The Planning and Land Development web experience provides a variety of property-related information such as lot boundaries, subdivisions, zoning district, and land use in East Baton Rouge Parish, Louisiana. Users have the options to turn on other various layers such as overlay districts, council districts, addresses and the tax parcel layer which is provided courtesy of the EBRP Assessor's Office.This application features three web maps including:Planning Cadastral Map at https://ebrgis.maps.arcgis.com/home/item.html?id=43e518581406442990aa676044796418Land Development Application Search Tool at https://ebrgis.maps.arcgis.com/home/item.html?id=c53a5bf66b994005a08978e84fc435d3Planning Reference Map at https://ebrgis.maps.arcgis.com/home/item.html?id=66ed92bfbe7446cabe0129009f8d3848

Baton Rouge's unique past has shaped the city that we live in today. The layout of the city's streets, the arrangement of prominent government and religious structures, the clustering of businesses, the distribution of residential neighborhoods, and the placement of parks and schools all speak to the long term processes of urban growth. Society invests tremendous effort in creating its urban centers and citizens develop attachments to those places. It is the investment of human effort that stimulates a sense of place and allows individuals to develop strong feelings about their home city. Sense of place is constantly reinforced by contact with the common, everyday landscapes that surround us. In Baton Rouge, the two principal university campuses, the state government complex, along with various historic neighborhoods and structures all stand as perpetual reminders of the city's past. Many familiar and, at the same time, unique landscape features of Baton Rouge shape our sense of place.

The General Plan is comprehensive and long-range in its scope. It will be used on an ongoing basis to direct the City’s decision making. The Land Use Element and the Land Use and Circulation Map designate the proposed location, distribution, and extent of land uses, which shape future physical development, community design, and quality of life. The Land Use Element sets forth specific goals, policies, and implementation actions to guide land use for the City through 2040. Each Land Use Classification represents a desired use for that area. These classifications include population density and commercial and industrial intensity, which assist in the determination of pedestrian and vehicular circulation and public facility needs. They reflect the environmental carrying capacity limitations that can be used in assessing new or rehabilitated growth.

Land cover has been interpreted from Satellite images and field checked, other information has been digitized from topographic maps

 Members informations:
 Attached Vector(s):
  MemberID: 1
 Vector Name: Land use
 Source Map Name: SPOT Pan
 Source Map Scale: 50000
 Source Map Date: 1989/90
 Projection: Polyconic on Modified Everest Ellipsoid
 Feature_type: polygon
 Vector 
 Land use maps, interpreted from SPOT panchromatic imagery and field
 checked (18 classes)

 Members informations:
 Attached Vector(s):
  MemberID: 2
 Vector Name: Administrative boundaries
 Source Map Name: topo sheets
 Source Map Scale: 50000
 Source Map Date: ?
 Feature_type: polygon
 Vector 
 Dzongkhags (Districts) and Gewogs

 Members informations:
 Attached Vector(s):
  MemberID: 3
 Vector Name: Roads
 Source Map Name: topo sheets
 Source Map Scale: 50000
 Source Map Date: ?
 Feature_type: lines
 Vector 
 Road network

 Attached Report(s)
 Member ID: 4
 Report Name: Atlas of Bhutan
 Report Authors: Land use planning section
 Report Publisher: Ministry of Agriculture, Thimpu
 Report Date: 1997-06-01
 Report 
 Land cover (1:250000) and area statistics of 20 Dzongkhags

Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity, and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system that meets land management needs. Local capacity may be replaced by external consultants or methods which lack long-term sustainability. In this study, we characterize and respond to the key land cover mapping gaps and challenges encountered in the Lower Mekong (LMR) and Hindu Kush-Himalaya (HKH) region through a needs assessment exercise and a collaborative system design. Needs were assessed using multiple approaches, including focus groups, user engagement workshops, and online surveys. Efforts to understand existing limitations and stakeholder needs resulted in a co-developed and modular land cover monitoring system which utilizes state-of-the-art cloud computing and machine learning which leverages freely available Earth observations. This approach meets the needs of diverse actors and is a model for transnational cooperation.

Unlock precise, high-quality GIS data covering 4.5M+ verified locations across Italy. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

Polygons delineating Federal, Tribal, State, and Local government land ownership/management at a scale of 1:24,000 within Oregon. The Ownership Land Management feature class provides a current representation of statewide land management and ownership status by integrating the best available data for Federal, State and County sources. This is not a legal representation and should not be considered an official source of property ownership or management. The attributes include information on who is the title holder as well as the entity responsible for managing the property.

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

Discover the booming real estate surveying and mapping market! This comprehensive analysis reveals key trends, growth drivers, and regional insights for 2025-2033, featuring major players like Trimble and Fugro. Learn about market size, CAGR, and future opportunities in this dynamic sector.

Status: COMPLETED 2010. The data was converted from the most recent (2010) versions of the adopted plans, which can be found at https://cms3.tucsonaz.gov/planning/plans/ Supplemental Information: In March 2010, Pima Association of Governments (PAG), in cooperation with the City of Tucson (City), initiated the Planned Land Use Data Conversion Project. This 9-month effort involved evaluating mapped land use designations and selected spatially explicit policies for nearly 50 of the City's adopted neighborhood, area, and subregional plans and converting the information into a Geographic Information System (GIS) format. Further documentation for this file can be obtained from the City of Tucson Planning and Development Services Department or Pima Association of Governments Technical Services. A brief summary report was provided, as requested, to the City of Tucson which highlights some of the key issues found during the conversion process (e.g., lack of mapping and terminology consistency among plans). The feature class "Plan_boundaries" represents the boundaries of the adopted plans. The feature class "Plan_mapped_land_use" represents the land use designations as they are mapped in the adopted plans. Some information was gathered that is implicit based on the land use designation or zones (see field descriptions below). Since this information is not explicitly stated in the plans, it should only be viewed by City staff for general planning purposes. The feature class "Plan_selected_policies" represents the spatially explicit policies that were fairly straightforward to map. Since these policies are not represented in adopted maps, this feature class should only be viewed by City staff for general planning purposes only. 2010 - created by Jamison Brown, working as an independent contractor for Pima Association of Governments, created this file in 2010 by digitizing boundaries as depicted (i.e. for the mapped land use) or described in the plans (i.e. for the narrative policies). In most cases, this involved tracing based on parcel (paregion) or street center line (stnetall) feature classes. Snapping was used to provide line coincidence. For some map conversions, freehand sketches were drawn to mimick the freehand sketches in the adopted plan. Field descriptions for the "Plan_mapped_land_use" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number LU_DES: Land use designation (e.g., Low density residential) LISTED_ALLOWABLE_ZONES: Allowable zones as listed in the Plan LISTED_RAC_MIN: Minimum residences per acre (if applicable), as listed in the Plan LISTED_RAC_TARGET: Target residences per acre (if applicable), as listed in the Plan LISTED_RAC_MAX: Maximum residences per acre (if applicable), as listed in the Plan LISTED_FAR_MIN: Minimum Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_TARGET: Target Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_MAX: Maximum Floor Area Ratio (if applicable), as listed in the Plan BUILDING_HEIGHT_MAX Building height maximum (ft.) if determined by Plan policy IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator IMPLIED_ALLOWABLE_ZONES: Implied (not listed in the Plan) allowable zones IMPLIED_RAC_MIN: Implied (not listed in the Plan) minimum residences per acre (if applicable) IMPLIED_RAC_TARGET: Implied (not listed in the Plan) target residences per acre (if applicable) IMPLIED_RAC_MAX: Implied (not listed in the Plan) maximum residences per acre (if applicable) IMPLIED_FAR_MIN: Implied (not listed in the Plan) minimum Floor Area Ratio (if applicable) IMPLIED_FAR_TARGET: Implied (not listed in the Plan) target Floor Area Ratio (if applicable) IMPLIED_FAR_MAX: Implied (not listed in the Plan) maximum Floor Area Ratio (if applicable) IMPLIED_LU_CATEGORY: Implied (not listed in the Plan) general land use category. General categories used include residential, office, commercial, industrial, and other.PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactJohn BeallCity of Tucson Development Services520-791-5550John.Beall@tucsonaz.govUpdate FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Download linkSizeType2019 NLCD2.28 GBapplication/zipThe U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011 and 2016. The 2016 release saw land cover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019.The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.National Land Cover Database (NLCD) 2019 Impervious ProductsNational Land Cover Database (NLCD) 2019 Land Cover Products

This hosted feature layer has been published in RI State Plane Feet NAD 83. THIS IS A FUTURE LAND USE PLANNING MAP CREATED IN 2006. THIS DOES NOT SHOW CURRENT 2025 LAND USE LAND COVER. The Land Use 2025 dataset was developed for the Division of Planning, RI Statewide Planning Program as part of an update to a state land use plan. It evolved from a GIS overlay analysis of land suitability and availability and scenario planning for future growth. The analysis focused on the 37% of the State identified as undeveloped and unprotected in a land cover analysis from RIGIS 1995 land use land cover data. The project studied areas for suitability for conservation and development, based on the location of key natural resources and public infrastructure. The results identified areas with future use potential, under three categories of development intensity and two categories of conservation.These data are presented in the Plan as Figure 121-02-(01), Future Land Use Map. Land Use 2025: State Land Use Policies and Plan was published by the RI Statewide Planning Program on April 13, 2006. The intent of the Plan is to bring together the elements of the State Guide Plan such as natural resources, economic development, housing and transportation to guide conservation and land development in the State. The Plan directs the state and communities to concentrate growth inside the Urban Services Boundary (USB) and within potential growth centers in rural areas. It establishes different development approaches for urban and rural areas.These data have several purposes and applications: They are intended to be used as a policy guide for directing growth to areas most capable of supporting current and future developed uses and to direct growth away from areas less suited for development. Secondly, these data are a guide to assist the state and communities in making land use policies. It is important to note these data are a generalized portrayal of state land use policy. These are not a statewide zoning data. Zoning matters and individual land use decisions are the prerogative of local governments. The land use element is the over arching element in Rhode Island's State Guide Plan. The Plan articulates goals, objectives and strategies to guide the current and future land use planning of municipalities and state agencies. The purpose of the plan is to guide future land use and to present policies under which state and municipal plans and land use activities will be reviewed for consistency with the State Guide Plan. The Map is a graphical representation of recommendations for future growth patterns in the State. It depicts where different intensities of development (e.g. parks, urban development, non-urban development) should occur by color. The Map contains a USB that shows where areas with public services supporting urban development presently exist, or are likely to be provided, through 2025. Within the USB, most land is served by public water service; many areas also have public sewer service, as well as, public transit. Also included on the map are growth centers which are potential areas for development and redevelopment outside of the USB. Growth Centers are envisioned to be areas that will encourage development that is both contiguous to existing development with low fiscal and environmental impacts.NOTE: These data will be updated when the associated plan is updated or upon an amendment approved by the State Planning Council. NOTE: Wetlands were not categorized within the Land Use 2025 dataset.When using this dataset, the RIGIS wetlands dataset should be overlaid as a mask. Full descriptions of the categories and intended uses can be found within Section 2-4, Future Land Use Patterns, Categories, and Intended Uses, of the Plan. https://www.planning.ri.gov/documents/guide_plan/landuse2025.pdf

Explore the burgeoning Real Estate Surveying and Mapping market, driven by urban development and real estate expansion. Discover market size, growth drivers, key trends, and regional dynamics shaping this vital industry through 2033.

This layer represents the boundaries for existing and in-progress BLM Land Use Planning Area (LUPA) polygons. Land Use Planning Areas are geographic areas within which the BLM will make decisions during a land use planning effort. Land Use Planning Area Boundaries shift from an "in-progress" status and become Existing Land Use Planning Areas when the Land Use Plan has been approved and a Record of Decision Date has been established.

As one of the most wealth-generating industries in the world, tourism has been the center of attention by many governments worldwide. Depending on the purpose of tourism, it has been classified into different types, one of which is ecotourism. Given that ecotourism currently accounts for a small part of the whole, however, it has a rapid growth rate. The present study aims at zoning suitable areas in Ilam, a province in western Iran, for ecotourism development purposes. Accordingly, the digital maps of elevation, slope, land cover/land use, mineral springs, and water resources were prepared, at first. Afterwards, the suitable and unsuitable areas were segregated by Boolean functions. Overlaying the map layers by GIS software, the suitable areas were identified. The obtained results revealed that the top attractions are mainly distributed from the northern and central province to the southeastern parts where climatic condition is favorable, and rich in natural land cover and water resources. Moreover, the southern and western parts were evaluated poor in term of eco-tourist attractions.

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

[Metadata] Description: Land Use Land Cover of main Hawaiian Islands as of 1976Source: 1:100,000 1976 Digital GIRAS (Geographic Information Retrieval and Analysis) files. Land Use and Land Cover (LULC) data consists of historical land use and land cover classification data that was based primarily on the manual interpretation of 1970's and 1980's aerial photography. Secondary sources included land use maps and surveys. There are 21 possible categories of cover type. The spatial resolution for all LULC files will depend on the format and feature type. Files in GIRAS format will have a minimum polygon area of 10 acres (4 hectares) with a minimum width of 660 feet (200 meters) for manmade features. Non-urban or natural features have a minimum polygon area of 40 acres (16 hectares) with a minimum width of 1320 feet (400 meters). Files in CTG format will have a resolution of 30 meters. May 2024: Hawaii Statewide GIS Program staff removed extraneous fields that had been added as part of the 2016 GIS database conversion and were no longer needed.For additional information, please refer to https://files.hawaii.gov/dbedt/op/gis/data/lulc.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of sports facility

EditDate type: Stringwidth: 4precision: 0

Detailed information on individual parcels within Cumberland County, NC, including the City of Fayetteville, the Town of Hope Mills, the Town of Spring Lake, the Town of Eastover, the Town of Falcon, the Town of Godwin, the Town of Linden, the Town of Stedman, and the Town of Wade. Attributes include:Parcel REID (PIN Number): A unique identifier assigned to each parcel for tax purposes.Owner Information: Name and contact details of the property owner(s).Parcel Boundaries: Geospatial data defining the exact boundaries of each parcel.Assessed Value: The assessed value of the land and any improvements for property tax purposes.Land Use: Current land use classification (e.g., residential, commercial, agricultural).Size: Area of the parcel in square feet or acres.Zoning: Zoning classification and any applicable zoning restrictions.Legal Descriptions: Detailed legal description of the parcel boundaries and location.This layer is crucial for tax assessors, urban planners, developers, and other stakeholders who require accurate and up-to-date parcel information for decision-making and operational purposes. It supports a wide range of applications, including property tax assessments, land use planning, infrastructure development, and real estate transactions.More information at https://cumberlandgis.maps.arcgis.com/apps/webappviewer/index.html?id=a6ea68995c2349e9a177366288589be7