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

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

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

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

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

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.

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 development plan (BPL) contains the legally binding determinations for the urban planning order. In principle, the development plan must be developed from the land use plan. The available data is the development plan “Great Molten — 1st Amendment” of the city of Oberriexingen from XPlanung 5.0. Description: Large moltens — 1st change.

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

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.

According to INSPIRE transformed development plan "Satzung Großflächenwerbung Kernstadt Teil 1-3" of the city of Vaihingen an der Enz based on an XPlanung data set in version 5.0.

The infrastructure database or POI database of the city of Gelsenkirchen offers you extensive information about infrastructure in Gelsenkirchen. You currently have the option of accessing over 100 different types of infrastructure and over 7000 data sets from the areas of family, education, leisure, infrastructure, culture, administration, social affairs and business. In addition to the spatial location, information on contact details and other specialist information is stored. The range is constantly being expanded and maintained by the responsible departments.

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.

The Horizon 2020 project WeCount is a citizen science project that involves citizens in all steps from problem definition to data collection and analysis. This is currently one of the most common methods of citizen participation. The ethical criteria that such a project must meet in order to be classified as citizen science, and the form of transparency or informed consent that should be a necessary part of the ethical conduct of citizen science projects, were on Telraam platform for examination at the international and national level during the collection of data on traffic flows for WeCount Ljubljana. Engaged citizens were given low-cost sensors which they placed on the inside of the windowpane in their home or office facing the street at different distances (from 3 to 15 meters).

Urban development plan “large molten page — 2nd change” of the city of Oberriexingen transformed according to INSPIRE based on an XPlanung dataset in version 5.0.

According to INSPIRE transformed development plan “Statute Large Area Advertising Core City II Part 1 and 2” of the city of Vaihingen an der Enz based on an XPlanung dataset in version 5.0.

This dataset provides values for INFLATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset includes the processing results used to create the interactive climate service Thermal Assessment Tool. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.

A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.

To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative Copernicus Climate Change Service (C3S). For the observations the e-OBS dataset is used and for the future projections the EURO-CORDEX dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the Copernicus Climate Data Store (CDS).

The database is organized in three datasets:

Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:

region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.

tmax: daily maximum temperature threshold.

tmin: daily minimum temperature threshold.

Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:

region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.

start: first date of the heatwave.

tmax: maximum temperature reached during the heatwave.

intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.

duration: duration of the heatwave.

Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:

hazard_level: it can be a warning, an alert or an alarm.

region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.

experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.

period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.

decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.

decade_frequency_best: only applicable to the future. It determines the best projection among the models.

decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.

year_days: average annual days.

year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.

year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.