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

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

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.

This historical weather dataset provides hourly weather data for a number of major European Cities between 2003-01-01 and 2022-12-31. You can use this data to analyze and understand how weather has impacted your business, enrich your website with weather-related information, or enhance your data science projects with weather data. In addition to standard weather measurements such as air pressure, temperature, precipitation, and wind speed, this dataset includes solar radiation and UV index data as well. The full list of fields is provided in the documentation.

Key features:

• Hourly historical weather data between 2003 and 2022
• Includes solar radiation data
• Provides information on standard weather measurements such as air pressure, temperature, precipitation, and wind speed
• Offers insights into how weather has impacted your business

This Historical Weather Data is crucial for businesses needing detailed Climate Data, including Precipitation Data and Wind Data, to make informed decisions

Generated using Copernicus Climate Change Service information 2023 Contains modified Copernicus Climate Change Service information 2023

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.

Costs of coastal flooding and protection are essential information for risk assessment and natural hazards research, but there are few systematic attempts to quantify cost curves beyond the case study level. Here, we present a set of systematically derived damage and protection cost curves for the 600 largest (by area) European coastal cities. The city clusters were identified by an automated cluster algorithm from CORINE land cover 2012 data, following the Urban Morphological Zone (UMZ) definition.The data provides detailed cost curves for direct flood damages at flood heights between 0 and 12 m on a 0.5 m increment. Costs estimates are based on depth damage functions for different land use obtained from the European Joint Research Center. The necessary mapping between land use and land cover is based on Land Use/Cover Area frame Survey (LUCAS) 2015 primary data. The underlying inundation maps were derived from the European Digital Elevation Model (EU-DEM).Furthermore, the data contain curves for the cost of protection at the same heights and increments as the damage curves, assuming no previously installed protection. These curves are available both for a low and high cost scenario and are based on hypothetical protection courses derived from cluster data and inundation maps.All cost estimates are given in Euro and were inflation-adjusted to 2016 price levels. For spatial reference, we include the individual raster tiles depicting the extent of each city cluster.The research leading to these results has received funding from the European Community's Seventh Framework Programme under Grant Agreement No. 308497 (Project RAMSES). Supplement to: Prahl, Boris F; Boettle, Markus; Costa, Luis; Kropp, Jürgen P; Rybski, Diego (2018): Damage and protection cost curves for coastal floods within the 600 largest European cities. Scientific Data, 5(1), 180034

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.

The number of international air arrivals in Athens increased sharply in 2023 over the previous year, also surpassing the figures reported before the coronavirus (COVID-19) pandemic. Overall, inbound air arrivals in the Greek city peaked at nearly 7.1 million in 2023.

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

Attitudes towards the European Union and towards digitization. 1. Attitudes towards the EU: more positive or more negative thoughts and feelings with regard to the EU; expectations for the future related to Germany and to the EU; expected positive or negative impact of a possible EU exit of Britain (Brexit) to the other EU Member States; assessment of the likelihood of the occurrence of selected consequences of a Brexit; role of the Federal Government at the European level (enforcement of national interests vs. reaching compromises between EU Member States). 2. Attitudes towards digitization: rather hopes or fears associated with digitization; strength of the changes in everyday life by digitization and evaluation of these changes; expected benefits due to digitization in various fields (transportation, medicine, schools and universities, the fight against crime, private communication, access to information from government and business, communication with public authorities as well as the purchase of products and services); desired influence of politics on digitization; assessment of political tasks related to digitization (creation of new Internet companies, promoting digital education in schools and for senior citizens, strengthening of data protection, expansion of fast Internet in urban and rural areas, uniform rules for the use of music and video files across Europe, protection against cyber crime and application of digital technology in health care); Opinion on the digitization of services and information in public authorities and institutions; personally used digital offers of the public service and satisfaction with these offers; opinion on the constant evolution of digital technology; opinion on digital education (as important as reading and writing, as many as possible digital services for seniors, digital education beginning in elementary school); satisfaction with the speed of Internet at home. Demography: age; sex; highest educational degree; employment; occupational position; marital status; confession; income (groups); party preference (Sunday question). Also encoded was: state; city size; weighting factor. Einstellungen zur Europäischen Union und zur Digitalisierung. 1. Einstellungen zur EU: eher positive oder eher negative Gedanken und Gefühle in Verbindung mit der EU; Zukunftssorgen in Bezug auf Deutschland und die EU; erwartete positive oder negative Auswirkung eines möglichen EU-Austritts Großbritanniens auf die übrigen EU-Mitgliedsstaaten; Einschätzung der Wahrscheinlichkeit des Eintretens ausgewählter Folgen eines möglichen EU-Austritts Großbritanniens; Rolle der Bundesregierung auf europäischer Ebene (Durchsetzen nationaler Interessen vs. Erreichen von Kompromissen zwischen EU-Mitgliedsstaaten). 2. Einstellungen zur Digitalisierung: eher Hoffnungen oder Befürchtungen in Verbindung mit Digitalisierung; Stärke der Veränderungen im alltäglichen Leben durch die Digitalisierung und Bewertung dieser Veränderungen; erwarteter Nutzen durch die Digitalisierung in verschiedenen Bereichen (Verkehr und Transport, Medizin, Schulen und Universitäten, Kampf gegen Kriminalität, private Kommunikation, Zugang zu Informationen aus Politik und Wirtschaft, Kommunikation mit Behörden und Ämtern sowie beim Kauf von Produkten und Dienstleistungen); gewünschter Einfluss der Politik auf das Thema Digitalisierung; Beurteilung der Aufgaben der Politik im Rahmen der Digitalisierung (Neugründung von Internetfirmen, Förderung der digitalen Bildung an Schulen und für Senioren, Stärkung des Datenschutzes, Ausbau des schnellen Internets in Stadt und Land, einheitliche Regelung für die Nutzung von Musik- und Videodateien in ganz Europa, Schutz vor Internetkriminalität sowie Anwendung digitaler Technik im Gesundheitswesen); Meinung zur Digitalisierung von Dienstleistungen und Informationen in Behörden und öffentlichen Einrichtungen; persönlich genutzte digitale Angebote der öffentlichen Verwaltung und Zufriedenheit mit diesen Angeboten; Meinung zur ständigen Weiterentwicklung der digitalen Technik; Meinung zur digitalen Bildung (genauso wichtig wie Lesen und Schreiben, möglichst viele digitale Angebote für Senioren, digitaler Bildungsbeginn bereits in der Grundschule); Zufriedenheit mit der Internetgeschwindigkeit zuhause. Demographie: Alter; Geschlecht; höchster Bildungsabschluss; Berufstätigkeit; berufliche Stellung; Familienstand; Konfession; Einkommen gruppiert); Parteipräferenz (Sonntagsfrage Bundestagswahl). Zusätzlich verkodet wurde: Bundesland; Ortsgröße; Gewichtungsfaktor.

Annual consolidated main financial indicators of implementation of financial plans of the State Enterprise "Dniprovsky Electric Transport" DMC

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

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 10 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 7.75. 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.

Attitudes to current national and international questions. Topics: most important national problem; most important international problem; countries in conflict with the FRG; major problems and differences between FRG and USA; major problems between FRG and other countries; opinion on France, Great Britain, USA, USSR, Red China; reasons for negative and positive attitude to countries USA, USSR and China; trust in USA and USSR in treatment of world problems; reasons for little trust in USA and USSR; effort of USA and USSR for world peace; relationship of USA to USSR; strongest current nuclear power; strongest nuclear power in 5 years; desired strongest nuclear power; reasons for desire for balanced nuclear potential between USA and USSR; knowledge about the SALT negotiations; countries participating in the SALT negotiations; purpose and chances for success of the SALT negotiations; beneficiary of a treaty between USA and USSR; relying on USA in negotiations; security conference; threat to national security of Germany; support for FRG in the case of conflict; knowledge of international organizations; purpose of NATO; membership in NATO; reasons for desired membership; trust in defense ability of NATO; stationing troops in Western Europe; reduction of US troop strength in Europe; necessity of USA for security of Western Europe; defense budget of FRG; navy forces in the Mediterranean; strongest naval power in the Mediterranean; relationship of Israel and Arab nations; support of FRG for Israel; significance of result of the Middle East Conflict for FRG; peace process in the Middle East; European unification process; powers of a European Government; attitude of the USA to European integration; solving the problem of environmental pollution by international organizations; economic aid for other countries. Demography: age; marital status; education; occupation; income; religious denomination; church attendance; sex; city size; state. Also encoded was: length of interview; number of contact attempts; presence of others during interview; willingness to cooperate; difficulty; end time; date of interview; interviewer number. Einstellungen zu aktuellen nationalen und internationalen Fragen. Themen: wichtigstes nationales Problem; wichtigstes internationales Problem; Länder im Konflikt mit der BRD; Hauptprobleme und Differenzen zwischen BRD und USA; Hauptprobleme zwischen BRD und anderen Ländern; Meinung über Frankreich, Großbritannien, USA, UdSSR, Rot-China; Gründe für negative und positive Einstellung zu den Ländern USA, UdSSR und China; Vertrauen in die USA und die UdSSR bei der Behandlung von Weltproblemen; Gründe für geringes Vertrauen in die USA und UdSSR; Bemühen der USA und der UdSSR um den Weltfrieden; Verhältnis der USA zur UdSSR; stärkste derzeitige Atommacht; Stärkste Atommacht in 5 Jahren; gewünschte stärkste Atommacht; Gründe für Wunsch nach ausgeglichenem Nuklearpotential zwischen USA und UdSSR; Kenntnis der SALT-Verhandlungen; Teilnehmerstaaten der SALT-Verhandlungen; Zweck und Erfolgschancen der SALT-Verhandlungen; Nutznießer eines Abkommens zwischen USA und UdSSR; Verlaß auf USA bei Verhandlungen; Sicherheitskonferenz; Bedrohung der nationalen Sicherheit Deutschlands; Beistand für BRD im Konfliktfall; Kenntnis internationaler Organisationen; Zweck der NATO; Mitgliedschaft in der NATO; Gründe für gewünschte Mitgliedschaft; Vertrauen in Verteidigungsfähigkeit der NATO; Truppenstationierungen in Westeuropa; Reduktion der US-Truppenstärke in Europa; Notwendigkeit der USA für die Sicherheit Westeuropas; Verteidigungsbudget der BRD; Marinestreitkräfte im Mittelmeer; stärkste Seemacht im Mittelmeer; Verhältnis Israel und arabische Staaten; Unterstützung der BRD für Israel; Bedeutung des Ausganges des Nahostkonfliktes für die BRD; Friedensprozess im Nahen Osten; europäischer Einigungsprozess; Kompetenzen einer europäischen Regierung; Haltung der USA zur europäischen Integration; Lösung des Problems der Umweltverschmutzung durch internationale Organisationen; Wirtschaftshilfe für andere Staaten. Demographie: Alter; Familienstand; Bildung; Beruf; Einkommen; Konfession; Kirchgang; Geschlecht; Ortsgröße; Bundesland. Zusätzlich verkodet wurden: Interviewdauer; Anzahl der Kontaktversuche; Anwesenheit anderer während des Interviews; Kooperationsbereitschaft; Schwierigkeit; Endzeit; Interviewdatum; Interviewer-Nummer.

The dataset consists of five files for arrival trajectories at five major European airports (Amsterdam Schiphol, Dublin, London City, London Heathrow and Paris Charles-de-Gaulle) in October and November 2019. Each file (using a parquet format) contains ADS-B positional information with their derivative and a unique identifier for each trajectory. Data has been first used to evaluate the environmental impact of arrival procedures at these airports (article under submission) and is also relevant for other air transportation related research.

Abstract copyright UK Data Service and data collection copyright owner.

The theoretical aim of this research is to examine and understand why innovative and competitive firms tend to cluster in a limited number of particular cities. The project is also seeking to understand the observed variety of supplier and customer arrangements among firms and the interactions between these and the firms' home city regions.
These concerns raise questions about the characteristics of different stages of the innovation process and why firms' activities have been seen to vary from flexibly specialised local production networks, in mainly craft-based older industries, in new industrial districts; to individually produced innovations linked primarily in the context of competitive secrecy to major international customers.
Research on the London region (further London data are also held separately in the companion study to this one, SN:4360 'Innovation in the London Region, 1999-2000') was informed by the comparative perspective of innovation studies in the four European cities of Amsterdam, Milan, Paris and Stuttgart. A common questionnaire was administered in the five cities to a common sample frame of innovative companies who had won awards for basic research in industrial technologies for Europe (BRITE). In addition to this common sample frame, innovative firms drawn from local databases were also interviewed. The lessons from this first stage of the research were taken forward into a more in-depth research study of innovative and external support systems in the London metropolitan region where the sampling frame was identified using a variety of innovation awards. The purpose of gathering data for the five European cities in one study was to implement a common methodology for five of the most innovative regions in Europe. The regions were selected from a group of ten cities identified by the European Union as the ten most significant islands of innovation within the EU. Data were collected from 160 telephone interviews with industrial firms who were asked about specific innovative projects - an average of 32 firms from each city.
Main Topics:

The dataset includes comparative data from five European cities on the relationship between innovation and external support systems. The questionnaire was developed in London in consultation with the four other European research teams, and was then translated into the four European languages as appropriate. The interviews were administered in the respective cities, and interviewees were asked to consider innovation as a development process, characterise its attributes and then estimate the importance of different external support factors and relationships during its development.
The innovations were identified from lists of BRITE award-winning projects. BRITE is an EU award to fund pre-competitive collaborative and cooperative research projects in materials, design and manufacturing technologies. It is therefore a comparable way of identifying innovative firms across the five cities (including London - see SN:4360). Where the number of BRITE firms was insufficient for comparison, they were supplemented from existing databases of innovative firms within the respective city regions. These respondents were asked to answer the questions with respect to their most recent innovation.

This respository contains the CLUE-LDS (CLoud-based User Entity behavior analytics Log Data Set). The data set contains log events from real users utilizing a cloud storage suitable for User Entity Behavior Analytics (UEBA). Events include logins, file accesses, link shares, config changes, etc. The data set contains around 50 million events generated by more than 5000 distinct users in more than five years (2017-07-07 to 2022-09-29 or 1910 days). The data set is complete except for 109 events missing on 2021-04-22, 2021-08-20, and 2021-09-05 due to database failure. The unpacked file size is around 14.5 GB. A detailed analysis of the data set is provided in [1].

The logs are provided in JSON format with the following attributes in the first level:

• id: Unique log line identifier that starts at 1 and increases incrementally, e.g., 1.
• time: Time stamp of the event in ISO format, e.g., 2021-01-01T00:00:02Z.
• uid: Unique anonymized identifier for the user generating the event, e.g., old-pink-crane-sharedealer.
• uidType: Specifier for uid, which is either the user name or IP address for logged out users.
• type: The action carried out by the user, e.g., file_accessed.
• params: Additional event parameters (e.g., paths, groups) stored in a nested dictionary.
• isLocalIP: Optional flag for event origin, which is either internal (true) or external (false).
• role: Optional user role: consulting, administration, management, sales, technical, or external.
• location: Optional IP-based geolocation of event origin, including city, country, longitude, latitude, etc.

In the following data sample, the first object depicts a successful user login (see type: login_successful) and the second object depicts a file access (see type: file_accessed) from a remote location:

{"params": {"user": "intact-gray-marlin-trademarkagent"}, "type": "login_successful", "time": "2019-11-14T11:26:43Z", "uid": "intact-gray-marlin-trademarkagent", "id": 21567530, "uidType": "name"}

{"isLocalIP": false, "params": {"path": "/proud-copper-orangutan-artexer/doubtful-plum-ptarmigan-merchant/insufficient-amaranth-earthworm-qualitycontroller/curious-silver-galliform-tradingstandards/incredible-indigo-octopus-printfinisher/wicked-bronze-sloth-claimsmanager/frantic-aquamarine-horse-cleric"}, "type": "file_accessed", "time": "2019-11-14T11:26:51Z", "uid": "graceful-olive-spoonbill-careersofficer", "id": 21567531, "location": {"countryCode": "AT", "countryName": "Austria", "region": "4", "city": "Gmunden", "latitude": 47.915, "longitude": 13.7959, "timezone": "Europe/Vienna", "postalCode": "4810", "metroCode": null, "regionName": "Upper Austria", "isInEuropeanUnion": true, "continent": "Europe", "accuracyRadius": 50}, "uidType": "ipaddress"}

The data set was generated at the premises of Huemer Group, a midsize IT service provider located in Vienna, Austria. Huemer Group offers a range of Infrastructure-as-a-Service solutions for enterprises, including cloud computing and storage. In particular, their cloud storage solution called hBOX enables customers to upload their data, synchronize them with multiple devices, share files with others, create versions and backups of their documents, collaborate with team members in shared data spaces, and query the stored documents using search terms. The hBOX extends the open-source project Nextcloud with interfaces and functionalities tailored to the requirements of customers.

The data set comprises only normal user behavior, but can be used to evaluate anomaly detection approaches by simulating account hijacking. We provide an implementation for identifying similar users, switching pairs of users to simulate changes of behavior patterns, and a sample detection approach in our github repo.

Acknowledgements: Partially funded by the FFG project DECEPT (873980). The authors thank Walter Huemer, Oskar Kruschitz, Kevin Truckenthanner, and Christian Aigner from Huemer Group for supporting the collection of the data set.

If you use the dataset, please cite the following publication:

[1] M. Landauer, F. Skopik, G. Höld, and M. Wurzenberger. "A User and Entity Behavior Analytics Log Data Set for Anomaly Detection in Cloud Computing". 2022 IEEE International Conference on Big Data - 6th International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2022), December 17-20, 2022, Osaka, Japan. IEEE. [PDF]

Living and housing situation in rural municipalities in Tunisia and the influence of television on the rural population. Topics: knowledge of selected personalities from the areas of politics, sport and music; preferred names for children (index of modernity); children or grandchildren without work; preferred work for sons or grandchildren; unmarried children; preferred place of residence for the children after marriage; personal marriage as well as previous marriage; preferred place of residence after personal marriage; importance of owning selected pieces of furniture and consumer goods as well as objects from the area of home decoration; attitude to life in a large European or American city as dangerous; knowledge of a person who spent time in Europe in the last year; judgement on the life of politician as easy or difficult; judgement on the housing situation in the city; preferred dress style for men; preferred occupations (index of modernity); attitude to family planning; personal opinion leadership in the city; assumed frequency of selected activities of politicians; frequency of political discussions about the Tunisian government; unmarried daughters; attitude to employment of a daughter in a modern city hotel; judgement on personal housing situation; observing obligatory prayer within the family; preferred dress style for women; perceived changes in living conditions in the region; time of last visit of a welfare worker to the region; ownership of a television set by the most politically aware people in the city; judgement on life in a large Tunisian city; interest in a discussion with a politician about personal problems; political discussion partners; assumed attitude of Islam to birth control; frequency of watching television as well as use of television programs in French and Arabic; place of television; social situation of television as well as number of people present; main television partner; length of watching television; frequency of watching selected programs; ownership of television; the right of wife and children to watch television in the absence of the head of household; preferred place to watch television; frequency of discussions about television programs; judgement on television program as suitable for the whole family; ownership of television and radio by friends and neighbors; length and frequency of use of radio; ownership of a radio; ability to read; frequency of reading magazines; marital status; size of household; education level; employment status; occupation; ownership of various objects of decoration, consumer goods and pieces of furniture. Additionally encoded were: respondent´s place of residence; place of interview; presence of third party during the interview; date of interview; length of interview; interviewer identification. Lebens- und Wohnsituation in ländlichen Gemeinden Tunesiens sowie der Einfluß des Fernsehens auf die ländliche Bevölkerung. Themen: Kenntnis ausgewählter Persönlichkeiten aus den Bereichen Politik, Sport und Musik; präferierte Namen für Kinder (Modernitätsindex); Kinder oder Enkel ohne Beschäftigung; präferierter Beruf für die Söhne und Enkel; unverheiratete Kinder; präferierter Wohnort für die Kinder nach der Heirat; eigene Heirat sowie fühere Ehe; präferierter Wohnort nach einer eigenen Heirat; Wichtigkeit des Besitzes ausgewählter Einrichtungsgegenstände und Konsumgüter sowie von Gegenständen aus dem Bereich der Wohnungsausstattung; Einstellung zum Leben in einer europäischen oder amerikanischen Großstadt als gefährlich; Kenntnis einer Person, die sich im letzten Jahr in Europa aufgehalten hat; Beurteilung des Lebens von Politikern als leicht oder schwer; Beurteilung der Wohnsituation im Ort; präferierter männlicher Kleidungsstil; präferierte Berufe (Modernitätsindex); Einstellung zur Familienplanung; eigene Meinungsführerschaft im Ort; vermutete Häufigkeit ausgewählter Aktivitäten von Politikern; Häufigkeit politischer Gespräche über die tunesische Regierung; unverheiratete Tochter; Einstellung zu einer Beschäftigung der Tochter in einem modernen Großstadthotel; Beurteilung der eigenen Wohnsituation; Einhaltung der Gebetsverpflichtungen innerhalb der Familie; präferierter weiblicher Kleidungsstil; wahrgenommene Veränderungen der Lebensbedingungen in der Region; Zeitpunkt des letzten Besuchs eines Sozialfürsorgers im Ort; Besitz eines Fernsehgeräts bei den politisch informiertesten Personen des Ortes; Beurteilung des Lebens in einer tunesischen Großstadt; Interesse an einem Gespräch über persönliche Probleme mit einem Politiker; politische Gesprächspartner; vermutete Einstellung des Islams zur Geburtenkontrolle; Häufigkeit des Fersehkonsums sowie der Nutzung französisch- und arabischsprachiger Fernsehprogramme; Ort des Fernsehens; soziale Situation des Fernsehens sowie Anzahl der anwesenden Personen; hauptsächliche Fernsehpartner; Dauer des Fernsehkonsums; Häufigkeit des Konsums ausgewählter Fernsehprogramme; Fernsehbesitz; Recht der Frau und Kinder zum Fernsehen bei Abwesenheit des Familienoberhaupts; präferierter Fernsehort; Häufigkeit der Diskussion über Fernsehprogramme; Beurteilung des Fersehprogramms als geeignet für die gesamte Familie; Fernseh- und Radiobesitz von Freunden und Nachbarn; Dauer und Häufigkeit der Radionutzung; Radiobesitz; Lesekenntnisse; Häufigkeit des Lesens von Zeitschriften; Familienstand; Haushaltsgröße; Bildungsstand; Erwerbsstatus; Beruf; Besitz ausgewählter Einrichtungsgegenstände, Konsumgüter sowie von Gegenständen der Wohnungsausstattung. Zusätzlich verkodet wurde: Wohnort des Befragten; Ort des Interviews; Anwesenheit Dritter beim Interview; Interviewdatum; Interviewdauer; Intervieweridentifikation.