The source codes that support the paper 'Automatic road network selection method considering functional semantic features of roads with graph convolutional networks' published in the International Journal of Geographical Information Science.Abstract: Road network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people’s needs. This paper develops two functional semantic features (i.e., travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography.Keywords: road network selection; graph convolutional network; functional features; map generalization; POI data

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. America's private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled ?Insufficient private forest for this analysis"and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) Metadata

Risk maps of points of particular importance corresponding to a scenario of average probability of flooding (return period of 100 years), taking into account information related to IPPC facilities, WWTP, Cultural Heritage and elements of special importance for Civil Protection.

High Precision Smart Travel Digital Map Market Outlook

The global High Precision Smart Travel Digital Map market size was valued at approximately USD 5.4 billion in 2023 and is expected to reach around USD 11.2 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 8.4% during the forecast period. This remarkable growth trajectory can be attributed to the increasing demand for accurate and real-time navigation solutions, advancements in mapping technology, and the proliferation of smart devices.

One of the primary growth factors for the High Precision Smart Travel Digital Map market is the rapid integration of these maps into various smart devices, including smartphones, tablets, and in-car navigation systems. As consumers become more reliant on digital maps for daily commuting and traveling, the demand for high-precision maps that offer real-time updates and accurate route information is surging. The advent of autonomous vehicles and connected cars is further propelling this demand, as these technologies require precise mapping data to ensure safety and efficiency.

Another significant driver is the increasing adoption of Geographic Information System (GIS) and Global Positioning System (GPS) technologies across multiple sectors. Industries such as transportation, logistics, and tourism are leveraging these technologies to enhance their operational efficiency and provide better services to their customers. For instance, logistics companies use high-precision digital maps to optimize delivery routes, reducing fuel consumption and delivery times. Similarly, the tourism industry utilizes these maps to offer tourists detailed information about destinations, improving their travel experience.

The growing emphasis on smart city initiatives worldwide is also contributing to the market's growth. Governments and municipal bodies are investing heavily in digital infrastructure to create smarter, more connected urban environments. High precision smart travel digital maps play a crucial role in these initiatives by providing accurate data for urban planning, traffic management, and public transportation systems. Additionally, the advent of 5G technology is expected to enhance the capabilities of these maps by enabling faster data transmission and more reliable connectivity.

The role of Digital Map Service providers has become increasingly vital in this evolving landscape. These services offer comprehensive mapping solutions that cater to various industries, from logistics to tourism. By providing real-time data and seamless integration with other digital platforms, Digital Map Services enhance the user experience and operational efficiency. As businesses and consumers alike demand more sophisticated and interactive mapping solutions, the importance of reliable Digital Map Services continues to grow. This trend is further amplified by the rise of smart cities and the need for precise navigation tools in urban environments.

Regionally, North America holds a significant share of the High Precision Smart Travel Digital Map market, driven by the presence of major technology companies and high consumer adoption rates. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid urbanization, increasing smartphone penetration, and government initiatives aimed at developing smart cities. Europe also represents a substantial market, supported by advanced infrastructure and a strong emphasis on technological innovation.

Component Analysis

The High Precision Smart Travel Digital Map market is segmented by component into software, hardware, and services. The software segment encompasses mapping and navigation software that provides users with accurate and up-to-date travel information. This segment is expected to dominate the market due to the increasing use of mobile applications and in-car navigation systems. Companies are continuously enhancing their software offerings with features like real-time traffic updates, 3D mapping, and augmented reality, making them indispensable tools for modern travelers.

Hardware components include GPS devices, sensors, and other equipment used to collect and process mapping data. While the hardware segment is essential for the functioning of digital maps, it is expected to grow at a moderate pace compared to software and services. The ongoing innovation in sensor technology and the integration of advanced hardware

The Blue Habitats website has been established as a portal for information on the global distribution of marine ‘blue’ habitats. Knowledge on the distribution of blue habitats is an important input into ocean management, marine spatial planning and biodiversity conservation. Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from this website.

Autonomous Vehicles HD Map Market Outlook

The global autonomous vehicles HD map market size in 2023 is valued at approximately USD 2.5 billion and is projected to reach USD 15.8 billion by 2032, growing at a CAGR of 22.5% during the forecast period. This market growth is primarily driven by the increasing demand for high-definition (HD) maps that provide real-time information to support the navigation and operational control of autonomous vehicles.

One of the primary growth factors for the autonomous vehicles HD map market is the rapid advancement in autonomous driving technologies. As major automotive manufacturers and tech companies invest heavily in developing autonomous vehicles, the need for precise and reliable HD maps has become crucial. These maps are essential for autonomous vehicles to navigate complex urban environments accurately and safely. Additionally, HD maps provide crucial data layers such as lane markings, road geometry, and traffic signals, which are vital for autonomous driving systems to make informed decisions.

Another significant growth factor is the increasing adoption of cloud-based solutions for HD mapping. Cloud-based HD maps offer several advantages, including real-time updates, scalability, and lower operating costs. These solutions enable autonomous vehicles to access the most up-to-date maps, ensuring that they can adapt to changing road conditions and traffic patterns. Moreover, cloud-based HD maps facilitate the integration of data from various sources, such as vehicle sensors and IoT devices, enhancing the map's accuracy and reliability.

The growing demand for enhanced safety features in vehicles is also driving the market for autonomous vehicles HD maps. HD maps play a crucial role in enabling advanced driver assistance systems (ADAS) and other safety features in both passenger and commercial vehicles. By providing detailed and accurate information about the road environment, HD maps help in reducing the risk of accidents and improving overall road safety. This has led to increased investments in HD mapping technologies by automotive OEMs and other stakeholders in the autonomous driving ecosystem.

Regionally, the Asia Pacific region is expected to witness significant growth in the autonomous vehicles HD map market. Countries like China, Japan, and South Korea are at the forefront of autonomous vehicle research and development. The strong presence of leading automotive manufacturers, coupled with supportive government policies and investments in smart city infrastructure, is driving the demand for HD maps in this region. Additionally, the increasing adoption of electric and autonomous vehicles in Asia Pacific is further propelling the market growth.

Solution Analysis

The autonomous vehicles HD map market is segmented into cloud-based and embedded solutions. Cloud-based HD mapping solutions are gaining popularity due to their numerous advantages, including real-time updates and scalability. These solutions allow autonomous vehicles to access the most current maps, ensuring that they can navigate accurately and safely. Moreover, cloud-based solutions facilitate the integration of various data sources, such as vehicle sensors and IoT devices, enhancing the map's accuracy and reliability. The lower operating costs associated with cloud-based solutions also make them an attractive option for automotive OEMs and fleet management companies.

Embedded HD mapping solutions, on the other hand, provide a robust alternative for autonomous vehicles that require high levels of data security and reliability. Unlike cloud-based solutions, embedded HD maps are stored locally within the vehicle's onboard systems, reducing the dependency on external data networks. This is particularly important for autonomous vehicles operating in remote or low-connectivity areas. Additionally, embedded solutions offer faster data processing and lower latency, which are critical for real-time decision-making in autonomous driving scenarios.

The choice between cloud-based and embedded HD mapping solutions often depends on the specific requirements of the end-users. For instance, automotive OEMs and fleet management companies may prefer cloud-based solutions for their cost-effectiveness and ease of integration with existing systems. In contrast, mobility as a service providers might opt for embedded solutions to ensure high levels of reliability and data security. Both solution types are expected to see significant growth, driven by the increasing adoption of autonomous vehicles and the demand for advanced navig

Electronic maps (E-maps) provide people with convenience in real-world space. Although web map services can display maps on screens, a more important function is their ability to access geographical features. An E-map that is based on raster tiles is inferior to vector tiles in terms of interactive ability because vector maps provide a convenient and effective method to access and manipulate web map features. However, the critical issue regarding rendering tiled vector maps is that geographical features that are rendered in the form of map symbols via vector tiles may cause visual discontinuities, such as graphic conflicts and losses of data around the borders of tiles, which likely represent the main obstacles to exploring vector map tiles on the web. This paper proposes a tiled vector data model for geographical features in symbolized maps that considers the relationships among geographical features, symbol representations and map renderings. This model presents a method to tailor geographical features in terms of map symbols and ‘addition’ (join) operations on the following two levels: geographical features and map features. Thus, these maps can resolve the visual discontinuity problem based on the proposed model without weakening the interactivity of vector maps. The proposed model is validated by two map data sets, and the results demonstrate that the rendered (symbolized) web maps present smooth visual continuity.

Recognizing the activities being performed on a map is crucial for adaptive map design based on user context. Despite eye tracking (ET) demonstrating potential in recognizing map activities and electroencephalography (EEG) measuring map users’ cognitive load, no studies have yet combined ET and EEG for recognition of the user’s activity on maps. Our study collected participants’ ET and EEG data during four types of map activities. After feature extraction and selection, we trained LightGBM (light Gradient-Boosting Machine) to classify these activities, and achieved 88.0% accuracy when combining ET and EEG features in the entire map usage trial, which is higher than using ET (85.9%) or EEG (53.9%) alone. Acceptable recognition accuracy could also be achieved with the early time windows (73.1% when using the first 3 seconds). Saccade features of ET were the most important for differentiating map activities, indicating selective map content for different tasks. Our findings demonstrate the feasibility and advantages of combining ET and EEG for activity recognition in map use. The results not only improve our understanding of visual patterns and cognitive processes in map use, but also enable the design of adaptive maps that can automatically adapt to the activities a map user is performing.

This dataset provides the supporting files for the paper entitled "Content-based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback", which has been accepted by PLOS ONE. The DOI of the paper is 10.1371/journal.pone.0166098. The dataset includes11689 layers from 653 OGC WMSs. It contains a archive of 11689 thumbnail images, two WMS layer metadata description files (in SQL script and CSV format respectively), an extracted image feature files and a data introduction document. Specially, the thumbnails are obtained by invoking WMS GetMap Operation for the available layers. The two layer metadata description files depict attribute fields of the layers, including keywords, abstract, boundingbox and etc. The image features were extracted from the WMS layer thumbnail images and contains total 11689 records. If you are interested in this research, please contact with hukai@whu.edu.cn or zhipeng.gui@whu.edu.cn.

Digital Map Service Market Outlook

The global digital map service market size is projected to grow significantly, from approximately $18.9 billion in 2023 to an estimated $53.1 billion by 2032, reflecting a compelling Compound Annual Growth Rate (CAGR) of 12.5%. This robust growth is driven by the increasing adoption of digital mapping technologies across diverse industries and the rising demand for real-time geographic and navigation data in both consumer and enterprise applications.

One of the primary growth factors for the digital map service market is the expanding use of digital maps in the automotive sector, particularly in the development of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. These technologies rely heavily on precise and up-to-date mapping data for navigation, obstacle detection, and other functionalities, making digital maps indispensable. Additionally, the proliferation of mobile devices and the integration of mapping services in applications such as ride-sharing, logistics, and local search have significantly contributed to market expansion.

Another significant driver is the increasing reliance on Geographic Information Systems (GIS) across various industries. GIS technology enables organizations to analyze spatial information, improve decision-making processes, and enhance operational efficiencies. Industries such as government, defense, agriculture, and urban planning utilize GIS for land use planning, disaster management, and resource allocation, among other applications. The continuous advancements in GIS technology and the integration of artificial intelligence (AI) and machine learning (ML) are expected to further propel market growth.

The rising demand for real-time location data is also a crucial factor fueling the growth of the digital map service market. Real-time location data is essential for applications such as fleet management, asset tracking, and public safety. Businesses leverage this data to optimize routes, monitor assets, and enhance customer service. The increasing implementation of Internet of Things (IoT) devices and the growing importance of location-based services are likely to sustain the demand for real-time mapping solutions in the coming years.

Regionally, North America leads the digital map service market, driven by the high adoption rate of advanced technologies and the presence of major players in the region. However, the Asia Pacific region is expected to witness the fastest growth, attributed to rapid urbanization, increasing smartphone penetration, and government initiatives to develop smart cities. Europe, Latin America, and the Middle East & Africa are also anticipated to experience substantial growth, fueled by the rising demand for digital mapping solutions across various sectors.

Service Type Analysis

In the digital map service market, the service type segment includes mapping and navigation, geographic information systems (GIS), real-time location data, and others. Mapping and navigation services hold a significant share in the market, primarily due to their extensive use in personal and commercial navigation systems. These services provide detailed road maps, traffic updates, and route planning, which are essential for everyday commuting and logistics operations. The continuous advancements in navigation technologies, such as integration with AI and ML for predictive analytics, are expected to enhance the accuracy and functionality of these services.

Geographic Information Systems (GIS) represent another critical segment within the digital map service market. GIS technology is widely used in various applications, including urban planning, environmental management, and disaster response. The ability to analyze and visualize spatial data in multiple layers allows organizations to make informed decisions and optimize resource allocation. The integration of GIS with other emerging technologies, such as drones and remote sensing, is further expanding its application scope and driving market growth.

Real-time location data services are gaining traction due to their importance in applications like fleet management, asset tracking, and location-based services. These services provide up-to-the-minute information on the geographical position of assets, vehicles, or individuals, enabling businesses to improve operational efficiency and customer satisfaction. The growing adoption of IoT devices and the increasing need for real-time visibility in supply chain operations are expected to bolster the demand for real-time location data services.</p&

Conservation International, GRID-Arendal and Geoscience Australia recently collaborated to produce a map of the global distribution of seafloor geomorphic features. The global seafloor geomorphic features map represents an important contribution towards the understanding of the distribution of blue habitats. Certain geomorphic feature are known to be good surrogates for biodiversity. For example, seamounts support a different suite of species to abyssal plains. A detailed description and analysis of the global geomorphic features map can be found in in the scientific paper published in Marine Geology (http://dx.doi.org/10.1016/j.margeo.2014.01.011). The map and the underlying spatial data can be accessed from http://www.bluehabitats.org/

Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24. is licensed under a Creative Commons Attribution 4.0 International License. The data is available as ESRI shapefiles in a single zipped archive and contains shapefiles for the following geomorphic features: abysses, basins, bridges, canyons, escarpments, fans, glacial troughs, guyots, hadals, plateaus, ridges, rift valleys, rises, seamounts, shelfs, sills, slopes, spreading ridges, terraces, trenches, and troughs.

The Automotive HD Maps market is experiencing rapid growth, driven by the increasing adoption of Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. The market's Compound Annual Growth Rate (CAGR) of 17% from 2019 to 2024 suggests a significant expansion, projecting a substantial market size by 2033. Key drivers include the rising demand for enhanced safety features in vehicles, the increasing need for precise localization in autonomous driving, and the continuous improvement in mapping technologies. Trends like the development of highly accurate, real-time map updates and the integration of HD maps with cloud-based services are further fueling market growth. However, challenges remain, including the high cost of HD map development and maintenance, concerns regarding data privacy and security, and the need for robust infrastructure to support widespread deployment. The market is segmented by map type (e.g., lane-level, point cloud) and application (e.g., ADAS, autonomous driving), with various leading companies employing diverse competitive strategies to gain market share. These strategies include strategic partnerships, technological innovations, and aggressive expansion into new markets. Consumer engagement focuses on highlighting the enhanced safety and convenience offered by HD map-enabled features. Regional variations exist, with North America and Europe currently holding significant market shares, followed by Asia Pacific, which is poised for substantial growth in the coming years driven by increasing automotive production and technological advancements. The competitive landscape is characterized by a mix of established players and emerging startups. Companies like Alphabet Inc., TomTom, and HERE Technologies leverage their existing expertise in mapping and location services, while newer entrants are focusing on niche technologies like high-definition point cloud mapping or specialized data analytics. The focus on strategic acquisitions, collaborations, and R&D investments highlights the importance of securing valuable data, enhancing map accuracy, and developing sophisticated algorithms for effective map usage. The continued refinement of sensor technologies (LiDAR, cameras, radar) and advancements in artificial intelligence (AI) are set to further accelerate market growth, leading to increased accuracy, wider coverage, and more comprehensive map functionalities. This overall trend indicates a bright future for the Automotive HD Maps market, although successful navigation of the challenges mentioned above will be crucial for sustained expansion and profitability.

National Transit Map RoutesThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the Bureau of Transportation Statistics, displays national transit routes. Per BTS, "the National Transit Map is a nationwide catalog of fixed-guideway and fixed-route transit service in America that is gleaned from General Transit Feed Specification (GTFS) Schedule data. This geospatial database includes transit routes for the purpose of supporting research, analysis, and planning. The U.S. Department of Transportation (USDOT) uses this national, openly available map to demonstrate the importance and role of transit in American society and to identify and address gaps in access to public transportation."Metrorail Silver LineData currency: current Federal Service (Layer: National Transit Map Routes)NGDAID: 148 (National Transit Map - Routes)OGC API Features Link: Not AvailableFor more information: National Transit MapFor feedback, please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Transportation Theme Community. Per the Federal Geospatial Data Committee (FGDC), Transportation is defined as the "means and aids for conveying persons and/or goods. The transportation system includes both physical and non-physical components related to all modes of travel that allow the movement of goods and people between locations".For other NGDA Content: Esri Federal Datasets

BioMap is the result of an ongoing collaboration between MassWildlife and the Massachusetts Chapter of The Nature Conservancy (TNC). Since its inception in 2001, this comprehensive tool has become a trusted source of information to guide conservation that is used by a wide spectrum of conservation practitioners. Today’s BioMap builds on previous iterations with the continuing goal of protecting the diversity of species and natural ecosystems within the Commonwealth. BioMap is an important tool to guide strategic protection and stewardship of lands and waters that are most important for conserving biological diversity in Massachusetts.More details...Map service also available.

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 descriptionsField descriptions for the "Plan_boundaries" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number ADOPT_DATE: Date of Plan adoption IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator 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.

IMPORTANT: This is the source of the feature layer template in the LearnArcGIS Lesson: Prepare for SAR Incidents and for the MapSAR Solution. If this layer is cloned or copied, the owner of the items needs to update the item details to reflect this. Purpose: This is a feature layer template for use in missing person search operations. It is based on the MapSAR (ArcGIS Desktop) Data Model but simplified for use in web maps and apps. Please see MapSAR GitHub for more information on this project.Maps are at the core of any Search and Rescue (SAR) operation. Geographic information system (GIS) software allows rescue personnel to quickly generate maps that depict specific aspects of the operation and show what is happening on the ground over time. The maps and operations data can be shared over a network to supply an enhanced common operating picture throughout the Incident Command Post (ICP). A team of GIS and SAR professionals from Sierra Madre Search and Rescue Team, Esri, Sequoia and Kings Canyon National Park, Yosemite National Park, Grand Canyon National Park, and the Mountaineer Rescue Group came together to develop the tools and instructions to fit established SAR workflows. The goal is to meet the critical need to provide standards, documents, and training to the international SAR community and establish more widespread and effective integration of GIS into operations.See Comments below for updates to the data model.

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

Natural Earth was built through a collaboration of many volunteers and is supported by NACIS (North American Cartographic Information Society).

Natural Earth Vector comes in ESRI shapefile format, the de facto standard for vector geodata. Character encoding is Windows-1252.

Natural Earth Vector includes features corresponding to the following:

Cultural Vector Data Thremes:

• Countries: matched boundary lines and polygons with names attributes for countries and sovereign states. Includes dependencies (French Polynesia), map units (U.S. Pacific Island Territories) and sub-national map subunits (Corsica versus mainland Metropolitan France).
• Disputed areas and breakaway regions - From Kashmir to the Elemi Triangle, Northern Cyprus to Western Sahara.
• First order admin (provinces, departments, states, etc.): internal boundaries and polygons for all but a few tiny island nations. Includes names attributes and some statistical groupings of the same for smaller countries.
• Populated places: point symbols with name attributes. Includes capitals, major cities and towns, plus significant smaller towns in sparsely inhabited regions. We favor regional significance over population census in determining rankings.
• Urban polygons: derived from 2002-2003 MODIS satellite data.
• Parks and protected areas: US National Park Service units.
• Pacific nation groupings: boxes for keeping these far-flung islands tidy.
• Water boundary indicators: partial selection of key 200-mile nautical limits, plus some disputed, treaty, and median lines.

Physical Vector Data Themes:

• Coastline: ocean coastline, including major islands. Coastline is matched to land and water polygons.
• Land: Land polygons including major islands
• Ocean: Ocean polygon split into contiguous pieces.
• Minor Islands: additional small ocean islands ranked to two levels of relative importance.
• Reefs: major coral reefs from WDB2.
• Physical region features: polygon and point labels of major physical features.
• Rivers and Lake Centerlines: ranked by relative importance. Includes name and line width attributes. Don’t want minor lakes? Turn on their centerlines to avoid unseemly data gaps.
• Lakes: ranked by relative importance, coordinating with river ranking. Includes name attributes.
• Glaciated areas: polygons derived from DCW, except for Antarctica derived from MOA. Includes name attributes for major polar glaciers.
• Antarctic ice shelves: derived from 2003-2004 MOA. Reflects recent ice shelf collapses.
• Bathymetry: nested polygons at 0, -200, -1,000, -2,000, -3,000, -4,000, -5,000, -6,000, -7,000, -8,000, -9,000,and -10,000 meters. Created from SRTM Plus.
• Geographic lines: Polar circles, tropical circles, equator, and International Date Line.
• Graticules: 1-, 5-, 10-, 15-, 20-, and 30-degree increments. Includes WGS84 bounding box.

Important Note: This item is in mature support. There are new versions of basemaps available for your use. Esri recommends updating your maps and apps to use the appropriate new version. This map features the National Geographic World Map, which is a cartographically rich and distinctive reference map of the world. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the National Geographic World Map map service description.

Important Note: This item is in mature support as of June 2021 and is no longer updated. This map presents land cover and detailed topographic maps for the United States. It uses the USA Topographic Map service. The map includes the National Park Service (NPS) Natural Earth physical map at 1.24km per pixel for the world at small scales, i-cubed eTOPO 1:250,000-scale maps for the contiguous United States at medium scales, and National Geographic TOPO! 1:100,000 and 1:24,000-scale maps (1:250,000 and 1:63,000 in Alaska) for the United States at large scales. The TOPO! maps are seamless, scanned images of United States Geological Survey (USGS) paper topographic maps.The maps provide a very useful basemap for a variety of applications, particularly in rural areas where the topographic maps provide unique detail and features from other basemaps.To add this map service into a desktop application directly, go to the entry for the USA Topo Maps map service. Tip: Here are some famous locations as they appear in this web map, accessed by including their location in the URL that launches the map:Grand Canyon, ArizonaGolden Gate, CaliforniaThe Statue of Liberty, New YorkWashington DCCanyon De Chelly, ArizonaYellowstone National Park, WyomingArea 51, Nevada

Master's Thesis. In 1837 the Ioway Indians drew a map to bring to treaty talks with the United States government. The 1837 Ioway Map project uses Geographic Information Systems (GIS) to help extract cultural, archaeological, and historical information from this rare document. Project goals include: documenting Ioway cartographic conventions; georeferencing the Ioway map to a modern base map; extracting spatial, historical, ecological and archaeological information from the georeferenced map; and designing a variety of digital (CD, web site) and non-digital (museum exhibit) presentation formats to broadly disseminate the project results. Centered on what is now the state of Iowa, the 1837 map shows 51 rivers, nine lakes, 23 villages, and over two dozen important Ioway Indian trails. Map features are unlabeled, but historic records indicate that it was designed around two major rivers, the Mississippi and the Missouri. GIS tools were helpful in evaluating the probable identifications of a number of the other hydrographic features. The Ioway encoded information about village size and population in their symbology, information that was systematically documented using pan, zoom, measurement, and geostatistical tools, with the results stored in attribute tables.