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.

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

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.

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

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.

A large number of European cities are covered by this dataset; for each city you can find one or more Cartosat-1 ortho image products and one or more Euro-Maps 3D DSM tiles clipped to the extent of the ortho coverage. The Euro-Maps 3D DSM is a homogeneous, 5 m spaced Digital Surface Model semi-automatically derived from 2.5 m Cartosat-1 in-flight stereo data with a vertical accuracy of 10 m. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. The final product includes several pixel-based quality and traceability layers: The dsm layer (_dsm.tif) contains the elevation heights as a geocoded raster file The source layer (_src.tif) contains information about the data source for each height value/pixel The number layer (_num.tif) contains for each height value/pixel the number of IRS-P5 Cartosat-1 stereo pairs used for the generation of the DEM The quality layer (_qc.tif) is set to 1 for each height/pixel value derived from IRS-P5 Cartosat-1 data and which meets or exceeds the product specifications The accuracy vertical layer (*_acv.tif) contains the absolute vertical accuracy for each quality controlled height value/pixel. The ortho image is a Panchromatic image at 2.5 m resolution. The following table defines the offered product types. EO-SIP product type Description PAN_PAM_3O IRS-P5 Cartosat-1 ortho image DSM_DEM_3D IRS-P5 Cartosat-1 DSM

The world’s largest noise complaint dataset including labeled noise sources. Ideal for AI training in acoustic event detection and urban noise analysis. Available via CSV, S3, and API (coming soon). GDPR-compliant.

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 ---

A dataset of grid level (500m by 500m) accessibility indicators measured in peak traffic and free flow conditions for all cities (represented by the Functional Urban Area) with more than 250 thousand people in EU27, the UK, Switzerland and Norway.

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.

Top European Cities by Number of Cinema Seats, 2017 Discover more data with ReportLinker!

This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics".

The different files contain:

Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc: Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010.

Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx: Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100.

Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.

Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.

Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.

Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.

Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.

Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx: Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics.

Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.

Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models.

Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models.

Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx: GCM-RCM matrices for the three heat metrics.

Silencio’s Street Noise-Level Dataset provides unmatched value environmental data industry, delivering highly granular noise data to researchers, developers, and governments. Built from over 35 billion datapoints collected globally via our mobile app and refined through AI-driven interpolation, this dataset offers hyper-local average noise levels (dBA) covering streets, neighborhoods, and venues across the whole USA.

Our data helps assess the environmental quality of any location, supporting residential and commercial property valuations, site selection, and urban development. By integrating real-world noise measurements with AI-powered models, we enable real estate professionals to evaluate how noise exposure impacts property value, livability, and buyer perception — factors often overlooked by traditional market analyses.

Silencio also operates the largest global database of noise complaints, providing additional context for understanding neighborhood soundscapes from both objective measurements and subjective community feedback.

We offer on-demand visual delivery for mapped cities, regions, or even specific streets and districts, allowing clients to access exactly the data they need. Data is available both as historical and up-to-date records, ready to be integrated into valuation models, investment reports, and location intelligence platforms. Delivery options include CSV exports, S3 buckets, PDF, PNG, JPEG, and we are currently developing a full-featured API, with flexibility to adapt to client needs. We are open to discussion for API early access, custom projects, or unique delivery formats.

Fully anonymized and fully compliant, Silencio’s data ensures ethical sourcing while providing real estate professionals with actionable insights for smarter, more transparent valuations.

Table of Content: 1. General context of the data set "lsUDPs" ; 2. Background and aims of the study using the data set lsUDPs; 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalisation 1. General context of the data set "lsUDPs" The data set "lsUDPs" has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2020. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, strategic spatial plans) into quantitative land-change modelling approaches at the urban regional level. The first stage (2016-2017) of the CONCUR project focussed on 21 urban regions in Western Europe. The urban regions were selected through a multi-stage strategy for empirical research (see Hersperger, A. M., Grădinaru, S., Oliveira, E., Pagliarin, S., & Palka, G. (2019). Understanding strategic spatial planning to effectively guide development of urban regions. Cities, 94, 96–105. https://doi.org/10.1016/j.cities.2019.05.032 ). 2. Background and aims of the study using the data set lsUDPs As part of the CONCUR project, a specific task was to examine the relationship between strategic spatial plans and the formulation and implementation (i.e. urban land change) of large-scale urban development projects in Western Europe. Strategic urban projects are typically large-scale, prominent urban transformations implemented locally with the aim to stimulate urban growth, for instance in the form of urban renewals of deprived neighborhoods, waterfront renewals and transport infrastructures. While strategic urban projects are referred to in the literature with multiple terms, in the CONCOR project we call them large-scale urban development projects (lsUDPs). Previous studies acknowledged both local and supra-local (or structural) factors impacting the context-specific implementation of lsUDPs. Local governance factors, such as institutional capacity, coordination among public and private actors and political leadership, intertwine with supra-local conditions, such as state re-scaling processes and devolution of state competencies in spatial planning, de-industrialisation and increasing social inequality. Hence, in implementing lsUDPs, multi-scalar factors act in combination. Because the formulation and implementation of lsUDPs require multi-scalar coordination among coalitions of public and private actors over an extended period of time, they are generally linked to strategic spatial plans (SSPs). Strategic spatial plans convey collective visions and horizons of action negotiated among public and private actors at the local and/or regional level to steer future urban development, and can contain legally binding dispositions, but also indicative guidelines. The key question remains as to what extent large-scale urban development projects and strategic spatial plans can be regarded as aligned. By alignment, or “concordance”, we mean that strategic projects are formulated and implemented as part of the strategic planning process (“high concordance”), or that the strategic role of projects is reconfirmed in (subsequent) strategic plans (“moderate concordance”). Lack of concordance is found when lsUDPs have been limitedly (or not at all) acknowledged in strategic spatial plans. We assume that certain local and supra-local factors, characterising the development of the projects, foster (but not strictly “cause”) the degree of alignment between lsUPDs and SSPs. In this study, we empirically examine how, and to what extent, the concordance between 38 European large-scale urban development projects and strategic plans (outcome: CONCOR) has been enabled by five multi-scalar factors (or conditions): (i) the role of the national state (STATE), (ii) the role of (inter)national private actors (PRIVATE), (iii) the occurrence of supra-regional external events (EVENTS), (iv) the degree of transport connectivity (TRANSP), and (v) local resistance from civil society (RESIST). We adopted a (multi-data) case-based qualitative strategy for empirical research and applied the formalised procedure of within- and cross-case comparison offered by fuzzy-set Qualitative Comparative Analysis appropriate for the goal of this study. Based on set theory, QCA formally integrates contextual sensitivity to case specificities (within-case knowledge) with systematic comparative analysis (across-case knowledge). The research question the data set has been created to reply to is the following: which conditions, and combinations of conditions, enable the concordance between large-scale urban development projects and strategic spatial plans? The conditions (“independent variables”) considered are. STATE: the set of large-scale urban projects characterized by a high degree of state intervention and support in their formulation and implementation, PRIVATE: the set of large-scale urban projects characterized by a high degree of involvement of (inter)national private actors in their formulation and implementation, EVENTS: the set of large-scale strategic projects whose formulation and implementation have been strongly affected by unforeseen international events and/or global trends, TRANSP: the set of large-scale strategic projects with a high degree of road and/or transit connectivity, and RESIST: set of large-scale strategic projects whose realization has been characterized by resistances that have substantially delayed or modified the project implementation. The outcome (“dependent variable”) under analysis is CONCOR: the set of large-scale urban projects having a high degree of concordance/alignment/integration with strategic spatial plans 3. The data set lsUDPs 3.1 Selection of cases and data collection To generate the current data set on large-scale urban development projects in European urban regions (data set "lsUDPs"), we identified 35 large-scale urban development projects in a sample of the 21 Western urban regions considered in the CONCUR project (see supra, Hersperger et al. 2019): Amsterdam, Barcelona, Copenhagen, Hamburg, Lyon, Manchester, Milan, Stockholm, Stuttgart. The criteria we followed to identify the 35 large-scale urban development projects are: geographical location, size (large-scale), site (located either in the city core or in the larger urban region) and urban function (e.g. housing, transportation infrastructures, service and knowledge economic functions). Employing these criteria facilitated the selection of diverse large-scale urban development projects while still ensuring sufficient comparability. In 2016, we performed 47 in-depth interviews with experts in urban and regional planning and large-scale strategic projects and infrastructure (i.e. academics and practitioners) about the formulation, implementation and development (1990s–2010s) of each project in each of the 9 selected urban regions. On average, each interviewee answered questions on 3.1 large-scale urban development projects. Three cases were subdivided into two cases because a clear differentiation between specific implementation stages was identified by the interviewees (expansion of the Barcelona airport, cases “bcn_airport80-90” and “bcn_airport00-16”; realisation of Lyon Part-Dieu, cases “lyo_partdieu70-90” and “lyo_partdieu00-16”; MediaCityUK, cases “man_salfordquays80-00” and “man_mediacityuk00-16”). Therefore, from the initial 35 cases, the final number of analysed cases in the lsUDPs dataset is 38. 3.2 The data set lsUDPs: Data management and operationalisation Interviews were fully transcribed and analysed through MAXQDA (version 12.3, VERBI GmbH, Berlin, Germany), and intercoder agreement was evaluated on a sample of nine interviews. We also compiled “synthetic case descriptions” (SCD) for each case (totalling more than 160 SCDs) to spot potential inconsistencies among interviewees’ accounts and to facilitate completion of the “calibration table” for each case (see below). An online expert survey distributed to the interviewees (response rate 78%) helped systematise the information collected during the interviews. We also consulted both academic and gray literature on the case studies to check for possible ambiguity and inconsistencies in the interview data, and to solve discrepancies between our assigned set membership scores and questionnaire values. Site visits were also carried out to retrieve additional information on the selected cases. For each case (i.e. each of the 38 selected large-scale urban development projects), we operationalised each condition (i.e. STATE, PRIVATE, EVENTS, TRANSP, RESIST) and the outcome (CONCOR) in terms of sets, for subsequent application of Qualitative Comparative Analysis. This process is called “calibration”; we used a number of indicators for each condition to qualitatively assess each large-scale project across the conditions. The case-based qualitative assessment was then transformed into fuzzy-set membership values. Fuzzy-set membership values range from 0 to 1, and should be conceived as “fundamentally interpretative tools” that “operationalize theoretical concepts in a way that enhances the dialogue between ideas and evidence” (Ragin 2000:162, in “Fuzzy-set Social Science”. Chicago: University Press). We employed a four-value fuzzy-set scale (0, 0.33, 0.67, 1) to “quantify” into set membership scores the individual histories of cases retrieved from interview data. Only the condition TRANSP was calibrated as a crisp-set (0, 1). The translation of qualitative case-based information into numerical fuzzy-set membership values was iteratively performed by populating a calibration table following standard practices recently emerged in

When using this data set, it should be bibliographically referred to as 'Urban Audit, 2004'.

The Urban Audit (UA) provides European urban statistics for a representative sample of large and medium-sized cities across 30 European countries. It enables an assessment of the state of individual EU cities and provides access to comparative information from other EU cities.

This spatial dataset will support the study and dissemination of the UA data. It allows the visualisation of participating cities at three conceptual levels: - UA City - the core city, using an administrative definition - UA City Kernel - a concept introduced to improve comparability between large cities - Larger Urban Zone (LUZ) - approximating the functional urban region

In addition, this spatial dataset allows visualisation of a 285 participating cities at two hierarchical sublevels to analyse the disparities within cities: - Sub City Districts level 1 (SCD L1) - Sub City Districts level 2 (SCD L2)

The extent of this dataset is the EU-27 plus Croatia (HR), Norway (NO) and Switzerland (CH).

The URAU_2004 dataset contains a polygonal feature class for UA Cities, UA City Kernels and Large Urban Zones, derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004). Polygonal feature classes for Sub City Districts are derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004) or spatial data supplied by URAU delegates which has been made coincident with UA City geometry.

A generalised version of each feature class allows for visualisation at the scale of 1:3 Million. UA Cities are also represented by a point topology that are derived from and synchronised with the GISCO STTL_V3 dataset of European Settlements. The UA city points are, when possible, synchronised to an Urban Fabric class in Corine Land Cover 2000.

Swiss Household Panel dataset. Visit https://dataone.org/datasets/sha256%3A24270a56fdcabda0b7138e1794de7e6ee21e7b8d40391fe210b071389da8d681 for complete metadata about this dataset.

Success.ai’s B2B Leads Data for Architecture, Planning, and Design Experts in Europe provides verified access to professionals shaping the built environment. Leveraging over 700 million LinkedIn profiles, this dataset delivers actionable insights, verified contact details, and firmographic data for architects, urban planners, interior designers, and more. Whether your objective is to market products, recruit talent, or explore industry trends, Success.ai ensures your data is accurate, enriched, and continuously updated.

Why Choose Success.ai’s B2B Leads Data for Architecture, Planning & Design Experts? Comprehensive Professional Profiles

Access verified profiles of architects, urban planners, landscape designers, and project managers in Europe. AI-driven validation ensures 99% accuracy, optimizing outreach efforts and minimizing bounce rates. Focused Coverage Across Europe

Includes professionals from major architectural firms, design studios, and urban planning organizations. Covers key markets like the UK, Germany, France, Italy, and Scandinavia. Continuously Updated Dataset

Real-time updates ensure your data remains relevant, reflecting changes in roles, organizations, and professional achievements. Tailored for Architectural Insights

Enriched profiles include professional histories, areas of specialization, certifications, and firmographic details for a deeper understanding of your audience. Data Highlights: 700M+ Verified LinkedIn Profiles: Gain access to a global network of architecture and design professionals. 170M+ Enriched Profiles: Includes work emails, phone numbers, and decision-maker insights for targeted communication. Industry-Specific Segmentation: Target professionals in architecture, urban planning, interior design, and landscape architecture with precision filters. Region-Specific Data: Focus on European design hubs, including London, Paris, Berlin, and Copenhagen. Key Features of the Dataset: Architecture and Design Professional Profiles

Identify and connect with architects, project managers, urban planners, and design experts leading major projects. Engage with professionals driving trends in sustainable building, smart cities, and innovative design. Detailed Firmographic Data

Leverage insights into company sizes, project scales, geographic reach, and specialization areas. Customize your approach to align with the needs of architectural firms, urban planning agencies, or independent designers. Advanced Filters for Precision Targeting

Refine searches by region, design specialty (residential, commercial, landscape), or years of experience. Tailor campaigns to address industry challenges such as sustainability, urbanization, or heritage conservation. AI-Driven Enrichment

Enhanced datasets provide actionable details for personalized campaigns, highlighting certifications, awards, and key projects. Strategic Use Cases: Marketing Products and Services

Promote building materials, design software, or urban planning tools to architects, designers, and planners. Engage with professionals managing construction, sustainability initiatives, or smart city developments. Collaboration and Partnerships

Identify architects, urban planners, and design studios for collaborative projects, competitions, or design innovations. Build partnerships with firms focused on sustainability, green architecture, and cutting-edge urban design. Recruitment and Talent Acquisition

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Analyze shifts in urban development, design trends, and sustainable construction practices across Europe. Use insights to refine product development and marketing strategies tailored to the architectural sector. Why Choose Success.ai? Best Price Guarantee

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Tailor datasets to specific architectural segments, regions, or roles to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

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Automate lead generation for a consistent pipeline of qualified professionals, scaling your outreach efficiently. Success.ai’s B2B Leads Data for Architecture, Planning & Design Experts positions you to connect with the creative minds shaping Europe’s...

Dataset to the article "Post-Socialist Large Housing Estates in Europe: What is the (Research) Problem?"

The Global Monthly and Seasonal Urban and Land Backscatter Time Series, 1993-2020, is a multi-sensor, multi-decadal, data set of global microwave backscatter, for 1993 to 2020. It assembles data from C-band sensors onboard the European Remote Sensing Satellites (ERS-1 and ERS-2) covering 1993-2000, Advanced Scatterometer (ASCAT) onboard EUMETSAT satellites for 2007-2020, and the Ku-band sensor onboard the QuikSCAT satellite for 1999-2009, onto a common spatial grid (0.05 degree latitude /longitude resolution) and time step (both monthly and seasonal). Data are provided for all land (except high latitudes and islands), and for urban grid cells, based on a specific masking that removes grid cells with > 50% open water or < 20% built land. The all-land data allows users to choose and evaluate other urban masks. There is an offset between C-band and Ku-band backscatter from both vegetated and urban surfaces that is not spatially constant. There is a strong linear correlation (overall R-squared value = 0.69) between 2015 ASCAT urban backscatter and a continental-scale gridded product of building volume, across 8,450 urban grid cells (0.05 degree resolution) from large cities in Europe, China, and the United States.

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.