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Green Space Distribution m2 per capita in Valladolid city
<p>Urban green infrastructures are key part of the sustainable development in our cities. They can provide important Ecosystem Services in them, including provisioning, regulating, supporting and cultural services. The total surface of green areas needs to be relativized in terms of total area or per capita, in order to compare results with other cities or to observe the evolution within the same city.</p>
Green areas sustainability for Valladolid city
<p>Recreational (number of visitors, number of recreational activities) or cultural (number of cultural events, people involved, children in educational activities) value is an idicator calculated during the timespan of the UrbanGreenUP project. </p>
Citizen Perception of the NBS in Valladolid city
<p>Citizens’ perceptions, both individuals and communities, are essential when evaluating the well-being benefits from urban green spaces (Kothencz et al, 2017). Public and stakeholder perceptions of urban nature, and specifically the quality or functionality of nature, are critical to our understanding of the “value” people place on local environments (Priego et al., 2008).</p> <p>Exploring visitors’ perceptions of green spaces is challenging as it depends on cognitive, affective, and behavioural components and, therefore, sensory perceptions are individually different (Kothencz et al, 2017).</p> <p>This KPI measures identified green space characteristics by the two following well-being variables and one geolocation variable:</p> <ul> <li>Green space visitors’ level of satisfaction. Directly related with the urban green space (UGS) quality.</li> <li>Self-reported quality of life (QoL).</li> <li>Frequency of green space visitors’ crowd-sourced geo-tagged data in NBS sites.</li> </ul> <p>Visitors’ level of satisfaction and perceived QoL contributions of UGS are key individual-level measures that are subjectively affected by area-based green space characteristics.</p> <p>This KPI will reflect on how people assess change in their local environments in terms of satisfaction, quality of life and citizens’ presence of urban green space (UGS) at a site (NBS), neighbourhood and city scale.</p>
Green Intelligence Awareness, inhabitants attended in Valladolid city
<p>Changes in behavior and human attitudes are fundamental to achieve a more sustainable world, so that, it is very interesting to analyze the potential of an activity or intervention to increase the green intelligence awareness of a population.</p> <p>There is enormous opportunity for nature-based solutions to promote understanding of sustainability in ways that positively influence citizen behavior. There are numerous available resources to learn and understand the fragility of our environmental and the responsibility of humans to protect, preserve and respect the world. Therefore, this KPI aims to reflect how the intervention is used for educational purposes and enhancement of public awareness. </p>
Green Intelligence Awareness, educational actions in Valladolid city
<p>Changes in behavior and human attitudes are fundamental to achieve a more sustainable world, so that, it is very interesting to analyze the potential of an activity or intervention to increase the green intelligence awareness of a population.</p> <p>There is enormous opportunity for nature-based solutions to promote understanding of sustainability in ways that positively influence citizen behavior. There are numerous available resources to learn and understand the fragility of our environmental and the responsibility of humans to protect, preserve and respect the world. Therefore, this KPI aims to reflect how the intervention is used for educational purposes and enhancement of public awareness. </p>
Noise Reduction by NBS in Valladolid city
<p>Noise pollution by traffic, construction works, etc. is a common city problem. Nuisance from noise is detrimental to neighbourhood liveability, living comfort and work environments, and can increase risk of serious health problems such as hearing loss and cardiovascular disease.</p> <p>Urban ecosystems provide noise reduction services by serving as a natural sound buffer. Vegetation provides both a direct and an indirect barrier to environmental noise. Starting with its direct functions, green belts attenuate noise by absorption, dispersal, and destructive interference of sound waves, though sound levels can intensify locally if measured right below tree crowns. Indirect noise reduction effects are generated by lessened wind speeds and the absorptive capacity of pervious soils. UGS also proved to offer noise reducing services via psychological effects: just observing the presence of a green wall can lead people to perceive less noise nuisance or alter the perception of noise as sounds such as flowing water, bird singing, and leaves rustling in the wind mask disturbing background noise.</p> <p>On the other hand, the methodology proposed for this KPI is based and uses the methodology and tools proposed by the European Commission Working Group Assessment of Exposure to Noise (WG-AEN).</p> <p>The Environmental Noise Directive (END) requires two main indicators to be applied in the assessment and management of environmental noise. The first indicator (Lden) is the noise level for the day, evening and night periods and is designed to measure ‘annoyance’. The END defines an Lden threshold of 55 dB. The second indicator (Lnight) is the noise level for night-time periods and is designed to assess sleep disturbance. The END defines an Lnight threshold of 50 dB. Member States must report the numbers of people who are exposed to noise levels above both thresholds for each noise source (e.g. roads, railways, airports, industry).</p>
Openness process in Valladolid city
<p>Nature-Based Solutions require planning approaches and governance architectures that support accessibility to green spaces, while maintaining their quality for the provision of ecosystem services. Urban environmental problems are often difficult to handle and successful solutions require combined efforts of different scientific disciplines but also an active dialogue between stakeholders from policy and society (Lemos and Morehouse, 2005).</p> <p>In this context, transdisciplinary approaches for knowledge co-production provide insights about the ways and the rationale for engaging with multiple knowledge holders: experts and scientists as well as citizens and practitioners (Bergmann et al., 2012, Jahn et al., 2012). The scientific frameworks of urban ecosystem services were brought into the interface between policy and science to inform urban planning and governance (Frantzeskaki and Tilie, 2014).</p> <p>The quality of the URBAN GreenUP project implementation depends on social learning and adequate technical solutions. This is possible through the support and cooperation between the involved parties and the resulting input of knowledge (Luyet, 2012).</p> <p>Participation is often reduced to the dissemination of information and the holding of workshops. These approaches generally do not take into account either the heterogeneity of stakeholders, or the complexity of the decision making process (Luyet, 2012).</p> <p>The KPI “Openness of participatory processes” is based on the participation actions delivered in the city of Valladolid. There are defined two steps, data collection and data evaluation.</p>
Driven distance by commuters to the center of major and minor cities in Europe
<p>The two files contains respectively a map of driving distances around major and minor cities in Europe. The distance is given by range of 5 km from 5 to 45 km in a resolution of 100m*100m. The encoding is uint8.</p> <p>The metropolitan areas considered in this study are those from this study of <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Archive:European_cities_%E2%80%93_the_EU-OECD_functional_urban_area_definition">functional urban area definition</a>.</p> <p>The corresponding geofile is available at <a href="https://circabc.europa.eu/ui/group/8eafb630-27cf-4fae-a4c2-f1234a123fd7/library/59bfa33a-8f4b-413d-8552-ac0a93ad7e5f/details">this adress</a>.</p> <p>Most of the cities with more than 50'000 habitants are included, some cities have been removed or added depending on the local context.</p> <p>As the geofile contains only polygones of the metropolitan areas and not the city point, the points form cities with the number of habitants have been dowloaded from <a href="http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-populated-places/">Natural Data</a> and then selected by comparison with the polygones.</p> <p>Cities with more than 20'000 have also been selected as commuting center for rural areas. Only the cities outside the metropolitan commuting zones are included.</p> <p>The calculation of driving distances around centers have been performed with <a href="https://openrouteservice.org/">OpenRouteService</a> (personal API key required, free of charge).</p> <p> </p> <p> </p>
Network Data of the District Heating System for the city of Sønderborg from 2016-2019
<p>The data set contains measurement data for heat load, as well as feed and return flow temperatures, from seven plants for the years 2016-2019 with a 15-minute time resolution. The heating plants belong to the district heating systems of Sønderborg, Denmark.</p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. 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.</p> <p>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.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>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:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: 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.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. 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.</p> <p>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.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_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:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: 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.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Supplementary material for the article "High-resolution projections of ambient heat for major European cities using different heat metrics"
<p>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".</p> <p>The different files contain:</p> <ul> <li>Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc:<br> Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010.</li> <li>Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx:<br> 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.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx:<br> 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.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx:<br> 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.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx:<br> 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.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx:<br> 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.</li> <li>Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> 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.</li> <li>Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx:<br> Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics.</li> <li>Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> 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.</li> <li>Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx:<br> 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.</li> <li>Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx:<br> 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.</li> <li>Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx:<br> GCM-RCM matrices for the three heat metrics.</li> </ul>
Challenges of cultural heritage adaptive reuse: a stakeholders-based comparative study in three European cities. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., van Wesemael, P. J. V., & Pereira Roders, A. R. (2023). Challenges of cultural heritage adaptive reuse: A stakeholders-based comparative study in three European cities. Habitat International, 136, [102807]. https://doi.org/10.1016/j.habitatint.2023.102807.</p> <ul> <li>Date of data collection: a) 31/05/2018, b) 27/11/2018, and c) 28/03/2019</li> <li>Geographic location of data collection: a) Amsterdam, The Netherlands. The venue of the data collection was Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands; b) Salerno, Italy. The venue of the data collection was Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy; and c) Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia.</li> <li>Activity of data collection: a) Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018; b) Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018; and c) Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019.</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges.</li> <li>Methods for collection/generation of data: See the methodology section in a) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability, 13(10), 5547. https://doi.org/10.3390/su13105547; b) Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 33, 100505. https://doi.org/10.1016/j.ccs.2023.100505; and c) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability, 13(7), 3603. https://doi.org/10.3390/su13073603.</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: a) Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez; b) Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh; and c) Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi.</li> <li>Language of the data: English.</li> <li>References: a) Pintossi, Nadia. (2021). Assessing cultural heritage adaptive reuse practices: multi-scale challenges and solutions in Rijeka. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4518743; b) Pintossi, Nadia. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4250495; and c) Pintossi, Nadia. (2023). Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3925602</li> </ul> <p><br> </p>
Block-group level mode choice parameters for New York City and New York State
<p>We provide two datasets of census block group-level mode choice parameters for New York City and New York State. The parameters are estimated by GLAM logit model using Replica's synthetic population datasets (For details of the GLAM logit model, please refer to <a href="https://github.com/BUILTNYU/GLAM-Logit">BUILTNYU/GLAM-Logit (github.com)</a>). Each row contains a set of mode choice parameters for each block-group OD pair and one of the four population segments (low-income, not low-income, students, and senior population). Six trip modes are considered: private auto, public transit (such as buses, light rail, and subways), on demand auto (taxi or TNC services such as Uber or Lyft), biking (including e-bike), walking, and carpool. Parameters of twelve mode attributes are estimated, including, auto travel time, transit in-vehicle time, transit access time, transit egress time, number of transit transfers, non-vehicle travel time, trip cost, and five alternative specific constants (setting carpool as the reference level).</p> <p>In New York City, the average value of time (VOT) of low-income population is 21.67$/hour, the average VOT of not low-income population is 28.05$/hour, the average VOT of student population is 10.96$/hour, and the average VOT of senior population is 10.93$/hour. In New York State, the average value of time (VOT) of low-income population is 9.63$/hour, the average VOT of not low-income population is 13.95$/hour, the average VOT of student population is 7.40$/hour, and the average VOT of senior population is 6.26$/hour. </p> <p>The empirical distribution of agent-level parameters is neither Gumbel nor Gaussian, which reveals a regional divergence of the value of time and mode preference, indicating potential inequity issues in the transportation system. This is infeasible for conventional discrete choice models (DCMs) to capture. </p>
Block-group level predicted mode share for New York City and New York State
<p>We provide two datasets of predicted mode share, one for New York City and another for New York State. Each row contains the mode proportion of trips along a census block group-level OD pair made by one of the four population segments: low-income, not low-income, students, and senior population. Six trip modes are considered: private auto, public transit (such as buses, light rail, and subways), on demand auto (taxi or TNC services such as Uber or Lyft), biking (including e-bike), walking, and carpool.</p> <p>The prediction is based on GLAM logit model calibrated with Replica's statewide synthetic population dataset. The in-sample prediction accuracy is quite competitive, with an overall accuracy of 90.28% in New York State and 88.63% in New York City. For more details of the model, please refer to our Github repository: <a href="https://github.com/BUILTNYU/GLAM-Logit">BUILTNYU/GLAM-Logit (github.com)</a></p>
FUME Local population projections in destination cities
<p>FUME data on projected distributions of migrants at local level between 2030 and 2050.</p> <p>The dataset contains a folder of data for each destination city as a gridded dataset at 100m resolution in GeoTIFF format. The examined destination cities are: Amsterdam, Copenhagen, Krakow and Rome. The dataset is provided as 100m grid cells based on the Eurostat GISCO grid of the 2021 NUTS version, using ETRS89 Lambert Azimuthal Equal-Area (EPSG: 3035) as coordinate system. The file names consist of the projected year, the corresponding scenario, and the reference migrant group. The projections have been performed for the years 2030, 2040 and 2050. The investigated scenarios are the following:<br> • benchmark (bs),<br> • baseline (bs),<br> • Rising East (re),<br> • EU Recovery (eur),<br> • Intensifying Global Competition (igc), and<br> • War (war).</p> <p>The migration background is derived from data about the Region of Origin (RoO) for migrants in Copenhagen and Amsterdam, and from Region of Citizenship (CoC) for migrants in Krakow and Rome.</p> <p>The case study of <strong>Copenhagen</strong> covers the two central NUTS3 areas (DK011, DK012) and the groups presented are the following:<br> • total population (totalpop),<br> • native population (DNK),<br> • Eastern EU European migrants (EU_East),<br> • Western EU Europeans migrants (EU_West),<br> • Non-EU European migrants (EurNonEU),<br> • migrants from Turkey (Turkey),<br> • the MENAP countries (MENAP; excluding Turkey),<br> • other non-Western (OthNonWest), and<br> • other Western countries (OthWestern).</p> <p>The case study of <strong>Amsterdam</strong> covers one NUTS3 area (NL329) and the presented groups are the following:<br> • total population (totalpop),<br> • native population (NLD),<br> • Eastern EU European migrants (EU East),<br> • Western EU European migrants (EU West),<br> • migrants from Turkey and Morocco (Turkey + Morocco),<br> • migrants from the Middle East and Africa (Middle East + Africa),<br> • migrants from the former colonies (Former Colonies), and<br> • migrants from the rest of the world (Other Europe etc).</p> <p>The case study of <strong>Krakow</strong> covers the Municipality of Krakow, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (POL),<br> • EU/EFTA European migrants (EU),<br> • non-EU European migrants (Europe_nonEU), and<br> • migrants from the rest of the world (Other).</p> <p>The case of <strong>Rome</strong> covers the Municipality of Rome, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (ITA),<br> • migrants from Romania (ROU),<br> • Philippines (PHL),<br> • Bangladesh (BGD),<br> • the EU (EU; excluding Romania),<br> • Africa (Africa),<br> • Asia (Asia; excluding Philippines and Bangladesh) and<br> • America (America).</p>
2021 New York City Daily Residential Parcel Volume and Stops
<p>These datasets give <strong>2021 NYC residential parcel volume and stops </strong>information. The four datasets represent the estimated volume and stops served by Amazon, FedEx, UPS, and USPS respectively.</p> <p>The dataset is generated by using the Pluto 2021, 2020 census, 2020 USPS postal diary, and the market share among the four companies in 2021. In total,1.92 million daily residential parcels are estimated in the whole NYC area. Each row represents a unique stop. It contains the census tract ID the stop belongs to, its coordinates, and the delivery (FTA) and pickup (FTP) volume. We assume that no pickup volume is assigned to Amazon and USPS due to their service characteristics. In the case of USPS, no direct pickup service will be provided. Instead, the parcel will be directly handed to post offices for pickup. The pickup volume proportional to Amazon's market share is evenly distributed to FedEx and UPS pickup services.</p> <p>"myGraph.pickle": pickle file storing the NYC OSM network graph.</p> <p>"Shapfile.zip": Shapefiles containing different geographic features of NYC.</p> <p>"Centroid_OSM.zip": OSM maps storing the centroid of NYC census tracts.</p> <p>"NTA_dist.zip": VKT result based on NTA. </p> <p>"Service Zone": facility locations represented by OSM node ID and the service areas defined by census tracts</p>
Dynamic traffic noise levels (LAeq) in the city of Tartu for two times of the day: 04:00 and 16:00
<p>Animations of the dynamic traffic noise levels (LAeq) in the city of Tartu, Estonia, for two times of the day: (a) 04:00 (sparse traffic) and (b) 16:00 (dense traffic). Blue circles represent the location of vehicles. Colours on receiver points represent noise levels according to legend (c).</p>
UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network
<p>The UBGG dataset provides easily access and leverage to researchers and analysts, which is stored in the following Zenodo repository (<a href="https://doi.org/10.5281/zenodo.8053333">https://doi.org/10.5281/zenodo.8352777</a>). The UBGG dataset consists of two main components:</p> <ul> <li><strong>UBGG-3m: the fine-grained UBGG map product of 36 metropolises in China.</strong> The UBGG-3m dataset captures the intricate urban landscape features with remarkable precision, providing a detailed representation at an impressive 3-meter resolution. Fig. 1 in User Guides shows the classification results for 36 Chinese metropolises. Researchers can delve into the nuances of the UBGG continuum, gaining invaluable insights into the interplay between the blue, green, and gray elements of urban environments in each metropolis.</li> </ul> <ul> <li><strong>UBGGset:</strong> <strong>the large-volume sample dataset to support the UBGG deep learning research.</strong> Complementing the UBGG-3m dataset, UBGGset serves as a large-volume sample dataset specifically tailored to support and foster UBGG research endeavors (Fig. 2). The UBGGset consists of 14,627 sample images (without data augmentation), with dimensions of 256 pixels in length and width, covering an urban area of approximately 2,272 km<sup>2</sup>. The UBGGset was constructed with co-registered pairs of 3 m Planet images and fine-annotated urban landscapes labeled on 1 m Google Earth image. This dataset encompasses 15 typical cities, offering researchers a rich and diverse resource to drive exploration, analysis, and innovation in the field of urban landscape studies.</li> </ul> <p> </p> <p><strong>Citation format for paper and dataset:</strong></p> <p>[1] Zhiyu Xu, Shuqing Zhao. Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network. <em>Sci Data</em> 11, 266 (2024). https://doi.org/10.1038/s41597-023-02844-2</p> <p>[2] Zhiyu Xu, Shuqing Zhao, Fine-grained urban landscape mapping reveals broad-scale homogeneity in urban environments,<br>Science Bulletin, (2024). https://doi.org/10.1016/j.scib.2024.03.060</p> <p>[3] Zhiyu Xu, Shuqing Zhao. UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network (v1.0) [Data set]. (2023). Zenodo. https://doi.org/10.5281/zenodo.8352777</p>
Greenhouse Gas and Water Chemistry Data from Ponds in the Twin-Cities area of Minnesota, 2021
Freshwaters are significant contributors of greenhouse gases to the atmosphere, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Small waterbodies such as ponds are now recognized to have disproportionate greenhouse gas emissions relative to their size, but recorded emissions from ponds have varied by several orders of magnitude. To assess drivers of variation in pond greenhouse gas dynamics, this study measured concentrations and emissions of CO2, CH4, and N2O across 26 ponds in Minnesota, USA during the ice-free season. The studied ponds ranged in land-use, from urban stormwater ponds to natural forested ponds. Water chemistry variables were measured with sonde profiles as well as surface water samples. Greenhouse gas emsisions of CO2 and CH4 were measured with a floating chamber at three locations on each pond, and gas concentrations of CO2, CH4, and N2O were measured using a headspace equilibrium technique in both the surface waters and bottom waters of each pond.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.