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135 results for “Urban Model”
Occupancy Modeling for invasive Rattus species in a peri-urban environment
<p>Occupancy Modeling for invasive Rattus species in a peri-urban environment. Files include a presence/absence matrix (0 = absence, 1 = presence) of invasive rats in a gradient of human disturbance, and the subsequent code for occupancy and detection models with a habitat covariate.</p>
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
<p>The attached files include the predictive features and the water depth from 2D hydrodynamic simulations that were used to train data driven models to predict water depth in Berlin.</p>
Data from: Hierarchical multi-taxa models inform riparian vs. hydrologic restoration of urban streams in a permeable landscape
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Data from: A worldwide model for boundaries of urban settlements
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Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid
The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.
Datasets for modelling direct CO2 emissions and uptake at neighborhood scale over the urban area of Beijing
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Parameterization of grass grid pavers for urban microclimate modelling to simulate the heat mitigation potential of unsealing measures in cities
<p><strong>README: </strong></p> <p>The exact version (V 5.1.2 Compatibility Release) of the ENVI-met model used for this paper, the parameterizations and the input data to run the ENVI-met model and to reproduce the model outputs of all the performed scenario simulations shown in this paper, as well as all the scripts for post-processing of the model output data needed to derive the presented results of this manuscript are provided in this permanent archive. The supplementary material (data and code) includes the following files: </p> <p>Appendix_1: ENVI-met model code of the version used to reproduce the outputs of this paper (V 5.1.2 Compatibility Release): <br> Appendix_1.exe: ENVImet model Version 5.1.2 setup </p> <p>Appendix_2: Model input data: for GGP scenarios including the model database parameterizations: <br> Appendix_2.1.zip: Input data for extreme scenario <br> Appendix_2.2.zip: Input data for realistic scenario <br>Eeach zip file contains: model driver file for LBCs, project file, project database, model domain file, and simulation task file. </p> <p>Appendix_3: Python scripts for postprocessing and extraction of model output data: <br> Appendix_3.1.enpy: Python script for exporting surface data <br> Appendix_3.2.enpy: Python script for exporting atmosphere data and soil data</p> <p>Appendix_4: R script for data analyses of model results: <br> Appendix_4.rmd: R script for data analyses of extracted simulation results and plotting </p> <p> </p>
Applying the LightGBM model and SHAP interpretation to understand the urban land price index and its influencing factors in China
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Energy Consumption Reduction in Historic Urban Residential Sectors: A Case Study of Kyoto City Using Bottom-Up Modeling and Future Climate Scenarios
<p>This dataset is a detailed simulation result of 21 scenarios in the paper. The results include:</p> <ol> <li>Annual daily energy consumption data divided by energy source and residential type.</li> <li>Photovoltaic power generation data, direct photovoltaic use, battery use, and photovoltaic power generation consumed by apartment houses and Kyomachiya through P2C systems. </li> <li>Annual daily net energy consumption data divided by energy source and residential type.</li> </ol>
Modelizations and analyzes of the urban fabric (2D and 3D) of Charleville from 1724 to 1876.
<p>The main characteristic of the city of Charleville (Ardennes), concerning the history of its population, is the realization by the municipal authorities of a nominative, spatial and annual census of the inhabitants, undertaken from the end of the 17th century, and until at the beginning of the 20th century.<br> This gives the possibility of exploiting Carolopolitan data within a geographic information system (GIS). Thanks to this GIS, to its informal quality and volume, it is possible to reconstruct in 3D the city of 1834, and to make full use of the methodological environment known as "BIM" (or Building Information Model). BIM will make it possible to exploit the architectural and demographic composition of each plot, block, dwelling in this city, from its genesis to the present day, like a real 3D GIS.<br> Spatial analyzes built from cartographic and topographic data, analyzed, vectorized (2D and 3D), georeferenced and linked to the occupation data of the city of Charleville.<br> <br> Data collected, digitized and structured from 01/15/2016 to 01/04/2021 in the context of:<br> - Axis 1 of the Roland Mousnier Center (UMR 8596), Sorbonne University / CNRS<br> - the C2EP2 Project funded by the Sorbonne University "Emergencies" call for projects (2019-21) for the web and BIM parts (CSTB partnership)<br> - scientific and technical collaboration (partnership agreement) between the National Archives and the Roland Mousnier Center.<br> <br> Creative Commons License<br> Dataset "Analyzes of the urban fabric (2D and 3D) of Charleville from 1724 to 1876.". by Sylvain Rassat, Roland Mousnier Center, CNRS, Departmental Archives, National Archives is made available under the terms of the Creative Commons Attribution - Share under the Same Conditions 4.0 International license.<br> Based on a Source Link work.<br> Permissions beyond the scope of this license can be obtained at https://www.researchgate.net/profile/Sylvain_Rassat.<br> (https://cesium.cstb.fr/Apps/cnrs/MN_Charleville3D.html)</p>
Input data for a spatial urban sprawl model
<p>Input data for projecting global urban extent under future shared socioeconomic pathways (2010-2100)</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>
Study data to "Crowdsourcing air temperature data for the evaluation of the urban microscale model PALM – a case study in central Europe"
<p>Supplementary material to the paper "Crowdsourcing air temperature data for the evaluation of the urban microscale model PALM – a case study in central Europe", http://dx.doi.org/10.1371/journal.pclm.0000197 </p><p>Provides the model input and configuration files as well as relevant output files for the analysis. Additionally delivers the required geo data and meteorological data in the scripts.</p><p>The scripts for processing the data can be found on github: https://github.com/lara-vdl/PALM-Crowdsourcing</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>
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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)
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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.