65 datasets found
  1. Getting to Know Web GIS, fourth edition

    • dados-edu-pt.hub.arcgis.com
    Updated Aug 13, 2020
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    Esri Portugal - Educação (2020). Getting to Know Web GIS, fourth edition [Dataset]. https://dados-edu-pt.hub.arcgis.com/datasets/getting-to-know-web-gis-fourth-edition
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    Dataset updated
    Aug 13, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Portugal - Educação
    License

    Attribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
    License information was derived automatically

    Description

    Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

  2. D

    Private Schools

    • data.seattle.gov
    • catalog.data.gov
    • +1more
    application/rdfxml +5
    Updated Feb 3, 2025
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    (2025). Private Schools [Dataset]. https://data.seattle.gov/d/yhx2-78xm
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    xml, csv, json, application/rdfxml, application/rssxml, tsvAvailable download formats
    Dataset updated
    Feb 3, 2025
    Description

    These points represent private schools as approved through the Washington State Board of Education. For more information please visit the SBE website.

    Displays data from CARTO.PRIV_SCH. Labels based on the attribute NAME. Data is downloaded from website as an .xlsx, then queried for City = Seattle, then geocoded.

    Updated as needed, last update August 2024.

  3. a

    High Schools, by Top to Bottom Percentile, 2013-14

    • datadrivendetroit-dcdev.hub.arcgis.com
    • detroitdata.org
    • +5more
    Updated Apr 6, 2016
    + more versions
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    Data Driven Detroit (2016). High Schools, by Top to Bottom Percentile, 2013-14 [Dataset]. https://datadrivendetroit-dcdev.hub.arcgis.com/datasets/D3::high-schools-by-top-to-bottom-percentile-2013-14
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    Dataset updated
    Apr 6, 2016
    Dataset authored and provided by
    Data Driven Detroit
    License

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

    Area covered
    Description

    These show the locations of open schools for the 2015-16 school year for the Detroit Tri-County area. State of Michigan Top to Bottom scores are attached to schools where available. Top to Bottom ranks come from MISchoolData.org

  4. e

    Marking Schedule - Find the Best Site - Geo 2.8

    • gisinschools.eagle.co.nz
    • resources-gisinschools-nz.hub.arcgis.com
    Updated Sep 11, 2023
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    GIS in Schools - Teaching Materials - New Zealand (2023). Marking Schedule - Find the Best Site - Geo 2.8 [Dataset]. https://gisinschools.eagle.co.nz/documents/79f870aeb66b496c8653a6b147118a72
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    Dataset updated
    Sep 11, 2023
    Dataset authored and provided by
    GIS in Schools - Teaching Materials - New Zealand
    Description

    Teachers need to be very familiar with the outcome being assessed by Achievement Standard Geography 91247. The achievement criteria and the explanatory notes contain information, definitions, and requirements that are crucial when interpreting the standard and assessing students against it.The following marking schedule is designed to help teachers mark the work submitted by students for the Find the Best Site Assessment.

  5. 2010-2014 ACS School Enrollment Variables - Boundaries

    • hub.arcgis.com
    • mapdirect-fdep.opendata.arcgis.com
    Updated Dec 1, 2020
    + more versions
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    Esri (2020). 2010-2014 ACS School Enrollment Variables - Boundaries [Dataset]. https://hub.arcgis.com/maps/643e0b5c942e435b9510ef97b59e822a
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    Dataset updated
    Dec 1, 2020
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows public vs. private school enrollment by sex by grade group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Any schools that receives public funding are considered public, including continuation schools and some charter & online schools. This layer is symbolized to show the percentage of students in kindergarten through 12th grade who are enrolled in a private school. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B14002 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

  6. a

    Elementary Schools, by Top to Bottom Percentile, 2013-14

    • d3-portal-v2-d176b-d3.opendata.arcgis.com
    • detroitdata.org
    • +1more
    Updated Apr 6, 2016
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    Data Driven Detroit (2016). Elementary Schools, by Top to Bottom Percentile, 2013-14 [Dataset]. https://d3-portal-v2-d176b-d3.opendata.arcgis.com/datasets/D3::schools-toptobottom?layer=0
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    Dataset updated
    Apr 6, 2016
    Dataset authored and provided by
    Data Driven Detroit
    License

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

    Area covered
    Description

    These show the locations of open schools for the 2015-16 school year for the Detroit Tri-County area. State of Michigan Top to Bottom scores are attached to schools where available. Top to Bottom ranks come from MISchoolData.org

  7. a

    Vocational School Districts

    • hub.arcgis.com
    Updated Jan 17, 2024
    + more versions
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    MassGIS - Bureau of Geographic Information (2024). Vocational School Districts [Dataset]. https://hub.arcgis.com/datasets/046086371d0f457aa3834317b0d747f9
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    Dataset updated
    Jan 17, 2024
    Dataset authored and provided by
    MassGIS - Bureau of Geographic Information
    Area covered
    Description

    The charter, collaborative, superintendency union, and vocational (CCUV) districts layer includes the following district types:Charter - Charter schools are independent public schools that operate under five-year charters granted by the Commonwealth's Board of Elementary and Secondary Education (BESE). The increased freedom available to charter schools coupled with increased accountability, infuses all aspects of BESE's oversight of charter schools, beginning with the rigorous application process that groups must go through to receive a charter. Once BESE has awarded a charter, the new charter school has the freedom to organize around a core mission, curriculum, theme, or teaching method. It is allowed to control its own budget and hire (and fire) teachers and staff. In return for this freedom, a charter school must demonstrate good results within five years or risk losing its charter.Collaborative - Educational Collaboratives are formed by local school committees and charter boards under the provisions of Chapter 40, Section 4E. The purpose of an educational collaborative is to supplement and strengthen the programs and services of member school committees and charter boards. All educational collaborative agreements and amendments must be approved by the member school committees and charter boards and BESE.Superintendency Union - Superintendency unions (sometimes called "school unions") are cooperative arrangements between two or more school committees (typically in small towns) to share the services of a superintendent of schools and central office staff, while allowing each town to keep its own school committee and school buildings. The law authorizing school unions was enacted in 1870, predating the law authorizing regional school districts, which was enacted in 1949.Regional Vocational Technical - Career/vocational technical education programs is the term used to denote Chapter 74-approved vocational technical education programs and non-Chapter 74 career and technical education programs. Administered by a regional vocational school committee.More details...Feature service also available.

  8. Elementary School Districts

    • hub.arcgis.com
    • gisnation-sdi.hub.arcgis.com
    Updated Jun 23, 2021
    + more versions
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    Esri U.S. Federal Datasets (2021). Elementary School Districts [Dataset]. https://hub.arcgis.com/maps/fedmaps::elementary-school-districts-1
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    Dataset updated
    Jun 23, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri U.S. Federal Datasets
    License

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

    Description

    Elementary School DistrictsThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays elementary school districts in the United States. Per the USCB, "School Districts are geographic entities within which state, county, local officials, the Bureau of Indian Affairs, or the U.S. Department of Defense provide public educational services for the area’s residents. Elementary school districts provide education to the lower grade/age levels."Edgartown School DistrictData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Elementary School Districts) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 78 (Series Information for Elementary School Districts State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (Elementary School Districts - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: School District BoundariesFor feedback, please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

  9. l

    Top 50: Safe Routes to Schools - Los Angeles

    • geohub.lacity.org
    • visionzero.geohub.lacity.org
    • +2more
    Updated Nov 2, 2015
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    Los Angeles Department of Transportation (2015). Top 50: Safe Routes to Schools - Los Angeles [Dataset]. https://geohub.lacity.org/datasets/ladot::top-50-safe-routes-to-schools-los-angeles
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    Dataset updated
    Nov 2, 2015
    Dataset authored and provided by
    Los Angeles Department of Transportation
    Area covered
    Description

    For more information on Safe Routes to School Los Angeles, go to saferoutes.lacity.org.

  10. World Imagery

    • cacgeoportal.com
    • inspiracie.arcgeo.sk
    • +5more
    Updated Dec 12, 2009
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    Esri (2009). World Imagery [Dataset]. https://www.cacgeoportal.com/maps/10df2279f9684e4a9f6a7f08febac2a9
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    Dataset updated
    Dec 12, 2009
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    World,
    Description

    World Imagery provides one meter or better satellite and aerial imagery for most of the world’s landmass and lower resolution satellite imagery worldwide. The map is currently comprised of the following sources:Worldwide 15-m resolution TerraColor imagery at small and medium map scales.Maxar imagery basemap products around the world: Vivid Premium at 15-cm HD resolution for select metropolitan areas, Vivid Advanced 30-cm HD for more than 1,000 metropolitan areas, and Vivid Standard from 1.2-m to 0.6-cm resolution for the most of the world, with 30-cm HD across the United States and parts of Western Europe. More information on the Maxar products is included below. High-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 30-cm to 3-cm resolution. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program.Maxar Basemap ProductsVivid PremiumProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product provides 15-cm HD resolution imagery.Vivid AdvancedProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product includes a mix of native 30-cm and 30-cm HD resolution imagery.Vivid StandardProvides a visually consistent and continuous image layer over large areas through advanced image mosaicking techniques, including tonal balancing and seamline blending across thousands of image strips. Available from 1.2-m down to 30-cm HD. More on Maxar HD.Updates and CoverageYou can use the World Imagery Updates app to learn more about recent updates and map coverage.CitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map.UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map.FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

  11. W

    California School District Areas 2018-19

    • wifire-data.sdsc.edu
    • data.ca.gov
    • +2more
    csv, esri rest +3
    Updated Feb 12, 2021
    + more versions
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    CA Governor's Office of Emergency Services (2021). California School District Areas 2018-19 [Dataset]. https://wifire-data.sdsc.edu/dataset/california-school-district-areas-2018-19
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    kml, html, geojson, esri rest, csvAvailable download formats
    Dataset updated
    Feb 12, 2021
    Dataset provided by
    CA Governor's Office of Emergency Services
    Area covered
    California
    Description

    This comprehensive layer of California public school districts combines all elementary, secondary and unified district area boundaries into a single file and serves as the authoritative geographic data source for all school district area boundaries in California for the 2018-19 academic year. School districts are single purpose governmental units that operate schools and provide public educational services to residents within geographically defined areas. Agencies considered school districts that do not use geographically defined service areas to determine enrollment are excluded from this data set. In order to view districts represented as point locations, please see the "California School District Offices" layer. The school districts in this layer are enriched with additional district-level attribute information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.

    School districts are categorized as either elementary (primary), high (secondary) or unified based on the general grade range of the schools operated by the district. Elementary school districts provide education to the lower grade/age levels and the high school districts provide education to the upper grade/age levels while unified school districts provide education to all grade/age levels in their service areas. Boundaries for the elementary, high and unified school district layers are combined into a single file. The resulting composite layer includes areas of overlapping boundaries since elementary and high school districts each serve a different grade range of students within the same territory. The 'DistrictType' field can be used to filter and display districts separately by type.

    Boundary lines are maintained by the California Department of Education (CDE) and are effective in the 2018-19 academic year . The CDE works collaboratively with the US Census Bureau to update and maintain boundary information as part of the federal School District Review Program (SDRP). The Census Bureau uses these school district boundaries to develop annual estimates of children in poverty to help the U.S. Department of Education determine the annual allocation of Title I funding to states and school districts. The National Center for Education Statistics (NCES) also uses the school district boundaries to develop a broad collection of district-level demographic estimates from the Census Bureau’s American Community Survey (ACS).

    The school district enrollment and demographic information are based on the 2018-19 academic year student enrollment counts collected on Fall Census Day in 2018 (first Wednesday in October). These data elements are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website.


    Source.

  12. d

    Shoreline Mapping Program of Back Bays, Great Egg Harbor Bay to Reed Bay,...

    • catalog.data.gov
    • fisheries.noaa.gov
    Updated Oct 31, 2024
    + more versions
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    NGS Communications and Outreach Branch (Point of Contact, Custodian) (2024). Shoreline Mapping Program of Back Bays, Great Egg Harbor Bay to Reed Bay, NJ, NJ1404D-TB-C [Dataset]. https://catalog.data.gov/dataset/shoreline-mapping-program-of-back-bays-great-egg-harbor-bay-to-reed-bay-nj-nj1404d-tb-c1
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    Dataset updated
    Oct 31, 2024
    Dataset provided by
    NGS Communications and Outreach Branch (Point of Contact, Custodian)
    Area covered
    New Jersey, Great Egg Harbor Bay
    Description

    These data provide an accurate high-resolution shoreline compiled from imagery of Back Bays, Great Egg Harbor Bay to Reed Bay, NJ . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

  13. a

    Best Practices Scores

    • gis.data.alaska.gov
    • egrants-hub-dcced.hub.arcgis.com
    • +5more
    Updated Jul 14, 2021
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    Dept. of Commerce, Community, & Economic Development (2021). Best Practices Scores [Dataset]. https://gis.data.alaska.gov/maps/DCCED::best-practices-scores/explore
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    Dataset updated
    Jul 14, 2021
    Dataset authored and provided by
    Dept. of Commerce, Community, & Economic Development
    Area covered
    Description

    Dataset containing all nine metrics for the Utility Management program Best Practice Scores, including data collected by Rural Utility Business Advisor (RUBA).These scores are collected in collaboration with the Department of Environmental Conservation twice a year. Data collection started in 2016 and is currently on-going. Best Practice scores help to determine funding for water utility projects.

  14. Digital Geologic-GIS Map of Great Sand Dunes National Park, Colorado (NPS,...

    • catalog.data.gov
    Updated Feb 15, 2025
    + more versions
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    National Park Service (2025). Digital Geologic-GIS Map of Great Sand Dunes National Park, Colorado (NPS, GRD, GRI, GRSA, GRSA digital map) adapted from a U.S. Geological Survey Scientific Investigations Map by Madole, VanSistine and Romig (2016) [Dataset]. https://catalog.data.gov/dataset/digital-geologic-gis-map-of-great-sand-dunes-national-park-colorado-nps-grd-gri-grsa-grsa-
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    Dataset updated
    Feb 15, 2025
    Dataset provided by
    National Park Servicehttp://www.nps.gov/
    Area covered
    Colorado
    Description

    The Digital Geologic-GIS Map of Great Sand Dunes National Park, Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (grsa_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (grsa_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (grsa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (grsa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (grsa_geology_metadata_faq.pdf). Please read the grsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (grsa_geology_metadata.txt or grsa_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:35,000 and United States National Map Accuracy Standards features are within (horizontally) 17.8 meters or 58.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

  15. School District Characteristics - Current

    • datasets.ai
    • s.cnmilf.com
    • +1more
    15, 21, 25, 3, 33, 55 +2
    Updated Aug 8, 2024
    + more versions
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    Department of Education (2024). School District Characteristics - Current [Dataset]. https://datasets.ai/datasets/school-district-characteristics-current-f96a2
    Explore at:
    15, 57, 55, 33, 3, 25, 21, 8Available download formats
    Dataset updated
    Aug 8, 2024
    Dataset provided by
    United States Department of Educationhttp://ed.gov/
    Authors
    Department of Education
    Description

    The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are special-purpose governments and administrative units designed by state and local officials to provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to develop demographic estimates and to support educational research and program administration. The NCES Common Core of Data (CCD) program is an annual collection of basic administrative characteristics for all public schools, school districts, and state education agencies in the United States. These characteristics are reported by state education officials and include directory information, number of students, number of teachers, grade span, and other conditions. The administrative attributes in this layer were developed from the most current CCD collection available. For more information about NCES school district boundaries, see: https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries. For more information about CCD school district attributes, see: https://nces.ed.gov/ccd/files.asp.


    Notes:

    -1 or M

    Indicates that the data are missing.

    -2 or N

    Indicates that the data are not applicable.

    -9

    Indicates that the data do not meet NCES data quality standards.

    Collections are available for the following years:

    All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

  16. GIS dataset of Onshore Energy Security Program (OESP) Seismic Surveys

    • ecat.ga.gov.au
    • datadiscoverystudio.org
    • +2more
    Updated Jan 1, 2011
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    Commonwealth of Australia (Geoscience Australia) (2011). GIS dataset of Onshore Energy Security Program (OESP) Seismic Surveys [Dataset]. https://ecat.ga.gov.au/geonetwork/srv/api/records/b0b62dea-f5d8-5060-e044-00144fdd4fa6
    Explore at:
    www:link-1.0-http--linkAvailable download formats
    Dataset updated
    Jan 1, 2011
    Dataset provided by
    Geoscience Australiahttp://ga.gov.au/
    Area covered
    Description

    This GIS dataset shows where Geoscience Australia (GA) has acquired regional seismic reflection data as a part of Australian Government's Onshore Energy Security Program (OESP) in collaboration with State and Territory geological surveys, AuScope and Australian National Seismic Imaging Resource (ANSIR). During 2006-2011 GA collected over 6,500 line kilometres of new world-class seismic reflection data within Australia for use by industry and government. This dataset is generated from files containing CDP (Common Depth Point) coordinates of all OESP seismic surveys. The CDP line is a curve of best fit through the midpoints between sources and receivers, which optimises the fold of the data while minimising the subsurface area of reflections contributing to each nominal CDP. Each trace (source-receiver pair) is allocated to the nearest CDP bin to its midpoint. The interval between each CDP traces is 20 metres.

  17. D

    Middle School Attendance Areas 2023-2024

    • data.seattle.gov
    • catalog.data.gov
    • +3more
    application/rdfxml +4
    Updated Feb 3, 2025
    + more versions
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    (2025). Middle School Attendance Areas 2023-2024 [Dataset]. https://data.seattle.gov/dataset/Middle-School-Attendance-Areas-2023-2024/xemx-mjad
    Explore at:
    csv, application/rdfxml, xml, application/rssxml, jsonAvailable download formats
    Dataset updated
    Feb 3, 2025
    Description

    An attendance area school is one to which elementary, middle and high-school students are assigned based on where they live, as long as the school offers the services the student needs.

    For questions, please contact enrollmentplanning@seattleschools.org

  18. California Schools and Legislative Districts

    • data.ca.gov
    • gis.data.ca.gov
    • +2more
    html
    Updated Dec 7, 2022
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    California Department of Education (2022). California Schools and Legislative Districts [Dataset]. https://data.ca.gov/dataset/california-schools-and-legislative-districts
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Dec 7, 2022
    Dataset authored and provided by
    California Department of Educationhttps://www.cde.ca.gov/
    Area covered
    California
    Description

    This web map of California legislative districts includes the geographically defined territories used for representation in the California State Assembly, California State Senate and the US House of Representatives from California. These three boundary layers are derived from the US Census Bureau's 2018 TIGER/Line database and are designed to overlay with the California Department of Education’s (CDE) education related GIS content.

    The 80 California State Assembly Districts represent the geographically defined territories used for electing members to the lower (house) chamber of the California State Legislature. The current state assembly boundaries were determined by the California Citizens Redistricting Commission following the completion of the 2010 United States Census and will remain valid until 2020.

    The 40 state senate districts represent the geographically defined territories used for electing members to the upper (senate) chamber of the California State Legislature. The current state senate boundaries were determined by the California Citizens Redistricting Commission following the completion of the 2010 United States Census and will remain valid until 2020.

    The 53 congressional districts within the State of California represent the geographically defined territories used for electing members to the U.S. House of Representatives. The current U.S. Congressional boundaries in California were determined by the California Citizens Redistricting Commission following the completion of the 2010 United States Census and will remain valid until 2020

  19. School Districts

    • psrc-psregcncl.hub.arcgis.com
    • hub.arcgis.com
    Updated Jan 1, 2021
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    Puget Sound Regional Council (2021). School Districts [Dataset]. https://psrc-psregcncl.hub.arcgis.com/datasets/school-districts
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    Dataset updated
    Jan 1, 2021
    Dataset authored and provided by
    Puget Sound Regional Councilhttp://www.psrc.org/
    Area covered
    Description

    This layer represents the boundaries of 295 Public School Districts in the PSRC region. This data was compiled by the Washington Office of Superintendent of Public Instruction (OSPI) to provide boundary and attribute information for the 295 Public School Districts in the State of Washington. The polygons are the best representation of current district boundaries based on legal descriptions, county, and other available GIS data. Users should contact the local school district(s) to confirm the interpretation of district boundaries in questions.

  20. a

    School Site Polygons, San Diego County

    • hhubsandiego-ucsdonline.hub.arcgis.com
    Updated Oct 30, 2022
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    University of California San Diego (2022). School Site Polygons, San Diego County [Dataset]. https://hhubsandiego-ucsdonline.hub.arcgis.com/datasets/school-site-polygons-san-diego-county
    Explore at:
    Dataset updated
    Oct 30, 2022
    Dataset authored and provided by
    University of California San Diego
    Area covered
    Description

    Polygons of public and private school sites. Includes elementary, middle, and high schools. Intended use is mapping and general analysis purposes. Provided by SANGIS and hosted by H-Hub at UC San Diego. School site addresses maintained by California Department of Education (CDE) were geocoded to the SanGIS parcels and street centerlines. Using aerial imagery, online research, site visits, and/or over-the-phone verification, attributes were corrected and/or polygons were adjusted to best represent the portion of the parcel in active use for school-related activities. This layer includes active schools.

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Esri Portugal - Educação (2020). Getting to Know Web GIS, fourth edition [Dataset]. https://dados-edu-pt.hub.arcgis.com/datasets/getting-to-know-web-gis-fourth-edition
Organization logo

Getting to Know Web GIS, fourth edition

Explore at:
Dataset updated
Aug 13, 2020
Dataset provided by
Esrihttp://esri.com/
Authors
Esri Portugal - Educação
License

Attribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
License information was derived automatically

Description

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

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