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3,709 results for “urbanization.”
A new perspective on evaluating high-resolution urban climate simulation with urban canopy parameters
<p>A new perspective on evaluating high-resolution urban climate simulation with urban canopy parameters</p>
2003 - Joint Urban 2003 - Oklahoma City - DATA -- Indiana University - Grimmond Urban Micromet Group
<p>Indiana University (IUB)/King's College London (KCL)/University of Reading <strong>Gr</strong><strong>immond Urban Micromet Group </strong></p> <p>Year:<strong>2003</strong></p> <p>City: <strong>Oklahoma City</strong> (OKC)</p> <p>Data Levels: multiple</p> <p>Sites: muliple</p> <p>Key Organizations: <strong>Joint Urban 2003</strong></p> <p>Links:</p> <ul> <li>https://zenodo.org/uploads/4244381 - all data</li> <li>https://zenodo.org/records/4244800 - 2004_AMS_Grimmond_OKC_JU2003.pdf -- metadata document</li> <li>https://zenodo.org/records/15660006 [ 2003-OKC-FromBenDir.zip]</li> </ul> <p>OKCweb - - gives the details of the data and the sites </p> <p>10.5281/zenodo.4244800</p> <p> </p> <p>Grimmond CSB, HB. Su., B Offerle, B Crawford, S Scott, S Zhong & C Clements 2004: Variability of sensible heat fluxes in a suburban area of Oklahoma City. 84th Annual Meeting AMS, Eighth Symposium on integrated observing & assimilation systems in the atmosphere, oceans & land surface & the Symposium on Planning, Nowcasting, & Forecasting in the Urban Zone. Seattle, January 11-15, 2004. [J7.2] 8pp CD https://ams.confex.com/ams/84Annual/webprogram/Paper67542.html</p> <p>https://ams.confex.com/ams/pdfpapers/67542.pdf</p> <p>This research was supported by DTRA Joint Urban 2003 and NSF BCS-0095284. Equipment was provided by Indiana University (Grimmond and Schmid/Grimmond), University of Houston (Zhong) and PNL (Allwine). Sites were provided by Tyler Media, Jim and Nadine Evans, Mary Harrison, South East High School, Oklahoma School District, Cojac Portable Buildings Company, John Griffin. Jim and Nadine Evans and Mary Harrison provided electricity to run the BH, WH, and GR sites. Field assistance was provided by S.B. Kim and S.T.Kim (University of Houston). Jerry Allwine provided significant support for the installation and removal of the instruments on to the TM tower. Joe Shinn and Marty Leach (LLNL) helped secure permission to use many of these sites.</p> <p> </p>
Dataset accompanying Korányi et al. 2021 Urban Ecosystems
<p>Dataset containing species abundance of birds and environmental background data and Google Earth file showing bird survey points. For details please see the original publication:<br> Korányi, D., Gallé, R., Donkó, B., Chamberlain, D.E. & Batáry, P. (2021). Urbanization does not affect green space bird species richness in a mid-sized city. Urban Ecosystems. DOI: 10.1007/s11252-020-01083-2</p>
Data from: Microbial diversity in the floral nectar of Linaria vulgaris along an urbanization gradient
Background: Microbes are common inhabitants of floral nectar and are capable of influencing plant-pollinator interactions. All studies so far investigated microbial communities in floral nectar in plant populations that were located in natural environments, but nothing is known about these communities in nectar of plants inhabiting urban environments. However, at least some microbes are vectored into floral nectar by pollinators, and because urbanization can have a profound impact on pollinator communities and plant-pollinator interactions, it can be expected that it affects nectar microbes as well. To test this hypothesis, we related microbial diversity in floral nectar to the degree of urbanization in the late-flowering plant Linaria vulgaris. Floral nectar was collected from twenty populations along an urbanization gradient and culturable bacteria and yeasts were isolated and identified by partially sequencing the genes coding for small and large ribosome subunits, respectively. Results: A total of seven yeast and 13 bacterial operational taxonomic units (OTUs) were found at 3 and 1 % sequence dissimilarity cut-offs, respectively. In agreement with previous studies, Metschnikowia reukaufii and M. gruessi were the main yeast constituents of nectar yeast communities, whereas Acinetobacter nectaris and Rosenbergiella epipactidis were the most frequently found bacterial species. Microbial incidence was high and did not change along the investigated urbanization gradient. However, microbial communities showed a nested subset structure, indicating that species-poor communities were a subset of species-rich communities. Conclusions: The level of urbanization was putatively identified as an important driver of nestedness, suggesting that environmental changes related to urbanization may impact microbial communities in floral nectar of plants growing in urban environments.
Data from: A worldwide model for boundaries of urban settlements
The shape of urban settlements plays a fundamental role in their sustainable planning. Properly defining the boundaries of cities is challenging and remains an open problem in the science of cities. Here, we propose a worldwide model to define urban settlements beyond their administrative boundaries through a bottom-up approach that takes into account geographical biases intrinsically associated with most societies around the world, and reflected in their different regional growing dynamics. The generality of the model allows one to study the scaling laws of cities at all geographical levels: countries, continents and the entire world. Our definition of cities is robust and holds to one of the most famous results in social sciences: Zipf's law. According to our results, the largest cities in the world are not in line with what was recently reported by the United Nations. For example, we find that the largest city in the world is an agglomeration of several small settlements close to each other, connecting three large settlements: Alexandria, Cairo and Luxor. Our definition of cities opens the doors to the study of the economy of cities in a systematic way independently of arbitrary definitions that employ administrative boundaries.
The genomic response to urbanization in the damselfly Ischnura elegans
Open the record for dataset details and reuse information.
Interpretable Deep Convolutional Neural Network-based Surrogates for Complex Urban Hydrodynamic Modeling using Random Chaotic Rainfall
<p>This data is part of the article "Interpretable Deep Convolutional Neural Network-based Surrogates for Complex Urban Hydrodynamic Modeling using. Random Chaotic Rainfall "data for model training and validation, as well as a dynamic runoff range generated by DHMUrban</p>
Unexpected levels of antibiotic resistance in marine bacterial communities not solely explained by urban river discharge
<p>This data and the subsequent code were used in the manuscript "Unexpected levels of antibiotic resistance in marine bacterial communities not solely explained by urban river discharge". Any questions and comments about the data or analysis should be forward to Brandon Chan, lead author.</p>
Data for: "The influence of urban and agricultural landscape context on forest diversity and structure across ecoregions "
<p>This is the data associated with "The influence of urban and agricultural landscape context on forest diversity and structure across ecoregions" by JP Schmit, LR Johnson, M Baker, L Darling, R Fahey, DH Locke, AT Morzillo, NF Sonti, TLE Trammell, MFJ Aronson, and ML Johnson, published in Ecosphere in 2025.</p>
A Multi-Centric Novel Coronavirus (COVID-19) Population-Based Age-Stratified Sero-Epidemiological Study (In Urban, Rural and Tribal Communities at Selected Centers in India: A Prospective Study)
<p>Results of population-based age stratified seroepidemiological investigation in India</p>
Urban Green Raster Germany 2018
<p><strong>Abstract</strong></p> <p>The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset.<br> The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation).<br> The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.</p> <p><strong>Data acquisition</strong></p> <p>The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.</p> <p><strong>Data collection</strong></p> <p>Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).</p> <p>The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).</p> <p>The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.</p> <p>GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.</p> <p><strong>Value of the data</strong></p> <p>The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].</p> <p>Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].</p> <p>Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).</p> <p><strong>Data description</strong></p> <p>A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).</p> <p>A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.</p> <p><strong>Experimental design, materials and methods</strong></p> <p>The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].</p> <p>Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.</p> <p>To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.</p> <p>Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.</p> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).</p> <p><strong>References</strong></p> <p>[1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: <a href="https://ec.europa.eu/eurostat/web/lucas/data/database">https://ec.europa.eu/eurostat/web/lucas/data/database</a></p> <p>[2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32</p> <p>[3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39</p> <p>[4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: <a href="https://doi.org/10.1016/j.jag.2020.102065">https://doi.org/10.1016/j.jag.2020.102065</a></p> <p>[5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030</p> <p>[6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687</p>
Bird habitat preference in urban environments
<p>We provide three data sets that were used to estimate bird habitat preference in an urban environment.<br> The Phi-bird preferences and Logistic-bird preference data sets contain the necessary information to estimate bird habitat preference via the Phi association coefficient and logistic regression, respectively. Additionally, the estimates obtained with each approach are presented. In the Phi-bird preferences data set, the bird records collected from unstructured citizen science data and structured scientific information are shown. <br> Finally, the Validation data set presents all the information used to validate the reliability of both, the Phi association coefficient and the Logistic regression methods</p>
Datasets used in: Modelling eye-level visibility of urban green space: Optimising city-wide point-based viewshed computations through prototyping
<p>Research data supporting our publication. Full workflows using the R programming language have been provided on <a href="https://github.com/STBrinkmann/protoVS">GitHub</a>. Here we provide external data that has been used for our research, as well as the resulting Viewshed Greenness Visibility Index (VGVI) raster.</p> <p><strong>Datasets</strong></p> <p>Digital Terrain Model (DTM):</p> <ul> <li>Spatial Resolution: 1 m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DTM_1m.tif<br> </li> </ul> <p>Digital Surface Model (DSM):</p> <ul> <li>Spatial Resolution: 1 m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DSM_1m.tif<br> </li> </ul> <p>Landuse</p> <ul> <li>Spatial Resolution: 2 m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_LULC_2m.tif<br> </li> </ul> <p>VGVI map</p> <ul> <li>Spatial Resolution: 5 m</li> <li>Source: Resulting dataset from our analysis</li> <li>Licence: MIT License</li> <li>File name: vgvi_van.tif</li> </ul>
Fig. 1 in Variation In Blackbird, Turdus Merula (Passeriformes, Turdidae), Nest Characteristics In Urban And Suburban Localities In Crimea
Fig. 1. The relation between external nest diameter and nest placement height (N = 27).
Fig. 9 in Breeding Bird Assemblage In A Mosaic Of Urbanized Habitats In A Cenral European City
Fig. 9. Distribution of occupied territories of Luscinia megarhynchos.
Data from: Hierarchical multi-taxa models inform riparian vs. hydrologic restoration of urban streams in a permeable landscape
The degradation of streams caused by urbanization tends to follow predictable patterns; however, there is a growing appreciation for heterogeneity in stream response to urbanization due to the local geoclimatic context. Furthermore, there is building evidence that streams in mildly sloped, permeable landscapes respond uncharacteristically to urban stress calling for a more nuanced approach to restoration. We evaluated the relative influence of local-scale riparian characteristics and catchment-scale imperviousness on the macroinvertebrate assemblages of streams in the flat, permeable urban landscape of Perth, Western Australia. Using a hierarchical multi-taxa model, we predicted the outcomes of stylized stream restoration strategies to increase the riparian integrity at the local scale or decrease the influences of imperviousness at the catchment scale. In the urban streams of Perth, we show that local-scale riparian restoration can influence the structure of macroinvertebrate assemblages to a greater degree than managing the influences of catchment-scale imperviousness. We also observed an interaction between the effect of riparian integrity and imperviousness such that the effect of increased riparian integrity was enhanced at lower levels of catchment imperviousness. This study represents one of few conducted in flat, permeable landscapes and the first aimed at informing urban stream restoration in Perth, adding to the growing appreciation for heterogeneity of the Urban Stream Syndrome and its importance for urban stream restoration.
Urban Tunnel Cameras (UTCam)
<p>This dataset contains recordings from french road tunnels and their associated flow rate (Q), occupancy (T) and velocity (V) values.</p> <p>Recordings were collected over two days at various hours on five different cameras. An approximate 10,000 datatpoints are available for a total of 57 hours of recordings.</p> <p>Each datapoint is composed of a RGB recording, its associated motion vector frames (as generated by the MPEG4 part2 encoding) and the associated flow rate (Q), occupancy (T) and velocity (V) values.</p>
Figure 1 from: Argañaraz CI, Gleiser RM (2017) Does urbanization have positive or negative effects on Crab spider (Araneae: Thomisidae) diversity? Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19987
Figure 1 - Location of sampling sites in Córdoba city, Argentina.
GIS integration for spatial analysis of urban expansion in Tisaleo canton (Ecuador).
<p><span>Political or economic interests have led to an empirical zonal delimitation, and the deficient use of Geographic Information Systems does not allow the specific characterisation of the territories. This article aims to determine the spatial analysis of the urban expansion areas of the canton of Tisaleo by integrating Geographic Information Systems, using a quantitative research method. The main components analysed are: the situational diagnosis of the canton through the characterisation of its biophysical component, its human settlements and the public system of facilities and social services that support it. Based on this information, an interrelation of the data collected is carried out to complement a spatial analysis with GIS software. The results show that in the canton of Tisaleo there are areas suitable for urban growth, but their limits are overdimensioned, i.e. they include areas that do not have the aptitude to be urbanised, as well as rural areas that have the characteristics to be considered urban.</span></p>
Figure 5. Per capita water footprint between 2004-2019 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 5. Per capita water footprint between 2004-2019 in Van province.
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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.