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

This map was prepared by the Center for Spatial Analysis, University of Oklahoma. School District Boundaries are based on information provided by the Oklahoma Department of Education. All boundary questions and changes should be directed to this agency. Map scale is 1:450,000.Last Updated: 06/016/2023

Analysis of ‘2015 - 2016 Iowa Public School District Boundaries’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/40275b66-6468-4c59-ad07-be2cee7dca3c on 21 November 2021.

--- Dataset description provided by original source is as follows ---

This map displays the boundaries for public school districts in Iowa for the 2015 - 2016 school year.

--- Original source retains full ownership of the source dataset ---

A. SUMMARY This dataset contains San Francisco Board of Supervisor district boundaries approved by the San Francisco Redistricting Task Force in April 2022 following redistricting based on the 2020 Decennial Census.

B. HOW THE DATASET IS CREATED The dataset was created from the final map submitted by the San Francisco Redistricting Task Force. Boundaries in this map were decided using data from the 2020 Census on the number of people living in each census block in the City and County. This data includes the number of individuals incarcerated in facilities under the control of the Department of Corrections and Rehabilitation based on their last known residential address. This information is made available by the Statewide Database based on U.S. Census Bureau Census Redistricting Data (P.L. 94-171).

These map boundaries were trimmed to align with the city and county's physical boundaries for greater usability. This trimming mainly consisted of excluding the water around the City and County from the boundaries.

C. UPDATE PROCESS Supervisor District boundaries are updated every 10 years following the federal decennial census. The Supervisor District boundaries reflected in this dataset will remain unchanged. A new dataset will be created after the next decennial census and redistricting process are completed.

The dataset is manually updated as new members of the Board of Supervisors take office. The most recent manual update date is reflected in the 'data_as_of' field.

Once the redistricting process is completed after the next decennial census and a new dataset is published, this dataset will become static and will no longer be updated.

D. HOW TO USE THIS DATASET This dataset can be joined to other datasets for analysis and reporting at the Supervisor District level.

If you are building an automated reporting pipeline using Socrata API access, we recommend using this dataset if you'd like your boundaries to remain static. If you would like the boundaries to automatically update after each decennial census to reflect the most recent Supervisor District boundaries, see the Current Supervisor Districts dataset or the Current Supervisor Districts (trimmed to remove water and other non-populated City territories) dataset.

E. RELATED DATASETS Supervisor Districts (2012) Current Supervisor Districts Current Supervisor Districts (trimmed to remove water and non-populated areas)

This dataset contains the New Mexico House District Boundaries as of July 2006. It is in a vector digital shapefile created to show the voting precinct coverage for New Mexico and the scale is unknown. The source software was shapefile provided by the Office of the NM Secretary of State. The precinct map was obtained from the NM Secretary of State's office as a composite of the voting precincts and the house and senate legislative boundaries, and then dissolved on the house attribute.

Dropout rates for Alaska public school districts. The dropout rate is defined by state regulation 4 AAC 06.895(i)(3) as a fraction of students grades 7-12 who have dropped out during the current school year out of the total students in grades 7-12 enrolled as of October 1st of the school year for which the data is reported.A student is considered to be a dropout when they have discontinued schooling for a reason other than graduation, transfer to another diploma-track program, emigration, or death unless the student is enrolled and in attendance at the same school or at another diploma-track program prior to the end of the school year (June 30).Students who depart a diploma track program in pursuit of GED certification, credit recovery, or non-diploma track vocational training are considered to have dropped out.This data set includes historic data from 1991 to present.GIS layers for individual years can be accessed using the Build Your Own Map application.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

Unified School DistrictsThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays the boundaries of all Unified 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. Unified school districts provide education to children of all school ages in their service areas."Kenmore-Tonawanda Union Free School DistrictData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Unified School Districts) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 96 (Series Information for Unified School Districts State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (Unified School District - 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

The aim of this article and associate Main Map is to highlight the social and economic diversity of the Ruhr area in Germany through the use of multivariate analysis and visualization. To this end we combine two different datasets. Demographic parameters stemming from the 2011 German census and socioeconomic indicators obtained from the microdialog of the German post service. Due to the different spatial resolution of the two datasets, we aggregated the data at the neighbourhood (Stadtteil) level. The multivariate analysis was carried out at this scale using Self-Organizing Maps (SOM), an artificial neuron network, which uses an unsupervised learning mechanism for projecting multidimensional data in a low (in our case two) dimensional space. First we used a visualization technique to better comprehend the relationship between our observations via reducing the dimensionality or complexity of our input data. At the same time, we established a global statistical relationships between the indicators. Finally, using these results we built clusters for revealing the distribution of socioeconomic profiles over the whole region. Our results demonstrate that structural inequalities resulting from the processes of industrialization/deindustrialization in the region are still widely persistent and result in characteristic patterns along the three main rivers, the Lippe, Emscher and the Ruhr. In close connection with this, three types of societal segregation patterns become clearly evident in the Ruhr area, namely nationality, age and economic power.

The regional flooding and shoreline overtopping analysis maps provided in the ART Bay Shoreline Flood Explorer website capture permanent and temporary flooding impacts from sea level rise scenarios from 0- to 108-inches above MHHW (mean higher high water) and storm surge events from the 1-year to the 100-year storm surge. The process used to develop the maps included discussions with key stakeholders in each county, who reviewed the preliminary maps and provided on-the-ground verification and supplemental data to improve the accuracy of the maps. The maps and information produced through this effort can inform adaptation planning, assist in managing climate change risks, and help identify trigger points for implementing adaptation strategies to address sea level rise and flooding hazards, at both local and regional scales. The Flood Explorer maps were produced using the latest LiDAR topographic data sets, water level outputs from the FEMA San Francisco Bay Area Coastal Study (which relied in hydrodynamic modeling using MIKE21) and the San Francisco Tidal Datums Study. The 2010/2011 LIDAR applied (collected by USGS and NOAA at a 1-m resolution) was further refined through the stakeholder review process and integration of additional elevation data where available. The Flood Explorer also includes the regional shoreline delineation developed by the San Francisco Estuary Institute to represent coastal flooding and overtopping throughout the Bay Area. In sum, the maps include: 1) Flooding at ten total water levels that capture over 90 combinations of future sea level rise and storm surge scenarios; 2) Shoreline overtopping maps for all ten total water levels that depict where the Bay may overtop the shoreline and its depth of overtopping at that specific location. Coupled with the flood maps, the overtopping data can help identify vulnerable shoreline locations and their respective flow paths that could lead to inland flooding, and; 3) Hydraulically disconnected low-lying areas that represent areas that may be vulnerable to flooding due to their low elevation. These areas are not directly within flooding locations, but could be connected to flood waters through culverts and storm drains that are not captured in this analysis.

Count of students in each grade (PK-12) for each Alaska public school district. 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.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: Alaska 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.

Number of students enrolled in free and reduced lunch programs by school district. Students qualify for free and reduced meals under the National School Lunch Program.GIS layers for individual years can be accessed using the Build Your Own Map application.*Mount Edgecumbe High School is a state-operated boarding school, and is therefore not included in a school district. This school is included in Alaska DCCED DCRA Data Portal school-scale education data.Source: Alaska 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 School Nutrition Programs.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This imagery layer shows national riparian areas for the conterminous United States. Riparian areas are an important natural resource with high biological diversity. These ecosystems contain specific vegetation and soil characteristics which support irreplaceable values and multiple ecosystem functions and are very responsive to changes in land management activities. Delineating and quantifying riparian areas is an essential step in riparian monitoring, planning, management, and policy decisions. USDA Forest Service supports the development and implementation of a national context framework with a multi-scale approach to define riparian areas utilizing free available national geospatial datasets.Why was this layer created? To estimate 50-year flood height riparian areas to support statistical analysis, map display, and model parameterization.Provide a framework and an end product to stakeholders and apply the information into management actions and strategies.Multi-scale approach to provide a national and regional report map. Create a product for managers to easily understand where to apply the information at various scales.Develop a national context inventory of riparian areas and their condition within national forests and rangelands.How was this layer created? Using freely available data.Develop cost effective modeling approach & technique.Multi-scale (national, regional, & local).Promote technology transfer to train/reach out to our partners.Fifty-year flood heights were estimated using U.S. Geological Survey (USGS) stream gage information. NHDPlus version 2.1 was used as the hydrologic framework to delineate riparian areas. The U.S. Fish and Wildlife Service's National Wetland Inventory and USGS 10-meter digital elevation models were also used in processing these data.The data are '1' if in the riparian zone and 'NoData' if outside the riparian zone. When displayed on a map, riparian zone cells are color-coded 'blue' with 25% transparency.For additional information regarding methodologies for modeling and processing these data, see Abood et al. (2012) and the National Riparian Areas Base Map StoryMapData Download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0030

Determining the technical and economic feasibility of geothermal district energy (district heat and cooling) systems is a two-track process. One is directed toward establishing the thermal and chemical characteristics of the resource and the other to establishing the economic and technical viability of building and operating a district energy system. To date most programs have been directed toward identification and characterization of the resource. However, exploration, confirmation drilling, resource characterization and reservoir engineering are all expensive activities that may or may not be justifiable unless the economics of the proposed use of that resource are extremely favorable. Fortunately, at least in the case of geothermal district energy, determining the technical and economic viability of using the resource can now be readily determined at a fraction of the cost of a detailed resource characterization process.

This dataset comprises a series of five land use and land cover (LULC) maps of western Murewha District, Zimbabwe, spanning the years 2002, 2007, 2013, 2018, and 2023. The overall accuracy scores for these maps are 0.93, 0.91, 0.90, 0.90, and 0.90, respectively. These maps were generated using open-access Landsat satellite imagery (30m resolution) from Landsat 5, 7, and 8, enabling consistent spatial resolution and temporal coverage. Each map integrates two images from the crop/wet and dry seasons, ensuring comprehensive seasonal representation. Key radiometric indices (NDVI, RVI, NDWI2, BI2) and a 30m resolution DEM were applied for enhanced classification accuracy. The algorythm used for the classification is a pixel random forest using Python 3.7.4 and the library sklearn. The study focuses on wards within Chitopi and Mushaninga sub-districts.

Uganda District Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2023 boundaries are provided by Michael Bauer Research GmbH. These were published in October 2023. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country Region

The purpose of the medication-assisted treatment (MAT) facility maps is to identify areas on a state-by-state basis that may be potentially underserved by existing treatment facilities. The maps are created with a methodology that seeks to include the highest potential need areas from individual counties so that county-level stakeholders are also informed. The maps are meant to be used as a tool for policy makers to determine potentially underserved areas—not as a definitive representation of these areas.

Analysis of ‘Detroit Local Historic Districts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2c6bb071-a7bc-440c-ada8-dcb380eb53ea on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Map of all local historic districts in the City of Detroit that is updated regularly and intended to be used for reference purposes only. Each local historic district is enacted by an ordinance containing a legal boundary description and elements of design. To obtain a copy of an ordinance, please consult Chapter 21 of the 2019 Detroit City Code or contact the Clerk's Office. Please note that all work conducted within a local historic district (construction, alteration, demolition, site work, etc.) requires review and approval by the Historic District Commission.

--- Original source retains full ownership of the source dataset ---

Polygon dataset representing local New Orleans Historic Districts. Local historic districts are created to regulate, preserve, and protect historic districts and landmarks within the City of New Orleans and may or may not correspond to districts listed on the National Register of Historic Places. As of 2007, there are 14 local historic districts within New Orleans/Orleans Parish, ten administered by the New Orleans Historic District Landmarks Commission and four by the Central Business District Historic District Landmarks Commission. The City of New Orleans Department of Information Technology & Innovation creates, collects and stores GIS infrastructure and other data. Data are provided by various departments within the City, other government entities, utilities, and private enterprise. The primary purpose for maintaining this enterprise GIS is to provide spatial analysis, decision support and mapping services to all City Departments.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.