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75 results for “urban heat”
Urban Heat and Desert Wildlife: Rodent Body Condition Across a Gradient of Surface Temperatures in the greater Phoenix, Arizona (USA) metropolitan area (2019-2020)
We live-trapped wild rodents from seven field sites spanning three strata of land-surface temperatures in the Phoenix, Arizona (USA) metropolitan area. We captured 116 adult pocket mice (Chaetodipus spp. and Perognathus spp.) and Merriam’s kangaroo rats (Dipodomys merriami) during 2019 and 2020 from mountainous urban parks and open spaces. Animal body condition was quantified as percent body fat (i.e., fat mass divided by body mass). We used a noninvasive quantitative magnetic resonance instrument to measure body condition.
Regional Heat Vulnerability Map and Cooling Solutions: A webtool of the Healthy Urban Environments Initiative
## Regional Heat Vulnerability Map and Cooling Solutions The regional heat vulnerability map and cooling solutions webtool offers two data sources for equitable heat mitigation. The dashboard layers vulnerability data onto land surface temperature regional rankings to identify areas with high and low heat exposure and vulnerability as well as the existing assets in each census block group. Additional layers can be added into the heat vulnerability map to highlight how heat affects critical infrastructures including schools, mobile home parks, parking lots, public transportation stops, pedestrian thoroughfares, and bikeways. The solutions tab showcases a variety of heat mitigation solutions and the research behind them. Heat-related solutions and resources from urban Maricopa County are included, including solutions funded through the Healthy Urban Environment Initiative. The data catalogued here are the underlying data that populate the webtool. ## Healthy Urban Environment (HUE) Initiative - Overview HUE is a solutions-focused research, policy and technology incubator to create healthier communities across Maricopa County (central Arizona, USA) through collaboration between researchers, practitioners and community members. As such, HUE funded rapid development, testing and deployment of heat-mitigation and air-quality improvement strategies and technologies. Heat emerged as the urgent focus, as urban centers across the desert Southwest continue to grow in size and density, aggravating existing challenges posed by the expansion of the built environment. In Phoenix, AZ, this expansion of the built environment creates conditions which magnify the intensity and duration of heat – making it difficult for residents to achieve thermal comfort throughout the day and night. Further, the legacies of urban sprawl and transportation planning in the Phoenix, Arizona metropolitan area have contributed to challenges with atmospheric pollutants. Importantly, urban heat and air qua
Urban-Rural Temperature Data-relation between land-cover and the Urban Heat Island in San Juan, Puerto Rico
Our objective in this study is to quantify the UHI created by the San Juan Metropolitan Area over space and time using temperature data collected by mobile and fixed-station measurements. We used the fixed-station measurements to examine the relation between average temperature at a given location and the density of vegetation located upwind. We then regressed temperatures against regional land-cover to predict future temperature with projected land-cover change. Our data show the existence of a nocturnal UHI, with average nighttime urban-rural temperature differences (ΔTU-R) of up to 3.02°C. Each of the stations listed in this excel file were used to calculate the urban heat island created by the San Juan Metropolitan Area. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona
<p><em><strong>The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets.</strong></em></p> <p><strong>How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a></strong></p> <p>This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions.</p> <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> <p>note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index</p> <p>We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is :</p> <p>VV_new_perNN_LSTWW</p> <p>where:</p> <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> <p>Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65. </p> <p> </p>
Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer
As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land-cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10, 30, 60 and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (about 7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0 to 100%, each) in a mid-sized city in the Upper Midwest, USA. Variability in daytime air temperature within the urban landscape averaged 3.5 degreeC (range 1.1 to 5.7 degreeC). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale, and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1C (range 1.2 to 3.0 degreeC), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.
Urban heat island: temperature climate trends in central Arizona-Phoenix: period 1948 to 2007
The question was to what degree are summer minimum temperature climate trends in the latter half of the 20th and early part of the 21st century attributed to local urban development as opposed to global climate change? The approach was to select a range of towns/cities in CA, NV, and AZ for which a pairing of sites from a town/city and a site outside that town/city was possible. Climate records for the period 1948 to 2007 were accessed, and statistical time trends determined for the urban vs. rural locations for towns/cities over a considerable range of population (i.e., from 3.5K to 3.2M). The urban heat island effect increased with the natural log of the population, ranging from a total change in minimum monthly temperatures of ca. 1.5F to over 12F over the population range of 3.5K to 3.2M. These rates of change in the 1948-2007 period overwhelm any background global climate change, with the exception of the rural sites and smaller towns. This study for the first time identified the temperature trends of a range of towns and cities in the Sonoran and Mojave deserts to unravel the impact of urban warming from that of global warming in the contemporary global warming era sometimes called the Anthropocene era. Previous literature investigatin these sites were only up to 1984 or did not address the urban warming contribution. The impact depends on land cover and extent of population development over time.
Future Projections of Temperature Extremes and Urban Heat Island in Paris using Deep Learning
<p>Future projections of 2-meter maximum and minimum temperature and land surface temperature in Paris, France, using Deep Learning, under four Shared Socioeconomic Pathways. ERA5 and GCM ensemble data at their original resolution are also included. The DL (Convolutional Neural Network) model architecture and trained weights are also available. The Python script to generate the boxplots of the future projections is also included.</p>
Data for "Do electric vehicles mitigate urban heat? The case of a tropical city"
<p>This dataset contains the underlying data used in the publication "Do electric vehicles mitigate urban heat? The case of a tropical city", which is under review in <em>Front. Environ. Sci. .</em></p> <p>The dataset includes two folders:</p> <p>1. <strong>data</strong> <br> Include COSMO-DCEP-BEP model inputs and output needed to reproduce the results in the manuscript (NetCDF). </p> <p>2. <strong>script</strong><br> Include post-processing scripts used to generate the figures in the manuscript (Jupiter Python 3 Notebook).</p> <p><em> </em></p>
IuliaMargineanGitHub/Projecting-Heat-Stress-Vulnerable-Populations-at-Intra-Urban-Scales: Projecting heat stress vulnerable populations at intra-urban scales
<p>This repository provides the data and scripts necessary for full reproduction of results, as well as example datasets that were used for data generation and analysis in the manuscript Marginean et al., "High-resolution Modelling and Projecting Local Dynamics of Differential Vulnerability to Urban Heat Stress".</p> <p>The folders in this repository contain the following:</p> <ol> <li>The input datasets: <ul> <li>shares of educational attainment in 2012 and 2020, by sex, age group and census tract</li> <li>internal migration by sex, age group and census tracts</li> <li>decadal mortality and fertility, by sex, age group, scenario, and census tract</li> </ul> </li> <li>Projected decadal data (2012 to 2050) by sex, age group, scenario and census tract</li> <li>Scripts for generating high resolution projections for three Shared Socioeconomic Pathways (SSPs): one script for SSP 2 projections and one scripts for SSP 1 and SSP 3 projections</li> <li>Example input data to reconstruct the projections in SSP 1, 2 and 3 for females ages 25 to 64</li> </ol>
Simulations of urban heat island effect in Paris Region during various types of heatwaves, and in different adaptation scenarios
<p><strong>Content</strong><br> - These data present air temperature, in the shade, 2m above grounds in Paris Region (projection: RGF93/Lambert 93, EPSG:2154) at different times of the day, for various heat waves conditions, and in different prospective scenarios for the built-up evolution and adaptation actions implementations.<br> - more information can be found here : https://www.umr-cnrm.fr/ville.climat/spip.php?rubrique45</p> <p><strong>Classification of the data</strong><br> - the first 5 letters (e.g. "CDFFA") present the prospective scenario<br> - the 4 following letters (e.g. "HW34") present the type of heat wave<br> - the following 2 letters (e.g. "D8") present the length of the heat wave (number of days after the beginning of the heat wave)<br> - the final letters (e.g. H15) represent the time (UTC : one hour should be added for French time) of the day</p> <p><strong>Prospective scenarios</strong><br> - the first letter is always C<br> - the second letter represents the expansion scenario. They are presented here : Lemonsu, A., Viguié, V., Daniel, M., Masson, V., 2015. Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in Paris (France). Urban Climate 14, 586–605.<br> - D stands for "dense development"<br> - F for business as usual scenario ("fil de l'eau" in French)<br> - V for a scenario with 10% more parks<br> - the third letter represents the building evolution scenario<br> - F stands for business as usual scenario<br> - V for a scenario with more insulation and reflective roofs<br> - the third letter represents AC use<br> - F stands for strong AC use<br> - M for moderate AC use<br> - N for no AC use<br> - the fourth letter represents vegetation watering<br> - N stands for no watering<br> - A for watering</p> <p><strong>Heat waves</strong><br> - the figure (e.g. "34" in "HW34") represents the intensity class, in °C of the heat wave. It is more precisely the maximum daily temperature observed without the impact of the urban heat island effect. (Tmax=34, 38, 42, or 46°C).</p> <p><strong>Other information</strong><br> - see the file "aggregated data.xls" for more information and data about energy consumption for AC, and averages of temperatures in the city over the entire day.</p> <p> </p>
Multi-disciplinary survey data for the assessment of regulating and recreational ecosystem services in urban parks under heat and drought conditions
<p>The research group GreenEquityHEALTH provides quantified knowledge on how urban green spaces contribute to the mitigation of climate change induced challenges and challenges from urbanization to improve health, well-being and environmental justice. The project identifies the mediating pathways or direct effects of divers urban green spaces that act to either promote health, encourage healthy behaviours like social interaction or physical activity, or to decrease risk factors such as air pollution or urban heat.</p> <p>Here we present core data of our interdisciplinary multi-method campaigns that included in-situ stationary and aerial (remote sensing-based) environmental measurements, mobile air quality measurements, and social science-informed surveys, namely, park vistor observations and countings and a questionnaire survey.</p> <p><strong>List of data and content</strong></p> <ul> <li>Aarial_survey: digital surface model, orthophoto and thermal infrared images as raster files (*.tif); flight and processing report (*.pdf)</li> <li>Air_quality: stationary PM measurement data (*.csv); coordinates (*.txt)</li> <li>Meteorology: stationary air temperature and humidity data (*.csv), sensor meta data (*.csv)</li> <li>ParkVisitor_Surveys: survey data and questionnaire replies (*.csv), survey sheets (*.docx), questionnaire form (*.pdf)</li> </ul> <p><strong>Data acquisition and processing</strong></p> <p>For details on the data (e.g. sensors, calibration, survey settings) please refer to the linked publication, incl. Supplementary Material.</p> <p><strong>Acknowledgments</strong><br> We would like to thank the City of Leipzig, Department for Urban Green and Waters, for supporting the project. We would like to thank Henrique Miguel Pereira (Head of Research Group Biodiversity Conservation of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig) for providing equipment for the meteorological field campaigns. We also thank Anhalt University of Applied Sciences, Institute of Geoinformation and Surveying with Lutz Bannehr for conducting the airborne campaigns, Marco Pohle and Helko Kotas (both Helmholtz Centre for Environmental Research - UFZ) for technical support, and Judith Rakowski for support during the field surveys. This work was carried out within the research project ‘Environmental-health Interactions in Cities<br> (GreenEquityHEALTH) – Challenges for Human Wellbeing under Global Changes’ (2017 to 2022) funded by the German Federal Ministry of Education and Research (BMBF), funding code: 01LN1705A.</p> <p><strong>Related publication</strong><br> Kabisch, N. et al. (2021). A methodological framework for the assessment of regulating and recreational ecosystem services in urban parks under heat and drought conditions. <em>Ecosystems and People</em>. <a href="https://doi.org/10.1080/26395916.2021.1958062">doi:10.1080/26395916.2021.1958062</a></p>
Exploring the Relationship Between Land Cover Classifications and Urban Heat Island Intensity
<p>This dataset is a collection of data and results from a research project conducted by NASA SEES Interns. The research project aimed to study urban heat islands and their relationship with land cover observations. This dataset upload consists of 12 files. One file is a poster pdf that includes all the information needed about the project. The other 11 files are png images of heatmaps, bar graphs, scatter plots, and tables used in the analysis of our project. For quick reference, the abstract to this project is below:</p> <p><strong>The urban heat island (UHI) effect refers to the phenomenon in which urban areas experience higher temperatures compared to their rural counterparts. This research aims to quantify and examine the UHI effect within three areas of interest (AOIs) by utilizing LANDSAT imagery. In addition, this study seeks to explore the relationship between land cover classifications, which represent the most green (rural) and the most urban areas, and the intensity of the UHI effect. To achieve this, temperature data from local weather stations are analyzed, and statistical methods are employed to determine whether a correlation exists between the difference in land cover classifications and the intensity of the UHI effect, as determined by the average temperature difference between urban and rural areas. Google Earth Engine is used to visualize LANDSAT data from 2013 to 2022 in the months of July and August for each AOI. Subsequently, the data is compared with the land cover classifications from Collect Earth Online using statistical models in Microsoft Excel. These tools were used to take data from three pre-selected areas of interest in GLOBE Observer. The data findings from this analysis suggest that the more tree cover and rural an area is according to our classification method, the lower the UHI intensity. On the other hand, the higher the urban area, the higher the UHI intensity. By beginning this research, we have reinforced the validity of land cover classifications, and we now have the capability to generally predict the UHI intensity of locations based on their classifications. Overall, this investigation aims to contribute to a better understanding of the GLOBE land cover classifications and their potential indications of UHI intensity.</strong></p>
Urban Heat: Forward-Looking Climate Modeling for Nis, Serbia
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for <strong>present-day and future conditions</strong> under selected climate scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>). The study domain focuses on Nis, Serbia.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>150 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32634</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The PNG files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Nis.docx</li> </ul>
Impact of urban heat islands on human mortality risk in European cities
<p>These data contain estimates of temperature-related human mortality, as well as the associated economic assessments, related to urban heat islands for 85 European cities over the years 2015-2017. They are based on temperature-mortality relationships from Masselot et al. 2023 and 100m resolution UrbClim urban climate model simulations of near-surface air temperature (De Ridder et al. 2015, Hooyberghs et al. 2019), re-gridded to 500m resolution.</p> <p> </p> <p>Details of the methodology are provided in the associated paper:</p> <p>Huang, W.T.K. et al. Economic valuation of temperature-related mortality attributed to urban heat islands in European cities. <em>Nat Commun</em> <strong>14</strong>, 7438 (2023). <a href="https://doi.org/10.1038/s41467-023-43135-z">https://doi.org/10.1038/s41467-023-43135-z</a></p> <p>And associated core analysis code is available on GitHub at https://github.com/hkatty/Paper_UHI_mortality_Europe (doi:10.5281/zenodo.8429209). </p> <p> </p> <p> </p> <p>The content of the files are as follows:</p> <p><strong>spatial_timeseries</strong> zip files: These contain the most unprocessed attributable fraction estimates, with the exposure-response relationships applied to the modelled temperature, prior to any further processing.</p> <p><strong>uhi</strong> csv files: These are tables of the average mortality and years of life lost, as well as associated economic assessment, related to urban heat islands for each city. They are identical to Tables S4-S11 in the supplementary materials of the above paper.</p> <p><strong>spatial_maps_time_averaged_diff_from_rural.zip</strong>: Spatial maps showing the difference from the rural average for each day and grid box, then averaged over time. </p> <p><strong>data_urbanruralavg_timeseries.nc</strong>: Time series of urban and rural averages, as well as the difference between the two (i.e. the urban heat island effect).</p> <p><strong>avg_diff_from_rural_urbanrural.nc</strong>: The above timeseries file temporally aggregated.</p> <p><strong>simulated_urbanruraldiff_timeseries.zip</strong>: Time series of urban-rural difference in attributable fraction for 1000-member ensembles representing uncertainties in the exposure-response relationships as captured by Monte Carlo simulations.</p> <p><strong>simulated_urbanruraldiff_averaged.zip</strong>: The above simulated timeseries temporally aggregated.</p> <p> </p> <p><strong>Some variables explained:</strong></p> <p>fAF = forward attributable fraction (i.e. fraction of total mortality associated with a single day's temperature, cumulative over lag time)</p> <p>fAD = forward attributable deaths (i.e. equivalent to fAF but for number of deaths)</p> <p>tas = temperature</p> <p>heat_ex = average over heat extreme days (i.e. the warmest 2% days in 2015-2017 for the city)</p> <p>cold_ex = average over cold extreme days (i.e. as heat_ex but for the coldest 2% days)</p> <p>heat = average over days warmer than the age-dependent optimal temperature</p> <p>cold = average over days colder than the age-dependent optimal temperature</p> <p>heat_count = number of days warmer than the optimal for the age group, note that for combined 2085.1 and 2085.5 age groups, days are counted if it is considered warm for at least one age group (therefore heat_count + cold_count ≠ total days over period)</p> <p>cold_count = number of days colder than the optimal for the age group</p> <p>rural = rural average</p> <p>imd = land imperviousness</p> <p>popden = population density</p> <p>age groups: </p> <p>20 = 20 to 44<br>45 = 45 to 64<br>65 = 65 to 74<br>75 = 75 to 84<br>85 = 85 and over<br>2085.1 = all above age groups combined, weighted by the local population age structure<br>2085.5 = all above age groups combined, weighted by the 2013 European standard population age structure</p> <p> </p> <p>References:</p> <p>De Ridder, K., Lauwaet, D., and Maiheu, B., (2015): UrbClim – A fast urban boundary layer climate model. Urban Climate, 12, 21–48. <a href="https://doi.org/10.1016/J.UCLIM.2015.01.001">https://doi.org/10.1016/J.UCLIM.2015.01.001</a>.</p> <p>Hooyberghs, H., Berckmans, J., Lauwaet, D., Lefebre, F., and De Ridder, K., (2019): Climate variables for cities in Europe from 2008 to 2017. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). <a href="https://doi.org/10.24381/cds.c6459d3a">https://doi.org/10.24381/cds.c6459d3a</a>.</p> <p>Masselot et al. (2023): Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health, <a href="https://doi.org/10.1016/S2542-5196(23)00023-2">https://doi.org/10.1016/S2542-5196(23)00023-2</a>. </p>
Data from: Physiological thermal limits predict differential responses of bees to urban heat-island effects
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Urban heat island conditions experienced by the Western black widow spider (Latrodectus hesperus): extreme heat slows development but results in behavioral accommodations
Herein lies data on urban h eat island conditions for black widow spiders across the CAP study area. It also includes data on the development speed and behavioral responses of spiders reared in the lab at these UHI temperatures. The urban heat island (UHI) effect describes the capture of heat by built structures (e.g. asphalt), resulting in elevated urban temperatures. The UHI is a well-studied phenomenon, but only a handful of studies have investigated trait-based shifts resulting from the UHI, and even fewer have attempted to quantify the magnitude of the UHI experienced at the microclimate scale. Here, using a common urban exploiter, the Western black widow spider (Latrodectus hesperus), we show that the UHI experienced by spiders in July in their urban Phoenix, AZ refuges is 6 degrees C hotter (33 degrees C) than conditions in the refuges of spiders from Sonoran Desert habitat outside of Phoenix-area development (27 degrees C). We then use this field microclimate UHI estimate to compare the development speed, mass gain and mortality of replicate siblings from 36 urban lineages reared at temperatures that reflect urban and desert habitats. We show that extreme heat is slowing the growth of spiderlings and increasing mortality. In contrast, we show that development of male spiders to their penultimate moult is accelerated by 2 weeks. Lastly, in terms of behavioral shifts, UHI temperatures caused late-stage juvenile male spiders to heighten their foraging voracity and late-stage juvenile female spiders to curtail their web-building behavior.
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Berlin, Germany
<p><strong>Berlin heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045).</strong></p> <p>Heat stress exposure maps for Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p><strong>Scenario: </strong>Urban planning scenario (situation LULC today + integrated urban planning projects 2030)</p> <p><strong>Exposure mapping variable: </strong><br /> Total population 2030<br /> Population density inhabitants per hectare 2030</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Berlin, Germany
<p>Heat stress maps for Berlin representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p>Scenario: Urban Planning</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Antwerp, Belgium
<p>Heat stress maps for Antwerp representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Almada, Portugal
<p>Heat stress maps for the city of Almada representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p>
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Understand access before you commit
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.