The East Point Planning & Community Development (EPCD) Planning Division utilizes a combination of Esri ArcGIS for Desktop and the geopoint application to provide citizens and other customers with current zoning and land use information throughout the city. The division manages and updates the city's geopoint application, which is a web-based map application that allows users to visually map out the current zoning designations in each zoning district and identify information about land use, buildings, and businesses. With the ability to view current zoning districts and easily navigate between maps and data, the planning division is able to provide current zoning and land use information to the public

Probability of Development, Northeast U.S. is one of a suite of products from the Nature’s Network project. Nature’s Network is a collaborative effort to identify shared priorities for conservation in the Northeast, considering the value of fish and wildlife species and the natural areas they inhabit.This index represents the integrated probability of development occurring sometime between 2010 and 2080 at the 30 m cell level. It was based on models of historical patterns of urban growth in the Northeast, including the type (low intensity, medium intensity and high intensity), amount and spatial pattern of development, and incorporates the influence of factors such as geophysical conditions (e.g., slope, proximity to open water), existing secured lands, and proximity to roads and urban centers. The projected amount of new development is downscaled from county level forecasts based on a U.S. Forest Service 2010 Resources Planning Act (RPA) assessment. A complementary product, Probability of Development, 2030, Northeast U.S., estimates the probability of development over a shorter time-scale.Note: based on revisions of the sprawl model, this version was revised in July 2017 to better reflect relatively higher probabilities of development in close vicinity to roads, which is most evident in rural areas.Description and DerivationThe derivation of the integrated probability of development layer was complex. Please consult the detailed technical documentation for a full description of the background data used, the computation of integrated probabilities from a stochastic model, and information about the related urban growth model. The following is a summary of the five major steps of the derivation: 1) Determining historical patterns of growthTo understand how past patterns of development have occurred, historical data from NOAA (for Maine and Massachusetts) and the Chesapeake Bay Watershed Landcover Data Series were obtained for the years 1984 (Chesapeake Bay only), 1996, and 2006. The data were used to model the occurrence of six different development transition types: New growthundeveloped to low-intensity (20-49% impervious surface; e.g., single-family homes)undeveloped to medium-intensity (50-79% impervious surface; e.g., small-lot single-family homes)undeveloped to high-intensity (80-100% impervious surface; e.g., apartment complexes and commercial/industrial development) Intensificationlow- to medium-intensitylow- to high-intensitymedium- to high-intensity Separate models were developed to represent development patterns at model points representing landscapes differing along two dimensions: intensity of development and amount of open water. Predictor variables in the models account for the intensity of existing development and landscape context (e.g. intensity and distance of nearest roads, amount of open water). Analysis of the historical data was based on dividing the landscape into “training windows,” 15km on a side, to determine the historical distribution of transition types and the total amount of historical development. 2) Application to current landscapesFuture patterns of development were projected based on the observed historical patterns. As the first step in this process, the entire Northeast was subdivided into 5km “application panes,” each of which was the center pane of a (3 x 3) “application window”, 15 km on a side. Each of these overlapping application windows was then matched to the three most similar training windows on the basis of intensity of development from the UMass integrated landcover layer, (derived in turn from the 2011 National Landcover Database and other sources), as well as geographic proximity, amount of open water, and density of roads. . For each application window, according to how it mapped on to the dimensions of development and open water modelled above, the relative probability of each of the six development transition types was determined on a scale of 30m cells. 3) Predictions for changing land-useFuture urban acreage by county was predicted as part of an assessment for the U.S. Forest Service 2010 Resources Planning Act. The derivation of this product, the new growth forecasted for the 70 years between 2010 and 2080 was transformed into demand in units of 30m cells. Demand for each county (or census Core Based statistical Area, where relevant) was allocated to the corresponding application windows based on the average of the total amount of historical development in the three matched training windows. 4) Combining models of past and predictions for the futureThe relative probability of a transition type occurring in each cell in a window was used to distribute the allocated demand of new growth throughout the window. The result was an actual probability of development for the transition occurring sometime between 2010- 2080 at the 30 m cell level. Already existing urban land-use was intensified (i.e., transitions 4-6) in proportion to historic patterns determined from the matched training windows, and distributed according to the probability of those transition types across the cells in the window. The combining of probabilities and demand to distribute development to cells was done for each transition type in turn; thus, each cell received a separate probability of being developed through each of the six transition types. Through the application of this process in every application window, an actual probability of development was determined for each cell with reference to nine slightly different contexts corresponding to each of the overlapping windows in which the pane was situated. 5) Smoothing and integrationAn additional step was used to create a smooth and continuous probability of development surface, not subject to abrupt differences along arbitrary boundaries. Cell by cell, actual probabilities of development from each of the overlapping windows were combined such that the closer to a window’s center a cell was located, the more weight the probability derived from it was given. Consequently, each cell had one weighted average probability that was part of a continuous probability of development surface for each transition type. Finally, the probability of development by each of six transition types was integrated for each cell. More weight was given to new growth, such that the probability of undeveloped land becoming urban had more impact than the probability of an intensification of development. The final product is a single layer of the integrated probability of development by 2080, extending across the entire Northeast on the scale of 30 m cells.Known Issues and Uncertainties As with any project carried out across such a large area, the Probability of Development dataset is subject to limitations. The results by themselves are not a prescription for on-the-ground action; users are encouraged to verify, with field visits and site-specific knowledge, the value of any areas identified in the project. Known issues and uncertainties include the following:Although this index is a true probability, it is best used in a relative manner to compare values from one location to anotherThe GIS data upon which this product was based, especially the National Land Cover Dataset (NLCD), are imperfect. Errors of both omission and commission affect the mapping of current development and in turn, models of the probability of future development. Likewise, the forecasts in the 2010 Resources Planning Act assessment, the basis of the projected demand for new growth, contains uncertainties. While the model is anticipated to generally correctly indicate where development is likely to occur, predictions at the cell level are not expected to be highly reliable.Users are cautioned against using the data on too small an area (for example, a small parcel of land), as the data may not be sufficiently accurate at that level of resolution.This model is built on the assumption that future patterns of development will match patterns in the past.It is important to recognize that the integrated probability of development is highest near existing roads, largely because the urban growth model does not attempt to predict the building of new roads and the development associated with them, nor does it incorporate county or town level planning for infrastructure. Because proximity to roads is an important and dominant predictor of development at the 30- m cell level in the model, the integrated probability of development surface is heavily weighted towards existing roads. It is not specifically designed to predict where a subdivision might be developed in the future.

The City of Fort Collins GIS Online Mapping tool (FCMaps) provide current, timely and local geographic information in an easy to use viewer. FCMaps is mobile friendly and will work well on tablets and smartphones as well as a desktop browser.

Here you will find tools to create your own maps. For example, you can find out what kind of zoning the lot next door has, locate an Art in Public Places project, confirm your Council district, map out a bike route on the City's bikeway system, check on the status of a current development project or locate a headstone in one of the City's cemeteries.

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of location-based services (LBS) across diverse sectors like automotive, logistics, and smart city initiatives is a primary catalyst. Furthermore, advancements in technologies such as AI, machine learning, and high-resolution satellite imagery are enabling the creation of more accurate, detailed, and feature-rich digital maps. The shift towards cloud-based deployment models offers scalability and cost-effectiveness, further accelerating market growth. While data privacy concerns and the high initial investment costs for sophisticated mapping technologies present some challenges, the overall market outlook remains overwhelmingly positive. The competitive landscape is dynamic, with established players like Google, TomTom, and ESRI vying for market share alongside innovative startups offering specialized solutions. The segmentation of the market by solution (software and services), deployment (on-premise and cloud), and industry reveals significant opportunities for growth in sectors like automotive navigation, autonomous vehicle development, and precision agriculture, where real-time, accurate mapping data is crucial. The Asia-Pacific region, driven by rapid urbanization and technological advancements in countries like China and India, is expected to witness particularly strong growth. The market's future hinges on continuous innovation. We anticipate a rise in the demand for 3D maps, real-time updates, and integration with other technologies like the Internet of Things (IoT) and augmented reality (AR). Companies are focusing on enhancing the accuracy and detail of their maps, incorporating real-time traffic data, and developing tailored solutions for specific industry needs. The increasing adoption of 5G technology promises to further boost the market by enabling faster data transmission and real-time updates crucial for applications like autonomous driving and drone delivery. The development of high-precision mapping solutions catering to specialized sectors like infrastructure management and disaster response will also fuel future growth. Ultimately, the digital map market is poised for continued expansion, driven by technological advancements and increased reliance on location-based services across a wide spectrum of industries. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Complexity in Integration of Traditional Maps with Modern GIS System. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

The City of Fort Collins GIS Online Mapping tool (FCMaps) provide current, timely and local geographic information in an easy to use viewer. FCMaps is mobile friendly and will work well on tablets and smartphones as well as a desktop browser.

Here you will find locations of current development proposals.

Data for download

Point features representing the locations of major development projects for the Town of Chapel Hill categorized my their current status within the development review process.

Suburban Cook County Election Precincts maintained by the Cook County Clerk's Election Department. Not included are the City of Chicago Election Precincts which are maintained by the Chicago Board Of Elections. An ArcGIS Service is available at: https://hub-cookcountyil.opendata.arcgis.com/datasets/0e91b48d49744346be343f0cb99d25bd_0/

Voting District Project of the Census Redistricting Data Program commenced in Summer of 2017 to establish the state’s Voting precincts within the bureau’s tiger geography. The initial phase used the Bureau’s GUPS plugin for QGIS to update the districts from the 2010 round, and then a secondary verification phase took place to ensure the updated voting districts were properly enacted into Tiger geographic files. A post census effort took place in December 2021 to cut the number of precincts down countywide to 1430. Election Data Services conducted Phase 2 of the Census Redistricting Data Program for the State of Illinois, as well as implemented changes made post census in Cook County.

Precincts are updated as needed before every election by the Cook County Clerk's Elections Department.

This dataset contains the boundaries of Nodes of the NJ State Development and Redevelopment Plan (NJSDRP). A Node is an existing or planned concentration of facilities and activities which are not organized in a compact form. Types of nodes include Commercial-Manufacturing nodes and Heavy Industry-Transportation-Utility nodes. An Existing Node is a concentration of facilities and activities that are not organized in a compact form and are encouraged to be retrofitted over time to reduce automobile dependency, diversify land uses, and enhance linkages to communities. A new Node may identify new heavy industry, transportation or utility facilities and activities as part of Plan Endorsement. A new Node should be organized in a compact form and located in Centers and other appropriate areas in Metropolitan or Suburban Planning Areas or Centers in Fringe, Rural or Environmentally Sensitive Planning Areas. Refer to the NJSDRP for further description of the geographic nature of Nodes.

This is not a comprehensive dataset of all projects.Projects and statuses are manually updated by Economic Development staff by reviewing applications submitted to Lee County Community Development for planning/zoning changes, development orders, and building permits. Project plans and statuses are subject to change regularly.

This dataset is designed to represent and identify the final development plan boundaries in Lexington-Fayette County, Kentucky. This feature class is created and maintained by the Lexington-Fayette Urban County Government (LFUCG) GIS office staff by selecting the parcels involved in the development plan from the LFUCG master parcel feature class, merging them together, and adding the appropriate attribution. Whena new development plan is an amendment to previous plans, the latest existing plan is copy and pasted and hte attributes are updated with the new information. The geometry of this data is not of survey quality and should not be used for survey purposes. The data is intended for general reference purposes only.As part of the basemap data layers, the parcel boundary map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output).The native projection for the data is Kentucky State Plane North (NAD83), but may have been reprojected for use in other applications. Please check metadata to determine current projection.

Statewide Florida Land Use layer updated with Landscape Development Intensity (LDI) and Landscape Support Index (LSI) values. The Land Use layer was compiled from the five Water Management Districts (WMD) in Florida (NWF,SR, SJR, SWF, SF) using the most recent version of data available. This layer will be updated when new versions of Land Use become available from the WMDs and updated with corresponding LDI and LSI values.

**ATTENTION USERS - This downloadable file will be retired by October 2025 **

The Digital Tax Map has been republished on Open Data as a growing collection of its map layers and data tables. You can find the collection here with the latest information: Digital Tax Map Collection.

• Updates: Data is extracted from DOF’s internal system on the last Friday of each month and refreshed on ArcGIS Online on the 1st. The online map always shows the most recent version.

• Accessing the Data:
• Digital Tax Map on NYC Open Data: See the complete collection.
• Individual layers: Downloadable from the Digital Tax Map Feature Server.
• Complete source: Available through the Digital Tax Map service, which always points to the latest monthly release.

Note: To ensure reliability, the Tax Map alternates between Set A and Set B each month. If one set has issues, the previous month’s copy remains online. Both sets are kept about a month apart and are available for download:
• Set A link
• Set B link

• Digital Alteration Book (DAB): The DAB is the official log of map changes—such as new lots, merges, or boundary shifts—providing a clear record of how the Tax Map evolves. It is available through the Property Information Portal. Coming soon the collection on Open Data will grow to include information from the Digital Alteration Book (DAB) that will keep track of historical changes to the Digital Tax Map.

Disclaimer:
This dataset reflects formal applications submitted to DOF but may not reflect the latest changes in other City systems (e.g., exemptions or buildings data). It is provided for informational purposes only and is not guaranteed to be accurate as of today’s date.

The Growth Centers data on the Future Land Use Map were developed for the Division of Planning, RI Statewide Planning Program as part of an update to a state land use plan. These data are included in the Plan as Figure 121-02-(01), Future Land Use Map. The growth centers were an end product of a GIS overlay analysis of land suitability and scenario planning for future growth. Initially the factors for centers included 9 urban communities; Providence, East Providence, Pawtucket, Cranston, Central Falls, Warwick, West Warwick, Newport and Woonsocket as potential urban centers as opposed to identifying specific neighborhoods in those municipalities. Historical downtowns and traditional mixed-use central business cores in urban fringe / suburban communities were included as potential town centers, as well as, some of the historical village downtowns and some traditional mixed-use cores in rural communities. All communities in the State either include one or more existing or potential centers or are within the Urban Services Boundary on the map. The growth centers shown in these data were selected by the Statewide Planning staff, the Technical Committee and the State Planning Council through a series of discussions at public meetings, and comments received at public hearings and workshops in the final adoption of Land Use 2025 in 2006. Centers depicted on the Future Land Use 2025 map are illustrative of potential new centers that may be established. It is not a intended as a comprehensive inventory of existing centers. Other centers may be illustrated and or proposed in municipal comprehensive plans. Full descriptions of the methodology for the GIS analysis and scenario planning can be found within the Technical Appendix D to Land Use 2025, Geographic Analysis for Land Available and Suitable for Development for Land Use 2025. Land Use 2025: State Land Use Policies and Plan was published by the RI Statewide Planning Program on April 13, 2006. 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. This Map has several purposes and applications: It is 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, the Map is a guide to assist the state and communities in making land use policies. It is important to note the Map is a generalized portrayal of state land use policy. It is not a statewide zoning map. Zoning matters and individual land use decisions are the prerogative of local governments. Growth Centers are envisioned to be areas that will encourage development that is both contiguous to existing development with low fiscal and environmental impacts. They are intended to be compact developed areas (existing or new) containing a defined central core that accommodate community needs for residential and economic functions. Centers are intended to provide optimum use of land and services, and offer a choice of diverse housing stock, economic functions, and cultural and governmental uses. Density will vary greatly between centers subject to site constraints; however, it is intended that they will share the common characteristic of compact development that capitalizes on existing infrastructure. Centers should reflect traditional New England development patterns with a human scale of blocks, streets, open spaces that offer walkability and access to transit where available. In suburban areas, centers should be distinguished from surrounding sprawling development by a closer proximity between residential and non-residential uses. In rural areas, centers should be surrounded by natural areas, farmland, or open space, and may have a mixed-use and or commercial area in the core for neighborhood-scale goods and services. 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. 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. Also included on the map are growth centers which are potential areas for development and redevelopment outside of the USB. These data will be updated when plan is updated or upon an amendment approved by the State Planning Council.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Following development of vegetation classifications after plot sampling, the preliminary vegetation map was further edited and refined in 2005. Using ArcGIS 9.0, polygon boundaries were revised on-screen based on plot data and additional field observations collected during 2004 field visits. Field notes and limited field mapping supplemented GIS mapping. Each polygon was attributed with a map class name that is the common name of a USNVC association, a park-specific map class name representing a variant of an association, or an Anderson Level II use/land cover map class based on plot data, field observations, aerial photography signatures, and topographic maps. Map units in the 2005 vegetation map were equivalent to the association level with few exceptions. The overall 2005 map accuracy and Kappa index was 76%, which fell below the USGS/NPS vegetation mapping protocol requirement of 80%. Revisions were subsequently made to the 2005 vegetation map to increase the accuracy of the final product. The final 2007 vegetation map accuracy was 85.7% and Kappa index was 84.6%.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The map was designed to facilitate ecologically- based natural resources management at a 1:24,000 scale with 0.5-ha minimum map unit size. Based on a provisional assessment, overall accuracy was 82.5% for Level 1 and 66.8% for Level 2. Level 1 units will likely be sufficient and most appropriate for many natural resource planning and evaluations, while Level 2 units provide added fine-scale information within major ecological groups. To support the map as a management tool, we provide an annotated map legend along with descriptions of each plant association, a corresponding diagnostic key, field forms, and a plant species list. The map was delivered in both printed form and as digital Geographic Information System (GIS) map files. The GIS format allows flexibility to update the map as new information becomes available, or as major vegetation changes, such as fire, disease or other impacts, occur in the park.

Detailed descriptions for each layer can be found in the metadata for that layer. Thumbnail image by Tony Moody.

This layer (hosted feature layer) depicts the approved development in the City of Canton, GA. This data set is maintained by the City of Canton's GIS division, and is updated on a regular basis to reflect the current approved development for the City of CantonFor specific questions about this data or to provide feedback, please contact the City's GIS division: Alaina Ellis GIS Analyst alaina.ellis@cantonga.gov (770) 546-6780 Canton City Hall 110 Academy Street, Canton, GA 30114

The ADA polygon coverage was developed to identify the geographic extent of county agricultural development areas (ADAs), as mapped by each individual county according to their unique adopted criteria, and certified by the New Jersey State Agriculture Development Committee (SADC).