Learn how to create and host a school locator map using ArcGIS Online. This video demonstrates how to add data, create a map, and share a map into a website or web map application. It also provides an example of using the School Locator web mapping application template using Web App Builder by Esri's ArcGIS for Local Government team. Please contact k12@esri.ca for more information.

Thinking Spatially Using GISThinking Spatially Using GIS is a 1:1 set of instructional materials for students that use ArcGIS Online to teach basic geography concepts found in upper elementary school and above.
Each module has both a teacher and student file.After Christopher Columbus found the New World in 1492, Spain and Portugal were eager to conquer and claim new lands. The two world powers decided to divide the world in half by drawing a line that ran through the Atlantic Ocean. Based on this line, Spain could claim new lands in the western half of the world, and Portugal could claim lands in the eastern half.The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG All Esri GeoInquiries can be found at http://www.esri.com/geoinquiries

I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.

Current data from 2023-24 school year. Dataset to be updated annually.Data sources:Public Schools (includes charter and Adult): CDE - https://www.cde.ca.gov/schooldirectory/report?rid=dl1&tp=txtPublic Schools enrollment and enhanced location: CDE - https://lacounty.maps.arcgis.com/home/item.html?id=61a4260e68b14a5ab91daf27d4415e7dPrivate Schools type and location: CDE - https://www.cde.ca.gov/schooldirectory/, query for private schoolsPrivate Schools enrollment and contact: CDE - https://www.cde.ca.gov/ds/si/ps/documents/privateschooldata2324.xlsxColleges and Universities: HIFLD - https://hifld-geoplatform.hub.arcgis.com/datasets/geoplatform::colleges-and-universities/aboutPublic schools use location from the CDE AGOL Layer where available. This source assigns X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy, often geocoding to parcel.Field Descriptions:Category1: Always "Education"Category2: School Level Category3: School Type Organization: School District for primary and secondary schools; data maintainer otherwise Source: Source of data (see source links above) Source ID: CDS Code for primary and secondary schools; IPEDS ID for colleges and universities Source Date: Date listed in source Enrollment: School EnrollmentLabel Class: School classification for symbology (matches either Category2 or Category3)Last Update: Date last updated by LA County Enterprise GIS

The Contact Us page details numerous options for communication as well as the names and titles of CelebrateOne Staff. Contact Us page contents:CelebrateOne Office Contact InformationSend A MessageCelebrateOne Staff Names And Titles Navigating The CelebrateOne Website E-Mailing List Sign Up

CelebrateOne 2017 Annual Report

This layer is sourced from gis.dot.state.oh.us.

© ODOT

Areas near schools that are designated as school speed zones.Refresh: DailyContact: DOT_IT_GIS@seattle.gov

The ability to analyze and classify three-dimensional (3D) biological morphology has lagged behind the analysis of other biological data types such as gene sequences. Here, we introduce the techniques of data mining to the study of 3D biological shapes to bring the analyses of phenomes closer to the efficiency of studying genomes. We compiled five training sets of highly variable morphologies of mammalian teeth from the MorphoBrowser database. Samples were labeled either by dietary class or by conventional dental types (e.g. carnassial, selenodont). We automatically extracted a multitude of topological attributes using Geographic Information Systems (GIS)-like procedures that were then used in several combinations of feature selection schemes and probabilistic classification models to build and optimize classifiers for predicting the labels of the training sets. In terms of classification accuracy, computational time and size of the feature sets used, non-repeated best-first search combined with 1-nearest neighbor classifier was the best approach. However, several other classification models combined with the same searching scheme proved practical. The current study represents a first step in the automatic analysis of 3D phenotypes, which will be increasingly valuable with the future increase in 3D morphology and phenomics databases.

Layer showing location of property owned by Virginia Beach City Public Schools. Property includes locations of elementary, middle and high schools, along with locations of administrative facilities. For related information on this dataset please see the VBCPS school locator website.https://www.vbschools.com/cms/One.aspx?portalId=78094&pageId=205225For questions, please email vbschzones@vbschools.com or call 757.263.1055.

School Districts

The construction of this data model was adapted from the Telvent Miner & Miner ArcFM MultiSpeak data model to provide interface functionality with Milsoft Utility Solutions WindMil engineering analysis program. Database adaptations, GPS data collection, and all subsequent GIS processes were performed by Southern Geospatial Services for the Town of Apex Electric Utilities Division in accordance to the agreement set forth in the document "Town of Apex Electric Utilities GIS/GPS Project Proposal" dated March 10, 2008. Southern Geospatial Services disclaims all warranties with respect to data contained herein. Questions regarding data quality and accuracy should be directed to persons knowledgeable with the forementioned agreement.The data in this GIS with creation dates between March of 2008 and April of 2024 were generated by Southern Geospatial Services, PLLC (SGS). The original inventory was performed under the above detailed agreement with the Town of Apex (TOA). Following the original inventory, SGS performed maintenance projects to incorporate infrastructure expansion and modification into the GIS via annual service agreements with TOA. These maintenances continued through April of 2024.At the request of TOA, TOA initiated in house maintenance of the GIS following delivery of the final SGS maintenance project in April of 2024. GIS data created or modified after April of 2024 are not the product of SGS.With respect to SGS generated GIS data that are point features:GPS data collected after January 1, 2013 were surveyed using mapping grade or survey grade GPS equipment with real time differential correction undertaken via the NC Geodetic Surveys Real Time Network (VRS). GPS data collected prior to January 1, 2013 were surveyed using mapping grade GPS equipment without the use of VRS, with differential correction performed via post processing.With respect to SGS generated GIS data that are line features:Line data in the GIS for overhead conductors were digitized as straight lines between surveyed poles. Line data in the GIS for underground conductors were digitized between surveyed at grade electric utility equipment. The configurations and positions of the underground conductors are based on TOA provided plans. The underground conductors are diagrammatic and cannot be relied upon for the determination of the actual physical locations of underground conductors in the field.The Service Locations feature class was created by Southern Geospatial Services (SGS) from a shapefile of customer service locations generated by dataVoice International (DV) as part of their agreement with the Town of Apex (TOA) regarding the development and implemention of an Outage Management System (OMS).Point features in this feature class represent service locations (consumers of TOA electric services) by uniquely identifying the features with the same unique identifier as generated for a given service location in the TOA Customer Information System (CIS). This is also the mechanism by which the features are tied to the OMS. Features are physically located in the GIS based on CIS address in comparison to address information found in Wake County GIS property data (parcel data). Features are tied to the GIS electric connectivity model by identifying the parent feature (Upline Element) as the transformer that feeds a given service location.SGS was provided a shapefile of 17992 features from DV. Error potentially exists in this DV generated data for the service location features in terms of their assigned physical location, phase, and parent element.Regarding the physical location of the features, SGS had no part in physically locating the 17992 features as provided by DV and cannot ascertain the accuracy of the locations of the features without undertaking an analysis designed to verify or correct for error if it exists. SGS constructed the feature class and loaded the shapefile objects into the feature class and thus the features exist in the DV derived location. SGS understands that DV situated the features based on the address as found in the CIS. No features were verified as to the accuracy of their physical location when the data were originally loaded. It is the assumption of SGS that the locations of the vast majority of the service location features as provided by DV are in fact correct.SGS understands that as a general rule that DV situated residential features (individually or grouped) in the center of a parcel. SGS understands that for areas where multiple features may exist in a given parcel (such as commercial properties and mobile home parks) that DV situated features as either grouped in the center of the parcel or situated over buildings, structures, or other features identifiable in air photos. It appears that some features are also grouped in roads or other non addressed locations, likely near areas where they should physically be located, but that these features were not located in a final manner and are either grouped or strung out in a row in the general area of where DV may have expected they should exist.Regarding the parent and phase of the features, the potential for error is due to the "first order approximation" protocol employed by DV for assigning the attributes. With the features located as detailed above, SGS understands that DV identified the transformer closest to the service location (straight line distance) as its parent. Phase was assigned to the service location feature based on the phase of the parent transformer. SGS expects that this protocol correctly assigned parent (and phase) to a significant portion of the features, however this protocol will also obviously incorretly assign parent in many instances.To accurately identify parent for all 17992 service locations would require a significant GIS and field based project. SGS is willing to undertake a project of this magnitude at the discretion of TOA. In the meantime, SGS is maintaining (editing and adding to) this feature class as part of the ongoing GIS maintenance agreement that is in place between TOA and SGS. In lieu of a project designed to quality assess and correct for the data provided by DV, SGS will verify the locations of the features at the request of TOA via comparison of the unique identifier for a service location to the CIS address and Wake County parcel data address as issues arise with the OMS if SGS is directed to focus on select areas for verification by TOA. Additionally, as SGS adds features to this feature class, if error related to the phase and parent of an adjacent feature is uncovered during a maintenance, it will be corrected for as part of that maintenance.With respect to the additon of features moving forward, TOA will provide SGS with an export of CIS records for each SGS maintenance, SGS will tie new accounts to a physical location based on address, SGS will create a feature for the CIS account record in this feature class at the center of a parcel for a residential address or at the center of a parcel or over the correct (or approximately correct) location as determined via air photos or via TOA plans for commercial or other relevant areas, SGS will identify the parent of the service location as the actual transformer that feeds the service location, and SGS will identify the phase of the service address as the phase of it's parent.Service locations with an ObjectID of 1 through 17992 were originally physically located and attributed by DV.Service locations with an ObjectID of 17993 or higher were originally physically located and attributed by SGS.DV originated data are provided the Creation User attribute of DV, however if SGS has edited or verified any aspect of the feature, this attribute will be changed to SGS and a comment related to the edits will be provided in the SGS Edits Comments data field. SGS originated features will be provided the Creation User attribute of SGS. Reference the SGS Edits Comments attribute field Metadata for further information.

This dataset demarcates the school district boundaries within Allegheny County

If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (https://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (https://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.

Category: Civic Vitality and Governance

Organization: Allegheny County

Department: Geographic Information Systems Group; Department of Information Technology

Temporal Coverage: current

Data Notes:

Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot

Development Notes: none

Other: none

Related Document(s): Data Dictionary (none)

Frequency - Data Change: As needed

Frequency - Publishing: As needed

Data Steward Name: Eli Thomas

Data Steward Email: gishelp@alleghenycounty.us

Feature class that compares the elevations between sand dune crests (extracted from available LiDAR datasets from 2010 and 2013) with published FEMA Base Flood Elevations (BFEs) from preliminary FEMA DFIRMS (Panels issued in 2018 and 2019) in coastal York and Cumberland counties (up through Willard Beach in South Portland). Steps to create the dataset included:Shoreline structures from the most recent NOAA EVI LANDWARD_SHORETYPE feature class were extracted using the boundaries of York and Cumberland counties. This included 1B: Exposed, Solid Man-Made structures, 8B: Sheltered, Solid Man-Made Structures; 6B: Riprap, and 8C: Sheltered Riprap. This resulted in the creation of Cumberland_ESIL_Structures and York_ESIL_Structures. Note that ESIL uses the MHW line as the feature base.Shoreline structures from the work by Rice (2015) were extracted using the York and Cumberland county boundaries. This resulted in the creation of Cumberland_Rice_Structures and York_Rice_Structures.Additional feature classes for structures were created for York and Cumberland county structures that were missed. This was Slovinsky_York_Structures and Slovinsky_Cumberland_Structures. GoogleEarth imagery was inspected while additional structures were being added to the GIS. 2012 York and Cumberland County imagery was used as the basemap, and structures were classified as bulkheads, rip rap, or dunes (if known). Also, whether or not the structure was in contact with the 2015 HAT was noted.MEDEP was consulted to determine which permit data (both PBR and Individual Permit, IP, data) could be used to help determine where shoreline stabilization projects may have been conducted adjacent to or on coastal bluffs. A file was received for IP data and brought into GIS (DEP_Licensing_Points). This is a point file for shoreline stabilization permits under NRPA.Clip GISVIEW.MEDEP.Permit_By_Rule_Locations to the boundaries of the study area and output DEP_PBR_Points.Join GISVIEW.sde>GISVIEW.MEDEP.PBR_ACTIVITY to the DEP_PBR_Points using the PBR_ID Field. Then, export this file as DEP_PBR_Points2. Using the new ACTIVITY_DESC field, select only those activities that relate to shoreline stabilization projects:PBR_ACTIVITY ACTIVITY_DESC02 Act. Adjacent to a Protected Natural Resource04 Maint Repair & Replacement of Structure08 Shoreline StabilizationSelect by Attributes > PBR_ACTIVITY IN (‘02’, ‘04’, ‘08’) select only those activities likely to be related to shoreline stabilization, and export the selected data as a DEP_PBR_Points3. Then delete 1 and 2, and rename this final product as DEP_PBR_Points.Next, visually inspect the Licensing and PBR files using ArcMap 2012, 2013 imagery, along with Google Earth imagery to determine the extents of armoring along the shoreline.Using EVI and Rice data as indicators, manually inspect and digitize sections of the coastline that are armored. Classify the seaward shoreline type (beach, mudflat, channel, dune, etc.) and the armor type (wall or bulkhead). Bring in the HAT line and, using that and visual indicators, identify whether or not the armored sections are in contact with HAT. Use Google Earth at the same time as digitizing in order to help constrain areas. Merge digitized armoring into Cumberland_York_Merged.Bring the preliminary FEMA DFIRM data in and use “intersect” to assign the different flood zones and elevations to the digitized armored sections. This was done first for Cumberland, then for York Counties. Delete ancillary attributes, as needed. Resulting layer is Cumberland_Structure_FloodZones and York_Structure_FloodZones.Go to NOAA Digital Coast Data Layers and download newest LiDAR data for York and Cumberland county beach, dune, and just inland areas. This includes 2006 and newer topobathy data available from 2010 (entire coast), and selected areas from 2013 and 2014 (Wells, Scarborough, Kennebunk).Mosaic the 2006, 2010, 2013 and 2014 data (with 2013 and 2014 being the first dataset laying on top of the 2010 data) Mosaic this dataset into the sacobaydem_ftNAVD raster (this is from the MEGIS bare-earth model). This will cover almost all of the study area except for armor along several areas in York. Resulting in LidAR206_2010_2013_Mosaic.tif.Using the LiDAR data as a proxy, create a “seaward crest” line feature class which follows along the coast and extracts the approximate highest point (cliff, bank, dune) along the shoreline. This will be used to extract LiDAR data and compare with preliminary flood zone information. The line is called Dune_Crest.Using an added tool Points Along Line, create points at 5 m spacing along each of the armored shoreline feature lines and the dune crest lines. Call the outputs PointsonLines and PointsonDunes.Using Spatial Analyst, Extract LIDAR elevations to the points using the 2006_2010_2013 Mosaic first. Call this LidarPointsonLines1. Select those points which have NULL values, export as this LiDARPointsonLines2. Then rerun Extract Values to Points using just the selected data and the state MEGIS DEM. Convert RASTERVALU to feet by multiplying by 3.2808 (and rename as Elev_ft). Select by Attributes, find all NULL values, and in an edit session, delete them from LiDARPointsonLines. Then, merge the 2 datasets and call it LidarPointsonLines. Do the same above with dune lines and create LidarPointsonDunes.Next, use the Cumberland and York flood zone layers to intersect the points with the appropriate flood zone data. Create ….CumbFIRM and …YorkFIRM files for the dunes and lines.Select those points from the Dunes feature class that are within the X zone – these will NOT have an associated BFE for comparison with the Lidar data. Export the Dune Points as Cumberland_York_Dunes_XZone. Run NEAR and use the merged flood zone feature class (with only V, AE, and AO zones selected). Then, join the flood zone data to the feature class using FID (from the feature class) and OBJECTID (from the flood zone feature class). Export as Cumberland_York_Dunes_XZone_Flood. Delete ancillary columns of data, leaving the original FLD_ZONE (X), Elev_ft, NEAR_DIST (distance, in m, to the nearest flood zone), FLD_ZONE_1 (the near flood zone), and the STATIC_BFE_1 (the nearest static BFE).Do the same as above, except with the Structures file (Cumberland_York_Structures_Lidar_DFIRM_Merged), but also select those features that are within the X zone and the OPEN WATER. Export the points as Cumberland_York_Structures_XZone. Again, run the NEAR using the merged flood zone and only AE, VE, and AO zones selected. Export the file as Cumberland_York_Structures_XZone_Flood.Merge the above feature classes with the original feature classes. Add a field BFE_ELEV_COMPARE. Select all those features whose attributes have a VE or AE flood zone and use field calculator to calculate the difference between the Elev_ft and the BFE (subtracting the STATIC_BFE from Elev_ft). Positive values mean the maximum wall value is higher than the BFE, while negative values mean the max is below the BFE. Then, select the remaining values with switch selection. Calculate the same value but use the NEAR_STATIC_BFE value instead. Select by Attributes>FLD_ZONE=AO, and use the DEPTH value to enter into the above created fields as negative values. Delete ancilary attribute fields, leaving those listed in the _FINAL feature classes described above the process steps section.

Public school district boundaries for all districts in New York State as of 2020Sourced from New York State GIS Clearinghouse:https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1326Metadata:Identification_Information:Citation:Citation_Information:Title:SchoolDistricts_2019_v3Geospatial_Data_Presentation_Form: vector digital dataDescription:Abstract:Data is updated when school distrcits merge or otherwise change.Purpose:NYS School DistrictsSpatial_Domain:Bounding_Coordinates:West_Bounding_Coordinate: -79.996911East_Bounding_Coordinate: -71.650182North_Bounding_Coordinate: 45.022656South_Bounding_Coordinate: 40.386493Keywords:Theme:Theme_Keyword_Thesaurus: NoneTheme_Keyword: NYS SchoolTheme_Keyword: School DistrictsAccess_Constraints: NoneUse_Constraints:NoneData_Set_Credit:NYS Education DepartmentNative_Data_Set_Environment:Esri ArcGIS 10.3.1.4959

Facilities (schools, colleges, etc.) used for the instruction of enrolled students.

CSV Table. This table includes coded descriptions for Property Class Codes in the St. Louis County, Missouri Parcel dataset. Property Class Codes are the Tax Subclass Codes for a property. Please see field PROPCLASS in the Parcel dataset. Link to Metadata.

Download In State Plane Projection Here. School district boundaries are based on legal documents filed in the office of the Recorder of Deeds and on court orders. Mapping is based on the legal descriptions from those documents, which relate to existing parcel, municipal, township and PLSS features. Attributes include the district name, address and district number.

Update Frequency: This dataset is updated on a weekly basis.

This dataset includes point feature showing the locations of Fire Stations in Middlesex County and Fire Stations within a 3 mile radius of the county boundary, since during emergencies near but within the boundary of the county, the nearest fire stations might be outside the county boundary. The primary source of the data was obtained from Fire Wiki, filtered to New Jersey, found at this url: https://fire.fandom.com/wiki/Category:New_Jersey This is a public/community curated website that has been checked and deemed to be accurate, though a few changes were made by Middlesex County GIS processors. Several fields were added to the feature class, including Company, Website, Phone #, Fire District, Municipality, Address, and Type (volunteer, professional), but these fields were only filled in for Middlesex County stations.

The geographic extent of a school district or attendance zone. This layer contains all the school districts that serve the City of Santa Ana and the nearby cities

This is an Esri geocoding web service based on the Montana Geographic Names Framework. It was published from a "POI Role" based locator constructed in ArcGIS Pro. The POI locator was built using a point feature class with data classified as "Montana" or "Official" for the primary location table. An alternate name table was constructed of names with a designation of "Alternate" or "BGN". ("BGN" designated names indicate contrast between "MT Names" and "Official" U.S. Board on Geographic Names. The name is the official name recognized by the Board on Geographic Names, but the State of Montana believes it is incorrect.) Output location properties are based on the following for the primary table: "Place Join ID " from MT GNIS "GNIS_ID" field, "Place Name" from MT GNIS "Name" field, "Category" classification from MT GNIS "Class" field, "County" (or subregion) from MT GNIS "County" field. Output location properties are based on the following for the alternate name table: "Join ID" from AltName "GNIS_ID" field, "Place Name" from AltName "Name" field. Geolocator was constructed with "Global High" precision type. The geocoding service can be used in ArcGIS or via the REST endpoint. More information on the Montana Geographic Names Framework: https://mslservices.mt.gov/Geographic_Information/Data/DataList/datalist_MetadataDetail.aspx?did={0c57ebe2-f8e8-4d55-b159-ab3202898956}