TY2024CY25 Class 1 & 2 Delinquencies for Schools.

This dataset attempts to represent the point locations of every educational program in the state of Minnesota that is currently operational and reporting to the Minnesota Department of Education. It can be used to identify schools, various individual school programs, school districts (by office location), colleges, and libraries, among other programs. Please note that not all school programs are statutorily required to report, and many types of programs can be reported at any time of the year, so this dataset is by nature an incomplete snapshot in time.

Maintenance of these locations is a result of an ongoing project to identify current school program locations where Food and Nutrition Services Office (FNS) programs are utilized. The FNS Office is in the Minnesota Department of Education (MDE). GIS staff at MDE maintain the dataset using school program and physical addresses provided by local education authorities (LEAs) for an MDE database called "MDE ORG". MDE GIS staff track weekly changes to program locations, along with comprehensive reviews each summer. All records have been reviewed for accuracy or edited at least once since January 1, 2020.

Note that there may remain errors due to the number of program locations and inconsistency in reporting from LEAs and other organizations. Some organization types (such as colleges and treatment programs) are not subject to annual reporting requirements, so various records included in this file may in fact be inactive or inaccurately located.

Note that multiple programs may occur at the same location and are represented as separate records. For example, an elementary and secondary school may be in the same building, but each has a separate record in the data layer. Users may leverage the "CLASS" and "ORGTYPE" attributes to filter and sort records according to their needs. In general, records at the same physical address will be located at the same coordinates.

This data is also available in CSV format. For that format only, OBJECTID and Shape columns are removed, and the Shape column is replaced by Latitude and Longitude columns.

The East Bay Regional Park District (EBRPD) initiated this project to map the topography, physical and biotic features, and diverse plant communities of the east bay region. This project was funded by the California Department of Forestry and Fire Protection (CAL FIRE), the California State Coastal Conservancy (SCC), and California Department of Fish and Wildlife (CDFW) grants. The mapping study area, consists of approximately 987,000 acres of Alameda and Contra Costa counties. This 115-class fine scale vegetation map was completed in May 2025 and contains 140,442 polygons. The map is based on summer 2020 National Aerial Imagery Program (NAIP) imagery. The map additionally contains lidar-derived information about stand height, canopy cover, and percentage of impervious cover as well as canopy mortality data for each polygon. The minimum mapping unit (MMU) for this project ranges from 1/5 to 1 acre depending on feature type, and is described in detail in the mapping report (Tukman Geospatial, 2025). Development of the Alameda and Contra Costa fine scale vegetation map was managed by EBRPD and staffed by personnel from Tukman Geospatial. Field surveys were completed by trained botanists from the California Native Plant Society (CNPS), who were assisted by botanists from Nomad Ecology Consulting. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the CNPS Vegetation Program, with support from the CDFW Vegetation Classification and Mapping Program (VegCAMP) to develop a county-specific vegetation classification. The floristic classification follows protocols compliant with the Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS). For more information on the field sampling and vegetation classification work, refer to the final report issued by CNPS and corresponding floristic descriptions (Sikes et al., 2025), which are bundled with the vegetation map published for BIOS here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/3200_3299/ds3206.zipThe foundation for this vegetation map is an enhanced lifeform map produced in 2023 with funding from CAL FIRE. This lifeform map was developed using fine scale segmentation in Trimble® Ecognition® with machine learning and further manual image interpretation. In 2023-2025, Tukman Geospatial and Nomad Ecology staff conducted countywide reconnaissance field work. Field-collected data was used to train automated machine learning algorithms, which produced a semi-automated countywide fine scale vegetation and habitat map. Throughout 2024 and 2025, Tukman Geospatial manually edited the fine scale maps, and Tukman Geospatial and Nomad Ecology went to the field for validation trips to inform and improve the manual editing process. In 2025, input from Alameda and Contra Costa counties’ community of land managers and by the funders of the project was used to further refine the map.Accuracy assessment plot data were collected in 2025. Accuracy assessment results were compiled and analyzed May of 2025. The overall accuracy of the vegetation map by lifeform is 97%. The overall accuracy of the vegetation map by fine scale vegetation map class is 80.8%, with an overall ‘fuzzy’ accuracy of 93.1%.For a complete datasheet of the product, click here. Map class definitions, as well as a dichotomous key for the map classes, can be found in the Alameda and Contra Costa Fine Scale Vegetation Map Key (https://vegmap.press/alcc_mapping_key). A key to map class abbreviations is also available (https://vegmap.press/alcc_vegmap_abbrevs).

The global Remote Sensing Interpretation Software market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $10 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $30 billion by 2033. This expansion is fueled by several key factors. The burgeoning adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting a wider range of users, including small and medium-sized enterprises (SMEs). Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are significantly enhancing the accuracy and speed of image interpretation, leading to improved decision-making in various applications. The increasing availability of high-resolution satellite imagery further contributes to market growth, enabling more detailed and precise analysis. Key application areas like agriculture (precision farming), petroleum and mineral exploration (resource mapping), and environmental monitoring are witnessing particularly strong adoption rates. While the on-premise segment currently holds a larger market share, the cloud-based segment is expected to experience faster growth in the forecast period due to its inherent flexibility and accessibility. However, factors such as high initial investment costs for advanced software and the need for skilled professionals to operate these systems pose some restraints on market growth. The market's competitive landscape is characterized by a mix of established players like Hexagon, Microsoft, and IBM, alongside specialized geospatial technology providers and emerging AI-focused companies. Regional growth is expected to be diverse, with North America and Europe maintaining substantial market shares due to high technological adoption and existing infrastructure. However, the Asia-Pacific region is projected to witness the fastest growth rate, driven by increasing government investments in infrastructure development and the rapid expansion of the agricultural and construction sectors. The ongoing development of innovative software features, such as 3D modeling and advanced analytics capabilities, will further drive market expansion. The continuous integration of AI and ML into remote sensing interpretation software will likely lead to the development of more automated and efficient solutions, potentially leading to further market consolidation and increased competition.

Discover the booming Geographic Information System (GIS) Analytics market, projected to reach $15.1B by 2025 with a 12.41% CAGR. This in-depth analysis explores key drivers, trends, restraints, and leading companies shaping this dynamic sector. Learn about market segmentation, regional growth, and future opportunities in GIS analytics.

Cloud GIS Market Outlook

According to our latest research, the global Cloud GIS market size reached USD 2.1 billion in 2024, reflecting robust demand across diverse industries. The market is experiencing a strong upward trajectory, registering a CAGR of 13.9% from 2025 to 2033. By the end of 2033, the Cloud GIS market is forecasted to achieve a value of approximately USD 6.56 billion. This impressive growth is primarily fueled by the increasing adoption of cloud-based geographic information systems for real-time analytics, data integration, and decision-making processes across both public and private sectors.

The accelerating digital transformation in industries such as government, utilities, transportation, and agriculture is a major growth driver for the Cloud GIS market. Organizations are increasingly leveraging cloud GIS solutions for their scalability, flexibility, and ability to facilitate remote access to geospatial data. This shift is further propelled by the growing need for collaborative platforms that enable multiple stakeholders to access, analyze, and share spatial information seamlessly. The transition from traditional, on-premises GIS systems to cloud-based platforms is reducing infrastructure costs and enabling organizations to focus on core business objectives, thereby expanding the addressable market for cloud GIS providers.

Another significant factor contributing to the growth of the Cloud GIS market is the rapid proliferation of Internet of Things (IoT) devices and the explosion of location-based data. The integration of IoT with cloud GIS is enabling real-time monitoring, predictive analytics, and enhanced asset management for sectors such as utilities, transportation, and agriculture. The ability to process and visualize large volumes of spatial data in the cloud is proving invaluable for disaster management, urban planning, and environmental monitoring applications. Additionally, advancements in artificial intelligence and machine learning are augmenting the capabilities of cloud GIS platforms, allowing organizations to derive deeper insights from geospatial data and automate critical workflows.

The widespread adoption of mobile devices and the growing popularity of location-based services (LBS) are further fueling demand for cloud GIS solutions. Enterprises and governments are increasingly deploying cloud GIS for mapping, surveying, and location intelligence, which is enhancing operational efficiency and service delivery. The cloud delivery model offers the advantage of rapid deployment, easy scalability, and lower maintenance costs, making it an attractive proposition for small and medium-sized enterprises (SMEs) as well. The increasing availability of high-speed internet and the expansion of cloud infrastructure, especially in emerging markets, are expected to further accelerate the growth of the Cloud GIS market in the coming years.

From a regional perspective, North America currently leads the Cloud GIS market in terms of revenue, driven by early technology adoption, strong presence of leading GIS vendors, and significant investments in smart city and infrastructure projects. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, owing to rapid urbanization, government initiatives for digital transformation, and increasing awareness about the benefits of cloud GIS solutions. Europe is also a key market, supported by stringent environmental regulations and the need for efficient resource management. Latin America and the Middle East & Africa are gradually emerging as promising markets, as organizations in these regions increasingly recognize the value of cloud-based geospatial solutions for addressing local challenges.

Component Analysis

The Cloud GIS market is segmented by component into software and services, each playing a pivotal role in shaping the industry landscape. The software segment comprises cloud-based GIS platforms and applications that enable users to collect, manage, analyze, and

Dataset description: This repository contains data pertaining to the manuscript "Mawrth Vallis, Mars, classified using the NOAH-H deep-learning terrain classification system." submitted to Journal of Maps. NOAH-H Mosaics: Mawrth_Vallis_NOAHH_Mosaic_DC_IG_25cm4bit_20230121_reclass.zip This folder contain mosaics of terrain classifications for Mawrth Vallis, Mars, made by the Novelty or Anomaly Hunter - HiRISE (NOAH-H) deep learning convolutional neural network developed for the European Space Agency (ESA) by SCISYS Ltd. In coordination with the Open University Planetary Environments Group. These folders contain the NOAH-H mosaics, as well as ancillary files needed to display the NOAH-H products in geographic information software (GIS). Included are two large raster datasets, containing the NOAH-H classification for the entire study area. One uses the 14 descriptive classes of the terrain, and the other with the five interpretative groups (Barrett et al., 2022). · Mawrth_Vallis_NOAHH_Mosaic_DC_25cm4bit_20230121_reclass.tif Contains the full 14 class “Descriptive Classes” (DC) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. · Mawrth_Vallis_NOAHH_Mosaic_IG_25cm4bit_20230121_reclass.tif Contains the 5 class “Interpretive Groups” (IG) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. Symbology layer files: NOAH-H_Symbology.zip This folder contains GIS layer file and colour map files for both the Descriptive Classes (DC) and interpretive Groups (IG) versions of the classification. These can be applied to the data using the symbology options in GIS. Georeferencing Control points: Mawrth_Vallis_Final_Control_Points.zip This file contains the control points used to georeferenced the 26 individual HiRISE images which make up the mosaic. These allow publicly available HiRISE images to be aligned to the terrain in Mawrth Vallis, and thus the NOAH-H Mosaic. Twenty-six 25 cm/pixel HiRISE images of Mawrth Vallis were used as input for NOAH-H. These are:

PSP_002140_2025_RED

PSP_002074_2025_RED

ESP_057351_2020_RED

ESP_053909_2025_RED

ESP_053698_2025_RED

ESP_052274_2025_RED

ESP_051931_2025_RED

ESP_051351_2025_RED

ESP_051219_2030_RED

ESP_050217_2025_RED

ESP_046960_2025_RED

ESP_046670_2025_RED

ESP_046525_2025_RED

ESP_046459_2025_RED

ESP_046314_2025_RED

ESP_045536_2025_RED

ESP_045114_2025_RED

ESP_044903_2025_RED

ESP_043782_2025_RED

ESP_043637_2025_RED

ESP_038758_2025_RED

ESP_037795_2025_RED

ESP_037294_2025_RED

ESP_036872_2025_RED

ESP_036582_2025_RED

ESP_035804_2025_RED NOAH-H produced corresponding 25 cm/pixel rasters where each pixel is assigned a terrain class based on the corresponding pixels in the input HiRISE image. To mosaic the NOAH-H rasters together, first the input HiRISE images were georeferenced to the HRSC basemap (HMC_11E10_co5) tile, using CTX images as an intermediate step. High order (spline, in ArcGIS Pro 3.0) transformations were used to make the HiRISE images georeference closely onto the target layers. Once the HiRISE images were georeferenced, the same control points and transformations were applied to the corresponding NOAH-H rasters. To mosaic the georeferenced NOAH-H rasters the pixel values for the classes needed to be changed so that more confidently identified, and more dangerous, classes made it into the mosaic (see dataset manuscript for details. To produce a HiRISE layer which fits the NOAH-H classification, download one of the listed HiRISE images from https://www.uahirise.org/, Select the corresponding control point file from this archive and apply a spline transformation through the GIS georeferencing toolbar. Manually Mapped Aeolian Bedforms: Mawrth_Manual_TARs.zip The manually mapped data was produced by Fawdon, independently of the NOAH-H project, as an assessment of “Aeolian Hazard” at Mawrth Vallis. This was done to inform the ExoMars landing site selection process. This file contains two GIS shape files, containing the manually mapped bedforms for both the entire mapping area, and the HiRISE image ESP_046459_2025_RED where the two datasets were compared on a pixel scale. The full manual map is offset slightly from the NOAH-H, since it was digitised from bespoke HiRISE orthomosaics, rather than from the publicly available HiRISE Red band images. It is suitable for comparison to the NOAH-H data with 100m-1km aggregation as in figure 8 of the associated paper. It is not suitable for pixel scale comparison. The map of ESP_046459_2025_RED was manually georeferenced to the NOAH-H mosaic, allowing for direct pixel to pixel comparisons, as presented in figure 6 of the associated paper. Two GIS shape files are included: · Mawrth_Manual_TARs_ESP_046459_2025.shp · Mawrth_Manual_TARs_all.shp Containing the high fidelity data for ESP_046459_2025, and the medium fidelity data for the entire area respectively. The are accompanied by ancillary files needed to view them in GIS. Gridded Density Statistics This dataset contains gridded density maps of Transverse Aeolian Ridges and Boulders, as classified by the Novelty or Anomaly Hunter – HiRISE (NOAH-H). The area covered is the runner up candidate ExoMars landing site in Mawrth Vallis, Mars. These are the data shown in figures; 7, 8, and S1. Files are presented for every classified ripple and boulder class, as well as for thematic groups. These are presented as .shp GIS shapefiles, along with all auxiliary files required to view them in GIS. Gridded Density stats are available in two zip folders, one for NOAH-H predicted density, and one for manually mapped density. NOAH-H Predicted Density: Mawrth_NOAHH_1km_Grid_TAR_Boulder_Density.zip Individual classes are found in the files: · Mawrth_NOAHH_1km_Grid_8TARs.shp · Mawrth_NOAHH_1km_Grid_9TARs.shp · Mawrth_NOAHH_1km_Grid_11TARs.shp · Mawrth_NOAHH_1km_Grid_12TARs.shp · Mawrth_NOAHH_1km_Grid_13TARs.shp · Mawrth_NOAHH_1km_Grid_Boulders.shp Where the text following Grid denotes the NOAH-H classes represented, and the landform classified. E.g. 8TARs = NOAH-H TAR class 8. The following thematic groups are also included: · Mawrth_NOAHH_1km_Grid_8_11continuousTARs.shp · Mawrth_NOAHH_1km_Grid_12_13discontinuousTARs · Mawrth_NOAHH_1km_Grid_8_10largeTARs.shp · Mawrth_NOAHH_1km_Grid_11_13smallTARs.shp · Mawrth_NOAHH_1km_Grid_8_13AllTARs.shp When the numbers denote the range of NOAH-H classes which were aggregated to produce the map, followed by a description of the thematic group: “continuous”, “discontinuous”, “large”, “small”, “all”. Manually Mapped Density Plots: Mawrth_Manual_1km_Grid.zip These GIS shapefiles have the same format as the NOAH-H classified ones. Three datasets are presented for all TARs (“_allTARs”), Continuous TARs (“_con”) and Discontinuous TARs (“_dis”) · Mawrth_Manual_1km_Grid_AllTARs.shp · Mawrth_Manual_1km_Grid_Con.shp · Mawrth_Manual_1km_Grid_Dis.shp Related public datasets: The HiRISE images discussed in this work are publicly available from https://www.uahirise.org/. and are credited to NASA/JPL/University of Arizona. HRSC images are credited to the European Space Agency; Mars Express mission team, German Aerospace Center (DLR), and the Freie Universität Berlin (FUB). They are available at the ESA Planetary Science Archive (PSA) https://www.cosmos.esa.int/web/psa/mars-express and are used under the Creative Commons CC BY-SA 3.0 IGO licence. SPATIAL DATA COORDINATE SYSTEM INFORMATION All NOAH-H files and derivative density plots have the same projected coordinate system: “Equirectangular Mars” - Projection: Plate Carree - Sphere radius: 3393833.2607584 m SOFTWARE INFORMATION All GIS workflows (georeferencing, mosaicking) were conducted in ArcGIS Pro 3.0. NOAH-H is a deep learning semantic segmentation software developed by SciSys Ltd for the European Space Agency to aid preparation for the ExoMars rover mission.

This dataset represents the boundary of each parcel of land in Fulton County recorded for the purpose of aiding in the appraisal of real property and the determination of property tax. A parcel dataset is created each year in association with that year's tax digest. The parcel dataset for any given year is not considered final until the completion of the digest, which generally occurs around mid-year. Until the completion of the digest, the parcel dataset is considered to be a work in progress. Any necessary corrections and omissions may continue to be made even after the completion of the digest. The parcel dataset in its published form incorporates information from the CAMA (computer-aided mass appraisal) database. The CAMA information included with the published dataset is selected based on its value to the typical consumer of the data and includes the parcel identification number, the property address, property owner, owner's mailing address, tax district, assessed and appraised value for land and improvements, the number of livable units, acreage, property class and land use class. The information in this data set represents the completed 2025 digest.

The Geospatial Analytics Artificial Intelligence market is poised for substantial growth, with an estimated market size of $10,500 million in 2025. This burgeoning sector is projected to expand at a robust Compound Annual Growth Rate (CAGR) of 22% through 2033, reaching an impressive value unit of millions. This significant expansion is primarily fueled by the increasing adoption of AI and machine learning techniques within the geospatial domain, enabling more sophisticated data analysis and actionable insights. Key drivers include the escalating demand for real-time location intelligence across diverse industries such as real estate for site selection and market analysis, sales and marketing for customer segmentation and targeted campaigns, and agriculture for precision farming and yield optimization. Furthermore, the growing need for enhanced situational awareness in transportation and logistics for route optimization and supply chain management, alongside applications in weather forecasting and disaster management, are propelling market growth. The integration of advanced analytics with spatial data allows for the identification of complex patterns, prediction of future trends, and automation of decision-making processes, making geospatial AI an indispensable tool for businesses and governments worldwide. The market is characterized by a dynamic interplay of technological advancements and evolving application needs. The increasing availability of high-resolution satellite imagery and aerial data, coupled with the proliferation of IoT devices generating location-based data, provides a rich foundation for geospatial AI. Trends such as the rise of cloud-based geospatial platforms, the development of sophisticated AI algorithms for image recognition and spatio-temporal analysis, and the growing emphasis on democratizing access to geospatial insights are shaping the market landscape. While the market enjoys strong growth, certain restraints, such as the high cost of implementing advanced AI solutions and a potential shortage of skilled geospatial AI professionals, may temper the pace of adoption in some segments. However, the inherent value proposition of geospatial analytics AI in driving efficiency, innovation, and informed decision-making across sectors like real estate, sales, agriculture, and transportation, alongside the continuous development of more accessible and powerful tools, ensures its sustained and significant expansion in the coming years. This report delves into the burgeoning field of Geospatial Analytics Artificial Intelligence (AI), analyzing its market dynamics, trends, and future trajectory from 2019 to 2033. With a base year of 2025 and a forecast period extending to 2033, this comprehensive study offers an in-depth examination of a market projected to reach multi-million dollar valuations. We will explore the intricate interplay of AI and location-based data, highlighting how sophisticated algorithms are revolutionizing various industries. The report identifies key players, emerging technologies, and critical growth drivers that are shaping this transformative sector. By understanding the challenges and opportunities, stakeholders can strategically position themselves for success in this rapidly evolving landscape.

The global Geographic Information System (GIS) Tools market is experiencing robust growth, projected to reach $2979.7 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.5% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing adoption of cloud-based GIS solutions offers scalability, cost-effectiveness, and improved accessibility for businesses of all sizes, particularly SMEs seeking efficient resource management. Secondly, the rising demand for precise location-based data analysis across diverse sectors like urban planning, environmental monitoring, and precision agriculture fuels market growth. Furthermore, technological advancements, including the integration of AI and machine learning capabilities within GIS platforms, enhance analytical power and facilitate more sophisticated spatial decision-making. Finally, government initiatives promoting smart cities and digital transformation worldwide further stimulate market expansion. The market is segmented by application (SMEs, Large Enterprises) and type (Cloud-Based, On-Premises), reflecting the diverse needs of various user groups. Large enterprises, with their extensive spatial data requirements and resources, are expected to drive significant market share, while cloud-based solutions are poised for faster growth due to their flexible deployment models. The regional landscape reveals a dynamic distribution of market share. North America, particularly the United States, holds a prominent position, driven by high technological adoption rates and the presence of major GIS solution providers. Europe follows closely, fueled by increasing government investments in infrastructure development and digitalization initiatives. The Asia-Pacific region is expected to experience significant growth, propelled by rapid urbanization and the expanding adoption of GIS technologies in developing economies like China and India. While the on-premises segment currently dominates, the cloud-based segment is anticipated to exhibit higher growth in the forecast period, driven by its inherent advantages in scalability, accessibility, and cost-efficiency. Competitive dynamics are shaped by both established players like IBM TRIRIGA and emerging technology companies, leading to innovation and diversification of GIS tool offerings. The market's future hinges on continuous technological innovation, the growing adoption of location intelligence across sectors, and the expansion of robust infrastructure supporting data accessibility and management.

The Computer Vision in Geospatial Imagery market is experiencing robust growth, driven by increasing demand for accurate and efficient geospatial data analysis across various sectors. Advancements in artificial intelligence (AI), deep learning, and high-resolution imaging technologies are fueling this expansion. The market's ability to extract valuable insights from aerial and satellite imagery is transforming industries such as agriculture, urban planning, environmental monitoring, and defense. Applications range from precision agriculture using drone imagery for crop health monitoring to autonomous vehicle navigation and infrastructure inspection using high-resolution satellite data. The integration of computer vision with cloud computing platforms facilitates large-scale data processing and analysis, further accelerating market growth. We estimate the 2025 market size to be approximately $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is expected to continue, driven by increasing adoption of advanced analytics and the need for real-time geospatial intelligence. Several factors contribute to this positive outlook. The decreasing cost of high-resolution sensors and cloud computing resources is making computer vision solutions more accessible. Furthermore, the growing availability of large datasets for training sophisticated AI models is enhancing the accuracy and performance of computer vision algorithms in analyzing geospatial data. However, challenges remain, including data privacy concerns, the need for robust data security measures, and the complexity of integrating diverse data sources. Nevertheless, the overall market trend remains strongly upward, with significant opportunities for technology providers and users alike. The key players listed—Alteryx, Google, Keyence, and others—are actively shaping this landscape through innovative product development and strategic partnerships.

Last update: July 21, 2025This polygon dataset represents golf course locations within the state of Utah. It should be noted that this is based on the Utah Golf Association"s website list of golf courses, golf course websites, and other public data and may be incomplete. This dataset also contains the name, city, number of holes, par, and type of golf course.

DEFINITION:Tax Law POINT is a generalized point representation of lands enrolled in the Managed Forest and Forest Crop Law Programs, collectively referred to as Tax Law Layers. Points are located at the center point of each 40-acre quarter-quarter section in which land is enrolled. Points do not identify specific enrollment location. Acreage enrolled from fractional or government lots are located either to the most approximate QQ, Q or S as possible. (Enrolled parcels are represented by the PLSS shape they lie within; however, the actual size of the enrolled property may be as small as 0.1 acres). The GIS layer was last updated March 5, 2025 to reflect conditions as of January 1, 2025. Corrections are made to the data throughout the year that may not be reflected in this snapshot.FEATURE TYPE(S):PointGEOGRAPHIC EXTENT:StatewideSOURCE SCALE:VariedPROJECTION:Wisconsin Transverse Mercator NAD 1983/1991 (WTM83/91)WKID: 3071PURPOSE/BACKGROUND:Wisconsin’s forest tax laws encourage sustainable forest management on private lands by providing a property tax incentive to landowners. Both the Managed Forest Law (MFL) and Forest Crop Law (FCL) encourage proper management of woodlands not only in their purposes and policies, but through a written management plan for a landowner’s property. The management plan incorporates landowner objectives, timber management, wildlife management, water quality and the environment as a whole to create healthy and productive forest. In exchange for following a written management plan and program rules, landowners pay forest tax law program rates in lieu of regular property taxes.FCL lands are open to the public for the following activities: hunting and fishing.MFL lands enrolled as open are open to the public for the following activities: hunting, fishing, hiking, sight-seeing, and cross-country skiing.Additional rules regarding public access may be reviewed here: https://dnr.wisconsin.gov/topic/forestlandowners/mflThe GIS feature class was created to be used in the Open Private Forest Lands web mapping application (Private Forest Lands Open to Public Recreation).Open Private Forest Lands (OPFL) Project Background:Provide a simple GIS web mapping application to display the approximate representations of over 1.3 million acres of Forest Tax Law lands (Managed Forest and Forest Crop Law) open to the public for hunting, hiking, fishing, cross-country skiing, and sightseeing. Display information to allow the public to access the lands without spending a lot of time cross checking plat books or contacting local county offices or the county Land Information Offices.Update Frequency:Semi-Annual (January, September). Edits to Tax Law entries can occur throughout the year, but most changes are not effective until January 1 except for landowner information. Landowner information edits are updated in the spatial views on a weekly basis. In addition, Forestry will re-generate taxlaw shapes as significant improvements to the data are completed. In January of each year, the feature class is re-generated to reflect new entries, changes to access, etc. effective January 1. January update: Update to reflect enrollments as of January.September update: Pre-hunting season update.The GIS layer was last updated March 5, 2025 to reflect conditions as of January 1, 2025. Corrections are made to the data throughout the year that may not be reflected in this snapshot.ATTRIBUTES:Field Descriptions:ORDER_NO: (c, 12) The Forestry property code of the feature. (Use as join field for if linking to landowner table information.)Format: 2-digit cnty – 3 digit seq no – 4 digit year of entryEx. 11-234-2013DNR_CTY_NO: (n, 2) The 2-digit DNR county code representing the predominant county in which the DTRSQQ falls.Format: Numbers, No commasEx: 37 (Marathon County)CNTY_NAME: (T, 11) County name of the predominant county in which the DTRSQQ falls.Ex: MarathonENTRY_YEAR: (t, 4) The year in which the order number was entered into the taxlaw program.Format: YYYYEx: 1999TAX_TYPE: (t, 3) Indicates whether the polygon is enrolled in MFL or FCL.Format: ALL CAPSPossible Values:MFL: Managed Forest LawFCL: Forest Crop LawAC_OP_PLS: (double) Acres, associated to the identified order number, that are enrolled in the identified PLSS as open. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_OP_ORD for total open acreage associated with this order number). NOTE: This is not the total number of acres open with this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.AC_CL_PLS: (double) Acres, associated to the identified order number, that are enrolled in the identified PLSS as closed. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_CL_ORD for total closed acreage associated with this order number). NOTE: This is not the total number of acres closed with this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.AC_TOT_PLS: (double) Total acres, associated to the identified order number, that are enrolled in the identified PLSS. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_TOT_ORD for total acreage associated with this order number). NOTE: This is not the total number of acres enrolled within this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.ORDER_YRS: (t, 2) Total number of years the order will be enrolled in the program (under the associated order number). Format: Plan or order lengths are either 25 or 50 yearsEx: 50ORDER_EXP: (t, 20) Date that order number expires. All orders end on December 31. Format: December 31, YYYYEx: December 31, 2015OWNER_TEXT: (t, 30) Type of ownership. Ownership could be: Individual, Joint, Corporation, LLC, Partnership, LLP, Trust, etc.ACCNT_TYPE: (t, 1) Type of account.Possible Values:S: Small Account – landowners generally have less than 1,000 acres of forest land and the accounts are managed by DNR field foresters.L: Large Account – landowners generally have 1,000 acres or more of forest land and the accounts are managed by DNR Forest TaxAC_OP_ORD: (double) Total open acreage associated with the order number. AC_CL_ORD: (double) Total closed acreage associated with the order number.AC_TOT_ORD: (double) Total acreage associated with the order number.DTRSQQ_CO: (long) A concatenation of direction, township, range, section, quarter section, and quarter-quarter section used to approximate the location of the order number (or part of the order number). Each order number has separate records for each DTRSQQ where the order number resides. (Data source: 24K Landnet Spatial Database Technical Documentation)Format:1st Digit = Direction2nd & 3rd Digits = Township4th & 5th Digits = Range6th & 7th Digits = Section8th Digit = Quarter9th Digit = Quarter-QuarterEx: 441012812LEGAL_D_CO: (t, 5) Code describing legal description identified by order number.Format: 1st character:Blank = Entire (Govt Lot)D = Entire (PLSS)P = Part ofE = Entire Excluding ROW2nd character:L = Govt LotBlank = PLSSCharacters 3-5:If PLSS, 001-016 are StandardIf PLSS, 017-060 are FractionalIf Govt Lot, this is the Govt Lot #Ex: PL003LEGAL_DESC: (t, 100) Translated legal description code. Ex: GOV LOT 3, PART OFDTRSLD_TXT: (t, 2380) Field generated to convert DTRSQQ and legal description codes to a text description of the PLSS where the enrollment is located. Includes a note indicating if a record includes a fractional correction.Ex: T02-R01W-S05, Part of the NE of the NW (fractional correction)PARCEL_NO: (t, 255) County created parcel number. (Parcel level information not yet available for all records.)Format: Varies by countyEx: 07-04-59MCD_NAME: (t, 50) Municipal Civil Division (MCD) name.Ex: Solon SpringsMCD_TYPE_C: (t, 1) Type of Municipal Civil Division (MCD). Format: ALL CAPSPossible Values:T: TownV: VillageC: CityPLSS_LEVEL: (t, 2) PLSS level to which the record is located. Format: ALL CAPSPossible Values:QQ: Quarter-quarterQ: QuarterS: SectionCHNG_BY: (c, 30) The user who last updated the record.Ex: klauscCHNG_DATE: (date) Date the record was last changed.Format: MM/DD/YYYY Ex: 10/23/2012ACCESS: (t, 1) Indicates whether the quarter-quarter contains areas which are open to the public, closed to the public, or both.Format: ALL CAPSPossible Values:O: QQ contains areas that are Open to the publicC: QQ contains areas that are Closed to the publicB: QQ contains Both open and closed areas.ADDITIONAL INFORMATION:Tax law programs: https://dnr.wisconsin.gov/topic/forestlandowners/mflWeb mapping application: https://dnr.wisconsin.gov/topic/forestlandowners/opentopublicappCONTACT PERSON(S):GIS contact: Laura Waddle - GIS Specialist, (608) 320-4648, Laura.Waddle@wisconsin.govResource contact: <>R.J. Wickham - Tax Law Section Chief, (920) 369-6248, Richard.Wickham@wisconsin.govCOPYRIGHT:The material is for the noncommercial use of the general public. The fair use guidelines of the U.S. copyright statutes apply to all material on the Department of Natural Resources Webpages and linked agency Webpages. The Department of Natural Resources shall remain the sole and exclusive owner of all rights, title and interest in and to all specifically copyrighted information created and posted for inclusion in this system. Photographs and graphics on the Department website are either the property of the state department or the state agency that holds a license to use and display the material. For copy or use of information on the Department website that is outside of the fair use provisions of copyright law, please seek permission from the individual listed as responsible for the page. If you have any questions on using material on the Department web pages please e-mail the specific

The global land displacement monitoring market is experiencing robust growth, driven by increasing urbanization, infrastructure development, and the escalating need for accurate land use planning and environmental protection. The market, estimated at $500 million in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $1.8 billion by 2033. Key drivers include advancements in satellite technology, including Synthetic Aperture Radar (SAR) and LiDAR, enabling precise and frequent monitoring of land surface changes. The growing adoption of GIS (Geographic Information Systems) and AI-powered analytics further enhances the accuracy and efficiency of displacement analysis. Government regulations promoting sustainable land management and disaster risk reduction are also significantly contributing to market expansion. Emerging trends include the integration of IoT sensors for real-time monitoring and the development of cloud-based platforms for data storage and processing, making the technology more accessible and cost-effective. However, the market faces certain restraints. High initial investment costs associated with advanced technology and skilled personnel can be a barrier for smaller companies and developing nations. Data security and privacy concerns surrounding the collection and usage of geospatial data also need to be addressed. Despite these challenges, the market's positive outlook is reinforced by the increasing demand for reliable land displacement data across various sectors, including agriculture, construction, and environmental conservation. The segmentation of the market includes software, hardware, services, and applications, each contributing to its overall growth. Major players like Hexagon, Synspective, Land Portal, CATALYST.Earth, EGMS, and Planetek are actively shaping market dynamics through technological innovation and strategic partnerships.

This feature class was derived from the GIS polygon datasets BLM Grazing Allotments and BLM Grazing Pastures, which were downloaded from the Geospatial Gateway in April 2025. Fields were added to the feature classes and calculated as needed to allow the Rangeland Administration System (RAS) tabular data to be joined to the GIS datasets. RAS tabular data for Billed allotments and pastures (as of April 2025) was provided by BLM Rangeland Management Specialist Josh Robbins in April 2025 and processed as dbfs, with fields added and calculated as needed to match the BLM GIS Grazing Allotments feature class. RAS tables and BLM GIS data for allotments were joined using the State Allotment Number, a concatenation of allotment number and BLM Administrative State for allotments (ST_ALLOT_NUM). To match numbered pastures in the RAS data and BLM GIS data, the pasture number (if present) was added to the State Allotment Number (ST_ALLOT_PAST_NUM). RAS records for Billed Allotments that did not match during a join operation were tracked in a separate excel sheet from the matching records. Matching records were then joined back to the BLM GIS Allotments grazing feature class and Allotment name fields were edited as necessary. A Status field was added to indicate if the data are either Billed or Authorized and a Source field was added to indicate if the data came from Allotments, Trailing Allotments, or Pastures. An additional field, TR_ALLOT_NUM, was added to designate any Trailing Allotments in the data. Trailing allotments were identified and processed separately for Nevada, since these allotments overlap portions of other allotments. Any overlaps in the data were removed via dissolve and Spatial Join. Billed Allotments and Pastures final feature classes were then unioned together, with fields examined to ensure that all data was captured. Input BLM GIS Grazing data:BLM Grazing Pastures and BLM Grazing Allotments are areas of land designated and managed for grazing of livestock. It may include private, state, and public lands under the jurisdiction of the Bureau of Land Management and/or other federal agencies. An allotment is derived from its pastures, where the grazing of livestock is occurring. The attributes of the BLM Grazing Allotment features may be duplicated in RAS, but are considered to be minimum information for unique identification and cartographic purposes.Input RAS Data:The Rangeland Administration System (RAS) provides grazing administrative support and management reports for the BLM and the public. The Rangeland Administration system serves as an electronic calendar for issuance of applications and grazing authorizations, including Permits, Leases, and Exchange-of-Use Agreements. The Billed data is current as of April 2025 and was provided by BLM Rangeland Management Specialist Josh Robbins in April 2025.

Current Effective Date: 0901Z 02 Oct 2025 to 0901Z 27 Nov 2025This Class Airspace data is provided as a vector geospatial-enabled file format and depicted on Enroute charts. Class Airspace data is published every eight weeks by the U.S. Department of Transportation, Federal Aviation Administration-Aeronautical Information Services.

Article I, section 3 of the Constitution requires the Senate to be divided into three classes for purposes of elections. Senators are elected to six-year terms, and every two years the members of one class—approximately one-third of the senators—face election or reelection. Terms for senators in Class I expire in 2025, Class II in 2021, and Class III in 2023.

The Federal Emergency Management Agency (FEMA) produces Flood Insurance Rate maps and identifies Special Flood Hazard Areas as part of the National Flood Insurance Program's floodplain management. Special Flood Hazard Areas have regulations that include the mandatory purchase of flood insurance for holders of federally regulated mortgages. In addition, this layer can help planners and firms avoid areas of flood risk and also avoid additional cost to carry insurance for certain planned activities. Dataset SummaryPhenomenon Mapped: Flood Hazard AreasGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Northern Mariana Islands, and American Samoa)Cell Sizes: 10 meters (default), 30 meters, and 90 metersUnits: NoneSource Type: ThematicPixel Type: Unsigned integerSource: Federal Emergency Management Agency (FEMA)Update Frequency: AnnualPublication Date: May 7, 2025 This layer is derived from the May 7, 2025 version Flood Insurance Rate Map feature class S_FLD_HAZ_AR. The vector data were then flagged with an index of 94 classes, representing a unique combination of values displayed by three renderers. (In three resolutions the three renderers make nine processing templates.) Repair Geometry was run on the set of features, then the features were rasterized using the 94 class index at a resolutions of 10, 30, and 90 meters, using the Polygon to Raster tool and the "MAXIMUM_COMBINED_AREA" option. Not every part of the United States is covered by flood rate maps. This layer compiles all the flood insurance maps available at the time of publication. To make analysis easier, areas that were NOT mapped by FEMA for flood insurance rates no longer are served as NODATA but are filled in with a value of 250, representing any unmapped areas which appear in the US Census boundary of the USA states and territories. The attribute table corresponding to value 250 will indicate that the area was not mapped.What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "flood hazard areas" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "flood hazard areas" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one. Processing TemplatesCartographic Renderer - The default. These are meaningful classes grouped by FEMA which group its own Flood Zone Type and Subtype fields. This renderer uses FEMA's own cartographic interpretations of its flood zone and zone subtype fields to help you identify and assess risk. Flood Zone Type Renderer - Specifically renders FEMA FLD_ZONE (flood zone) attribute, which distinguishes the original, broadest categories of flood zones. This renderer displays high level categories of flood zones, and is less nuanced than the Cartographic Renderer. For example, a fld_zone value of X can either have moderate or low risk depending on location. This renderer will simply render fld_zone X as its own color without identifying "500 year" flood zones within that category.Flood Insurance Requirement Renderer - Shows Special Flood Hazard Area (SFHA) true-false status. This may be helpful if you want to show just the places where flood insurance is required. A value of True means flood insurance is mandatory in a majority of the area covered by each 10m pixel. Each of these three renderers have templates at three different raster resolutions depending on your analysis needs. To include the layer in web maps to serve maps and queries, the 10 meter renderers are the preferred option. These are served with overviews and render at all resolutions. However, when doing analysis of larger areas, we now offer two coarser resolutions of 30 and 90 meters in processing templates for added convenience and time savings.Data DictionaryMaking a copy of your area of interest using copyraster in arcgis pro will copy the layer's attribute table to your network alongside the local output raster. The raster attribute table in the copied raster will contain the flood zone, zone subtype, and special flood hazard area true/false flag which corresponds to each value in the layer for your area of interest. For your convienence, we also included a table in CSV format in the box below as a data dictionary you can use as an index to every value in the layer. Value,FLD_ZONE,ZONE_SUBTY,SFHA_TF 2,A,, 3,A,,F 4,A,,T 5,A,,T 6,A,,T 7,A,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN CHANNEL,T 8,A,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN STRUCTURE,T 9,A,ADMINISTRATIVE FLOODWAY,T 10,A,COASTAL FLOODPLAIN,T 11,A,FLOWAGE EASEMENT AREA,T 12,A99,,T 13,A99,AREA WITH REDUCED FLOOD RISK DUE TO LEVEE,T 14,AE,,F 15,AE,,T 16,AE,,T 17,AE,,T 18,AE,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN CHANNEL,T 19,AE,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN STRUCTURE,T 20,AE,"1 PCT CONTAINED IN STRUCTURE, COMMUNITY ENCROACHMENT",T 21,AE,"1 PCT CONTAINED IN STRUCTURE, FLOODWAY",T 22,AE,ADMINISTRATIVE FLOODWAY,T 23,AE,AREA OF SPECIAL CONSIDERATION,T 24,AE,COASTAL FLOODPLAIN,T 25,AE,COLORADO RIVER FLOODWAY,T 26,AE,COMBINED RIVERINE AND COASTAL FLOODPLAIN,T 27,AE,COMMUNITY ENCROACHMENT,T 28,AE,COMMUNITY ENCROACHMENT AREA,T 29,AE,DENSITY FRINGE AREA,T 30,AE,FLOODWAY,T 31,AE,FLOODWAY CONTAINED IN CHANNEL,T 32,AE,FLOODWAY CONTAINED IN STRUCTURE,T 33,AE,FLOWAGE EASEMENT AREA,T 34,AE,RIVERINE FLOODWAY IN COMBINED RIVERINE AND COASTAL ZONE,T 35,AE,RIVERINE FLOODWAY SHOWN IN COASTAL ZONE,T 36,AE,STATE ENCROACHMENT AREA,T 37,AH,,T 38,AH,,T 39,AH,FLOODWAY,T 40,AO,,T 41,AO,COASTAL FLOODPLAIN,T 42,AO,FLOODWAY,T 43,AREA NOT INCLUDED,,F 44,AREA NOT INCLUDED,,T 45,AREA NOT INCLUDED,,U 46,D,,F 47,D,,T 48,D,AREA WITH FLOOD RISK DUE TO LEVEE,F 49,OPEN WATER,,F 50,OPEN WATER,,T 51,OPEN WATER,,U 52,V,,T 53,V,COASTAL FLOODPLAIN,T 54,VE,,T 55,VE,,T 56,VE,COASTAL FLOODPLAIN,T 57,VE,RIVERINE FLOODWAY SHOWN IN COASTAL ZONE,T 58,X,,F 59,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD,F 60,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD,T 61,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD,U 62,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN CHANNEL,F 63,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN STRUCTURE,F 64,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD IN COASTAL ZONE,F 65,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD IN COMBINED RIVERINE AND COASTAL ZONE,F 66,X,"1 PCT CONTAINED IN STRUCTURE, COMMUNITY ENCROACHMENT",F 67,X,"1 PCT CONTAINED IN STRUCTURE, FLOODWAY",F 68,X,1 PCT DEPTH LESS THAN 1 FOOT,F 69,X,1 PCT DRAINAGE AREA LESS THAN 1 SQUARE MILE,F 70,X,1 PCT FUTURE CONDITIONS,F 71,X,1 PCT FUTURE CONDITIONS CONTAINED IN STRUCTURE,F 72,X,"1 PCT FUTURE CONDITIONS, COMMUNITY ENCROACHMENT",F 73,X,"1 PCT FUTURE CONDITIONS, FLOODWAY",F 74,X,"1 PCT FUTURE IN STRUCTURE, COMMUNITY ENCROACHMENT",F 75,X,"1 PCT FUTURE IN STRUCTURE, FLOODWAY",F 76,X,AREA OF MINIMAL FLOOD HAZARD, 77,X,AREA OF MINIMAL FLOOD HAZARD,F 78,X,AREA OF MINIMAL FLOOD HAZARD,T 79,X,AREA OF MINIMAL FLOOD HAZARD,U 80,X,AREA OF SPECIAL CONSIDERATION,F 81,X,AREA WITH REDUCED FLOOD RISK DUE TO LEVEE,F 82,X,AREA WITH REDUCED FLOOD RISK DUE TO LEVEE,T 83,X,FLOWAGE EASEMENT AREA,F 84,X,1 PCT FUTURE CONDITIONS,T 85,AH,COASTAL FLOODPLAIN,T 86,AE,,U 87,AE,FLOODWAY,F 88,X,AREA WITH REDUCED FLOOD HAZARD DUE TO ACCREDITED LEVEE SYSTEM,F 89,X,530,F 90,VE,100,T 91,AE,100,T 92,A99,AREA WITH REDUCED FLOOD HAZARD DUE TO LEVEE SYSTEM,T 93,A99,AREA WITH REDUCED FLOOD HAZARD DUE TO NON-ACCREDITED LEVEE SYSTEM,T 94,A,COMBINED RIVERINE AND COASTAL FLOODPLAIN,T 250,AREA NOT INCLUDED,Not Mapped by FEMA, Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

[Superseded] This dataset is a single layer from [Superseded] City Plan 2014 – v33.00–2025 collection. Not all layers were updated in this amendment, for more information on past Adopted City Plan amendments.This feature class is shown on the Extractive resources overlay map (map reference: OM-005.1).This feature class includes the following sub-categories:(a) KRA resource/processing area sub-category;(b) KRA separation area sub-category;(c) KRA transport route separation area sub-category.For information about the overlay and how it is applied, please refer to the Brisbane City Plan 2014 document.

The Community Facilities Inventory for Cuyahoga County is a comprehensive geographic information system (GIS) layer developed by the Cuyahoga County Planning Commission staff. This inventory provides an extensive and up-to-date dataset of essential community facilities, including education institutions, libraries, recreation centers, parks, city halls, civic centers, emergency services, and healthcare centers. The dataset is derived from various authoritative sources, including SafeGraph, OpenStreetMap, Google Places, The State of Ohio, and The City of Cleveland, among others. Key Features: Parks and Recreation:Athletic Field: Athletic Field: Outdoor space for organized sports and activities.Pool: Public swimming and aquatic recreation facility.Skate Park: Area with ramps and features for skateboarding and biking.Dog Park: Fenced area designated for off-leash dog exercise.Recreation Center: Indoor facility for fitness, sports, and community programs.Trailhead: Designated access point to a trail or pathway.Education Facilities:Child Care Services: Locations of licensed child care service providers. Primary Schools: Locations of primary educational institutions catering to young learners.Secondary Schools: Locations of secondary educational institutions serving middle and high school students.Post-Secondary Institutions: Locations of colleges and universities offering higher education opportunities.Civic and Administrative Centers:City Halls: Locations of city halls providing administrative services for municipalities within Cuyahoga County.Civic Centers: Locations of community gathering places that host events, cultural activities, and public gatherings.Recreation Centers: Locations designed to provide a wide range of leisure and recreational activities, often including sports, fitness, cultural programs, and social gatherings, for people of all ages and interestsParks: Locations that are open, natural or landscaped areas set aside for public enjoyment, typically featuring green spaces, playgrounds, walking paths, and opportunities for outdoor activities, relaxation, and connection with nature.Libraries: Locations, public or private, that house collections of books, digital resources, and other educational materials, providing a quiet and conducive environment for reading, research, and intellectual exploration, as well as various educational programs and services.Emergency Services:Police Stations: Locations of law enforcement agencies responsible for ensuring public safety.Fire Stations: Locations of fire departments responsible for emergency response and fire prevention services.Hospitals and Healthcare Facilities:Hospitals: Locations of medical centers offering comprehensive healthcare services.Healthcare Facilities: Locations of clinics, doctors offices, and medical centers catering to specific medical needs.Data Sources:The data used to compile this inventory is sourced from a variety of reputable providers, including but not limited to:SafeGraph: Contributing valuable location data to enhance the accuracy of community facilities' information.OpenStreetMap: Contributing valuable location data to enhance the accuracy of community facilities' information.Google Places: Supplying location details and metadata on various establishments and amenities.The State of Ohio: Providing authoritative data on education institutions, early child care and education programs, and public services.The City of Cleveland: Offering precise location information on civic and administrative facilities.Coverage: Cuyahoga County Vintage: 2023 - 2025 Update Frequency: Annual Updates or by Request Last Update: March, 2025