the two poles are associated and together form the heart of a catchment area. — Municipalities that send at least 15 % of their assets to work in the pole are the crown of the area. The definition of the largest catchment areas of cities is consistent with the definition of “cities” and “functional urban areas” used by Eurostat and the OECD to analyse the functioning of cities. Zoning into catchment areas thus facilitates international comparisons and makes it possible to visualise the influence in France of major foreign cities. For example, seven areas have a town located abroad (Bâle, Charleroi, Geneva, Lausanne, Luxembourg, Monaco and Saarbrücken). The areas are classified according to the total number of inhabitants of the area in 2017. The main thresholds selected are: Paris, 700,000 inhabitants, 200,000 inhabitants and 50,000 inhabitants. Areas whose pole is located abroad are classified in the category corresponding to their total population (French and foreign). Urban catchment areas, dated 2020, were constructed with reference to commuting known in the 2016 Census. Downloadable files provide the characteristics of the city’s catchment areas (size slice, number of municipalities) and the municipal composition of the city’s catchment areas.

This dataset lists the main places of reception, information and orientation present in Île-France.

The data are largely derived from Service-public.fr – Directory of the administration – Local database which references more than 63000 local public offices (airies, social organisations, state departments, etc.).

List of structures of Service-public.fr

• IOC – Information and Orientation Centres
• Suio – Academic Information and Guidance Services
• Local missions
• Youth Information Network
• Job cap
• Employment hubs
• APEC – Association for the Employment of Executives

• Chambers of Agriculture

• CCI – Chambers of Commerce and Industry

• Chambers of crafts and crafts

• Business cities (data Île-de-France region)

This dataset supports scenario analysis using a high-resolution (5m) land cover classification of three European cities: Paris Region (France), Aarhus Municipality (Denmark), and Grad Velika Gorica (Croatia). The scenarios are: current (baseline) land cover, and a created new land cover that meets the 3-30-300 rule for urban greening (Konijnendijk 2023). In the 3-30-300 scenario, every building has two or more tree raster cells within a 30 m buffer, every neighbourhood has 30% or more green and blue space cover within a 300 m buffer, and each building has an accessible green space of at least 1 ha within 300 m. This rule was applied to the urban footprint of each city. In Paris, this applied only to the four central départements and not the entire Paris Region, Île-de-France.

Associated Paper

The full methodology behind the datasets is described in the following paper. This paper analyses the extent to which each city currently meets, and measures the land cover change required to meet the 3-30-300 rule. Please also cite this paper when using the dataset.

Owen, D., Fitch, A., Fletcher, D., Knopp, J., Levin, G., Farley, K., Banzhaf, E., Zandersen, M., Grandin, G., Jones, L. 2024. Opportunities and constraints of implementing the 3-30-300 rule for urban greening. Urban Forestry & Urban Greening, https://doi.org/10.1016/j.ufug.2024.128393" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ufug.2024.128393

Original Data Sources

These layers are based on the high resolution land cover layers produced by Knopp (2021, 2022a, 2022b). For Paris, the baseline land cover was modified, using the 10 m land cover by Wu (2022), to reclassify trees to either coniferous or deciduous.

Data

LC_Classification_Lookup_Table.docx

This word document is a lookup table for the baseline and 3-30-300 scenario land cover classification.

Baseline_and_3_30_300_HRLC_all_cities.zip

This file contains the baseline and 3-30-300 scenarios for Velika Gorica, Aarhus, and the four central départements of Paris Region (clipped to a 1km buffer). These files include all interventions from the 3-30-300 rule.

Original_and_Final_HRLC_3_30_300_Paris_Region.zip

This file contains the baseline and 3-30-300 scenario for the entire Paris Region only. Whilst there is land cover data for the entire Paris Region, the interventions from the 3-30-300 rule were only applied to the four central départements. This file has been uploaded separately because the file size is greater.

References

Knopp, J. M. (2021). High resolution land cover 2015 Aarhus, Denmark [Data set]. In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Version v1, Vol. 16, pp. 6545–6555). Zenodo. https://doi.org/10.5281/zenodo.5215792

Knopp, J. (2022a). High resolution land cover 2016 Velika Gorica (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7107514

Knopp, J. (2022b). High resolution land cover 2017 Ile-de-France [Data set]. REGREEN - Fostering nature‐based solutions for smart, green and healthy urban transitions in Europe and China. Horizon2020 Grant No. 821016. https://doi.org/10.5281/zenodo.7110027

Konijnendijk, C.C., 2023. Evidence-based guidelines for greener, healthier, more resilient neighbourhoods: Introducing the 3–30–300 rule. Journal of forestry research, 34(3), pp.821-830.

Owen, D., Fitch, A., Fletcher, D., Knopp, J., Levin, G., Farley, K., Banzhaf, E., Zandersen, M., Grandin, G., & Jones, L. (2024). Opportunities and constraints of implementing the 3–30–300 rule for urban greening. Urban Forestry & Urban Greening, 98, 128393. https://doi.org/10.1016/j.ufug.2024.128393

Wanben Wu. (2022). Europe and China Refined Land cover (ECRLC) (10m) (Version V2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5846090

The data addresses the dynamics of coexistence and conflict in increasingly diverse cities from a human-centred perspective. It was collected as part of the EU-funded project Coexistence and Conflict in the Age of Complexity (EmergentCommunity) in nine European cities in Finland, France, and Sweden. The dataset comprises of two parts: EmergentCommunityEthno (qualitative data) and EmergentCommunityVR (quantitative and qualitative data) that were collected during the project. In addition to these, desk research was conducted and these files have been included in the metadata description.

EmergentCommunityEthno (dataset 1):

Across the nine cities, participants consisted of people above 15-years of age, living in the studied urban neighbourhoods or using their public spaces. In Finland, data were collected in the neighbourhoods of Peltolammi and Multisilta in Tampere, in Malmi in Helsinki, and in Martti and Paavola in Hyvinkää. In Tampere, part of the data (n=31 interviews) was collected in collaboration with the EKOS research project (this part of the data is described and archived in the Finnish Social Science Data Archive, DoI: https://doi.org/10.60686/t-fsd3816). The second part of the data was collected in Sweden. The data collection sites there were the neighborhoods of Möllevången and Nydala in Malmö, Farsta and Rågsved in Stockholm, and Fröslunda and Årby in Eskilstuna. The French data were collected in the La Plaine area in Marseille; in La-Chapelle-Saint-Luc, Saint-Andre-Les-Vergers and Les Chartreux in Troyes; and in Guillotière in Lyon.

Across these sites shared methods were used in data collection, consisting of thematic interviews, walking interviews, and observations. The dataset emphasizes the diversity of experiences and the manifestations of distinctions in diverse urban environments and examines the ways in which people form bonds in relation to each other, their neighborhoods, and the broader society.

The first set of participants were located through social media groups (Facebook), from the premises of associations organizing community activities in the areas, libraries, cafes, community events, and youth centers. After this, snowball sampling was used, in addition to which targeted recruitment was applied if a population group represented in the area was completely missing from the dataset. Ethnographic observations were conducted in public spaces, community centres, cafés, stations, and shopping centres that were selected as potentially interesting places based on extant scholarship on living with difference and urban encounters. Here, attention was paid at how people used these sites, who were there and who were absent, as well as how people moved in and across the sites. Notes were made of what kinds of encounters, patterns of behaviour, cooperations, and conflicts occurred. These observations were made at various times of the day, to capture potential temporal changes. This resulted in a rich collection of fieldnotes, sketches, photographs, and movement maps.

Relevant files: 1) EmergentCommunity ethnographic matrix.pdf, 2) EmergentCommunityEthno interview questions.docx, 3) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 4) EmergentCommunityEthno_metadata.csv (contains metadata only on desk research, ethnographic interviews and fieldnotes).

EmergentCommunityVR (dataset 2):

Data collection was conducted in Helsinki, Marseille, and Malmö. The data was collected using 360-degree videos based on the aforementioned ethnographic data as stimuli to which participants were exposed. A separate video was created for each city, using specifically the data collected therein. We put together a mobile laboratory set-up that travelled to each city and collaborated with local NGOs whose premises were used as our laboratory space. The equipment and software used are explained in the document "EmergentCommunity mobile laboratory.pdf".

The inclusion criteria for participation were: being a major, healthy, not having hearing or vision impairments, being a resident in the city that the video depicted, and knowledge of the local language in which the video was executed. During the viewing of the video stimulus, participants' physiological responses were measured and their eye movements were tracked. VR eye tracking was used as it enables the precise analysis of gaze behaviour – such as fixations and saccades – within immersive, ecologically valid environments. Regarding physiological signals, the focus was on the electrical activity of the heart using electrocardiography (ECG), the electrical activity of the facial muscles using facial electromyography (fEMG), and the electrical conductivity of the skin using galvanic skin response (GSR). To complement the physiological data, a multimodal setup was established to assess the affective content of the stimulus in terms of arousal/valence, avoidance/approach, and unpredictability. After viewing, the participants were asked to evaluate the intensity of their emotional experience and to name the emotional reactions elicited by the video using a questionnaire carried out with Gorilla Experiment Builder. The questionnaire also contained background questions, from basic participant information, such as age and gender, to aspects that relate to diversity and inequality in contemporary societies: language, income, housing, education, political activity, participation, as well as political opinions and social values. After completing the measurements and the questionnaire, participants were interviewed about their experience and the thoughts it provoked, and they were asked to share information regarding their daily lives.

The purpose of the dataset was to help understand the formation of emotional experiences and the significance and functioning of emotions in the everyday life of increasingly diverse and unequal cities. The call for participation was distributed in several thematic Facebook groups (related to e.g., urban development, multiculturalism, neighborhood, local NGOs and minority communities) and via Instagram, as well as through flyers/posters in libraries, local associations, shopping centers, cafes, and on the project's Facebook page and Instagram profile. In the case of Marseille and Malmö, local assistants were used to spread the invitation within their networks and distribute participation invitation leaflets on the streets. In each city, it was possible for already registered participants to invite additional participants as well. Overall, the goal was to ensure the representativeness of the data in terms of age, gender, and minority status.

Relevant files: 1) EmergentCommunity video stimuli.pdf, 2) EmergentCommunityVR interview questions.pdf, 3) EmergentCommunityVR Gorilla questionnaires.pdf, 4) EmergentCommunity mobile laboratory.pdf, 5) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 6) EmergentCommunityVR interviews.csv (contains metadata on interviews done after watching the 360-degree video), 7) EmergentCommunityVR physio.csv (contains metadata on physiological measuring and questionnaires).

Purpose of the data

The EmergentCommunity project aimed at producing knowledge about what community means and how it is formed in increasingly diverse societies, as well as the conflicts and tensions that everyday life brings out. The project empirically examined the concrete challenges that societal changes produce for cities and coexistence. The aim was to identify how peaceful coexistence could be supported and population relations promoted in urban everyday life. The project emphasized that community relations and everyday coexistence are affective, social, and spatial phenomena, which is why a wide range of research methods from ethnography and observation to psychophysiological measurements and interviews were applied. These approaches were brought into dialogue through virtual reality by utilizing ethnography-based 360-degree videos depicting everyday life in the latter part of the project (EmergentCommunityVR). Thus, the project created new understanding of emotions formed in everyday life and produced unique knowledge in the fields of psychological and sociological emotion research. Bringing these areas together enabled a critical examination of the concept of community and the identification of the practices and ways in which communities are produced in the everyday life of diverse and unequal cities (see CORDIS database for public description, results, and reporting).

Throughout the data collection, the research focused on everyday life and the forms, practices, and interpretations of everyday coexistence in public urban spaces in the selected research neighbourhoods. Participants were also asked to share their experiences, interpretations, and views on societal change and how the change has been visible in their own neighborhoods and what thoughts and feelings it evokes in them. The data was formed through non-probability sampling (self-formed sample).

The research sites were selected by examining statistics, policy reports, and available data on demographic changes and diversity, income inequality, trends of residential and ethnic segregation in different countries and cities (desk research). We chose the countries and cities so that they would complement each other and that changes were observable in each selected context, although their forms, emphases, and manifestations might vary. After this extensive background review, we focused on the city level, complementing the available

The replication data includes all the scripts to generate the “German-French Police Relations Dataset” on which the book relies (denoted as “dataset”), as well as the scripts that produce all the tables and figures presented in the book (by chapters). The scripts were compiled in Stata (Version 13). The dataset was produced within the framework of the comparative German-French research project “Police and Adolescents in Multi-Ethnic Societies” (POLIS)” as part of the author’s PhD research project. The cross-sectional dataset includes detailed information on encounters with and perceptions of the police among young people in two French (Grenoble and Lyon) and two German (Mannheim and Cologne) cities. Moreover, the dataset includes information on socio-demographic and ethnic background of the respondents as well as their self-reported delinquency, family and peer relations, routine activities and neighborhood of residence. The dataset draws on two school survey datasets – one German and one French dataset – compiled by the POLIS research project team. This primary data is stored, among others, at the Max Planck Institute for the Study of Crime, Security and Law (https://csl.mpg.de). The school surveys were carried out between September 2011 and November 2012 in France and Germany, using the same study design. They took the form of a paper-and-pencil questionnaire during school time. The POLIS project resulted from a cooperation between the Max Planck Institute for the Study of Crime, Security and Law in Freiburg, Germany, and the Sciences Po Grenoble, Université Grenoble Alpes, France. Dietrich Oberwittler and Hans-Jörg Albrecht (Germany) as well as Sebastian Roché (France) were its principal investigators. For more information on the POLIS project visit the project website https://csl.mpg.de/en/research/projects/police-and-adolescents-in-multi-ethnic-societies-polis/.

This dataset provides sensory and nutritional information for 30 commercial cooked hams (without rind, not flavored) representative of the French commercial segment. The sensory data were collected in two phases. During the first phase (fall 2019, field experiment), 483 consumers, regular consumers of cooked hams, were recruited in seven cities and vicinities of France. They were instructed to choose and buy cooked hams at the supermarket and evaluate them at home over a period of three months. They were provided with a list of 30 eligible cooked hams selected by the experimenters. A total of 2,758 evaluations were collected (an average of 5.7 evaluations per consumer). During the second phase (fall 2020, lab experiment), a selection of 16 cooked hams were evaluated at blind by 86 consumers in a sensory analysis laboratory using a complete balanced design. Sensory evaluation at home and in the laboratory included liking Just-About-Right (colour, fat, salt and texture) measurements. In the field experiment, consumers were additionally asked to describe with free comments the appearance, texture and flavour of tested hams and of a virtual “ideal ham”. They also had to report the price they paid for a pack of four slices of ham and their intentions to repurchase the tested hams. Other data on cooked hams included actual salt and fat contents (measured using physicochemical analyses) and information displayed on the packaging (type of brand, nutritional claims, labels).

L_AIRE_ATT_VILLE_2020_ZSUP_FLA_000 Attractions of cities in 2020 in Corrèze and neighbouring departments. Objects located on the outskirts of neighbouring departments may not be complete if they overflow on the neighbouring deparetment. Sources: INSEE + GeoFLA IGN https://www.insee.fr/fr/information/4803954 The catchment area of a city is a group of municipalities, of a single enclave and enclave, which defines the extent of the influence of a cluster of population and employment on the surrounding municipalities, this influence being measured by the intensity of commuting to work. Urban area zoning follows the zoning into urban areas in 2010. An area consists of a pole and a crown. * Poles are determined mainly on the basis of density and total population criteria, using a methodology consistent with that of the municipal density grid. A threshold of jobs is added in order to prevent essentially residential municipalities with few jobs from being considered poles. Within the pole, the most populous commune is called the center commune. If a pole sends at least 15 % of its assets to work in another pole of the same level, the two poles are associated and together form the heart of a catchment area. * Municipalities that send at least 15 % of their assets work in the pole are the crown of the area. The definition of the largest catchment areas of cities is consistent with the definition of “cities” and “functional urban areas” used by Eurostat and the OECD to analyse the functioning of cities. Zoning into catchment areas thus facilitates international comparisons and makes it possible to visualise the influence in France of major foreign cities. For example, seven areas have a town located abroad (Bâle, Charleroi, Geneva, Lausanne, Luxembourg, Monaco and Saarbrücken). The areas are classified according to the total number of inhabitants of the area in 2017. The main thresholds selected are: Paris, 700,000 inhabitants, 200,000 inhabitants and 50,000 inhabitants. Areas whose pole is located abroad are classified in the category corresponding to their total population (French and foreign). Urban catchment areas, dated 2020, were constructed with reference to commuting known in the 2016 Census. Downloadable files provide the characteristics of the city’s catchment areas (size slice, number of municipalities) and the municipal composition of the city’s catchment areas.

The RGC® produced by the IGN is a text file containing the geographical position of the main towns of the municipality (town hall), coupled with administrative information.

Description of certain fields

• POPU: 1999 census, without double counting, rounded to the nearest hundred inhabitants.
• SURFACE: Area assessed in 1975 by the cadastre service of the Direction Générale des Impôts, corrected for municipal changes since 1975. This "fiscal" area does not necessarily correspond to the geographical area of ​​the municipality.
• NAME: Name of the entity communal. The name of the chief town of the commune (locality where the town hall is located) is not always identical to the name of the commune.
• XLAMB2, YLAMB2: Extended Lambert 2 coordinates, rounded to the hectometre
• XLAMB93, YLAMB93: Lambert 93 coordinates, rounded to the hectometre
• XLAMBZ, YLAMBZ: Lambert Carto coordinates
• LONGI_DMS, LATI_DMS: Geographical coordinates in Degrees Minute Second calculated in the geodetic system WGS84
  

The Law of 14 November 1996 implementing the City Recovery Pact (PRV) distinguished three levels of intervention: sensitive urban areas, urban revitalisation zones (ZRUs), urban free zones (ZFU). These three levels of intervention ZUS, ZRU and ZFU, characterised by devices of increasing importance, were intended to respond to different degrees of difficulties encountered in those neighbourhoods. Since then, the Planning Law for City and Urban Cohesion of 21 February 2014 has laid down (Article 5) the modalities for the reform of the priority geography of city policy. Two decrees issued in 2014 (No 2014-767 of 3 July 2014 and No 2014-1575 of 22 December 2014) set out these arrangements for the metropolis and for the ultramarine territories respectively. Thus, the national list of priority neighbourhoods of the city policy (Decrees n°2014-1750 and n° 2014-1751 of 30 December 2014) was produced and the national mapping of their perimeters was published. These perimeters replace sensitive urban areas (SEZs) and urban social cohesion contract (CUCS) neighbourhoods as of 1 January 2015. The surface objects present are a Francislian extraction of the file posted on the SG-CIV website. These are perimeters to which INSEE’s first accessible data and links to the NPNRU neighbourhoods were linked (New National Urban Renewal Program — December 15, 2014. The Law of 14 November 1996 implementing the City Recovery Pact (PRV) distinguished three levels of intervention: sensitive urban areas, urban revitalisation zones (ZRUs), urban free zones (ZFU). These three levels of intervention ZUS, ZRU and ZFU, characterised by devices of increasing importance, were intended to respond to different degrees of difficulties encountered in those neighbourhoods. Since then, the Planning Law for City and Urban Cohesion of 21 February 2014 has laid down (Article 5) the modalities for the reform of the priority geography of city policy. Two decrees issued in 2014 (No 2014-767 of 3 July 2014 and No 2014-1575 of 22 December 2014) set out these arrangements for the metropolis and for the ultramarine territories respectively. Thus, the national list of priority neighbourhoods of the city policy (Decrees n°2014-1750 and n° 2014-1751 of 30 December 2014) was produced and the national mapping of their perimeters was published. These perimeters replace sensitive urban areas (SEZs) and urban social cohesion contract (CUCS) neighbourhoods as of 1 January 2015. The surface objects present are a Francislian extraction of the file posted on the SG-CIV website. These are perimeters to which INSEE’s first accessible data and links to the NPNRU neighbourhoods were linked (New National Urban Renewal Program — December 15, 2014.

The empirical dataset is derived from a survey carried out on 25 estates in 14 cities in nine different European countries: France (Lyon), Germany (Berlin), Hungary (Budapest and Nyiregyha´za), Italy (Milan), the Netherlands (Amsterdam and Utrecht), Poland (Warsaw), Slovenia (Ljubljana and Koper), Spain (Barcelona and Madrid), and Sweden (Jo¨nko¨ping and Stockholm). The survey was part of the EU RESTATE project (Musterd & Van Kempen, 2005). A similar survey was constructed for all 25 estates.

The survey was carried out between February and June 2004. In each case, a random sample was drawn, usually from the whole estate. For some estates, address lists were used as the basis for the sample; in other cases, the researchers first had to take a complete inventory of addresses themselves (for some deviations from this general trend and for an overview of response rates, see Musterd & Van Kempen, 2005). In most cities, survey teams were hired to carry out the survey. They worked under the supervision of the RESTATE partners. Briefings were organised to instruct the survey teams. In some cases (for example, in Amsterdam and Utrecht), interviewers were recruited from specific ethnic groups in order to increase the response rate among, for example, the Turkish and Moroccan residents on the estates. In other cases, family members translated questions during a face-to-face interview. The interviewers with an immigrant background were hired in those estates where this made sense. In some estates it was not necessary to do this because the number of immigrants was (close to) zero (as in most cases in CE Europe).

The questionnaire could be completed by the respondents themselves, but also by the interviewers in a face-to-face interview.

Data and Representativeness

The data file contains 4756 respondents. Nearly all respondents indicated their satisfaction with the dwelling and the estate. Originally, the data file also contained cases from the UK.

However, UK respondents were excluded from the analyses because of doubts about the reliability of the answers to the ethnic minority questions. This left 25 estates in nine countries. In general, older people and original populations are somewhat over-represented, while younger people and immigrant populations are relatively under-represented, despite the fact that in estates with a large minority population surveyors were also employed from minority ethnic groups. For younger people, this discrepancy probably derives from the extent of their activities outside the home, making them more difficult to reach. The under-representation of the immigrant population is presumably related to language and cultural differences. For more detailed information on the representation of population in each case, reference is made to the reports of the researchers in the different countries which can be downloaded from the programme website. All country reports indicate that despite these over- and under-representations, the survey results are valuable for the analyses of their own individual situation.

This dataset is the result of a team effort lead by Professor Ronald van Kempen, Utrecht University with funding from the EU Fifth Framework.