This dataset provides information about the extent of municipal land use planning in Nova Scotia. Each municipality is listed along with the type of its land use planning described as either comprehensive for entire municipality, comprehensive for local areas only, comprehensive for towns, or single issue. It also lists the date of the municipal land use plans described. A comprehensive strategy is one that address all major areas of concern, for example: residential, commercial, industrial, recreational, environmental, social and economic. A single-issue plan only deals with specific land-use issues such as wind turbines, drinking water protection, general development. Municipal planning strategies may be developed for an entire municipality and/or a local area within its jurisdiction.

The classification of land according to what activities take place on it or how it is being used; for example, agricultural, industrial, residential, rural, or commercial. Land use information and analysis is a fundamental tool in the planning process.

DVRPC’s 2020 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2020. This dataset supports many of DVRPC's planning analysis goals.

Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.

lu20cat: Land use main category two-digit code.

lu20catn: Land use main category name.

lu20cat

lu20catn

1 - Residential

3 - Industrial

4 - Transportation

5 - Utility

6 - Commercial

7 - Institutional

8 - Military

9 - Recreation

10 - Agriculture

11 - Mining

12 - Wooded

13 - Water

14 - Undeveloped

lu20sub: Land use subcategory five-digit code. (refer to this data dictionary for code description)

lu20subn: Land use subcategory name.

lu20dev: Development status.

mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).

acres: Area of feature, in US acres.

geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).

state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

The downloadable ZIP file contains model documentation and contact information for the model creator. For more information, or a copy of the project report which provides greater model detail, please contact Ryan Urie - traigo12@gmail.com.This model was created from February through April 2010 as a central component of the developer's master's project in Bioregional Planning and Community Design at the University of Idaho to provide a tool for identifying appropriate locations for various land uses based on a variety of user-defined social, economic, ecological, and other criteria. It was developed using the Land-Use Conflict Identification Strategy developed by Carr and Zwick (2007). The purpose of this model is to allow users to identify suitable locations within a user-defined extent for any land use based on any number of social, economic, ecological, or other criteria the user chooses. The model as it is currently composed was designed to identify highly suitable locations for new residential, commercial, and industrial development in Kootenai County, Idaho using criteria, evaluations, and weightings chosen by the model's developer. After criteria were chosen, one or more data layers were gathered for each criterion from public sources. These layers were processed to result in a 60m-resolution raster showing the suitability of each criterion across the county. These criteria were ultimately combined with a weighting sum to result in an overall development suitability raster. The model is intended to serve only as an example of how a GIS-based land-use suitability analysis can be conceptualized and implemented using ArcGIS ModelBuilder, and under no circumstances should the model's outputs be applied to real-world decisions or activities. The model was designed to be extremely flexible so that later users may determine their own land-use suitability, suitability criteria, evaluation rationale, and criteria weights. As this was the first project of its kind completed by the model developer, no guarantees are made as to the quality of the model or the absence of errorsThis model has a hierarchical structure in which some forty individual land-use suitability criteria are combined by weighted summation into several land-use goals which are again combined by weighted summation to yield a final land-use suitability layer. As such, any inconsistencies or errors anywhere in the model tend to reveal themselves in the final output and the model is in a sense self-testing. For example, each individual criterion is presented as a raster with values from 1-9 in a defined spatial extent. Inconsistencies at any point in the model will reveal themselves in the final output in the form of an extent different from that desired, missing values, or values outside the 1-9 range.This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

This dataset consists of raster geotiff outputs of 30-year average annual land use and land cover transition probabilities for the California Central Valley modeled for the period 2011-2101 across 5 future scenarios. The full methods and results of this research are described in detail in “Integrated modeling of climate, land use, and water availability scenarios and their impacts on managed wetland habitat: A case study from California’s Central Valley” (2021). Land-use and land-cover change for California's Central Valley were modeled using the LUCAS model and five different scenarios were simulated from 2011 to 2101 across the entirety of the valley. The five future scenario projections originated from the four scenarios developed as part of the Central Valley Landscape Conservation Project (http://climate.calcommons.org/cvlcp ). The 4 original scenarios include a Bad-Business-As-Usual (BBAU; high water availability, poor management), California Dreamin’ (DREAM; high water availability, good management), Central Valley Dustbowl (DUST; low water availability, poor management), and Everyone Equally Miserable (EEM; low water availability, good management). These scenarios represent alternative plausible futures, capturing a range of climate variability, land management activities, and habitat restoration goals. We parameterized our models based on close interpretation of these four scenario narratives to best reflect stakeholder interests, adding a baseline Historical Business-As-Usual scenario (HBAU) for comparison. The TGAP raster maps represent the average annual transition probability of a cell over a specified time period for a specified land use transition group and type. Each filename has the associated scenario ID (scn418 = DUST, scn419 = DREAM, scn420 = HBAU, scn421 = BBAU, and scn426 = EEM), transition group (e.g. FALLOW, URBANIZATION), transition type, model iteration (= it0 in all cases as only 1 Monte Carlo simulation was modeled and no iteration data used in the calculation of the probability value), timestep of the 30-year transition summary end date (ts2041 = average annual 30-year transition probability from modeled timesteps 2012 to 2041, ts2071 = average annual 30-year transition probability from modeled timesteps 2042 to 2071, and ts101 = average annual 30-year transition probability from modeled timesteps 2072 to 2101). For example, the following filename “scn418.tgap_URBANIZATION_ Grass_Shrub to Developed [Type].it0.ts2041.tif” represents 30-year cumulative URBANIZATION transition group, for the Grass/Shrub to Developed transition type, for the 2011 to 2041 model period. More information about the LUCAS model can be found here: https://geography.wr.usgs.gov/LUCC/the_lucas_model.php. For more information on the specific parameter settings used in the model contact Tamara S. Wilson (tswilson@usgs.gov)

Land use totals for the Greater Philadelphia region, in five-years increments from 1990 to 2015. Data is derived from the corresponding year's Land Use GIS dataset, created by DVRPC.

Please note that DVRPC has altered our data creation methodology over time, as technology, processes, and higher resolution orthoimagery have become available. As such, you'll find that land use categories in different tables do not always completely correlate to one another. For example, in 2015, DVRPC created more detailed land use classifications, making comparisons with previous datasets problematic. For more information, please visit our publication "Land Use in the Delaware Valley, 2015 Enhanced Land Use Data" (ADR 026).

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.

This dataset is OBSOLETE as of 12/3/2024 and will be removed from the Open Data Portal on 12/3/2025.

An updated version of this dataset is available at https://data.cambridgema.gov/Planning/Land-Use-Data-2024/dnyu-v8e9/about_data

View full metadata https://www.cambridgema.gov/GIS/gisdatadictionary/CDD/CDD_LandUse

Description This data set derives from several sources. The primary source is a data dump from the VISION assessing data system, which provided data up to date as of January 1, 2012, and is supplemented by information from subsequent building permits and Development Logs. (Use codes provided by this system combine aspects of land use, tax status, and condominium status. In an effort to clarify land use type the data has been cleaned and subdivided to break the original use code into several different fields.) The data set has further been supplemented and updated with development information provided by building permits issued by the Inspectional Services Department and from data found in the Development Log publication. Information from these sources is added to the data set periodically. Land use status is up to date as of the Last Modified date.

Differences From “Official” Parcel Layer

The Cambridge GIS system maintains a separate layer of land parcels reflecting up to date subdivision and ownership. The parcel data associated with the Land Use Data set differs from the “official” parcel layer in a number of cases. For that reason this separate parcel layer is provided to work with land use data in a GIS environment. See the Assessing Department’s Parcel layer for the most up-to-date land parcel boundaries.

About Edit Dates This data is automatically updated on a set schedule. The Socrata edit date may not reflect the actual edit dates in the data. For more details please see the update date on the full metadata page or view the edit date within the data rows.

The Historical Generalized Land Use dataset encompasses the seven county Twin Cities (Minneapolis and St. Paul) Metropolitan Area in Minnesota. The dataset was developed by the Metropolitan Council, a regional governmental organization that deals, in part, with regional issues and long range planning for the Twin Cities area. The data were interpreted from 1984, 1990, 1997, 2000, 2005, 2010, 2016 and 2020 air photos and other source data, with additional assistance from county parcel data and assessor's information.

The Metropolitan Council has routinely developed generalized land use for the Twin Cities region since 1984 to support its statutory responsibilities and assist in long range planning for the Twin Cities area. The Council uses land use information to monitor growth and to evaluate changing trends in land consumption for various urban purposes. The Council uses the land use trend data in combination with its forecasts of households and jobs to plan for the future needs and financing of Metropolitan services (i.e. Transit, Wastewater Services, etc.). Also, in concert with individual local units of government, the land use and forecast data are used to evaluate expansions of the metropolitan urban service area (MUSA).

The Council does not specifically survey the rights-of-way of minor highways, local streets, parking lots, railroads, or other utility easements. The area occupied by these uses is included with the adjacent land uses, whose boundaries are extended to the centerline of the adjacent rights-of way or easements. The accuracy of Council land use survey data is suitable for regional planning purposes, but should not be used for detailed area planning, nor for engineering work.

Until 1997, the Metropolitan Council had manually interpreted aerial photos on mylar tracing paper into a 13-category land-use classification system to aggregate and depict changing land use data. In 1997, with technological advances in GIS and improved data, the Metropolitan Council was able to delineate land uses from digital aerial photography with counties' parcel and assessor data and captured information with straight 'heads-up' digitizing with GIS software. Also, understanding that land use data collected and maintained at the county and city level are collected at different resolutions using different classification schemes, the Metropolitan Council worked with local communities and organizations to develop a cooperative solution to integrate the Council's land use interpretation with a generally agreed upon regional classification system. By 2000, the Metropolitan Council had not only expanded their Generalized Land Use Classification system to include 22 categories, but had refined how they categorized land (removing all ownership categories) to reflect actual use. See the Entities and Atributes section of the metadata for a detailed description of each of the land use categories and available subcategories.

With the completion of the 2020 Generalized Land Use dataset, regional and local planners have the ability to map changes in urban growth and development in a geographic information system (GIS) database. By tracking land use changes, the Metropolitan Council and local planners can better visualize development trends and anticipate future growth needs.


NOTE ABOUT COMPARATIVE ANALYSIS:

It is important to understand the changes between land use inventory years and how to compare recent land use data to historical data.

In general, over the land use years, more detailed land use information has been captured. Understanding these changes can help interpret land use changes and trends in land consumption. For detailed category definitions, specific land use comparisons and how best to compare the land uses between 1984 and 2020, please refer to the Attribute Accuracy or the Data Quality section of the metadata.

It is also important to note that changes in data collection methodology also effects the ability to compare land use years:

- In 2000, the land use categories were modified to more accurately reflect the use of the land rather than ownership. Although this has minimal effect on associating categories between 1997 and 2000, is may have had an affect on some particular land use. For example, land owned by a community or county but had no apparent active use could have been classified as 'Public/ Semi-Public' prior to 2000. In 2000, land with no apparent use, regardless of who owns it, is classified as 'Undeveloped.'

- With better resolution of air photos beginning in 2000, the incorporation of property information from county assessors and the use of more accurate political boundaries (particularly on the exterior boundaries of the region), positive impacts were made on the accuracy of new land use delineations between pre-2000 land use data and data collected between 2000 and 2020. With the improved data, beginning in 2000, a greater effort to align land use designations, both new and old, to correspond with property boundaries (county parcels) where appropriate. In addition, individual properties were reviewed to assess the extent of development. In most cases, if properties under 5 acres were assessed to be at least 75% developed, then the entire property was classified as a developed land use (not 'Undeveloped'). As a result of these realignments and development assessments, changes in land use between early land use years (1984-1997) and more recent years (2000-2020) will exist in the data that do NOT necessarily represent actual land use change. These occurrences can be found throughout the region.

There are also numerous known deficiencies in the datasets. Some known deficiencies are specific to a particular year while others may span the entire time series. For more details, please refer to Attribute Accuracy of the Data Quality section of the metadata.

Managing 245 million acres of land and 700 million acres of mineral estate is a big task. The BLM recognizes that geospatial information is a critical tool for managing public lands. We’ve already made great strides in creating national datasets, supporting almost every program in the Bureau. The BLM has adopted a ground-up approach to managing public lands, and the geospatial program is providing the structure and tools to accomplish this strategy. We manage spatial data to support multiple activities at varying scales.

The BLM's geospatial strategy focuses on collection, organization, and use of baseline resource management data, like fenceline and transportation data and enhancing predictions based on geospatial data. Examples of activities that require geospatial data include planning and resource management, special status species monitoring, regional mitigation, and renewable energy projects, just to name a few.

An important factor in implementing our strategy is using a geographic information system (GIS) that is consistent and integrated within the Bureau and the Department of the Interior. This internal cohesion enhances the BLM's ability to partner with other Federal agencies, collaborate with State and Tribal governments, and communicate with the public.

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

Includes where particular Ministry of Natural Resources (MNR) land use planning initiatives have effect that have been approved or are established for a significant geographic area. Examples include the Far North Plan Area, Ontario's Living Legacy (OLL) Land Use Strategy, Temagami Land Use Plan Area, Madawaska Highlands Area.Additional Time Period Information: Date all boundaries were assembled into one digital layer. Currency may vary on actual creation date for individual Land Use Plan boundaries

Additional DocumentationLand Use Plan Area, MNR - Data Description (PDF)Land Use Plan Area, MNR - Documentation (Word)

Status

On going: data is being continually updated

Maintenance and Update Frequency

As needed: data is updated as deemed necessary

Contact

Nicole Mokrey, Integration Branch, Ministry of Natural Resources and Forestry, nicole.mokrey@ontario.ca

The data referenced here is licensed Electronic Intellectual Property of the Ontario Ministry of Natural Resources and Forestry and is provided for professional, non-commercial use only.

The classification of land according to what activities take place on it or how it is being used; for example, agricultural, industrial, residential, rural, or commercial. Land use information and analysis is a fundamental tool in the planning process.

DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.

Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.

lu23cat: Land use main category two-digit code.

lu23catn: Land use main category name.

lu23cat

lu23catn

• 1 - Residential

• 3 - Industrial

• 4 - Transportation

• 5 - Utility

• 6 - Commercial

• 7 - Institutional

• 8 - Military

• 9 - Recreation

• 10 - Agriculture

• 11 - Mining

• 12 - Wooded

• 13 - Water

• 14 - Undeveloped

lu23sub: Land use subcategory five-digit code. (refer to this data dictionary for code description)

lu23subn: Land use subcategory name.

lu23dev: Development status.

mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).

acres: Area of feature, in US acres.

geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).

state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.

MethodThis dataset includes a detailed example for using our method (described in paper linked to below) to digitize historical land-use maps in R.MapsWe also release all of the Swedish land-use maps that we digitized for this project. This includes the Economic Map of Sweden (Ekonomiska kartan) over Sweden's 15 southernmost counties (7069 25 km2 sheets), plus 11 sheets of the District Economic Map (Häradsekonomiska kartan - but see http://bolin.su.se/data/Cousins-2015 for more accurate manual digitization).SvenskaHär kan du ladda ner 7069 Ekonomiska kartblad som vi digitaliserade över södra Sverige. En kort beskrivning av metoden publicerades i tidningen Kart & Bildteknik (se länk nedan).--UpdatesVersion 2: The digitized Economic Maps have been resampled so that they are all at a 1m resolution. In the original version they were all very close to 1m but not exactly the same, which made mosaicking difficult. This should be easier now. We now also link to the published paper in Methods in Ecology and Evolution.For more information, please see the readme file. For help or collaboration, please contact alistair.auffret@natgeo.su.se. If you use the data here in your work or research, please cite the publication appropriately.

Human use of the land has a large effect on the structure of terrestrial ecosystems and the dynamics of biogeochemical cycles. For this reason, terrestrial ecosystem and biogeochemistry models require moderate resolution information on land use in order to make realistic predictions. Few such datasets currently exist.

This collection consists of output from models that estimate the spatial pattern of land use in four land-use categories by relating a high-resolution land-cover dataset to state-level census data on land use. The models have been parameterized using a goodness-of-fit measure.

The land cover product used was from the IGBP DISCover global product, derived from 1 km AVHRR imagery, with 16 land cover classes (Belward et al., 1999). Land-use data at state-level resolution came from the USDA's Major Land Uses database (USDA, 1996), aggregated into the four general land-use categories described below.

The model was used to generate maps of land use in 1992 for the conterminous U.S. at 0.5 degree spatial resolution. Two different parameterization schemes were used to spatially interpolate land use from land cover, based on the state-level land use census data: 1) a National Parameterization, and 2) a Regional Parameterization.

For the National Parameterization, a single parameterization relating aggregate land cover and state-level land use. For the Regional Parameterization, a separate parameterization was used for each of seven different regions. The seven regions include: Northeast, Southeast, East North-central, West North-central, Southern Plains, Mountain, and Pacific. These regions are substantially different in terms of land use and land cover. In both cases, the results are a nationally gridded map at 0.5 degrees of land use categories for cropland, pasture/range, forest, and other land use; the other land use category is also further spilt into three additional subcategories (forested, non-forested, non-vegetated).

This project is currently being extended to other regions of the globe, and for other time periods, where both land use census data and image-derived land cover data are available.

Available Datasets:

1) US Land Use - 1992 National Parameterization 2) US Land Use - 1992 Regional Parameterization

Each dataset has 4 major land use categories and 3 subcategories of the Other major land use category.

These data, denoted collectively as the spatial realizations, are comprised of 2001 rasters of 90-meter resolution, with each raster representing the conterminous United States (CONUS) land base. One raster represents the base land use in 2020; the other rasters represent projections according to year (decadal time step 2030 through 2070), scenario-climate future (four socioeceonomic pathways, intersected with five climate projections), and case (twenty cases of a parameter driving the degree of contagious allocation of projected land use changes). Projected rasters were generated by allocating county-level gross land use change projections across the base map. Collectively, the spatial realizations represent a wall-to-wall scenario-based projection of land use and its spatial pattern across the conterminous United States.The spatial realizations were generated to support the Resources Planning Act (RPA) 2020 Assessment (https://www.fs.usda.gov/research/inventory/rpaa), providing resource area research teams with the means to analyze landscape pattern and land use (e.g., forest) fragmentation projections. The spatial realizations also facilitate analyses by units other than geopolitical (e.g., hydrologic or ecoregion units).These data were published on 04/17/2023. Metadata updated on 10/17/2023 to include reference to published RPA Assessment and related published data.

Methods for the downscaling algorithm are detailed in Brooks et al. (2020).

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Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

This dataset contains land use authorization- rights-of-way cases derived from Legal Land Descriptions (LLD) contained in the US Bureau of Land Management's, BLM, Mineral and Land Record System(MLRS) and geocoded (mapped) using the Public Land Survey System (PLSS) derived from the most accurate survey data available through BLM Cadastral Survey workforce. The minimum data entry requirement for legal descriptions for linear authorizations is to the nearest 40 acre aliquot level (e.g.,NENW). Legal descriptions for non-linear authorizations are as described on the authorizing document. Geospatial representations might be missing for some cases that can not be geocoded using the MLRS algorithm. This data set contains cases with the dispositions of 'Authorized', 'Pending','Closed', and 'Interim'. Each case is given a data quality score based on how well it mapped. These can be lumped into seven groups to provide a simplified way to understand the scores. Group 1: Direct PLSS Match. Scores “0”, “1”, “2”, “3” should all have a match to the PLSS data. There are slight differences, but the primary expectation is that these match the PLSS. Group 2: Calculated PLSS Match. Scores “4”, “4.1”, “5”, “6”, “7” and “8” were generated through a process of creating the geometry that is not a direct capture from the PLSS. They represent a best guess based on the underlining PLSS Group 3 – Mapped to Section. Score of “8.1”, “8.2”, “8.3”, “9” and “10” are mapped to the Section for various reasons (refer to log information in data quality field). Group 4- Combination of mapped and unmapped areas. Score of 15 represents a case that has some portions that would map and others that do not. Group 5 – No NLSDB Geometry, Only Attributes. Scores “11”, “12”, “20”, “21” and “22” do not have a match to the PLSS and no geometry is in the NLSDB, and only attributes exist in the data. Group 6 – Mapped to County. Scores of “25” map to the County. Group 7 – Improved Geometry. Scores of “100” are cases that have had their geometry edited by BLM staff using ArcGIS Pro or MLRS bulk upload tool.

The Land Cover layer provides detailed classification of Earth's surface into 11 categories like urban areas, forests, and water bodies. Created using deep learning on Sentinel-2 data, it offers 10-meter resolution for precise land use analysis, supporting urban planning, agriculture, and environmental monitoring.

Latitudo 40's Super-Resolution layer enhances satellite imagery to achieve a 1-meter resolution from Sentinel-2 data, using AI and machine learning algorithms. It provides highly detailed visuals for urban planning, infrastructure monitoring, and environmental assessments, allowing for precise analysis of small-scale features.

Latitudo 40's Flooding Risk layer integrates Sentinel-2, Sentinel-1, DEM, and ERA5 data, offering a comprehensive risk index for pluvial, fluvial, and coastal floods. It helps insurers, urban planners, and real estate developers assess flood risks, enabling informed decision-making for risk management, climate change adaptation, and urban resilience. This layer supports both large-scale and localized risk analysis, ensuring safer, more sustainable developments.

Primary Use Cases;

Urban Planning & Development: Use high-resolution imagery from the Super-Resolution layer and land cover classifications to precisely plan urban developments, identify suitable locations for green infrastructure, and evaluate flood risks before construction in vulnerable areas.

Climate Resilience: Incorporate Flooding Risk and Land Cover data to design urban areas that are better protected from climate risks, such as floods and heatwaves, by strategically placing green spaces and improving land use planning.

Infrastructure Monitoring: Combine Super-Resolution and Flood Risk layers to monitor infrastructure in flood-prone areas, ensuring buildings and roads are resilient to potential flooding events and urban growth is sustainable.

Environmental Conservation: Leverage land cover data and high-resolution imagery to monitor biodiversity in urban areas, assess the impact of flooding on natural habitats, and promote conservation initiatives that protect green spaces.

Agriculture & Land Management: Use Super-Resolution data and land cover classifications to monitor agricultural zones, assess flood risks, and improve land-use planning for sustainable farming practices in both rural and peri-urban areas.

All of these data layers are integrated into Latitudo 40’s EarthDataPlace (EDP) platform. EDP provides a user-friendly interface to access and analyze high-resolution geospatial data tailored for urban planning, environmental monitoring, and climate resilience efforts. Through EDP, users can easily search, purchase, and download the datasets they need to make informed decisions. The platform offers comprehensive, actionable insights that enable planners, researchers, and governments to tackle pressing environmental challenges and enhance urban sustainability.

All of these data layers are integrated into Latitudo 40’s EarthDataPlace (EDP) platform. EDP provides a user-friendly interface to access and analyze high-resolution geospatial data tailored for urban planning, environmental monitoring, and climate resilience efforts. Through EDP, users can easily search, purchase, and download the datasets they need to make informed decisions. The platform offers comprehensive, actionable insights that enable planners, researchers, and governments to tackle pressing environmental challenges and enhance urban sustainability.