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3 results for “geographic weighted regression”
Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City
<div> <p><span><span>Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City</span></span></p> </div> <p> </p>
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>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.