The primary GIS platform, accessible through the City of Allen GIS portal, integrates hardware, software, and data to capture, manage, analyze, and display geographically referenced information. This system enables users to visualize and interpret data in various forms, such as maps and reports, revealing relationships, patterns, and trends that support planning and development efforts. Key FeaturesZoning and Land Use Maps: Interactive maps display current zoning boundaries, land use classifications, and related ordinances, assisting in understanding development regulations. Development Projects Map: Users can explore proposed, approved, under-construction, or recently completed development projects, with details on each project's status and related information. City Limits and ETJ Map: This map delineates the official city boundaries and extraterritorial jurisdiction (ETJ) areas, providing context for municipal planning and services.Aerial Imagery Viewer: An interactive aerial map offers high-resolution imagery of the city, useful for detailed site analysis and visualization. Open Data Hub: The Allen GIS Open Data Hub allows users to discover, analyze, and download various datasets in multiple formats, supporting transparency and data-driven decision-making.These tools are integral to the City of Allen's commitment to providing accessible and comprehensive geographic data, facilitating effective planning, development, and community engagement.Metadata updated: 05/2025

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

Use this regional model layer when performing analysis within a single continent. This layer displays a single global land cover map that is modeled by region 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

As users scroll through the story, they can find the following information:Regional overview of the Wasatch Choice 2050 VisionRegionally significant land uses and descriptionsRegional performance measuresSmall area stories with area desires and needs as well as county performance measuresMap to view transportation project/land use information and provide feedbackWithin the main map, the user can navigate to find information about draft transportation projects and land use. The user can also provide feedback on transportation/land use by utilizing the comment pushpin feature.

This interactive map is a collaborative project by the Geographical Names Board of Canada, illustrating a curated selection of places in Canada with names that have origins in multiple Indigenous languages. The names selected show the history and evolution of Indigenous place naming in Canada, from derived and inaccurate usage, to names provided by Indigenous organisations. Many Indigenous place names convey stories, knowledge, and descriptions of the land. By celebrating these names through this map, the Geographical Names Board of Canada hopes to increase the awareness of existing Indigenous place names and help promote the revitalization of Indigenous cultures and languages. Many more Indigenous place names exist in Canada and will be added in future releases of this map. The content of this map is a compilation of information obtained from many current and historical sources. The Geographical Names Board of Canada does not warrant or guarantee that the information is accurate, complete or current at all times. For more information, to report data errors, or to suggest improvements to this application, please contact the Geographical Names Board of Canada Secretariat.

Digital Map Market Outlook

According to our latest research, the global digital map market size reached USD 20.7 billion in 2024, reflecting robust demand across various industries. The market is expected to expand at a CAGR of 13.9% from 2025 to 2033, reaching a projected value of USD 67.4 billion by 2033. This impressive growth trajectory is fueled by rapid advancements in geospatial technologies, the proliferation of connected and autonomous vehicles, and the increasing reliance on real-time data for business and consumer applications. The digital map market is witnessing a paradigm shift as organizations embrace location intelligence to optimize operations, enhance customer experiences, and support critical decision-making processes.

One of the primary growth drivers for the digital map market is the widespread adoption of location-based services (LBS) across industries such as automotive, transportation, logistics, and retail. The integration of digital maps with mobile applications, IoT devices, and smart city initiatives is transforming the way businesses and governments manage assets and deliver services. The surge in demand for real-time navigation, route optimization, and geospatial analytics is prompting organizations to invest in advanced mapping solutions that offer high accuracy, scalability, and seamless integration with other digital platforms. As digital transformation accelerates, the need for dynamic, interactive, and cloud-based mapping solutions is becoming increasingly evident, further propelling market growth.

Another significant factor contributing to the expansion of the digital map market is the evolution of autonomous and connected vehicles. Automotive manufacturers are leveraging high-definition digital maps to enable advanced driver-assistance systems (ADAS), autonomous navigation, and predictive maintenance. The deployment of 5G networks and edge computing is enhancing the capabilities of digital maps by enabling real-time data processing and ultra-low-latency communication. This technological synergy is driving the adoption of digital maps in not only the automotive sector but also in transportation, logistics, and urban planning. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) is enhancing the accuracy, relevance, and predictive power of digital mapping solutions, unlocking new opportunities for innovation and efficiency.

The digital map market is also benefiting from increasing government investments in smart infrastructure and urban development projects. Governments and municipal authorities are utilizing digital maps for land management, disaster response, utility planning, and public safety initiatives. The growing emphasis on sustainability and resource optimization is prompting the use of digital mapping in environmental monitoring, renewable energy deployment, and infrastructure maintenance. Additionally, the rising popularity of location-based advertising and personalized marketing is encouraging retailers and service providers to harness the power of digital maps for targeted customer engagement. These multifaceted applications underscore the strategic importance of digital maps in the modern digital ecosystem.

From a regional perspective, North America currently dominates the digital map market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States remains at the forefront of innovation, driven by the presence of leading technology companies, high smartphone penetration, and robust investments in autonomous vehicle research. Europe is witnessing significant growth due to the expansion of smart city projects and regulatory support for geospatial data initiatives. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid urbanization, infrastructure development, and increasing adoption of digital technologies in countries such as China, Japan, and India. The regional dynamics reflect the global nature of the digital map market and the diverse opportunities for stakeholders across geographies.

As the digital map market continues to evolve, the concept of Network Digital Map and Inventory Reconciliation is gaining traction among industry players. This approach involves the integration of digital mapping technologies with inventory management systems to create a cohesive network that enhances op

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

The Los Angeles Stewardship Mapping and Assessment Project (STEW-MAP) was launched in 2013 by the Loyola Marymount University Center for Urban Resilience (CURes), with support from the USDA Forest Service Northern Research Station. The survey was sent in 2014-2015 to 715 groups and organizations participating in environmental stewardship in Los Angeles County. Responses were received from 140 stewardship organizations (19.5% response rate) and geographic descriptions of stewardship turfs from 115 organizations. The initial analyses revealed that survey respondents represented organizations from majority (57%) non-profit sector and about one-third (35%) public sector.One applied goal of the project is to inform development of a suite of online, publicly available tools that can facilitate local and regional natural resource planning and management. In 2016, the LA Urban Center for Natural Resources Sustainability partnered with CURes to support the development of STEW-MAP research driven products. Two participatory workshops were held in summer 2017 with Los Angeles practitioners to share LA County STEW-MAP results and gather input on how the data could be applied in their work. The workshops were attended by 27 participants, who provided feedback and helped prioritize STEW-MAP products. Deliverables from the workshops included the presentation slides, a white paper of LA County STEW-MAP results currently in development, and a publicly available data layer hosted on the CURes website.In 2018, CURes launched LA River STEW-MAP, with support from a USDA Forest Service Cost Share Challenge grant and the USDA Forest Service Pacific Southwest Research Station. This effort focuses on stewardship organizations working within the Los Angeles River watershed. The LA River STEW-MAP survey is scheduled to be sent in June 2019.STEW-MAP databases and interactive maps allow land managers, community organizations, non-profits, and the public to see where hundreds of environmental stewardship groups are working in a particular landscape of interest. This tool can be applied to strengthen capacity, promote engagement with on-the-ground projects, and build more effective partnerships among stakeholders. STEW-MAP data provide a rich complement to biophysical and geographic information on green infrastructure, improving outcomes for a wide range of applications.

Like other Assessor and Recorder data sets from First American, BlackKnight, ATTOM or HouseCanary, we provide both residential real estate and commercial restate data on homes, properties and parcels nationally.

Over 60M parcels reflecting over 330M permits over the past 20 years.

This comprehensive dataset contains building permits issued in the United States, providing valuable insights into residential and commercial construction activities. With over millions of records covering millions of homes, this dataset offers a vast opportunity for analysis and business growth.

Includes permits from various states across the US

Covers residential and commercial construction activities

Insights:

Residential vs. Commercial: Analyze the distribution of permits by type (residential, commercial) to understand local market trends.

Construction Activity: Track permit issuance over time to identify patterns and fluctuations in construction activity.

Geographic Patterns: Examine the concentration of permits by state, county, or city to reveal regional development opportunities.

Potential Applications:

Contractors and Builders: Utilize this dataset to identify potential projects, estimate job values, and stay up-to-date on permit requirements.

Local Governments: Analyze building permit data to inform land-use planning, zoning regulations, and infrastructure development.

Investors and Developers: Explore the types of construction projects being undertaken in specific areas, enabling informed investment decisions.

Value Propositions:

Understand Current Home Condition: Gain insights into the current state of homes by analyzing building permit data, allowing you to:

Identify areas with high concentrations of permits

Determine the scope and type of work being performed

Infer the potential for improved home values

Lender Lead Generation: Use this dataset to identify potential refinance candidates based on improved homes, enabling lenders to:

Target homeowners who have invested in their properties

Offer tailored financial solutions to capitalize on increased property value

Contractor Lead Generation:

Solar installers can target neighbors of solar customers, increasing the chances of successful referrals and upselling opportunities.

Pool cleaners can target new pools, identifying potential customers for maintenance and cleaning services.

Roofing contractors can target homes with recent roofing permits, offering replacement or repair services to homeowners.

Home Service Providers:

Handyman services can target homes with permit records, offering a range of maintenance and repair services.

Appliance installers can target new kitchens and bathrooms, identifying potential customers for appliance installation and integration.

Real Estate Professionals:

Realtors can analyze permit data to understand local market trends, adjusting their sales strategies to capitalize on areas with high construction activity.

Property managers can identify potential investment opportunities, using permit data to evaluate the feasibility of investment projects.

Data Analysis Ideas:

Trend Analysis: Identify trends in permit issuance by type (residential, commercial), project size, or location to forecast future demand.

Geospatial Analysis: Visualize permit data on a map to analyze the concentration of construction activity and identify areas with high growth potential.

Correlation Analysis: Examine the relationship between permit issuance and local economic indicators (e.g., GDP, unemployment rates) to understand the impact of construction on the local economy.

Business Use Cases:

Market Research: Analyze permit data to inform business decisions about market trends, competition, and growth opportunities.

Risk Assessment: Identify areas with high concentrations of permits and potential risks (e.g., building code non-compliance) to adjust business strategies accordingly.

Investment Analysis: Use permit data to evaluate the feasibility of investment projects in specific regions or markets.

Data Visualization Ideas:

Interactive Maps: Create interactive maps to visualize permit concentration by location, type, and project size.

Permit Issuance Charts: Plot permit issuance over time to illustrate trends and fluctuations in construction activity.

Bar Charts by Category: Display the distribution of permits by category (e.g., residential, commercial) to highlight market trends.

Additional Ideas:

Combine with other datasets: Integrate building permit data with other sources (e.g., crime statistics, weather patterns) to gain a more comprehensive understanding of local conditions.

Analyze by demographic factors: Examine how permit issuance varies across different demographics (e.g., age, income level) to understand market preferences and behaviors.

Develop predictive models: Create statistical models to forecast future permit issuance based on historical trends and external factors.

Project and Permit...

Soil Survey Project Boundaries (soil mapping study areas) contains the soil survey project area and attributes describing each project (project level metadata), plus links to the locations of other data associated with the project (e.g., soil survey reports, polygon datasets, plotfiles, scanned maps, legends). Soil Mapping divides the landscape into units according to soil association, name, type, drainage, parent material, and texture. This layer is derived from the STE_TEI_PROJECT_BOUNDARIES_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: SOIL, TIMSOI, and SOILSW. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01) The Soil Survey dataset contains project boundaries as well as the soil survey polygons which are available in a variety of formats including: 1) via the Soil Information Finder Tool Mapping App (interactive app), 2) Soil Survey Spatial data with Soil Name and Layer Files (for download or viewing via iMapBC), or as 3) Soil Mapping Data Packages with geodatabase or shape files, and a data dictionary.

The landscape Change Program is an archive of paired historic and recent photos of Vermont landscapes. The program is funded by the National Science Foundation to digitally document how the Vermont landscape has changed over time.

The landscape of Vermont has changed considerably since it first emerged from the ocean during the collision of huge tectonic plates. For a time, geologically speaking, sediments that became Vermont had been in a warm tropical sea at the equator. Slowly they had moved north. Mountains were born and began to erode. Massive glaciers more than a kilometer thick blanketed Vermont. Soon after the glaciers left, Native Americans inhabited the area. Colonial settlers moved in, clearing the land and leaving just a quarter of the total area forested, making way for agriculture, then sheep, then dairy. Hundreds of hill farms sprang up and many were later abandoned as western soils called. Now the Vermont landscape is mostly forested and yet increasingly developed. The face of Vermont has changed dramatically over time. The shared appreciation and acknowledgement of this rich landscape history is the goal of this project.

[Summary provided by the University of Vermont.]

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: Digital Elevation Model for the Northern Experimental Forest

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

Howland DEM is a digital elevation model of the 10km X 10km area located within the Northern Experimental Forest. The contours and elevation benchmarks from the United States Geological Survey 7.5'quadsheets for Howland and Lagrange were digitized and then rasterized into a 10m X 10m grid.

The data was revised by projecting it into NAD83 datum by L. Prihodko at NASA Goddard Space Flight Center. Although the data was received at GSFC with an undeclared datum, it was assumed to be in North American Datum of 1927 (NAD27) because the original map from which the data were digitized was in NAD27. Also, the data fit exactly within the bounds of the FED site grid (even Universal Transverse Mercator projections) in NAD27. After projecting the data into NAD83 it was checked to insure that the change was a linear translation of the coordinates only and that the gridded values did not undergo any changes.

These Soil Mapping Data Packages include 1. a Soil Map dataset which includes the equivalents to Soil Project Boundaries, Soil Survey Spatial View mapping polygons with attributes from the Soil Name and Layer Files, plus + A Soil Site dataset which includes soil pit site information and detailed soil pit descriptions and any associated lab analyses, and + The Soil Data Dictionary which documents the fields and allowable codes within the data. The Soil Map geodatabase contains the 'best available' data ranging from 1:20,000 scale to 1:250,000 scale with overlapping data removed. The choice of the datasets that remain is based on connectivity to the soil attributes (soil name and layer files), map scale and survey date. (Note: the BC Soil Landscapes of Canada (BCSLC) 1:1,000,000 data has not been included in the Soil_Map or SIFT, but is available from: CANSIS. (A complete soils data package with overlapping soil survey mapping and BCSLC is available on request. Note that the soil survey data with attributes can also be viewed interactively in the [Soil Information Finder Tool](The Soil Map dataset is also available for interactive map viewing or as KMZs from the Soil Information Finder Tool website.

Cristy Parsons · Geospatial Portfolio is a dynamic online platform that highlights my expertise and passion for geospatial technologies. This portfolio features a variety of GIS projects I've worked on, showcasing my skills in spatial analysis, mapping, and data visualization. Each project demonstrates the use of GIS tools to address real-world problems, from community art mapping to land use analysis. The site includes interactive maps, embedded StoryMaps, web mapping applications, and other geospatial content, offering visitors an in-depth look at my professional capabilities and projects. It's also a space where I can continue to grow, share new work, and connect with the geospatial community.

The data included in this spreadsheet were downloaded from the World Resource Institute's Rights to Resources interactive map. The Rights to Resources interactive map presents the results of a legal review of national laws in sub-Saharan Africa in their treatment of local-use rights to natural resources. As part of WRI’s Land and Resource Rights (LRR) project, national framework laws were reviewed to answer eleven questions regarding rights to five natural resources: water, trees, wildlife, minerals and petroleum. This spreadsheet contains a tab for each resource and answers to the eleven questions by country. For more information, please visit: http://www.wri.org/our-work/project/land-and-resource-rights Cautions

This interactive map provides important geospatial information pertaining to the Sequoia National Forest Land Management Plan 2023 (Record of Decision signed on May 26, 2023). The Plan, Environmental Impact Statement, and Record of Decision provide thorough explanations of the planning process, analysis undertaken, and public input. To understand the definitions, differences, important distinctions among terms used in the map layers, and the details of the planning process (including studies and alternatives), we highly recommend reviewing the Forest Plan Revision documents here: https://www.fs.usda.gov/project/?project=3375Geospatial information presented was developed during the Forest Plan Revision Process for the Sequoia National Forest or is relevant to the Land Management Plan but was not created or altered during the Forest Plan Revision Process.

3D geology models have been created for London, Glasgow, Cardiff, Liverpool and Gateshead. Users can create geological cross-sections, synthetic boreholes and horizontal slices through the 3D models. Underpinned by BGS geological data and expertise, the models and reports provide an enhanced understanding of the geological structures and sediments for urban practitioners to inform construction projects, infrastructure design, groundwater assessments and land use planning. These models cover depths from +300 m OD (Ordnance Datum) to -600 m OD. The models which cover Liverpool and London include superficial and bedrock units. The models which cover Glasgow and Cardiff include superficial units on an undifferentiated bedrock base model. The Gateshead Model includes undifferentiated superficial deposits on a bedrock model, but the Superficial Deposits top layer shows the hydro domains map produced for Project Groundwater Northumbria. The models were constructed in the National Geological Model, Urban Geoscience programmes and Project Groundwater Northumbria between 2008-2025 in GSI3D, Groundhog and ASPEN SKUA for deployment to the web viewer. These datasets are managed by the 3D GeoModel project (National Geoscience).

Alaska Survey Boundary contains miscellaneous state, federal, and private surveys. This shape file characterizes the geographic representation of land parcels within the State of Alaska contained by the Base - Survey Boundary category. It has been extracted from data sets used to produce the State status plats. This data set includes cases noted on the digital status plats up to one day prior to data extraction. Each state survey feature has an associated attribute record, including a Land Administration System (LAS) file-type and file-number which serves as an index to related LAS case-file information. Additional LAS case-file and customer information may be obtained at: https://dnr.alaska.gov/projects/las/ Those requiring more information regarding State land records should contact the Alaska Department of Natural Resources Public Information Center directly.

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: Elevation Contours for the Northern Experimental Forest

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

This data layer contains elevation contours for the 10 X 10 km area located within the Northern Experimental Forest. Contours and elevation benchmarks from the United States Geological Survey 7.5" Maine quadsheets for Howland and Lagrange were digitized, and elevation data in feet were added.

The data was revised by projecting it into NAD83 datum by L. Prihodko at NASA Goddard Space Flight Center. Although the data was received at GSFC with an undeclared datum, it was assumed to be in North American Datum of 1927 (NAD27) because the original map from which the data were digitized was in NAD27. Also, the data fit exactly within the bounds of the FED site grid (even Universal Transverse Mercator projections) in NAD27. After projecting the data into NAD83 it was checked to insure that the change was a linear translation of the coordinates.

The PALEOMAP project produces paleogreographic maps illustrating the Earth's plate tectonic, paleogeographic, climatic, oceanographic and biogeographic development from the Precambrian to the Modern World and beyond.

A series of digital data sets has been produced consisting of plate tectonic data, climatically sensitive lithofacies, and biogeographic data. Software has been devloped to plot maps using the PALEOMAP plate tectonic model and digital geographic data sets: PGIS/Mac, Plate Tracker for Windows 95, Paleocontinental Mapper and Editor (PCME), Earth System History GIS (ESH-GIS), PaleoGIS(uses ArcView), and PALEOMAPPER.

Teaching materials for educators including atlases, slide sets, VHS animations, JPEG images and CD-ROM digital images.

Some PALEOMAP products include: Plate Tectonic Computer Animation (VHS) illustrating motions of the continents during the last 850 million years.

Paleogeographic Atlas consisting of 20 full color paleogeographic maps. (Scotese, 1997).

Paleogeographic Atlas Slide Set (35mm)

Paleogeographic Digital Images (JPEG, PC/Mac diskettes)

Paleogeographic Digital Image Archive (EPS, PC/Mac Zip disk) consists of the complete digital archive of original digital graphic files used to produce plate tectonic and paleographic maps for the Paleographic Atlas.

GIS software such as PaleoGIS and ESH-GIS.