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6 results for “GIS tools”
Selkie GIS Techno-Economic Tool input datasets
<p>This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the</p><p>Irish and Welsh maritime areas. Read more here:</p><p>https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/</p><p> </p><blockquote><p>This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.</p></blockquote><p> </p><p>********************</p><p><strong>File Formats</strong></p><p>********************</p><p>Results are presented in three file formats:</p><p> </p><p><strong>tif</strong> Can be imported into a GIS software (such as ARC GIS)</p><p><strong>csv</strong> Human-readable text format, which can also be opened in Excel</p><p><strong>png</strong> Image files that can be viewed in standard desktop software and give a spatial view of results</p><p> </p><p> </p><p>******************</p><p><strong>Input Data</strong></p><p>******************</p><p>All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.</p><p> </p><p>Hourly Data from 2000 to 2019</p><p> </p><p><i>- Wind -</i></p><p>Copernicus ERA5 dataset</p><p>17 by 27.5 km grid </p><p>10m wind speed</p><p> </p><p><i>- Wave -</i></p><p>Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset</p><p>3 by 5 km grid</p><p> </p><p> </p><p>*********************</p><p><strong>Accessibility</strong></p><p>*********************</p><p>The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site. </p><p>The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.</p><p> </p><p>Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if </p><p>the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.</p><p> </p><p>Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset. </p><p>Wind hourly data is from the ERA 5 dataset. </p><p> </p><p> </p><p>********************</p><p><strong>Availability</strong></p><p>********************</p><p>A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather </p><p>windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the </p><p>relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between</p><p>accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.</p><p> </p><p>The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the </p><p>environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits. </p><p>Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined </p><p>by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total </p><p>number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship</p><p>between the two. A mature technology reliability was assumed.</p><p> </p><p> </p><p>**********************</p><p><strong>Weather Window</strong></p><p>**********************</p><p>The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the </p><p>given duration for the month.</p><p> </p><p>The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset </p><p>(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.</p><p> </p><p>The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep. </p><p>The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?</p><p> </p><p>Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather </p><p>windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain </p><p>suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable </p><p>weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available </p><p>at any given point in the month.</p><p> </p><p>*****************************</p><p><strong>Extreme Wind and Wave</strong></p><p>*****************************</p><p>The Extreme wave layers show the highest significant wave height expected to occur during the given return period.</p><p>The Extreme wind layers show the highest wind speed expected to occur during the given return period. </p><p> </p><p>To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced </p><p>portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model </p><p>that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values </p><p>for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.</p><p> </p><p>The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.</p><p> </p><p>The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The</p><p>second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected </p><p>extremes and used to calculate the extreme value for the selected return period.</p>
GIS as a heuristic tool to interpret ancient historiography: A case study to reconstruct what could plausibly have happened according to the accounts in New Testament texts
<p>This presentation summarizes the research findings of the TiG article (doi 10.1111/tgis.12762) which examines how GIS can be used as a heuristic tool to reconstruct spatial–temporal events from narratives in order to examine whether a scenario is conceivable within the narrative world. The narrative about Paul's escape from Berea (Acts 17:14–15) is used as a case study. Several interpretive issues related to spatial and temporal questions surround these texts. In the case study, three methods are applied: (a) least-cost path analysis on elevation data to construct journeys and travel times for Roman roads; (b) network analysis to find seafaring routes valid for ancient times; and (c) the integration of spatial and temporal data in a space-time cube. Our main finding is that the method yields insights into the spatial–temporal dynamics of the narrative. This helps a modern reader to better understand the narrative conceivability of a story in the mind of a first-century reader.</p>
Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Integrating GIS Tools and Probability-Risk Matrix – Case Study: Guarda Region, Portugal
<p>In these files we can find the final risk map of heavy metal contamination for the guarding area in Portugal obtained according to the methodology explained in the paper "Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Instegrating GIS Tools and Probability-Risk Matrix - Case Study: Guarda Region (Portugal)</p> <p>Final Risk Equal.tiff: GeoTiff with a pixel size of 30m. EPSG:3763 - ETRS89 / Portugal TM06</p> <p>Also attached is the symbolisation for the image in .qml (Quantum GIS Layer Style File) format.</p> <p>A file called RISK RECLASS is also available, where you can find the risk classification maps for each of the studied factors: </p> <ul> <li>Proximity to roads</li> <li>Proximity to industrial areas</li> <li>Ph</li> <li>Soil organic content</li> <li>Slope</li> <li>Soil texture</li> <li>Mining extraction areas </li> <li>Drainage</li> </ul> <p>finally a DATABASE file where the data of the 360 points for the calculation of the risk maps can be found. </p>
Fig. 3 in The Implementation Of Nature Management Plans For Specially Protected Nature Territories In Latvia: Stakeholder Awareness, Applying Gis Tools, Indicators For Management
Fig. 3. Map of the landscape terrain at Nature park "Dridža ezers" ("Lake Dridža")
Fig. 1 in The Implementation Of Nature Management Plans For Specially Protected Nature Territories In Latvia: Stakeholder Awareness, Applying Gis Tools, Indicators For Management
Fig. 1. Interactive map of recreation sites at Nature park "Dridža ezers" ("Lake Dridža")
Morph_CNeT: A new GIS tool to extract morphometric attributes characterising channel network topology of Indian catchments
<p><span>Morph_CNeT” (Morphometric Channel Network Extraction Tool) can facilitate extraction of the topology based new morphometric attributes by processing DEM datasets within GIS framework. Morph_CNeT tool is used to create a repository named as Morph_CNeT-India of topological catchment attributes for 1749 gauging stations maintained by the Central Water Commission (CWC) across 22 River basin Systems of India.</span></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.