This dataset includes boundaries for the Wisconsin Technical College System districts. This layer was created using unified and secondary public school district boundaries for the state of Wisconsin. Authoritative information used to create this dataset came from the Wisconsin Technical College board policy manual which was current as of August 2021 and last updated in April 2020.

This dataset includes point location data for public schools in Wisconsin. The points are placed on or near each school building's centroid. Out of the 2,277 schools listed in the attribute table, 2,152 have a physical location while 125 are completely virtual campuses. School data was pulled from the Department of Public Instruction's schools database on January 17, 2025. This data gets updated biannually so for more up-to-date information, please visit the DPI public school directory.

This dataset includes boundaries for secondary school district boundaries within the state of Wisconsin. By law, all territory in the state must be included within a public school district. The US Census Bureau identifies three types of school districts. Unified school districts serve children of all grade levels, Elementary primarily serve students in the elementary grades, and Secondary primarily serve children in grades 9-12. Out of 421 school districts in Wisconsin, 43 are considered elementary districts, 10 are secondary districts, and 368 are unified districts. Elementary and secondary school districts overlap. This layer is an aggregate of county-submitted data for school district boundaries.Each year, there is a chance for reorganizations to take place that either transfer territory between school districts or consolidate/dissolve/create districts. These reorganizations go into effect on July 1st of each year. In 2024, there was one (1) reorganization involving secondary and elementary districts. The reorganization took place in Waukesha County. Transfers of territory took place between the following pairs of districts: Norris and Washington-Caldwell; and Norris and Waterford UHS. Please refer to the property transfer log for more information.This is not an official authoritative statewide dataset for school district boundaries nor does one exist for Wisconsin. These boundaries are updated annually around July 1 to reflect boundary changes from the reorganization process and as needed throughout the rest of the year.

Data in this digital opportunity map comes from students' and families' answers to the Internet Access at Home Survey, which school districts use to gather data on home internet and learning device access for students in their districts. While this is an optional data collection, DPI encouraged districts to collect this information and push it to WISEdata to help drive statewide initiatives to improve digital learning opportunity in Wisconsin. Data is given in percentages to protect student privacy. View statewide digital opportunity data on the WISEdash Public Portal.The digital opportunity questions are the result of a coordinated effort with the Council of Chief State School Officers (CCSSO), Education SuperHighway, and the Ed-Fi Alliance (affiliated with the Dell Foundation). In May 2021, the US Department of Education added these questions as data elements to the Common Educational Data Standard (CEDS). CEDS is the federal government’s framework for all education data, adding significant validation to the questions and items. See the questions DPI provided to districts to use in their surveys here.

Poverty data from American Community Survey (ACS) is shown at the block group level and contains 5-year estimates from 2012-2017.Network analysis done in ArcGIS Online.

This dataset contains geocoded locations of higher education institutions located in Wisconsin. Information exported from National Center for Education Statistics (NCES) College Navigator.

This polygon feature class represents boundaries of Wisconsin Department of Workforce Development's Workforce Development Areas. Since each WDA region is comprised of one or more counties, this feature layer was built from Wisconsin DNR's county feature layer. The county data is derived from 1:24,000-scale sources. The county feature class was last updated in June 2015; refer to the Data Lineage section of the ArcGIS metadata for information about the nature of the update. For more information, contact the WI DNR Bureau of Technology Services.

This dataset includes point locations for public libraries, public library branches, and public library system offices within the state of Wisconsin. The data that comprises this dataset is submitted by each library through a DPI public library annual report survey. The assigned person at each library submits the information for the central library as well as any branch libraries. It is the libraries responsibility to for keeping their information current in the directory. Any errors in the data should be reported to Melissa Aro (Melissa.Aro@dpi.wi.gov) when they are found.

Data in this feature layer and related digital opportunity maps comes from students' and families' answers to the Internet Access at Home Survey, which school districts use to gather data on home internet and learning device access for students in their districts. While this is an optional data collection, DPI encouraged districts to collect this information and push it to WISEdata to help drive statewide initiatives to improve digital learning opportunity in Wisconsin. Data is aggregated by district but given in percentages to protect student privacy. View statewide digital opportunity data on the WISEdash Public Portal. The digital opportunity questions are the result of a coordinated effort with the Council of Chief State School Officers (CCSSO), Education SuperHighway, and the Ed-Fi Alliance (affiliated with the Dell Foundation). In May 2021, the US Department of Education added these questions as data elements to the Common Educational Data Standard (CEDS). CEDS is the federal government’s framework for all education data, adding significant validation to the questions and items. See the questions DPI provided to districts to use in their surveys here.

An Image Service of select Alaska Geologic raster map images. These geologic maps are scanned hardcopy maps or direct raster exports from GIS. The raster images are stored as TIFFs typically at 300 dpi which results in an average raster cell size of ~30 meters. If required TIFFs are georeferenced within ArcPro with their native coordinate system. Each raster is added to a mosaic dataset which converts all rasters into Albers Equal Area Projection, and then referenced in this Image Service. This Image Service clips all map rasters to the main map portion of each map. To view an Image Service of geologic maps with full collars, see the Alaska Geologic Maps Images with collars Image Service instead.Key Field include:ZOrder: field that is used as the default drawing over. Larger values draw first.Map_type: general map classification; Geologic, Bedrock, Surficial, Permafrast, Engineering Geologic.url: link the DGGS citation page for the map.citation_id: reference id for the map’s citation that can be used to relate the map index record for this map.Numerous other fields that are copies of the DGGS map Index Record are included as well. To view the map index feature service, see: the Map Index feature service.For question contact the Alaska DGGS GIS group.

WEOP’s mission is to prepare youth and adults to pursue higher educational opportunities by providing college and career readiness programs, resources, and support. All WEOP services are free. This is accomplished through four statewide programs and three regional programs. The boundaries for the five WEOP regions are based on the boundaries for Wisconsin public school districts.

Point Dataset of Charter Schools in Wake County. Last updated August 17, 2021. The feature service is updated annually from an Excel file available here: https://www.dpi.nc.gov/students-families/alternative-choices/charter-schools

Wisconsin Assembly, Wisconsin Senate, and US Congressional districts.

This dataset contains yearly certified enrollment for all public school districts (with physical boundaries) in Wisconsin for the 2023-2024 school year. This data is also available in the WISEdash Public Portal. This dataset is derived from publicly available files on the WISEdash Download Page. Enrollment Count is the number of students enrolled on specific dates as determined by school enrollment/exit dates that cover those dates. Percent Enrollment by Student Group is a percent of the enrollment count for all student groups combined. Reporting Disability is indicated in the pupil’s individualized education program (IEP) or individualized service plan (ISP). A person's race or ethnicity is the racial and/or ethnic group to which the person belongs or with which he or she most identifies. Ethnicity is self-reported as either Hispanic/Not Hispanic. Race is self-reported as any of the following 5 categories: Asian, American Indian or Alaskan Native, Black or African American, Native Hawaiian or other Pacific Islander, or White. The data displayed reflects the race/ethnicity that is reported by school districts to DPI.An economically disadvantaged student is one who is identified by Direct Certification (only if participating in the National School Lunch Program) OR a member of a household that meets the income eligibility guidelines for free or reduced-price meals (less than or equal to 185 percent of Federal Poverty Guidelines) under the National School Lunch Program (NSLP) OR identified by an alternate mechanism, such as the alternate household income form.English Learner status is any student whose first language, or whose parents' or guardians' first language, is not English and whose level of English proficiency requires specially designed instruction, either in English or in the first language or both, in order for the student to fully benefit from classroom instruction and to be successful in attaining the state's high academic standards expected of all students at their grade level.A child is eligible for the Migrant Education Program (MEP) (and thereby eligible to receive MEP services) if the child: meets the definition of “migratory child” in section 1309(3) of the ESEA,[1] and is an “eligible child” as the term is used in section 1115(c)(1)(A) of the ESEA and 34 C.F.R. § 200.103; and has the basis for the State’s determination that the child is a “migratory child” properly recorded on the national Certificate of Eligibility (COE). Eligibility determination is made by a Wisconsin state migrant recruiter during a face-to-face family interview.

2016-17 State, District, and School Level Drilldown Performance DataPercentages greater than 95 are displayed as >95 and percentages less than 5 are displayed as <5.An * indicates a school does not have tested grades/sufficient data for reporting.Note: Rows not having sufficient data for the 'All Students' subgroup are excluded from this file.EDS=Economically Disadvantaged, LEP=Limited English Proficient, SWD=Students with Disabilities, AIG=Academically or Intellectually GiftedUpdated yearly.Reference: https://www.dpi.nc.gov/

Pocatello, Idaho historical orthomosaic for 1968 was created by collecting, scanning, merging and georectifying historic photography of Pocatello. The total spatial error is less than 1 meter. These historical orthomosaic images were derived using SfM (Structure-from-motion photogrammetry). SfM uses a series of overlapping images aligned to form a 3D representation. Classification resulted in raster and vector data with discrete classes grouped into objects located in the urban corridor of Pocatello. High-resolution aerial photography of the Pocatello area was provided by Valley Air Photos and the Idaho State Historical Society for 1968. All images were transferred from a traditional 9x9 photograph and scanned at a 1210 dpi resolution. (Date: 10/17/1968, Scale: 1:12,000, Total GSD [GSD = photo scale x scanning resolution]: 42, Scanned resolution: 11432x11241 1210 dpi). The general workflow for processing was as follows: Image collection, image pre-processing combined with gps positioning and differential correction. Photo alignment, point cloud generation, point cloud meshing, orthomosaic and DSM (Digital Surface Models) output. Photos were aligned using Agisoft Photoscan. Focal lengths for data sets were 152mm. GPS points were collected for ground truthing. Photo alignment, dense cloud, and mesh generation using ground control points, resulted in orthomosaics and DSMs (Digital Surface Models) for time periods. Orthomosaics were produced at a fine scale spatial resolution: .25m resolution in all cases except the final year at .5m due to differences in scale of the original imagery. Each orthomosaic and DEM was outputted at .5 m and 1 m resolution respectively, in order to maintain continuity between data sets. See Brock Lipple Thesis, 2015 for more information about the scanning and merging process.Data are sourced from: https://data.nkn.uidaho.edu/dataset/pocatello-idaho-historic-orthoimagery-1968-1-meter-resolutionPlease cite as: Delparte, D., & Lipple, B. (2016). Pocatello, Idaho Historic Orthoimagery for 1968 (~1 meter resolution) [Data set]. University of Idaho. https://doi.org/10.7923/G4J1012NIndividual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Users should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of these data as described in the lineage or elsewhere.

The Deteriorated Paint Index (DPI) data predicts areas at-risk of containing several pre-1980 households with large areas of deteriorated paint, a significant and common predictor of lead dust. Funding for remediation and abatement is limited. To adequately target households eligible for home remediation and associated intervention efforts, local healthy homes and environmental health program administrators must identify neighborhoods that are the most “at risk” of residential lead exposure where deteriorated paint is the primary source. To address this need, the DPI uses household-level data to predict a household’s risk of deteriorated paint. Predicted risk scores were calculated using microdata from the 2011 American Housing Survey (AHS) and the 2009-2013 American Community Survey (ACS) to develop a predicted risk measure. This metric estimates the predicted percentage of occupied housing units with large areas of deteriorated paint for three geographic levels: state, county, and tract. The primary methodological goal of the analysis was to post-fit ACS households with beta parameters from an AHS model that predicted the presence of a large area of deteriorated paint. Prior research shows this methodology can be used for small area estimation.Analyses were conducted using SAS Version 9.1.4 (SAS Institute Inc., Cary, NC). Household-level ACS and AHS microdata were used. Analyses were conducted in a Census-approved partner institution Federal Statistical Research Data Center. Exposure to residential lead dust will continue to be a public health problem until housing with deteriorated lead paint is remediated. Public health practitioners interested in strategically allocating healthy homes funding should consult this dataset and overlay predicted rates of deteriorated paint with important and unique local data to develop comprehensive targeting strategies.To learn more about the Deteriorated Paint Index and associated datasets visit: Deteriorated Paint Index Interactive Web MapDeteriorated Paint Index Interactive Map User's GuideMapping Efforts to Identify Populations at Higher Risk of Lead Exposure: HUD PerspectiveFor questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2009 - 2013

1855 Map of the city of Pittsburgh with some of the surrounding communities. This map service includes a mosaic of several volumes of plat maps that were originally bound atlases. The mosaic was created using ArcGIS software from the scanned JPEG images, varying from 300-600 dpi. These images were obtained from the Historic Pittsburgh website (http://historicpittsburgh.org/maps-hopkins) The images were georeferenced to WGS84 Web Mercator and the borders were clipped to create a contiguous map.This product is to be used for reference purposes only. The original historical paper maps were sometimes damaged or distorted to varying degrees due to age and use. There are spatial inaccuracies and some places where the footprints do not lineup perfectly or in some cases overlap.

2015 3in Cupertino Aerial Photo Tile Info: Height: 256 Width: 256 DPI: 96 Levels of Detail: 9 Full Extent: XMin: 6097999.999999999 YMin: 1926999.9999999995 XMax: 6127999.999999999 YMax: 1950999.9999999995 Spatial Reference: PROJCS["NAD_1983_California_zone_3_ftUS",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["false_easting",6561666.667],PARAMETER["false_northing",1640416.667],PARAMETER["central_meridian",-120.5],PARAMETER["standard_parallel_1",37.06666666666667],PARAMETER["standard_parallel_2",38.43333333333333],PARAMETER["latitude_of_origin",36.5],UNIT["Foot_US",0.3048006096012192]] Pixel Size X: 0.25 Pixel Size Y: 0.25 Band Count: 3 Pixel Type: U8 Raster Type Infos: Name: Raster Dataset Description: Supports all ArcGIS Raster Datasets

This image is a lidar point cloud of downtown Reno, looking east along the Truckee River. The point cloud was symbolized by classified elevation and modulated by intensity. Prominent in the composition are the Virginia Street Bridge and the Pioneer Center’s geodesic dome, both strong geometric influences on the landscape.Original file dimensions are 36"x 48" at 300 dpi.Google "blueprint reno nv" to find local companies that can print this graphic and assist you with framing.