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196 results for “Spatial map”
Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction
<p>This dataset contains the spatially-explicit gridded database based on social media data for over 150 different tourism-related classes that depicts tourism density (supply and demand) and perceived satisfaction in Europe, and the related exposure to selected climate extreme events. Information on tourism density (supply and demand) and perceived satisfaction are categorised for Attractions, Culinary, and Hospitality, while the exposure analysis of those clases are provided in separate, specific files. The provided dataset is made accessible to support large-scale and regional tourism research and extends its relevance to other fields that are part of tourism as a complex system, such as risk assessment and vulnerability studies. For citing this work, please refer to the research article "Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction", DOI 10.1088/1748-9326/ad3e91. Suggested citation: "Camatti, N., Hrast Essenfelder, A., & Giove, S. (2024). Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction. Environmental Research Letters."</p>
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
<p>This dataset contains 365 hyperspectral albedo maps of Earth with a temporal resolution of 1 day. The hyperspectral albedo maps are built from a 10-year average of the MODIS Surface Reflectance dataset (MCD43D42-48, version 6.1). Using a Principal Component Analysis (PCA) regression algorithm we combine different hyperspectral laboratory and in-situ measurements datasets of various dry soils, vegetation surfaces and mixure of both to reconstruct the albedo maps in the entire wavelength range from 400 to 2500 nm. The hyperspectral albedo maps have a spatial resolution of 0.25° in latitude and longitude.</p> <p>Additional hyperspectral albedo maps with a coarser spatial and spectral resolutions are available in the "Data sets" supplemetary material of "HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution" (Roccetti et al., 2024, https://doi.org/10.5194/egusphere-2024-167) or upon request to the corresponding author.</p>
Data for manuscript : Effect of spatial training on space-number mapping: A situated cognition account
<p><span>From an embodied perspective of cognition, sensorimotor mechanisms play a crucial role in abstract processing, such as the understanding of Arabic numerals. For instance, spatial cognition can influence number processing. These </span><span>spatial–numerical</span><span> associations (SNAs) have been thoroughly investigated since the pioneering </span><span>spatial–numerical association of response codes (SNARC) effect</span><span>, which demonstrates faster left/right responses to small/large numbers, respectively. While there </span><span>has been</span><span> no systematic assessment of SNAs on other planes in three-dimensional space in the literature, recent primary </span><span>evidence has</span><span> revealed that SNAs along </span><span>the transverse and sagittal planes </span><span>are</span><span> mutually exclusive </span><span>with respect</span><span> </span><span>to the required spatial reference frames used by the participant.</span><strong><span> </span></strong><span>Specifically, under </span><span>egocentric</span><span> spatial reference frames</span><span>,</span><span> SNAs have been observed only along the sagittal plane, </span><span>whereas</span><span> under</span><span> allocentric reference </span><span>frames, </span><span>the </span><span>reverse</span><span> pattern has been observed</span><span>,</span><span> with SNAs present exclusively along the transverse plane of the body. Given </span><span>this</span><span> empirical </span><span>evidence</span><span>, we have hypothesized that the subject's ability to switch spatial reference frames to match that of another person could significantly </span><span>influence</span><span> the occurrence of SNAs according to the processed plane. Therefore, this study has two aims. The first is to replicate </span><span>previous</span><span> seminal findings. The second is to investigate how referential </span><span>frame</span><span> switching (RFS) training can affect this organization. </span><span>While the results of</span><span> the two experiments reveal a general replication, more importantly, we find that RFS training enables </span><span>the development of</span><span> new situated cognition strategies </span><span>from</span><span> egocentric perspectives and </span><span>the generalization of</span><span> transverse SNAs to other spatial planes </span><span>from</span><span> allocentric perspectives.</span></p>
GIS data for the maps in publication Spatial perspectives enhance modeling of nanomaterial risks
<p>These files include the datasets utilized to perform geospatial modeling in the publication: Spatial perspectives enhance modeling of nanomaterial risks in the Journal of Industrial Ecology. </p> <p>The following data sources were used in this modeling effort:</p> <p><strong>National Hydrography Dataset (NHD): United States Geological Survey (USGS)</strong></p> <p>Upstate NY Lakes, ponds, streams, rivers, springs, and wells</p> <p><strong>Critical Environmental Areas in New York State: New York State Department of Environmental Conservation </strong></p> <p>Areas designated as critical under 6 NYCRR Part 617: “ecological, geological, or hydrological sensitivity that may be adversely affected by any change” (NY DEC)</p> <p><strong>National Land Cover Dataset (NLCD): United States Geological Survey (USGS)</strong></p> <p>National Land Cover Database classification schemes based primarily on Landsat data (2011)</p> <p><strong>Elevation Data: United States Geological Survey (USGS)</strong></p> <p>Digital Elevation Models (10-meter) for New York, elevation values were derived from USGS contour lines mapped at a scale of 1:24,000. </p> <p><strong>Interstate Highway: Federal Highway Administration’s National Transportation Atlas Database</strong></p> <p>Rural and urban highways for New York</p> <p> </p> <p><strong>Other references</strong></p> <p>Bureau, U.S. Census., American community survey 5-year estimates. 2017.</p> <p>EPA, Toxics Resource Inventory. 2019</p> <p> </p> <p>.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
FIG. 4 in Spatial Variation of False Map Turtle (Graptemys pseudogeographica) Bacterial Microbiota in the Lower Missouri River, United States
FIG. 4. Gut bacteria community relative abundances shown with each bar representing an individual sample in the corresponding location. Each color is representative of a corresponding genera. Where genera is not classified, the source family is given preceding ''unclassified.'' Bars remain unnormalized with remaining space composed of excluded low-representation groups (Ĺ2% of total abundance).
FIG. 3 in Spatial Variation of False Map Turtle (Graptemys pseudogeographica) Bacterial Microbiota in the Lower Missouri River, United States
FIG. 3. Gut bacteria community relative abundances shown with each bar representing an individual sample in the corresponding location. Each color is representative of a corresponding phylum (or in the case of ''Bacteria_unclassified,'' unidentified members of the bacteria). Bars remain unnormalized with remaining space composed of excluded low representation groups (Ĺ2% of total abundance).
FIG. 1 in Spatial Variation of False Map Turtle (Graptemys pseudogeographica) Bacterial Microbiota in the Lower Missouri River, United States
FIG. 1. Locations where cloacal microbiota samples were taken from False Map Turtles (Graptemys pseudogeographica) along the lower Missouri River between South Dakota and Nebraska, United States. Sites include: 1) James River, 2) Goat Island, and 3) Vermillion River. Major habitat types along the Missouri River are indicated (green = riparian forests; tan = sandbars) and modified from Dixon et al. (2015).
FIG. 2. Nonmetric multidimensional scaling calculated with the Bray-Curtis distance metric using a in Spatial Variation of False Map Turtle (Graptemys pseudogeographica) Bacterial Microbiota in the Lower Missouri River, United States
FIG. 2. Nonmetric multidimensional scaling calculated with the Bray-Curtis distance metric using a square root transformation and Wisconsin double-standardization. Location is represented by color, and sex is represented by shape. Stress of fit for the ordination is reported at 0.145. Axis titles represent the two dimensions to which the data have been ordinated.
Data from: Spatial structure of above-ground biomass limits accuracy of carbon mapping in rainforest but large scale forest inventories can help to overcome
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Data from: Range-wide spatial mapping reveals convergent character displacement of bird song
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High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)
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Data from: Uncovering spatial variation in acoustic environments using sound mapping
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Data from: High resolution spatial mapping of human footprint across Antarctica and its implications for the strategic conservation of avifauna
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The Pacific lamprey genomic divergence, association mapping, temporal Willamette Falls, spatial rangewide datasets
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Data from: Spatially consistent high-resolution land surface temperature mosaics for thermophysical mapping of the Mojave Desert
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Data from: Misuse of bird digital distribution maps creates reversed spatial diversity patterns in the Amazon
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Data from: Improving public safety through spatial synthesis, mapping, modeling, and performance analysis of emergency evacuation routes in California localities
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Data from: Mapping phosphorus hotspots in Sydney's organic wastes: a spatially-explicit inventory to facilitate urban phosphorus recycling
Phosphorus is an essential element for food production whose main global sources are becoming scarce and expensive. Furthermore, losses of phosphorus throughout the food production chain can also cause serious aquatic pollution. Recycling urban organic waste resources high in phosphorus could simultaneously address scarcity concerns for agricultural producers who reply on phosphorus fertilisers, and waste managers seeking to divert waste from landfills to decrease environmental burdens. Recycling phosphorus back to agricultural lands however requires careful logistical planning to maximize benefits and minimize costs including, processing and transportation. The first step towards such analyses is quantifying recycling potential in a spatially-explicit way. Here we present such inventories and scenarios for the Greater Sydney Basin's recyclable phosphorus supply and agricultural demand. In 2011, there was fifteen times more phosphorus available in organic waste than agricultural demand for phosphorus in Sydney. Hypothetically, if future city residents shifted to a plant-based diet, eliminated edible food waste, and removed animal production in the Greater Sydney Basin, available phosphorus supply would decrease to 7.25 kt of phosphorus per year, even when accounting for population growth by 2031, and demand would also decrease to 0.40 kt of phosphorus per year. Creating a circular phosphorus economy for Sydney, in all scenarios considered, would require effective recycling strategies which include transport outside of the Greater Sydney Basin. These spatially explicit scenarios can be used as a tool to facilitate stakeholders engagement to identify opportunities and barriers for appropriate organic waste recycling strategies.
Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)
The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two-step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two-step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow-up with MSR generally reduced (already low) false-positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual-based and population-based sampling by leveraging spatial information that has hitherto been neglected.
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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.