This layer shows public vs. private school enrollment by sex by grade group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. 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. Current Vintage: 2019-2023ACS Table(s): B14002 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National 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 is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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.

Thinking Spatially Using GIS

Thinking 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.

The animal kingdom is quite large, with thousands of animal species identified around the world and more being discovered all the time. To make sense of

all these species, scientists typically classify animals based on their physical characteristics. They start with a general classification and then get more detailed until they end up with a scientific name for the animal. For example, in the Linnaeus classification, the scientific name for a brown bear is Ursus arctos. This means that it has a backbone, is a mammal and a carnivore, and is part of the bear family.

Usually, it is easier to use common names to identify animals. In addition to their physical features, animals have many other characteristics: What country or area do they come from? What habitat do they live in? What kind of food do they like to eat?

The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG

All Esri GeoInquiries can be found at: http://www.esri.com/geoinquiries

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2024-25 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to TK-12 public schools that were open in October 2024 to coincide with the official 2024-25 student enrollment counts collected on Fall Census Day in 2024 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data 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. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

This layer serves as the authoritative geographic data source for all school district area boundaries in California. 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 2022-23 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 student enrollment counts collected on Fall Census Day (first Wednesday in October) in the 2022-23 academic year. 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 https://www.cde.ca.gov/ds.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program develops bi-annually updated point locations (latitude and longitude) for private schools included in the NCES Private School Survey (PSS). The PSS is conducted to provide a biennial count of the total number of private schools, teachers, and students. The PSS school location and associated geographic area assignments are derived from reported information about the physical location of private schools. The school geocode file includes supplemental geographic information for the universe of schools reported in the most current PSS school collection, and they can be integrated with the survey files through use of institutional identifiers included in both sources. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations and https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries Previous collections are available for the following year: 2021-22 2019-20 2017-18 2015-16 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.

Pursuant to D.C. Official Code § 38-2803, the Mayor of the District of Columbia is required to prepare a 10-year Master Facilities Plan for public education facilities and additional annual supplements. As part of this, the Office of the Deputy Mayor for Education collects and updates information about public school facilities in active use and the public schools that occupy them. This dataset contains historic data on public school facilities from SY13-14 to SY25-26 and serves as a record of change of the public school ecosystem over this period. For each year contained in the dataset, users can explore the number of Local Education Agencies (LEA) and Schools as well as their locations and, for District of Columbia Public Schools (DCPS), where they were located during modernization projects.

The State Lands Commission has prepared the Significant Lands Inventory (report) for the California Legislature as a general identification and classification of those unconveyed State school lands and tide and submerged lands which possess significant environmental values. The publication incorporates evaluated and pertinent comments received on the initial draft report which was circulated statewide in February 1975.The absence of a particular digitized waterway in the dataset does not mean that the State does not claim ownership of that parcel or waterway, or that such specific parcel or waterway has no significant environmental values. This dataset is not intended to establish ownership, only to identify those parcels which possess significant environmental values. Staff was unable to physically inventory all of the considered lands; instead, the advice and participation of those with known enviornmental expertise was utilized as additional to staff survey.Tide and submerged lands are digitized in the WaterBody and WaterLine feature classes; WaterLines for coastal areas, WaterBody for inland areas. Tide and submerged lands under the jurisdiction of the State Lands Commission are those sovereign lands received from the Federal Government by virtue of California's admission to the Union on an equal footing with the original States. Such lands, and State interest therein, are generally the lands waterward of the ordinary high water mark of the Pacific Ocean (seaward to a three-mile limit); tidal bays, sloughs, estuaries; and, navigable lakes and streams within the State.School Lands are digitized in the SchoolLand feature class. State school lands under the jurisdiction of the Commission are largely composed of the 16th and 36th sections of each township. The Federal Government transferred these lands to the State in 1853, in order to establish a financial foundation for a public school system. In cases where the 16th and 36th sections were mineral in character, incomplete as to acreage total, or already claimed or granted by the Federal Government, the State was permitted to select other lands "in lieu" of the specific sections.The public trust of commerce, navigation and fisheries which the State retains on patented sovereign lands should also be considered included in this inventory. Wherever a waterway, or body of water, is listed or mapped, the common trust state interest in patented sovereign lands, if any, is also included.The State Lands Commission emphasized when it adopted this report at its December 1, 1975 meeting that all tide and submerged lands are significant by the nature of their public ownership. Only because of the methodology used for this report are all of these waterways not specifically listed in this inventory.It is the intent of the State Lands Commission that the Significant Lands Inventory be periodically updated. This dataset should be considered informational, to assist the Legislature, the Commission, and the public in considering the environmental aspects of a proposed project and the significant values to be protected therein.

SCHOOL PROFICIENCY INDEXSummaryThe school proficiency index uses school-level data on the performance of 4th grade students on state exams to describe which neighborhoods have high-performing elementary schools nearby and which are near lower performing elementary schools. The school proficiency index is a function of the percent of 4th grade students proficient in reading (r) and math (m) on state test scores for up to three schools (i=1,2,3) within 1.5 miles of the block-group centroid. S denotes 4th grade school enrollment:Elementary schools are linked with block-groups based on a geographic mapping of attendance area zones from School Attendance Boundary Information System (SABINS), where available, or within-district proximity matches of up to the three-closest schools within 1.5 miles. In cases with multiple school matches, an enrollment-weighted score is calculated following the equation above. Please note that in this version of the data (AFFHT0004), there is no school proficiency data for jurisdictions in Kansas, West Virginia, and Puerto Rico because no data was reported for jurisdictions in these states in the Great Schools 2013-14 dataset. InterpretationValues are percentile ranked and range from 0 to 100. The higher the score, the higher the school system quality is in a neighborhood. Data Source: Great Schools (proficiency data, 2015-16); Common Core of Data (4th grade school addresses and enrollment, 2015-16); Maponics (attendance boundaries, 2016).Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 7.

To learn more about the School Proficiency Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

2018 DC School Report Card. The sum of the student group scores using all applicable STAR framework metrics. This is a number from 0 – 100 points. Overall STAR score for the school based on all applicable framework scores and student groups. Star value assigned to the school based on the STAR score.1, 2, 3, 4, or 5. Supplemental:Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide).

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE.
A spatial representation of school district boundaries. This polygon feature class was initially derived from the 1998 Parcel polygon feature class, based on a common school district code as maintained in the Oakland County Land Records database. The key attribute is Name (the school district name).

Schools, centers and administrative support locations for Fairfax County public schools.

Learn the basics of GIS. Work with ArcGIS Online to interact with GIS maps, explore real world problems, and tell a story. Find out how workers use GIS and what it takes to become a GIS professional.

Count of students in each grade (PK-12) enrolled in each Alaska public school. These data are taken from the official October 1 student count. This data set features historical data from the 2012-2013 school year to the present. Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center.

Number of teachers, and types of teachers, by school in Alaska. Includes historical data from 2016 to present. Select 'Open in Map Viewer', or add this data to the Build Your Own Map application. From the Layer List, expand this map service to change what is visible on the map.Source: Department of Education & Early DevelopmentThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center.

The California State Lands Commission (CSLC) was created by the California Legislature in 1938 and given the authority and responsibility to manage certain public lands within the state. The public lands under the Commission’s jurisdiction are of two distinct types—sovereign lands acquired upon California’s admission into the Union in 1850; and certain federally granted lands including school lands, and swamp and overflowed lands. For purposes of this GIS data, sovereign lands are considered to be further divided into two general categories—fixed-boundary sovereign lands and ambulatory-boundary sovereign lands. The following lands are included in this data: School lands: These are what remain of the nearly 5.5 million acres throughout the state originally granted to California by Congress in 1853 to benefit public education. NOT INCLUDED IN THIS DATA: Ambulatory-boundary state sovereign lands, which include the beds of California’s naturally navigable rivers, streams and lakes. Swamp and overflowed lands: These are what remain of federal lands granted to California by Congress in 1850 to encourage reclamation and development of agricultural lands. Fixed-boundary sovereign lands: These are sovereign, public trust lands having fixed boundaries as the result of land exchanges, boundary line agreements or court orders. ALSO NOT INCLUDED IN THIS DATA: Ownership details within the U.S. Government meanders of Owens Lake. THIS DATA SUPERSEDES all previously published GIS information with respect to the above described state-owned lands under the jurisdiction of the CSLC.

School regions within Fairfax County. Regions are administrative divisions of the Fairfax County Public Schools and there are 5 regions in Fairfax County.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD program annually collects administrative and fiscal data about all public schools, school districts, and state education agencies in the United States. The data are supplied by state education agency officials and include basic directory and contact information for schools and school districts, as well as characteristics about student demographics, number of teachers, school grade span, and various other administrative conditions. CCD school and agency point locations are derived from reported information about the physical location of schools and agency administrative offices. The point locations and administrative attributes in this data layer were developed from the 2021-2022 CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. For more information about these CCD attributes, as well as additional attributes not included, see: https://nces.ed.gov/ccd/files.asp.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.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.

School Districts of NYS.

These maps display the areas throughout the city that were impacted by the new school attendance zones as of SY2015-16. The area is considered impacted if it was reassigned to a different school or reassigned to just one of its current school options (previously some areas had rights to multiple schools due to school closures). These maps do not take into account phasing in policies. For more information about the 2014 Student Assignment and Boundaries Review Process, visit https://dme.dc.gov.