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300 results for “Urban area”
Impact of IPT With Sulfadoxin Pyrimetamin on Pregnant Women and Their Babies Outcomes in Peri-urban Areas of Bobo-Dioulasso(Burkina Faso)
ClinicalTrials.gov study NCT01255605. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Mindfulness or Brain Stimulation Intervention for Late-life Adults in Taiwan Urban and Rural Areas
ClinicalTrials.gov study NCT07186023. IPD Sharing: NO. Countries: 1. Publications: 0.
Impact of Microbe Literacy Initiative on Improved Vaccine Uptake in Peri-urban Slum Areas in Kathmandu, Nepal
ClinicalTrials.gov study NCT05378438. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Physical Performance Levels and Social Participation in Elderly Living In Urban and Rural Areas
ClinicalTrials.gov study NCT05579457. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluation of Digital Decision-support Tool for Child Nutritional Monitoring: a Protocol for Cluster Randomized Controlled Trial in Urban and Semi-Urban Areas in Indonesia
ClinicalTrials.gov study NCT07027384. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3
The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications. Users can find the paper "Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990-2015: How data choices matter" (MacManus, et al. 2021) in order to evaluate selected inputs for modeling Low Elevation Coastal Zones. According to the paper, the following are considered core data sets for the purposes of LECZv3 estimates: Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT-DEM), Global Human Settlement (GHSL) Population Grid R2019 and Degree of Urbanization Settlement Model Grid R2019a v2, and the Gridded Population of the World, Version 4 (GPWv4), Revision 11. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and the City University of New York (CUNY) Institute for Demographic Research (CIDR).
Low Elevation Coastal Zone (LECZ) Global Delta Urban-Rural Population and Land Area Estimates, Version 1
The Low Elevation Coastal Zone (LECZ) Global Delta Urban-Rural Population and Land Area Estimates, Version 1 data set provides country-level estimates of urban, quasi-urban, rural, and total population (count), land area (square kilometers), and built-up areas in river delta- and non-delta contexts for 246 statistical areas (countries and other UN-recognized territories) for the years 1990, 2000, 2014 and 2015. The population estimates are disaggregated such that compounding risk factors including elevation, settlement patterns, and delta zones can be cross-examined. The Intergovernmental Panel on Climate Change (IPCC) recently concluded that without significant adaptation and mitigation action, risk to coastal commUnities will increase at least one order of magnitude by 2100, placing people, property, and environmental resources at greater risk. Greater-risk zones were then generated: 1) the global extent of two low-elevation zones contiguous to the coast, one bounded by an upper elevation of 10m (LECZ10), and one by an upper elevation of 5m (LECZ05); 2) the extent of the world's major deltas; 3) the distribution of people and built-up area around the world; 4) the extents of urban centers around the world. The data are layered spatially, along with political and land/water boundaries, allowing the densities and quantities of population and built-up area, as well as levels of urbanization (defined as the share of population living in "urban centers") to be estimated for any country or region, both inside and outside the LECZs and deltas, and at two points in time (1990 and 2015). In using such estimates of populations living in 5m and 10m LECZs and outside of LECZs, policymakers can make informed decisions based on perceived exposure and vulnerability to potential damages from sea level rise.
Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2
The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2 data set consists of country-level estimates of urban population, rural population, total population and land area country-wide and in LECZs for years 1990, 2000, 2010, and 2100. The LECZs were derived from Shuttle Radar Topography Mission (SRTM), 3 arc-second (~90m) data which were post processed by ISciences LLC to include only elevations less than 20m contiguous to coastlines; and to supplement SRTM data in northern and southern latitudes. The population and land area statistics presented herein are summarized at the low coastal elevations of less than or equal to 1m, 3m, 5m, 7m, 9m, 10m, 12m, and 20m. Additionally, estimates are provided for elevations greater than 20m, and nationally. The spatial coverage of this data set includes 202 of the 232 countries and statistical areas delineated in the Gridded Rural-Urban Mapping Project version 1 (GRUMPv1) data set. The 30 omitted areas were not included because they were landlocked, or otherwise lacked coastal features. This data set makes use of the population inputs of GRUMPv1 allocated at 3 arc-seconds to match the SRTM elevations, and at 30 arc-seconds resolution in order to reflect uncertainty levels in the product resulting from the interplay of input population data resolutions (based on census Units) and the elevation data. Urban and rural areas are differentiated by the GRUMPv1 Urban Extents. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
Annual PM2.5 Concentrations for Countries and Urban Areas, 1998-2016
The Annual PM2.5 Concentrations for Countries and Urban Areas, 1998-2016, consists of mean concentrations of particulate matter (PM2.5) for countries and urban areas. The PM2.5 data are from the Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. The urban areas are from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02, and its time series runs from 1998 to 2016. The country averages are population-weighted such that concentrations in populated areas count more toward the country average than concentrations in less populated areas, and its time series runs from 2008 to 2015.
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Land and Geographic Unit Area Grids
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Land and Geographic Unit Area Grids measure land areas in square kilometers and the mean Unit size (population-weighted) in square kilometers. The land area grid permits the summation of areas (net of permanent ice and water) at the same resolution as the population density, count, and urban-rural grids. The mean Unit size grids provide a quantitative surface that indicates the size of the input Unit(s) from which population count and density grids are derived. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019 v1
The Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of the chemical concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and at a high resolution (1km x 1km grid cells) in non-urban areas for the years 2000 to 2019. Particulate matter with an aerodynamic diameter less than 2.5 �m (PM2.5) increases mortality and morbidity. PM2.5 is composed of a mixture of chemical components that vary across space and time. Due to limited hyperlocal data availability, less is known about health risks of PM2.5 components, their U.S.-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM2.5 mass. The national super-learned models were developed across the U.S. for hyperlocal estimation of annual mean elemental carbon, ammonium, nitrate, organic carbon, and sulfate concentrations across 3,535 urban areas at a 50m spatial resolution, and at a 1km resolution for non-urban areas from 2000 to 2019. Using Machine-Learning models (ML), combined with either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA) or Super-Learning (SL) and approximately 82 billion predictions across 20 years, hyperlocal super-learned PM2.5 components are now available for further research. The overall R-squared values of 10-fold cross validated models ranged from 0.910 to 0.970 on the training sets for these components, while on the test sets the R-squared values ranged from 0.860 to 0.960. Remarkable spatiotemporal intra-urban and inter-urban variabilities were found in PM2.5 components. The Coordinate Reference System (CRS) for predictions is the World Geodetic System 1984 (WGS84) and the Units for the PM2.5 Components are �g/m^3. The data are provided in RDS tabular format, a file format native to the R programming language, but can also be opened by other languages such as Python.
FIGURE 1 in Description of larva and pupa of Phylloicus cressae Prather 2003 (Trichoptera Calamoceratidae) from a montane forest stream in the peri-urban area of Caracas Venezuela
FIGURE 1: Phylloicus cressae, larva; head capsule. 1A, dorsal; 1B, ventral. Head chaetotaxy in dorsal view, 1A: FC1–FC6 = frontoclypeal tactile setae, A1–A2 = anterior tactile seta, L1 = lateral tactile seta, posterolateral to stemmata, S2 and S3 = stemmatal tactile seta, P1–P4 = posterior tactile seta.
Datasets for modelling direct CO2 emissions and uptake at neighborhood scale over the urban area of Beijing
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Annual Mean PM2.5 Components Trace Elements (TEs) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1
The Annual Mean PM2.5 Components Trace Elements (TEs) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of trace elements concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and a high resolution (1km x 1km grid cells) in non-urban areas, for the years 2000 to 2019. Particulate matter with an aerodynamic diameter of less than 2.5 �m (PM2.5) is a human silent killer of millions worldwide, and contains many trace elements (TEs). Understanding the relative toxicity is largely limited by the lack of data. In this work, ensembles of machine learning models were used to generate approximately 163 billion predictions estimating annual mean PM2.5 TEs, namely Bromine (Br), Calcium (Ca), Copper (Cu), Iron (Fe), Potassium (K), Nickel (Ni), Lead (Pb), Silicon (Si), Vanadium (V), and Zinc (Zn). The monitored data from approximately 600 locations were integrated with more than 160 predictors, such as time and location, satellite observations, composite predictors, meteorological covariates, and many novel land use variables using several machine learning algorithms and ensemble methods. Multiple machine-learning models were developed covering urban areas and non-urban areas. Their predictions were then ensembled using either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA), or Super-Learners. The overall best model R-squared values for the test sets ranged from 0.79 for Copper to 0.88 for Zinc in non-urban areas. In urban areas, the R-squared model values ranged from 0.80 for Copper to 0.88 for Zinc. The Coordinate Reference System (CRS) used in the predictions is the World Geodetic System 1984 (WGS84) and the Units for the PM2.5 Components TEs are ng/m^3. The data are provided in RDS tabular format, a file format native to the R programming language, but can also be opened by other languages such as Python.
Emeprical Studies on the difference between urban and rural of different areas in China
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Research on the impact of the digital economy on narrowing the gap between urban and rural areas—An empirical analysis based on provincial panel data
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Geographically Weighted Regression Modeling of a Nighttime Urban Heat Island in Dar es Salaam Metropolitan Areas
<p>Urban Heat Islands (UHI) is the urban microclimate with higher air temperature than surrounding areas. It is caused by both man-made and natural factors which vary geographically based on weather periods. To have a sustainable future in the environment, there is a need to regulate influence levels of various causative factors that generate UHI. Geographically Weighted Regression (GWR) model is among the spatial regression models that define the non-stationarity of variables. It generates a new equation on each sampled data unlikely global models like Ordinary Least Square (OLS). The study used GWR model to determine the influencing levels of three independent factors named Indexed-based Built-up Index (IBI), albedo and wind speed. Datasets were retrieved from MODIS satellite during the dry period of July from 2000 to 2019. IBI and Albedo were observed to have a strong negative influence with the maximum value of -0.045 and -0.053 respectively although, we expected to observe a positive influence on IBI since buildings emit absorbed energy during the night. Wind speed has a positive influence with the maximum value of 0.028 leading to the shift of heatwaves hence being termed as the secondary driving factor while IBI and Albedo as the primary driving factors. Wind speed is the highest driving factor that shifts emitted energies to other areas. We encourage an innovation in technology that produce higher albedo construction materials. We should improve environmental policies by introducing green cities through horizontal and vertical forests which might decrease the emitted energy into the atmosphere.</p>
Geographically Weighted Regression Modeling of a Daytime Urban Heat Island in Dar es Salaam Metropolitan Areas.
<p>Urban heat island is the phenomenon of having higher temperatures in urban areas compared to surrounding areas. It is caused by the replacement of natural vegetation with construction materials. Geographically Weighted Regression Model (GWR) determines non-stationarity among variables by generating a new equation for each sample size. Moderate Resolution Imaging Spectroradiometer (MODIS) products such as MOD11A1, MCD43A1, MOD09A1 and MOD13A1 are used to acquire Land Surface Temperature (LST), Albedo, Indexed-Based Built-Up Index (IBI) and Enhanced Vegetation Index (EVI) respectively. The highest and lowest coverage of Urban Heat Island of 69% and 43% were observed in 2000 and 2005 respectively. IBI is the leading causative factor by having an influence of 0.023 followed by Albedo, wind speed and EVI with an average of 0.019, 0.016 and -0.015 respectively. We recommend innovation in producing higher albedo construction materials and introducing green city policies.</p>
Data and Code for: Evaluation of the SPARTACUS-Urban Radiation Model for Vertically Resolved Shortwave Radiation in Urban Areas
<p>This contains the model outputs and analysis code for the article: Evaluation of the SPARTACUS-Urban Radiation Model for Vertically Resolved Shortwave Radiation in Urban Areas, Stretton et al. 2022, Boundary Layer Meteorology, 10.1007/s10546-022-00706-9</p>
The dataset of China's Urban area (CUD) and Urban Built-up area (CUBD)
<p>The dataset is based on national unified high-precision surface coverage data (geographic condition monitoring results), including urban areas and built-up areas of 337 cities above prefecture level in China in 2015 and 2020, with the city differentiation field "CITY" and cities differentiated by administrative codes, e.g. "Urban_110100" for Beijing.</p>
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.