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391 results for “Spatial Analysis”
Fig. 4 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 4: Spatial representation of the Coastal fishery suitability index (Sc).
Fig. 3 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 3: Spatial representation of the criteria ranking taken into account in MCDA.
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures".</p> <p>The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.</p>
Figure 7 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 7. Results of MADIFA-mapping of Vallonia pulchella ecological niche.
Code + simulated + publically accessable data for "Evaluating health facility access using Bayesian spatial models and location analysis methods"
<p># README</p> <p>These files contain r data objects and R files that represent the key details of the paper, "Evaluating health facility access using Bayesian spatial models and location analysis methods".</p> <p>The following datasources are available for simulation of some of the ideas in the paper.</p> <p>- dat_grid_sim: simulated data of the grid and grid cells<br> - dat_ohca_cv_sim: simulated data containing the cross validated test/training sets of OHCA data<br> - dat_ohca_sim: simulated OHCA event data<br> - dat_aed_sim: simulated AED location data<br> - dat_bldg_sim: simulated building location data<br> - dat_municipality_sim: simulated municipality information<br> - table_1: Table 1 information containing key demographic data</p> <p>These data were produced using the code in 01-create-sim-data.R, and one of the statistical models is demonstrated in 02-demo-inla-model.R</p> <p>In terms of the paper itself, the functions and code used in the manuscript are located in:</p> <p>* 01_tidy.Rmd - analysis code used to tidy up the data</p> <p>* 02_fit_fixed_all_cv.Rmd - analysis code used to place AEDs</p> <p>* 02_model.Rmd - analysis code used to fit the model in INLA</p> <p>* 03_manuscript.Rmd - Full code and text used to create the paper</p> <p>* 04_supp_materials.Rmd - full code and text used to create the supplementary materials</p> <p>The following files are a part of an R package "swatial" that was developed along with the paper. These files are:</p> <p>* DESCRIPTION</p> <p>* NAMESPACE</p> <p>* LICENSE</p> <p>* LICENSE.md</p> <p>* decay.R</p> <p>* spherical-distance.R</p> <p>* test-figure-data-matches.R</p> <p>* test-table-data-matches.R</p> <p>* testthat.R</p> <p>* tidy-inla.R</p> <p>* tidy-posterior-coefs.R</p> <p>* tidy-predictions.R</p> <p>* utils-pipe.R</p> <p>* All files that end in .Rd are documentation files for the functions.</p> <p>## Regarding data sources</p> <p>Census information for Ticino was transcribed from the Annual Statistical Report of Canton Ticino from years 2010 to 2015. This data was taken from their publicly accessible annual reports - for example: (https://www3.ti.ch/DFE/DR/USTAT/allegati/volume/ast_2015.pdf). The raw data was extracted from these annual reports, and placed into the file: "swiss_census_popn_2010_2015.xlsx". These data are put into analysis ready format in the file “01_tidy.Rmd”</p> <p>Housing and other relevant geospatial data can be accessed via http://map.housing-stat.ch/ and https://data.geo.admin.ch/. The maps of buildings from the REA (Register of Buildings and Dwellings) can be found here: https://map.geo.admin.ch/?zoom=11&bgLayer=ch.swisstopo.pixelkarte-grau&lang=en&topic=ech&layers=ch.bfs.gebaeude_wohnungs_register,ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill,ch.bfs.volkszaehlung-gebaeudestatistik_gebaeude,ch.bfs.volkszaehlung-gebaeudestatistik_wohnungen,ch.swisstopo.swissbuildings3d_1.metadata,ch.swisstopo.swissbuildings3d_2.metadata&E=2717616.28&N=1096597.25&catalogNodes=687,696&layers_timestamp=,,2016,2016,,&layers_visibility=true,false,false,false,false,false&layers_opacity=1,1,1,1,1,0.75</p> <p>For further enquiries on this data, contact the Swiss federal Office of Statistics at the details listed here: https://www.bfs.admin.ch/bfs/en/home/services/contact.html</p> <p>The shapefiles of the Comuni can be accessed here: https://www4.ti.ch/dfe/de/ucr/documentazione/download-file/?noMobile=1</p> <p>Data from the people living in the Municipalities in Ticino can be downloaded here: https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=dati.home&tema=33&id2=61&id3=65&c1=01&c2=02&c3=02</p> <p>## Future work</p> <p>In the future, these functions from the paper may be generalised and put into their own package. If that happens, this repository will be updated with a link to updated functions.</p>
Analysis and visualization of the Fasciola hepatica spatial transcriptomics dataset
<p>This repository contains various files related to the analysis of the paper: Spatial transcriptomics of a parasitic flatworm provides a molecular map of drug targets and drug-resistance genes.</p>
Supplementary files: Machine Learning Insights into Türkiye's Climate Variability: Predictive Modelling and Spatial Analysis
<p>This dataset and python code were used in the study titled "Machine Learning Insights into Türkiye's Climate Variability: Predictive Modelling and Spatial Analysis".</p>
Data and Analysis Files Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
<p>Data and Analysis Files from "Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics"</p> <p>ArraySeq_Method.zip contains the following folder and contents:</p> <ul> <li>STARSolo: All code and count matrix output from fastq spatial barcode demultiplexing. </li> <li>Images: All resolution-downsampled H&E image scans from analyzed tissues</li> <li>Space_Ranger: All 10x Space Ranger output from Visium datasets generated in the paper. </li> <li>Analysis: All scripts for analyzing and plotting Array-seq and Visium datasets generated in this paper. Also contains output h5ad files. </li> </ul> <p>ArraySeq_Barcode_generation_n12.rmd: The script used to generate the Array-seq probes with 12-mer spatial barcodes. </p>
Spatially Gridded Vehicle Count Analysis for Connaught Place, New Delhi
<p>Our Gridded Vehicle Count Analysis for Connaught Place, New Delhi, is a detailed geospatial analysis focusing on vehicular emissions in a highly trafficked urban area. Here's an overview:</p> <h3>Objective</h3> <p>The analysis aims to quantify vehicle density in Connaught Place, a major commercial and tourist hub, by generating a spatially gridded vehicle count dataset. By doing this, the project provides insights into traffic patterns and potential emission hotspots, supporting air quality forecasts and urban planning.</p> <h3>Data Collection</h3> <ol> <li><strong>Satellite Imagery</strong>: You collected high-resolution WorldView satellite imagery, zoomed specifically over Connaught Place to ensure the detail needed for vehicle detection.</li> <li><strong>Image Preprocessing</strong>: Using PyQGIS, you loaded the satellite imagery, created a 0.09-degree buffer around Connaught Place, and generated a grid layer with 150m x 150m cells to spatially segment the area for analysis.</li> <li><strong>Geotagged .tiff Files</strong>: The images were exported as geotagged .tiff files, preserving spatial information for each grid cell, facilitating precise location-based analysis.</li> </ol> <h3>Detection and Analysis</h3> <ol> <li><strong>Object Detection Models</strong>: You employed YOLOv8 through YOLOv10 and other state-of-the-art deep learning models to detect various vehicle types, such as cars, buses, and trucks. This approach helped accurately identify vehicle counts in real time.</li> <li><strong>Class-wise Detection</strong>: Specific object classes were defined, allowing for detailed counts by vehicle type, which is crucial for emission factor calculations.</li> <li><strong>Result Export</strong>: The detected vehicle counts and corresponding latitude/longitude data were stored in netCDF files, enabling the creation of gridded emission inventories.</li> </ol> <h3>Outcomes and Applications</h3> <p>The gridded dataset enables real-time monitoring of vehicular density and emissions over time. This data:</p> <ul> <li>Enhances air quality models by integrating localized emission sources.</li> <li>Assists policymakers in targeting emission reduction initiatives.</li> <li>Provides a foundational layer for studies on urban traffic flow and its environmental impacts in high-density zones like Connaught Place.</li> </ul> <p>In summary, this analysis provides a scientifically robust and spatially precise view of vehicular density, supporting both atmospheric studies and practical urban management decisions.</p>
Epidemiological geography at work. An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year (DATASET)
<p><strong>Literature review dataset</strong></p> <p>This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1<sup>st</sup>, 2020 to December 15<sup>th</sup>, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.</p> <p>This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.</p> <p>The reference to cite the related paper is the following:</p> <p><strong>Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p>To read the manuscript please follow this link: <strong>https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p> </p>
Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales
<p><span><span>Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damages to agriculture and livestock. Spatial risk modelling is a useful tool to gain understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (<i>Ursus arctos</i>) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damages. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damages and help mitigate conflicts in a proactive and preventive manner. </span></span></p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 1
<p>This deposit contains the supporting records of images and image analysis presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Associated Zenodo repositories:</p> <table> <thead> <tr> <th scope="col">Description</th> <th scope="col">DOI</th> </tr> </thead> <tbody> <tr> <td>Main figures PART 1, Figure 1,2,3,5</td> <td>10.5281/zenodo.7653239</td> </tr> <tr> <td>Main figures PART 2, Figure 6</td> <td>10.5281/zenodo.7900973</td> </tr> <tr> <td>Supplemental 3DTC figures: S1, S4, S5, S7, S8, S9</td> <td>10.5281/zenodo.7894632</td> </tr> </tbody> </table> <p>Contents:1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figures 2, 3 and 5. Figure 6 analyses are included in a compansion repository: 10.5281/zenodo.7900973. Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figure 1 P,Q. The supplemental figure data for RNAScope. Figures S1,S4 and S5 are found in: 10.5281/zenodo.7894633.</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", Supplemental 3DTC figures
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218 found in supplemental figures.</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses by figures in the supplemental figure data for 3D tissue cytometry. The main figure data is found at: 10.5281/zenodo.7653239 and 10.5281/zenodo.7900973.</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figures S1,S4 and S5. The main RNAScope figure data is found at: 10.5281/zenodo.7653239</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 2
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figure 6 analyses.</p> <p>Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p> </p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p> <p> </p>
Fig. 2 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis
Fig. 2. DCA analysis of the trapdays data for all of the sampling sites.
Fig. 1 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis
Fig. 1. PCA distribution of the sampling sites with full two-year catches.
A modeling framework for quantifying spatial recruitment dynamics using abundance estimation and sibship analysis: code and simulation study output
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Temporal and spatial pattern analysis of escaped prescribed fires in California from 1991 to 2020
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Data from: Rapid colonisation of synanthropic stone martens in a highly urbanised region: Insights from temporal and spatial analysis
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Dispersal increases spatial synchrony of populations but has weak effects on population variability: a meta-analysis
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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.