100+ datasets found
  1. d

    Bighorn Mountains Forest Mapping Study Area

    • catalog.data.gov
    • data.usgs.gov
    Updated Jul 6, 2024
    + more versions
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    U.S. Geological Survey (2024). Bighorn Mountains Forest Mapping Study Area [Dataset]. https://catalog.data.gov/dataset/bighorn-mountains-forest-mapping-study-area
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Bighorn Mountains
    Description

    This is the study area associated with the project: “Status and Trends of Deciduous Communities in the Bighorn Mountains”. The aim of the study is to assess the current trends of deciduous communities in the Bighorn National Forest in north-central Wyoming. The data here represents phase I of the project, completed in FY2017. The USGS created a synthesis map of coniferous and deciduous communities in the Bighorn Mountains of Wyoming using a species distribution modeling approach developed in the Wyoming Landscape Conservation Initiative (WLCI) (Assal et al. 2015). The modeling framework utilized a number of topographic covariates and temporal remote sensing data from the early, mid and late growing season to capitalize on phenological differences in vegetation types. We used the program RandomForest in the R statistical program to generate probability of occurrence models for deciduous and coniferous vegetation. The binary maps were combined into a synthesis map using the procedure from Assal et al. 2015. In Phase II of this project (to be completed in FY2018 and 2019), the USGS will conduct a preliminary assessment on the baseline condition of riparian deciduous communities. This will be a proof-of-concept study where the USGS will apply a framework used in prior research in upland aspen and sagebrush communities to detect trends in riparian vegetation condition from the mid-1980s to present. Literature Cited Assal et al. 2015: https://doi.org/10.1080/2150704X.2015.1072289

  2. H

    Replication Data for: Maps in People’s Heads: Assessing A New Measure of...

    • dataverse.harvard.edu
    • dataone.org
    Updated May 1, 2018
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    Jake Bowers; Cara Wong; Daniel Rubenson; Mark Fredrickson; Ashlea Rundlett (2018). Replication Data for: Maps in People’s Heads: Assessing A New Measure of Context [Dataset]. http://doi.org/10.7910/DVN/9XWGHN
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 1, 2018
    Dataset provided by
    Harvard Dataverse
    Authors
    Jake Bowers; Cara Wong; Daniel Rubenson; Mark Fredrickson; Ashlea Rundlett
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    To understand the relationship between place and politics, we must measure both political attitudes and the ways in which place is represented in the minds of individuals. In this paper, we assess a new measure of mental-representation of geography, in which survey respondents draw their own local communities on maps and describe them. This mapping measure has been used in Canada, the UK, Denmark, and the U.S. so far. We use a panel study in Canada to present evidence that these maps are both valid and reliable measures of a personally relevant geographic area, laying the measurement groundwork for the growing number of studies using this technology. We hope to set efforts to measure ‘place’ for the study of context and politics on firmer footing. Our validity assessments show that individuals are thinking about people and places with which they have regular contact when asked to draw their communities. Our reliability assessments show that people can draw more or less the same map twice, even when the exercise is repeated months later. Finally, we provide evidence that the concept of community is a tangible consideration in the minds of ordinary citizens and is not simply a normative aspiration or motivation.

  3. a

    HJA Studies Map

    • hub.arcgis.com
    Updated Apr 2, 2019
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    Oregon State University GISci (2019). HJA Studies Map [Dataset]. https://hub.arcgis.com/maps/fcaa4ae3f46042f88d2b2c21fb0e1e08
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    Dataset updated
    Apr 2, 2019
    Dataset authored and provided by
    Oregon State University GISci
    Area covered
    Description

    H.J. Andrews Studies Map is a compilation of study site locations and GIS base layers (e.g. administrative boundaries, roads, streams, etc.). This was updated in 2019 during the transition to the ArcGIS Online and Enterprise platforms.

  4. CGS Information Warehouse: Mineral Land Classification Maps (SMARA Study...

    • data.cnra.ca.gov
    • data.ca.gov
    • +7more
    Updated Jun 4, 2019
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    California Department of Conservation (2019). CGS Information Warehouse: Mineral Land Classification Maps (SMARA Study Areas) [Dataset]. https://data.cnra.ca.gov/dataset/cgs-information-warehouse-mineral-land-classification-maps-smara-study-areas
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    arcgis geoservices rest api, geojson, kml, csv, html, zipAvailable download formats
    Dataset updated
    Jun 4, 2019
    Dataset authored and provided by
    California Department of Conservationhttp://www.conservation.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description
    Mineral Land Classification studies are produced by the State Geologist as specified by the Surface Mining and Reclamation Act (SMARA, PRC 2710 et seq.) of 1975. To address mineral resource conservation, SMARA mandated a two-phase process called classification-designation. Classification is carried out by the State Geologist and designation is a function of the State Mining and Geology Board. The classification studies contained here evaluate the mineral resources and present this information in the form of Mineral Resource Zones.

    The objective of the classification-designation process is to ensure, through appropriate local lead agency policies and procedures, that mineral materials will be available when needed and do not become inaccessible as a result of inadequate information during the land-use decision-making process.
  5. GEOGRAPHY TOOLKIT - TODALSIGS -MAP SKILLS/ELEMENTS

    • library.ncge.org
    Updated Jul 27, 2021
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    NCGE (2021). GEOGRAPHY TOOLKIT - TODALSIGS -MAP SKILLS/ELEMENTS [Dataset]. https://library.ncge.org/documents/26b6a0f425ad49e8b7bd885e4f468c1f
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    Dataset updated
    Jul 27, 2021
    Dataset provided by
    National Council for Geographic Educationhttp://www.ncge.org/
    Authors
    NCGE
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    Author: ANN WURST, NGS TEACHER CONSULTANTGrade/Audience: grade 6, grade 7, grade 8, high school, ap human geography, post secondary, professional developmentResource type: activitySubject topic(s): cartography, maps, regional geographyRegion: worldStandards: TEXAS TEKS (19) Social studies skills. The student applies critical-thinking skills to organize and use information acquired through established research methodologies from a variety of valid sources, including technology. The student is expected to: (A) analyze information by sequencing, categorizing, identifying cause-and-effect relationships, comparing, contrasting, finding the main idea, summarizing, making generalizations and predictions, and drawing inferences and conclusions; (B) create a product on a contemporary government issue or topic using critical methods of inquiry; (D) analyze and evaluate the validity of information, arguments, and counterarguments from primary and secondary sources for bias, propaganda, point of view, and frame of reference; Objectives: Students will keep a list of the toolkit 'helpers' in their notebook and use the elements to process/apply information in various formats such as short answers responses, tickets out the door, setting up writing samples for world geo, AP Human Geo and other courses involving the study of geographic concepts. Summary: Students can use these 'hooks' in their study of cartography/map making , can be applied in every unit where map skills are needed. Helps further critical thinking skills.

  6. N

    Thresholded Maps: White and Gray Matter Correlates of Theory of Mind in...

    • neurovault.org
    zip
    Updated May 5, 2023
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    (2023). Thresholded Maps: White and Gray Matter Correlates of Theory of Mind in Autism: A Voxel-Based Morphometry Study [Dataset]. http://identifiers.org/neurovault.collection:13948
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    zipAvailable download formats
    Dataset updated
    May 5, 2023
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    A collection of 34 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

    Collection description

    This collection includes thresholded SPM12 statistical maps for the study titled "White and Gray Matter Correlates of Theory of Mind in Autism: A Voxel-Based Morphometry Study," to be published in Brain Structure and Function in 2023. Details of the corresponding publication will be added, once the manuscript is published.

  7. f

    Map package (ArcMap version) with geomorphological and geographical datasets...

    • uvaauas.figshare.com
    7z
    Updated Jun 7, 2023
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    Matheus G.G. De Jong; Henk Pieter Sterk; Stacy Shinneman; A.C. Seijmonsbergen (2023). Map package (ArcMap version) with geomorphological and geographical datasets used to generate maps for Au West study area in Vorarlberg, Austria [Dataset]. http://doi.org/10.21942/uva.13713751.v4
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    7zAvailable download formats
    Dataset updated
    Jun 7, 2023
    Dataset provided by
    University of Amsterdam / Amsterdam University of Applied Sciences
    Authors
    Matheus G.G. De Jong; Henk Pieter Sterk; Stacy Shinneman; A.C. Seijmonsbergen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Vorarlberg, Austria
    Description

    For complete collection of data and models, see https://doi.org/10.21942/uva.c.5290546.Map package for use in ArcMap containing three-tiered geomorphological data and geographical datasets such as rivers and hillshading. Datasets were used to generate figures for publication: Hierarchical geomorphological mapping in mountainous areas. Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021. All data is in MGI Austria GK West projected coordinate system (EPSG: 31254) and was clipped to the study area.

  8. d

    Geospatial Data for a Flood-Inundation Mapping Study of the South Platte...

    • catalog.data.gov
    • data.usgs.gov
    Updated Jul 6, 2024
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    U.S. Geological Survey (2024). Geospatial Data for a Flood-Inundation Mapping Study of the South Platte River at Fort Morgan, Colorado, 2018 [Dataset]. https://catalog.data.gov/dataset/geospatial-data-for-a-flood-inundation-mapping-study-of-the-south-platte-river-at-fort-mor
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    South Platte River, Fort Morgan
    Description

    The development and generation of the datasets that are published through this data release, were based on the results and findings of the report: Kohn, M.S. and Patton, T.T., 2018, Flood-Inundation Maps for the South Platte River at Fort Morgan, Colorado, 2018: U.S. Geological Survey Scientific Investigations Report 2018-5114, 14 p., https://doi.org/10.3133/sir20185114. The geospatial dataset contain final versions of the raster and vector geospatial data and related metadata. The geospatial data include inundation extents, corresponding inundation depths, and the study area boundaries. Digital flood-inundation maps for a 4.5-mile reach of the South Platte River at Fort Morgan, Colorado from Morgan County Road 16 to Morgan County 20.5, were created by the U.S. Geological Survey (USGS) in cooperation with the Colorado Water Conservation Board. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site (https://water.usgs.gov/osw/flood_inundation/), depict estimates of the areal extent and depth of flooding corresponding to select water levels (stages) at USGS streamgage 06759500, South Platte River at Fort Morgan. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information are available through the National Water Information System web interface or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (http:/water.weather.gov/ahps/). Water-profiles were computed for the stream reach by means of a one-dimensional, step-backwater model. The September 15, 2013 and May 20, 2017 floods were used to calibrate the model, and the June 15, 2015 and May 29, 2017 floods were used to independently validate the model. Nine pressure transducers were deployed to record the stage at nine different locations along the reach and to document the floods of May 20 and 29, 2017 at the South Platte River at Fort Morgan streamgage. The calibrated hydraulic model was then used to determine 16 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from 12 ft (3.66 m) or below bankfull to 27 ft (8.23 m), which is 1 ft (0.3 m) greater than the highest recorded water level (25.73 ft [7.84 m] on September 15, 2013) at the South Platte River at Fort Morgan streamgage during its period of record and the 2013 flood exceeds the major flood stage of 21.5 ft (6.55 m) by more than 4 ft (1.2 m) as defined by the National Weather Service. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging) to delineate the area flooded at stages ranging from 12-ft to 27-ft. The availability of these inundation maps, along with internet information regarding the current stage from the USGS streamgage 06759500, South Platte River at Fort Morgan, Colorado, and forecast river stages from the NWS Advanced Hydrologic Prediction Service, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

  9. Making Speed Seriously Sample Data

    • figshare.com
    txt
    Updated Jun 3, 2023
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    TIAGO GIL (2023). Making Speed Seriously Sample Data [Dataset]. http://doi.org/10.6084/m9.figshare.12939635.v2
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    txtAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    TIAGO GIL
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Dataset based on maps presented in the "Annales" and "Past & Present" journals, between 1950 and 2000, focusing on motion maps.The data are a set of organized maps but does not include the maps themselves (only data on the maps), which are in two different repositories: www.jstor.org and persee.fr. Every map registered in the table indicates a URL where the original map is available freely, at persee.fr, and by request at jstor.org.

  10. d

    GIS data of urchin barren mapping in Northeastern New Zealand

    • dataone.org
    • datadryad.org
    Updated Feb 6, 2024
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    Vince Kerr (2024). GIS data of urchin barren mapping in Northeastern New Zealand [Dataset]. http://doi.org/10.5061/dryad.8gtht76w3
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    Dataset updated
    Feb 6, 2024
    Dataset provided by
    Dryad Digital Repository
    Authors
    Vince Kerr
    Time period covered
    Jan 1, 2023
    Area covered
    New Zealand
    Description

    On shallow rocky reefs in northeastern Aotearoa, New Zealand, urchin barrens are recognised as indicators of the ecosystem effects of overfishing reef predators. Yet, information on their extent and variability is lacking. We use aerial imagery to map the urchin barrens and kelp forests on reefs (<30 m depth) across seven locations, including within two long-established marine reserves and a marine protected area that allows recreational fishing. Urchin barrens were present in all locations and were restricted to reefs <10-16 m deep. This archive contains ArcGIS shapefiles and layer files for all of the maps used in this study. The study area extends from Cape Reinga in the far north of the North Island to Tawharanui in the Hauraki Gulf near Auckland. Regional scale base maps of the prominent marine habitats were included along with the seven fine-scale maps where the kelp forests and urchin barrens were mapped., The GIS shapefiles produced in this study were hand-drawn over layers of low-level aerial photography taken in specific conditions, which maximised the visible depth observable to create polygons to depict the habitat boundaries of the shallow reef. Of particular interest was the mapping of urchin barrens. Ground truthing surveys creating point data and underwater imagery were also brought into the GIS project to assist in drawing the reef habitat polygons. Arc layer files contain a common symbology across the seven study maps to aid the interpretation of the mapping. Further information on the methodology used in the mapping can be found in two published papers and four technical reports corresponding to the maps. The Readme file details where technical reports and published reports can be downloaded from the internet., , # GIS data of urchin barren mapping in Northeastern New Zealand

    GIS mapping resources supporting the research article: Kerr, V.C. Grace R.V. (deceased), and Shears N.T., 2004. Estimating the extent of urchin barrens and kelp forest loss in northeastern Aotearoa, New Zealand. Kerr and Associates, Whangarei, New Zealand.

    Description of the data and file structure

    Four folders in this archive contain ArcGIS shapefiles with the extension (.shp). The shapefiles can be uploaded to ArcGIS or any ArcGIS-compatible software to view and access the files' spatial data and habitat attributes. It is essential to retain the associated files in each folder as these are system files required by ArcGIS to open and use the shapefiles. Each shapefile has six associated files with extensions: .avi, .CPG, .dbf, .prf, .sbn, and .sbx. In this archive are maps based on polygons drawn to depict habitat boundaries of biological and physical habitats in the shallow coastal areas of Northeastern New Zealan...

  11. a

    RESTYLE Study map

    • hub.arcgis.com
    Updated Jun 1, 2021
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    Center for Geographic Analysis @Harvard University (2021). RESTYLE Study map [Dataset]. https://hub.arcgis.com/maps/9ebe06ae2bd142e697150a9659965bc1
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    Dataset updated
    Jun 1, 2021
    Dataset authored and provided by
    Center for Geographic Analysis @Harvard University
    Area covered
    Description

    RESTYLE pilot study results by neighborhood, c.o. Marissa Chan.

  12. e

    Land-cover mapping of the central Arizona region based on 2015 National...

    • portal.edirepository.org
    application/vnd.dbf +4
    Updated Sep 25, 2020
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    Yujia Zhang; Billie Turner II (2020). Land-cover mapping of the central Arizona region based on 2015 National Agriculture Imagery Program (NAIP) imagery [Dataset]. http://doi.org/10.6073/pasta/e671ed549a55fda3338b177a2ad54487
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    csv(898 bytes), tiff(441087182 byte), kml(20287 byte), application/vnd.dbf(842 byte), csv(354 bytes), bin(112 byte)Available download formats
    Dataset updated
    Sep 25, 2020
    Dataset provided by
    EDI
    Authors
    Yujia Zhang; Billie Turner II
    Time period covered
    May 29, 2015 - Jun 1, 2015
    Area covered
    Variables measured
    Name, class_id, Tile_Name, class_name, description, raster_value, user_accuracy, reference_count, classified_count, producer_accuracy, and 1 more
    Description

    Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science, especially for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) program. This project provides a 1-meter land-cover mapping of the CAP LTER study area (greater Phoenix metropolitan area and surrounding Sonoran desert). The mapping is generated primarily using 2015 National Agriculture Imagery Program (NAIP) four-band data, with auxiliary GIS data used to improve accuracy. Auxiliary data include the 2015 cadastral parcel data, the 2014 USGS LiDAR data (1-meter), the 2014 Microsoft/OpenStreetMap Building Footprint data, the 2015 Street TIGER/Line, and a previous (2010) NAIP-based land-cover map of the study area (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=623). Among auxiliary data, building footprints and LiDAR data significantly improved the boundary detection of above-ground objects. Post-classification, manual editing was applied to minimize classification errors. As a result, the land-cover map achieves an overall accuracy of 94 per cent. The map contains eight land cover classes, including: (1) building, (2) asphalt, (3) bare soil and concrete, (4) tree and shrub, (5) grass, (6) water, (7) active cropland, and (8) fallow. When compared to the aforementioned, previous (2010) NAIP-based land-cover map for the study area, buildings and tree canopies are classified more accurately in this 2015 land-cover map.

  13. a

    Study Area Boundaries

    • hub.arcgis.com
    Updated Jun 9, 2016
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    Federal Communications Commission (2016). Study Area Boundaries [Dataset]. https://hub.arcgis.com/maps/cbd47993de8d4a78934ccb5221398f3a
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    Dataset updated
    Jun 9, 2016
    Dataset authored and provided by
    Federal Communications Commission
    Area covered
    Description

    The map displays the study area boundaries submitted and certified by incumbent local exchange carriers and state commissions through May 5th, 2016. As a result of confidentiality requests, certain boundaries for Verizon and AT&T are not displayed.

  14. c

    What does the primary school student know about the map in 2013?

    • datacatalogue.cessda.eu
    • researchdata.se
    • +1more
    Updated Feb 13, 2019
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    Hennerdal, Pontus (2019). What does the primary school student know about the map in 2013? [Dataset]. http://doi.org/10.5878/m5fe-9t56
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    Dataset updated
    Feb 13, 2019
    Dataset provided by
    Stockholm University
    Authors
    Hennerdal, Pontus
    Time period covered
    Feb 1, 1968 - May 17, 1968
    Area covered
    Sweden
    Variables measured
    Individual
    Measurement technique
    Educational measurements and tests, Educational measurements and tests
    Description

    In 1968, Sonja Peterson conducted a study that included all students from grade 1 to grade 9 in Arvika, Sweden. That study was named: Vad vet grundskoleeleven om kartan? [What does the primary school student know about the map?] and was published in 1971 in the report series: Rapporter från Pedagogiska institutionen i Göteborg [Reports from the Department of Education in Gothenburg]. As a part of the doctoral studies Pontus Hennerdal conducted at Stockholm University, he recreated parts of the test Peterson used in her study. Together with some new questions, Hennerdals study was also carried out with students in Arvika during 2013. This time with students from grade 2 to year 9. This archive consists of answers from the 1206 completed questionnaires that were filled in during the spring term 2013 during Hennerdals Arvika study. Also the archive include some comparisons with the data Peterson collected 45 years earlier.

    During the spring term of 2013, tests(see attached files) were distributed to all classes (grade 2 to 9) in Arvika, Sweden. All grades did not have all test sheets. The teachers conducted the test with each class based on the instruction:

    The intention is that the questions should be self-instructing, that the only thing the students need in addition to the questions is a pen and an eraser, and that the answers to the questions should be received during an hour. But the study is not ruined by helping students to understand question they find unclear (explain the question, not the concept), let them use draft paper and their own ruler, or let someone draw a little over the time if the teacher finds it possible.

  15. g

    TCCS - Google Maps Case Study | gimi9.com

    • gimi9.com
    Updated Jul 1, 2025
    + more versions
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    (2025). TCCS - Google Maps Case Study | gimi9.com [Dataset]. https://gimi9.com/dataset/au_dz6v-nett
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    Dataset updated
    Jul 1, 2025
    Description

    This case study document provides information on how Google Maps is using our open datasets and articulates citizen benefits.

  16. s

    A discrete choice experiment to validate the use of areal wombling for...

    • orda.shef.ac.uk
    html
    Updated Aug 28, 2024
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    Meng Le Zhang; Aneta Piekut; Zanib Rasool; Lydia Warden; Henry Staples; Gwilym Pryce (2024). A discrete choice experiment to validate the use of areal wombling for detecting social boundaries [Dataset]. http://doi.org/10.15131/shef.data.25731387.v1
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    htmlAvailable download formats
    Dataset updated
    Aug 28, 2024
    Dataset provided by
    The University of Sheffield
    Authors
    Meng Le Zhang; Aneta Piekut; Zanib Rasool; Lydia Warden; Henry Staples; Gwilym Pryce
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Data, code and materials from a discrete experiment to test the validity of an Bayesian areal wombling algorithm for predicting social boundaries. The experiment was conducted as a part of project ‘Life at the Frontier: Researching the Impact of Social Frontiers on the Social Mobility and Integration of Migrants’ (2020-2023; NordForsk/ESRC, project no 95193), and experiment data was collected in Rotherham (UK).About the experimentEach border on a map is assigned a boundary value based on how dissimilar the adjacent neighbourhoods are (higher = more dissimilar = more likely to be a social boundary).The experiment was carried out as follows:- We created three maps of the same area with different boundaries using the Bayesian areal wombling approach.- Map A contained the boundaries with the highest boundary values, whilst map C had the lowest boundary values. Map B contained boundaries that were in between.- During an interview, participants were then shown pairs of maps and asked which map in each pair best corresponds to local community boundaries.- The sequence and order of the maps shown were randomised.- Assuming that residents and experts can recognise (but not necessarily recall) social boundaries, we conjecture that participants would choose the map containing borders with higher boundary values.Hypothesis: We hypothesise that participants will agree with the predictions of the areal wombling algorithm and choose boundaries with higher boundary values.Null hypothesis: Participants are not more or less likely to choose boundaries with higher boundary values.Aside from testing a hypothesis, another motivation behind the study is to explore the feasibility of the method for future replications and follow-up research.More informationThis study was approved by the University of Sheffield ethics committee (application number 042378).Please read the README file for a more detailed description of the content of this repository.

  17. R

    Mental Mapping and Spatial Awareness among Urban Policymakers in CEE/FSU

    • rds.icm.edu.pl
    Updated Jun 16, 2025
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    Drozda, Łukasz (2025). Mental Mapping and Spatial Awareness among Urban Policymakers in CEE/FSU [Dataset]. http://doi.org/10.60894/UH77LS
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    Dataset updated
    Jun 16, 2025
    Dataset provided by
    Repozytorium Danych Społecznych
    Authors
    Drozda, Łukasz
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2021 - 2024
    Dataset funded by
    Polish National Agency for Academic Exchange
    University of Warsaw
    National Science Centre (Poland)
    Description

    The dataset comprises mental maps of five cities in CEE/FSU produced by a broad spectrum of urban policymakers, understood here as any actors who influence the formulation of urban policies. These actors fall into three categories:Public officials (both elected and appointed), including civil servants and urban planners employed by municipal administrations at managerial and subordinate levels;Business representatives - primarily real-estate developers, property owners, and employees of IT companies whose operations depend on urban space, and companies active in the leisure and tourism sectors;Urban reviewers - individuals who critically evaluate urban issues, such as analysts, researchers, journalists, and activists.However, these roles may overlap when someone has experience in multiple fields.Respondents’ sketches were collected during individual semi-structured, in-depth interviews (IDI) conducted with the aforementioned policymakers in five cities: Leipzig (Germany), Warsaw (Poland), Kyiv and Lviv (Ukraine), and Tallinn (Estonia). The material was gathered during field-research trips carried out between 2021 and 2024. Ukrainian interviews occurred in the summer and autumn of 2021, shortly before Russia’s full-scale invasion. Although the study focuses on so-called post-socialist cities, the dataset can be used for broader investigations of contemporary urbanization processes.During the interviews, respondents were asked to identify positive, neutral, and negative elements, places, and problems relevant to their city using three colors - green for positives, black for neutrals, and red for negatives. They produced freehand sketches in any technique they preferred - stand-alone cartographic renderings, perspective drawings, mind, or word maps. To minimize interviewer influence, no base maps (printed or digital) were provided; respondents generated their associative maps entirely from memory.

  18. a

    Geodatabase Templates - Watershed Study Map Package

    • hub.arcgis.com
    • data.geospatialhub.org
    Updated Feb 6, 2018
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    wrds_wdo (2018). Geodatabase Templates - Watershed Study Map Package [Dataset]. https://hub.arcgis.com/documents/51404fcbb949455f930a0db8e0ef2e70
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    Dataset updated
    Feb 6, 2018
    Dataset authored and provided by
    wrds_wdo
    Description

    This package contains a project specific geodatabase and map (.mxd) for Watershed study projects. For directions on using this file, see the GIS Standards Technical Memorandum on the Standards Page.

  19. D

    Stimulus Data for "Comparative Study on the Perception of Direction in...

    • darus.uni-stuttgart.de
    Updated May 26, 2023
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    Alexandra Hirsch; Max Franke; Steffen Koch (2023). Stimulus Data for "Comparative Study on the Perception of Direction in Animated Map Transitions Using Different Map Projections" [Dataset]. http://doi.org/10.18419/DARUS-3463
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 26, 2023
    Dataset provided by
    DaRUS
    Authors
    Alexandra Hirsch; Max Franke; Steffen Koch
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Dataset funded by
    DFG
    Description

    We compare how well participants can determine the geographical direction of an animated map transition. In our between-subject online study, each of three groups is shown map transitions in one map projection: Mercator, azimuthal equidistant projection, or two-point equidistant projection. The distances of the start and end point are varied. Map transitions zoom out and pan towards the middle point, then zoom in and continue panning, following the recommendations by Van Wijk and Nuij (IEEE InfoVis, 2003). We measure response time and accuracy in the task. We evaluate the results by the sample means per participant, using interval estimation with 95% confidence intervals. We construct the confidence intervals by using BCa bootstrapping. The study is pre-registered on OSF.io, but due to file size limitations, we were not able to submit the video stimuli there. Instead, we provide them here. This repository contains the MPEG-4 video files that were shown to the participants in the videos/ folder. These are numbered from 0 to 1199 for each of the three map projections, which are also stated in the file name, for a total of 3,600 video stimuli. An additional 3×6 example stimuli are also included. For each video stimulus, a JSON file with the same prefix file name (projection + number) is located in the metadata/ folder. These files contain the ground truth metadata for the respective stimulus. The stimuli shown for teaching the participants the task are located with the same structure under the examples/ folder. The entire source code for the study is also available in the related publication. The related repository includes: The code for generating the individual PNG frames, and JSON metadata, for each stimulus. The server and front-end code for the online study itself. The Python and R code for evaluating the study results.

  20. f

    Data from: Perceptual complexity of soil-landscape maps: a user evaluation...

    • tandf.figshare.com
    mp4
    Updated May 31, 2023
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    Arzu Çöltekin; Alžběta Brychtová; Amy L. Griffin; Anthony C. Robinson; Mark Imhof; Chris Pettit (2023). Perceptual complexity of soil-landscape maps: a user evaluation of color organization in legend designs using eye tracking [Dataset]. http://doi.org/10.6084/m9.figshare.4033545.v1
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    mp4Available download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Arzu Çöltekin; Alžběta Brychtová; Amy L. Griffin; Anthony C. Robinson; Mark Imhof; Chris Pettit
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    We compared the ability of two legend designs on a soil-landscape map to efficiently and effectively support map reading tasks with the goal of better understanding how the design choices affect user performance. Developing such knowledge is essential to design effective interfaces for digital earth systems. One of the two legends contained an alphabetical ordering of categories, while the other used a perceptual grouping based on the Munsell color space. We tested the two legends for 4 tasks with 20 experts (in geography-related domains). We analyzed traditional usability metrics and participants’ eye movements to identify the possible reasons behind their success and failure in the experimental tasks. Surprisingly, an overwhelming majority of the participants failed to arrive at the correct responses for two of the four tasks, irrespective of the legend design. Furthermore, participants’ prior knowledge of soils and map interpretation abilities led to interesting performance differences between the two legend types. We discuss how participant background might have played a role in performance and why some tasks were particularly hard to solve despite participants’ relatively high levels of experience in map reading. Based on our observations, we caution soil cartographers to be aware of the perceptual complexity of soil-landscape maps.

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U.S. Geological Survey (2024). Bighorn Mountains Forest Mapping Study Area [Dataset]. https://catalog.data.gov/dataset/bighorn-mountains-forest-mapping-study-area

Bighorn Mountains Forest Mapping Study Area

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Dataset updated
Jul 6, 2024
Dataset provided by
United States Geological Surveyhttp://www.usgs.gov/
Area covered
Bighorn Mountains
Description

This is the study area associated with the project: “Status and Trends of Deciduous Communities in the Bighorn Mountains”. The aim of the study is to assess the current trends of deciduous communities in the Bighorn National Forest in north-central Wyoming. The data here represents phase I of the project, completed in FY2017. The USGS created a synthesis map of coniferous and deciduous communities in the Bighorn Mountains of Wyoming using a species distribution modeling approach developed in the Wyoming Landscape Conservation Initiative (WLCI) (Assal et al. 2015). The modeling framework utilized a number of topographic covariates and temporal remote sensing data from the early, mid and late growing season to capitalize on phenological differences in vegetation types. We used the program RandomForest in the R statistical program to generate probability of occurrence models for deciduous and coniferous vegetation. The binary maps were combined into a synthesis map using the procedure from Assal et al. 2015. In Phase II of this project (to be completed in FY2018 and 2019), the USGS will conduct a preliminary assessment on the baseline condition of riparian deciduous communities. This will be a proof-of-concept study where the USGS will apply a framework used in prior research in upland aspen and sagebrush communities to detect trends in riparian vegetation condition from the mid-1980s to present. Literature Cited Assal et al. 2015: https://doi.org/10.1080/2150704X.2015.1072289

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