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Dataset results
19 results for “Urban infrastructures”
Evaluation of stormwater urban ecological infrastructure in Phoenix, Arizona (USA): a case study of a small-scale bioretention basin system
In 2017, Arizona State University finished construction on a pedestrian mall central to its Tempe campus, which included a small-scale bioretention basin system for stormwater management. This study analyzed the flood control and water quality improvement performance of the small-scale bioretention basin system in the Phoenix Metropolitan Area, AZ USA. Flood control efficacy was quantified by calculating discharge from the basin system using water level loggers and measuring soil moisture levels using soil moisture probes. Stormwater runoff samples were collected for twenty-one storm events and analyzed for nitrogen and phosphorus constituent concentrations. Nutrient concentrations at the system inflow and outflow were used to determine percent change in concentration. Water quality improvement performance was compared to results from previous studies on bioretention basin system performance. These data were used to create relevant graphical figures. Results were obtained by performing statistical analysis calculations on the data measurements. The results indicated that the bioretention basin system performed adequately for flood control and water quality improvement, supporting the use of stormwater urban infrastructure systems in arid and semi-arid climates. Further research can reveal how these systems may perform during more severe storm events and offer improvements for future designs.
Urban Ecological Infrastructure (UEI) in the greater Phoenix, Arizona metropolitan area and surrounding Sonoran desert region (2010-2017)
Urban ecological infrastructure (UEI) encompasses all infrastructure in a city that supports ecological structure and function, and by extension, provides ecosystem services to urban residents and is a broad, all-encompassing concept for "nature in cities". This idea includes commonly recognized forms of infrastructure, such as parks, residential yards, community gardens, lakes and rivers, and street trees. But UEI also includes less recognized forms, such as vacant lots, agricultural fields, canals, and water retention basins. Despite being widely recognized as important to urban landscapes, the wide variety, and various forms of urban ecological infrastructure are rarely documented in a single source. To address this, we consolidated various aquatic, terrestrial, and wetland UEI throughout the Phoenix Metropolitan area so researchers can incorporate this UEI into project designs and models. Since people’s perceptions of UEI differ not only by the three broad classifications but also by the individual characteristics of UEI, each feature is classified not only as aquatic, terrestrial, or wetlands but also given on of fifteen unique classifications. Incorporation of UEI into both planning and research design can promote practices that increase both biodiversity and human well-being while also possibly limiting negative landscape perceptions.
Data on public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki
<p>A public participatory GIS -survey dataset detailing public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki, Finland.</p>
Dateset on 'Disentangling associations of human wellbeing with green infrastructure, degree of urbanity, and social factors around an Asian megacity'
<p>The data was collected a part of the baseline survey on household socio-economics among the Bengalurian along the rural-urban interface. </p>
Data on public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland
<p>A public participatory GIS -survey dataset detailing public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland.</p>
The impact of small-scale green infrastructure on the affective wellbeing associated with urban sites
<p>The database contains participants' reported affective perceptions of 18 images of street images with different levels of green coverage.</p>
Ecohydrological monitoring of urban ecological infrastructure at the Arizona State University Tempe Campus (Tempe, Arizona)
Urban Ecological Infrastructure (UEI) are urban ecological structures whose ecological functions are increasingly being used to deliver key urban services and address urban sustainability challenges. The growing use of UEI to address urban sustainability challenges can bring together teams of urban researchers and practitioners to co-produce UEI design, monitoring and maintenance. However, this co-production process received little attention in the literature, and has not been studied in the Phoenix Metro Area. This study examined several components of a co-produced design process and related project outcomes associated with a small-scale UEI project – bioretention basins installed at the Arizona State University (ASU) Orange Mall and Student Pavilion in Tempe, AZ. We explored the design process associated with the collaborative development of an ecohydrological monitoring protocol for assessment of post-construction performance of this UEI system. This co-production process involved both CAP LTER researchers and practitioners, designers, and managers at ASU and associated third-party consultants involved in site design and management. Together, these researchers and practitioners co-produced a suite of ecohydrological metrics to monitor the performance of the bioretention basins at Orange Mall. Specifically, this protocol evaluated the performance of the UEI system with regards to two of it’s key design goals: storm water capture and storm water quality improvement. The ecohydrological data produced by the implementation of this monitoring protocol are presented here. Monitoring equipment were installed throughout the site in June and July 2018. Site monitoring and data collection began in August 2018, and continued through Feb 2019.
Dataset from publication "Predicting context-sensitive urban green space quality to support urban green infrastructure planning"
<p><strong>Data Description:</strong></p><p>This dataset presents the spatial outcome of an analysis modelling perceived green space quality across the city of Espoo, Finland. The analysis relies on data gathered through the My Espoo on the Map survey (<i>Mun Espoo kartalla</i>) in the autumn of 2020 as part of the NordForsk-funded research project NORDGREEN. A comprehensive account of the analytical process and potential applications of the dataset is available in the associated publication, "<i>Predicting context-sensitive urban green space quality to support urban green infrastructure planning</i>" (open access: <a href="https://doi.org/10.1016/j.landurbplan.2023.104952">https://doi.org/10.1016/j.landurbplan.2023.104952</a>).</p><p><strong>Data Processing:</strong></p><p>This dataset results from an analysis that integrates both primary and secondary sources of geospatial data. The primary data were collected with an online public participation GIS (PPGIS) survey directed for the adult inhabitants of Espoo. The data collection took place in September-October 2020 and was executed in collaboration with Aalto University and the City of Espoo. For a detailed overview of the data collection process, please refer to the related publication.</p><p><strong>Data characteristics:</strong></p><p>Format: Shapefile (50m x 50m grid)</p><p>Geographical area: Espoo, Finland</p><p>Spatial reference: EUREF FIN TM35FIN</p><p>Note: Only grid cells intersecting with greenspace have been included in the dataset. For the employed definition of green areas, please consult the related publication.</p><p><strong>Data attributes and their descriptions:</strong></p><p><strong>"</strong><i>P_PROB"</i>: Probability (P), positive perceived quality</p><p><i>"N_PROB"</i>: Probability (P), negative perceived quality</p><p><strong>Funding: </strong></p><p>This research was funded by NordForsk, Sustainable Urban Development and Smart Cities Programme, Project Smart Planning for Healthy and Green and Nordic Cities – NORDGREEN, under Grant Number: 95322.</p>
Supporting Data for "Local exposure misclassification in national models: relationships with urban infrastructure and demographics"
<p><strong>Overview:</strong> This dataset accompanies the recent publication "Local exposure misclassification in national models: relationships with urban infrastructure and demographics" (DOI: 10.1038/s41370-023-00624-z). It provides essential data for replicating and extending the analysis conducted in the study. The script for the analysis is available at https://github.com/SEChambliss-AQ/LD-analysis. The dataset consists of four key files.</p> <p><strong>Files Included:</strong></p> <ol> <li> <p><strong>Gridded OSM and GSV Data (gridded_OSM_GSV.RDS):</strong></p> <ul> <li>This R object offers a 100mx100m grid covering select neighborhoods in the San Francisco Bay Area.</li> <li>Each grid cell includes average air pollution levels (Ultrafine Particle Count in thousand count per cubic meter; Nitrogen Dioxide in ppb) from mobile monitoring.</li> <li>The file also provides normalized z-scores of local density of urban infrastructure related to air pollutants based on OpenStreetMap (OSM) data.</li> <li>Further details can be found in the associated publication.</li> </ul> </li> <li> <p><strong>BartMachine Outputs (bartMachine R objects.zip):</strong></p> <ul> <li>A collection of R object outputs from the bartMachine package, an R-Java Bayesian Additive Regression Trees implementation.</li> <li>These objects, which can be regenerated using the provided script, are included to reduce computational requirements for future analyses.</li> <li>The script for generating these outputs is available at the above <a href="https://github.com/SEChambliss-AQ/LD-analysis">GitHub Repository</a>.</li> </ul> </li> <li> <p><strong>NO2 Predictions (CACES_criteria.csv):</strong></p> <ul> <li>Land Use Regression (LUR) data for Nitrogen Dioxide, provided by the Center for Air, Climate, and Energy Solutions.</li> <li>Methodologies for integrating these data with the gridded dataset are described in the publication and script.</li> </ul> </li> <li> <p><strong>UFP Predictions (CACES_UFP.csv):</strong></p> <ul> <li>Similar to the NO2 dataset, this file contains LUR data for Ultrafine Particle predictions.</li> <li>Methods for data integration are detailed in the publication and available script.</li> </ul> </li> </ol> <p><strong>Usage:</strong> These files are intended for researchers and analysts aiming to replicate or build upon the study's findings. They provide a source of data for exploring the complex interplay between air pollution, urban infrastructure, and demographic factors in urban environments. For detailed methodology and analysis, refer to the original publication and the accompanying GitHub repository.</p>
Data from: neglected puzzle pieces of urban green infrastructure: richness, cover, and composition of insect-pollinated plants in traffic-related green spaces
<p>Insect-pollinated vascular plants in spontaneous vegetation provide essential ecosystem services and benefit wildlife. However, floral communities associated with traffic-related green spaces are rarely considered valuable elements of urban green infrastructure (UGI). The dataset contains information on species-based floral communities of vascular insect-pollinated plants in traffic-related green spaces in three highly populated Finnish cities. Those are Helsinki (665 558 inhabitants), Tampere (244 029 inhabitants), and Turku (175 645 inhabitants). Data were collected during the mean flowering phenophase of vascular plants in July-August 2022 from two types of locations: (i) urban (city centers) and (ii) suburban (city outskirts), and from three types of traffic-related green spaces: (i) traffic islands, (ii) parking lots, (iii) road verges. The dataset contains information for the 93 vascular insect-pollinated plant species flowering during the survey. Sampling campaign was conducted in 90 sampling sites, and the dataset contains information on the location coordinates. In addition, the dataset possesses information on the amount of garbage pieces (cigarette filters, plastic boxes, or scraps) revealed for each sampling point in traffic-related green spaces.</p>
Urban Transportation Infrastructure and Cyclist and Pedestrian Safety
<p>The goal of this project was to perform a comprehensive evaluation of crash causes and risk factors to identify the root causes of crashes involving bicyclists and pedestrians in San Antonio, TX. The research included the development of a database of bicycle and pedestrian crash reports in the target area, calculation of crash counts and rates, identifying road segments and intersections with highly concentrated bicycle and pedestrian crashes, and the development of effective safety countermeasures. Several variables and factors were analyzed, including driver characteristics such as age and gender, road-related factors, and environmental factors such as weather conditions and time of the day. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian/bicyclist crashes. Geospatial analysis was used to investigate crash frequency and severity. High-risk locations were identified through heat maps and hotspot analysis. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. The strongest predictors of severe injury include lighting condition, road class, road speed limit, traffic control, collision type, and the age and gender of the pedestrian/bicyclist. Fatal and incapacitating injury risk increased substantially when the pedestrian/bicyclist was at fault. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, and raised medians, and the use of leading pedestrian/bicyclist interval and hybrid beacons are recommended.</p>
Vehkaoja etal Infrastructure influences urban smooth newts
<p>Dataset contains information on smooth newts in metropolitan Helsinki, Finland. The file also contains land use information obtained through GIS analysis.</p>
Data on youth understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki
<p>A workshop survey -dataset detailing youth understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki, Finland.</p>
BQE WIM Data Year 4 Project (Implementation and Effectiveness of Autonomous Enforcement of Overweight Trucks in an Urban Infrastructure Environment)
<p>BQE (Brooklyn-Queens Expressway) WIM Data for QB (Queens Bound) and SIB (Staten Island Bound)</p>
Data from: neglected puzzle pieces of urban green infrastructure: richness, cover, and composition of insect-pollinated plants in traffic-related green spaces
Open the record for dataset details and reuse information.
Infrastructure and the ethnographic-cartographic production of urban bird species richness
<p>The qualitative bio-geographies of human geographers and the quantitative mappings of biogeographers share a goal: how to understand living with non-human life. Yet they rarely bridge the conceptual and methodological gap between them. This paper theorizes how the concept of infrastructure can bridge this ethnographic-cartographic divide. Infrastructure is not just an inanimate shell. It is also a system of relation, a dynamic patterning of socionatural form emerging out of the experiences and affective moments of its constituents. As a proof of concept, we quantified and compared urban bird species richness and frequency for Tallahassee, Florida over a 17-year period (2000–2017) for two co-occurring observational infrastructures, eBird and a wildlife rehabilitation center that serves the city. Species common to both infrastructures comprised 94% of all eBird observations and 99% of all rehab records. Their differences reflected contrasts in how the motivations for experiencing birds intersected with bird habitat preferences, behavior, and contingencies of urban history and development. eBird observations had high species richness (295 spp) and reflected the growing popularity among birds and a small number of active birders for visiting stormwater retention lakes recently modified to improve bird habitat. Rehabilitation records had a lower richness (194 spp) and exhibited a much more even distribution of bird encounters among individual residents as well as community institutions like schools, universities, law enforcement, and other government organizations. Infrastructural perspectives convey how affective and individualistic encounters with non-humans can link to emergent biogeographic mappings and how urban biodiversity is relationally and heterogeneously produced rather than simply contained in cities. </p>
Infrastructure and the ethnographic-cartographic production of urban bird species richness
Open the record for dataset details and reuse information.
CoUDlabs_WP8_T811_UDC_001 Analysis and assessment of new techniques to build-up the topography/geometry of Urban Drainage infrastructure with high resolution
<p>The quality of the results obtained in hydraulic numerical models is strongly conditioned by the accuracy of the input data, especially in the case of shallow waters such as those occurring in urban floods. In addition, urban catchments have kerbs and other urban elements, such as ditches or grates, that condition the flow of the runoff generated during an event. This also occurs in the case of laboratory studies in large-scale facilities, with traditional elevation measurement techniques being too time-consuming to obtain not very high-resolution topographies. Therefore, this dataset includes data from an analysis and assessment of three different devices and techniques to obtain accurate elevation maps with a high resolution: i) conventional camera with the Structure from Motion (SfM) photogrammetric technique, ii) Intel® RealSense™ LiDAR Camera L515 and iii) Depth camera Intel d435i. The measurements from cameras ii) and iii) were combined with a 3D reconstruction software. Finally, a topographic manual survey was used as a base for coordinate referencing and elevation comparison. Surveys data, including the images used during the SfM survey, and resulted DEM were included in this dataset. </p><p>This dataset is a result from the Joint Research Activity 3 (WP8, Improving Resilience and Sustainability in Urban Drainage solutions), Task 8.1.1. (Development of Scalable Hydrodynamic Performance Protocols.) within Co-UDlabs project, funded under the European Union's Horizon 2020 research and innovation programme under grant agreement No 101008626.</p>
Dataset on 'Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India'
<p>This dataset includes the raw data of a survey of 649 residents of 61 villages along a rural-urban gradient in Bengaluru, India. It presents socio-demographic characteristics (village, village rural-urban state, age, gender, level of education, caste, and sources of household income) and Likert-scale answers (ranging from 1, i.e., not important to 5, i.e., very important) to rank the perceived importance of ecosystem services and disservice from five types of small-scale green infrastructure: Domestic trees (DT), Farm trees (FT), Street trees (ST), Platform trees (PT), Temple trees (TT).</p> <p>The complete method is described in Thapa, P., Torralba, M., Bhaskar, D., Nagendra, H., Plieninger, T. (2023): Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India. Ecosystems and People, in press.</p>
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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.