Global Geographic Information System Software Market was valued at USD 8.5 billion in 2022 and will reach USD 21.0 billion by 2030, registering a CAGR of 12.1% for the forecast period 2023-2030. Factor Impacting the Geographic Information System Software Market:

The development of smart cities and Modern urban Planning is expected to drive the Geographic Information System Software Market

The process of site selection, land acquisition, planning, designing, visualizing, building, project management, operations, and reporting are all aided by geographic information system (GIS) software for smart cities. Moreover, geographic information system (GIS) solutions are used in urban planning by experts to better properly analyze, model, and visualize places. By processing geospatial data from satellite imaging, aerial photography, and remote sensors, geographic information system (GIS) software systems offer a comprehensive perspective of the land and infrastructure. Additionally, the industry for geographic information system software is growing over the forecast period as a result of such geographic information system (GIS) software applications.

Restraining factor for Geographic Information System Software Market

The high cost of the system has impacted the Geographic Information System Software Market

The pricey geographic information system will further derail the overall market’s growth. The geographic information system (GIS) is expensive because, in addition to the technology and software, it is necessary to have a properly qualified human workforce. Moreover, Specialized knowledge is needed to comprehend and interpret the information gathered by a geographic information system (GIS) system, which is expensive to hire and train. This factor will therefore obstruct market growth over the forecast period. What is Geographic Information System Software?

Geographic Information System Software is used to develop, hold, retrieve, organize, display, and perform analyses on many kinds of spatial and geographic data. The geographic information system (GIS) Industry is majorly driven by infrastructural developments, such as smart cities, water and land management, utility, and urban planning. The services segment provides various applications such as location-based services and, thus, is one of the prominent contributors to the market share. Advancements in GIS technologies, such as geo-analytics and integrated location-based data services, are also boosting the adoption of GIS in various regional markets, thereby driving the market demand over the forecast period.

The Geographic Information System (GIS) market is experiencing robust growth, projected to reach $2979.7 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.5% from 2025 to 2033. This expansion is driven by several key factors. Increasing urbanization and infrastructure development necessitate sophisticated spatial data management and analysis, fueling demand for GIS solutions across various sectors. The construction industry, for instance, leverages GIS for project planning, site surveying, and resource management, while utilities companies use it for network optimization and asset management. Furthermore, the growing adoption of cloud-based GIS platforms enhances accessibility, scalability, and cost-effectiveness, attracting a wider user base. Precision agriculture, another significant driver, utilizes GIS for efficient land management, crop monitoring, and yield optimization. Technological advancements, particularly in areas like sensor technology (imaging sensors, LIDAR), GNSS/GPS, and improved data analytics capabilities, continuously enhance GIS functionalities and expand its applications. Competitive landscape includes major players like Esri, Hexagon, and Autodesk, driving innovation and fostering market competitiveness. However, the market faces some challenges. The high initial investment required for implementing GIS solutions, along with the need for specialized technical expertise, can be barriers to entry, particularly for smaller businesses. Data security and privacy concerns also remain a significant factor influencing market growth. Despite these restraints, the long-term outlook for the GIS market remains positive, driven by continued technological progress, increasing data availability, and growing awareness of the benefits of spatial data analysis across diverse industries. The market is expected to witness substantial growth in regions like Asia Pacific and North America owing to high adoption rates and increasing investment in infrastructure projects. The consistent improvements in accuracy and cost-effectiveness of GIS technology will continue to open up new application areas, further fueling market expansion throughout the forecast period.

The Geographic Information System (GIS) Services market is experiencing robust growth, driven by increasing adoption across various sectors. While the provided data lacks specific market size figures, based on industry reports and observed trends in related technology sectors, we can estimate a 2025 market size of approximately $15 billion USD. This reflects the significant investments being made in spatial data infrastructure and the growing demand for location-based analytics. Assuming a Compound Annual Growth Rate (CAGR) of 8%, the market is projected to reach roughly $25 billion by 2033. Key drivers include the rising need for precise mapping and location intelligence in environmental management, urban planning, and resource optimization. Furthermore, advancements in cloud-based GIS platforms, the increasing availability of big data, and the development of sophisticated geospatial analytics tools are fueling market expansion. The market is segmented by service type (Analyze, Visualize, Manage, Others) and application (primarily Environmental Agencies, but also extending to various sectors such as utilities, transportation, and healthcare). North America currently holds a significant market share due to early adoption and advanced technological infrastructure. However, regions like Asia-Pacific are demonstrating rapid growth, driven by increasing urbanization and infrastructure development. While the lack of readily available detailed market figures presents a challenge for complete precision in projection, the overall trend points to a considerable expansion of the GIS services sector over the forecast period. The competitive landscape is characterized by a mix of large multinational corporations like Infosys and Intellias and smaller, specialized firms like EnviroScience and R&K Solutions, reflecting the diverse needs of the market. These companies compete based on their technological capabilities, industry expertise, and geographical reach. The ongoing integration of GIS with other technologies, such as artificial intelligence (AI) and machine learning (ML), will further shape the market landscape, creating opportunities for innovation and differentiation. Challenges include the high initial investment costs associated with implementing GIS solutions and the need for skilled professionals to effectively utilize these technologies. However, the long-term benefits of improved decision-making and operational efficiency are driving wider adoption despite these hurdles. The future growth of the GIS services market hinges on the continued development of innovative technologies and the increasing awareness of the value that location-based insights provide across various industries.

An introduction to ArcView 3.x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-204.)

The Global GIS Mapping Tools Market is poised for significant expansion, projected to reach a substantial market size of $10 billion by 2025, with an anticipated Compound Annual Growth Rate (CAGR) of 12.5% through 2033. This robust growth trajectory is fueled by the increasing demand for advanced spatial analysis and visualization capabilities across a multitude of sectors. Key drivers include the escalating need for accurate geological exploration to identify and manage natural resources, the critical role of GIS in planning and executing complex water conservancy projects for sustainable water management, and the indispensable application of GIS in urban planning for efficient city development and infrastructure management. Furthermore, the burgeoning adoption of cloud-based and web-based GIS solutions is democratizing access to powerful mapping tools, enabling broader use by organizations of all sizes. The market is also benefiting from advancements in data processing, artificial intelligence integration, and the growing availability of open-source GIS platforms. Despite the optimistic outlook, certain restraints could temper the market's full potential. High initial investment costs for sophisticated GIS software and hardware, coupled with a shortage of skilled GIS professionals in certain regions, may pose challenges. However, the overwhelming benefits of enhanced decision-making, improved operational efficiency, and the ability to gain deep insights from spatial data are compelling organizations to overcome these hurdles. The competitive landscape is dynamic, featuring established players like Esri and Autodesk alongside innovative providers such as Mapbox and CARTO, all vying for market share by offering specialized features, user-friendly interfaces, and integrated solutions. The continuous evolution of GIS technology, driven by the integration of remote sensing data, big data analytics, and real-time information, will continue to shape the market's future. Here's a comprehensive report description on GIS Mapping Tools, incorporating your specified requirements:

This in-depth report provides a panoramic view of the global GIS Mapping Tools market, meticulously analyzing its landscape from the Historical Period (2019-2024) through to the Forecast Period (2025-2033), with 2025 serving as both the Base Year and the Estimated Year. The study period encompasses 2019-2033, offering a robust historical context and forward-looking projections. The market is valued in the millions of US dollars, with detailed segment-specific valuations and growth trajectories. The report is structured to deliver actionable intelligence to stakeholders, covering market concentration, key trends, regional dominance, product insights, and critical industry dynamics. It delves into the intricate interplay of companies such as Esri, Hexagon, Autodesk, CARTO, and Mapbox, alongside emerging players like Geoway and Shenzhen Edraw Software, across diverse applications including Geological Exploration, Water Conservancy Projects, and Urban Planning. The analysis also differentiates between Cloud Based and Web Based GIS solutions, providing a granular understanding of market segmentation.

Urban Planning Expert Questionnaire on Neighborhood Service Weighting

"Hi, I'm Adam Burke. I am the Lead Specialist Advisor for Geospatial at Natural Resources Wales. Read on to find out more about the work I do and how I got here."I graduated from Aberystwyth University with a BSc in Physical Geography and a MSc in Geographic Information Systems.

The Traffic Geographic Information System (TGIS) market is experiencing robust growth, projected to reach a market size of $223.41 million in 2025. While the provided CAGR is missing, considering the rapid advancements in data analytics, location intelligence, and the increasing adoption of smart city initiatives, a conservative estimate of the Compound Annual Growth Rate (CAGR) for the forecast period (2025-2033) would be around 8%. This growth is fueled by several key drivers: the increasing need for efficient traffic management in urban areas, the proliferation of connected vehicles generating real-time data, and the growing demand for advanced analytics to optimize traffic flow and reduce congestion. Furthermore, the integration of TGIS with other smart city technologies, such as smart parking and public transportation systems, is contributing to market expansion. Software solutions within the TGIS market are likely to dominate due to their scalability and flexibility, while applications focusing on route optimization and traffic prediction are experiencing high demand. The market faces certain restraints, primarily including the high initial investment costs associated with implementing TGIS solutions and the need for skilled professionals to manage and interpret the complex data generated by these systems. However, the long-term benefits of improved traffic management, reduced emissions, and enhanced public safety are likely to outweigh these challenges, ensuring sustained market growth. The regional distribution of the TGIS market mirrors global urbanization trends, with North America and Europe anticipated to hold significant market shares. However, the Asia-Pacific region is poised for rapid growth driven by increasing investments in infrastructure development and the adoption of smart city technologies in rapidly developing economies like China and India. The competitive landscape is marked by established players like Autodesk, Bentley Systems, ESRI, Hexagon, MDA, and Pitney Bowes, constantly innovating and expanding their product offerings to meet the evolving needs of the market. The market segmentation across software, services, and various applications—including route optimization, traffic tracking, and other specialized solutions—reflects the diverse functionalities and applications of TGIS within various sectors, including transportation, logistics, and urban planning.

The PaleoPerm database was created on the basis of published studies of the natural history of the of the Perm Kama region. The objects of the database are paleoarchives (peatlands, lacustrine, alluvial, cover, cultural, polygenetic sediments) of the Late Pleistocene and Holocene age.

The database contains information about the location of paleoarchives in the WGS84 coordinate system. For each object, a set of data is provided on its name, study area, geographical and morphometric characteristics, information on dating, and a set of analyzes (proxy-data) performed. In total, more than 20 parameters. For each paleoarchive, links to the original source of the study data are also indicated.

The database can be visualized using geographic information systems. It is intended for specialists in the field of paleogeography, paleoecology, Quaternary geology, archeology and all those interested in the natural history of the region.

The information is systematized in the form of two files: "Таблица.xlsx" and "PaleoPerm.shp". As additional materials, the "Описание и инструкция по использованию.docx", "Карточка палеоархива.docx" (for further replenishment of the database) and "Библиографический список публикаций.docx" are attached to the database.

Get an accurate and fresh 2D geospatial representation of the world's road networks, points of interest (POIs), land use and land cover across the globe.

Across multiple industries, you can select the best map type and location content products for your specific use case or application. Build your own tailored interactive map with road segments, addresses, cartographic data and administrative areas.

HERE Maps can be further enriched with additional curated and specialized location content products that enable you to build differentiating location-enabled services and applications.

The Southwestern Region is 20.6 million acres. There are six national forests in Arizona, five national forests and a national grassland in New Mexico, and one national grassland each in Oklahoma and the Texas panhandle.The region ranges in elevation from 1,600 feet above sea level and an annual rain fall of 8 inches in Arizona's lower Sonoran Desert to 13,171-foot high Wheeler Peak and over 35 inches of precipitation a year in northern New Mexico. Geographic Information Systems or GIS are computer systems, software and data used to analyze and display spatial or locational data about surface features. One of the strengths of GIS is the capability to overlay or compare multiple feature layers. A user can then analyze the relationship between the layers. Data, reports and maps produced through GIS are used by managers and resource specialists to make decisions about land management activities on National Forests. The National Forests of the Southwestern Region maintain and utilize GIS data for various features on the ground. Some of these datasets are made available for download through this page. Resources in this dataset:Resource Title: GIS Datasets. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=STELPRDB5202474 Selected GIS datasets for the Southwestern Region are available for download from this page.Resource Software Recommended: ArcExplorer,url: http://www.esri.com/software/arcexplorer/index.html

Cloud GIS Market Outlook

The global Cloud GIS market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% over the forecast period. The growth of the Cloud GIS market can be attributed to several factors, including the increasing demand for cloud-based geographic information systems (GIS) across various sectors, advancements in geospatial technologies, and rising investments in smart city projects.

One of the primary growth factors driving the Cloud GIS market is the increasing demand for real-time geospatial data and location-based services. As businesses and governments recognize the value of real-time data for decision-making, there has been a surge in the adoption of Cloud GIS solutions. These solutions offer scalable, flexible, and cost-effective ways to collect, store, analyze, and visualize geographic data, making them indispensable in sectors such as transportation, logistics, and urban planning.

Another significant growth driver is the rapid advancement in geospatial technologies, such as remote sensing, satellite imagery, and geographic data analytics. These technological advancements have expanded the capabilities of GIS systems, enabling more sophisticated data analysis and mapping solutions. The integration of AI and machine learning with GIS is further enhancing the ability to derive actionable insights from complex geospatial data, thus fueling the market growth.

Investments in smart city projects are also contributing to the growth of the Cloud GIS market. Governments and urban planners are increasingly leveraging Cloud GIS to manage and optimize urban infrastructure, transportation systems, and public services. Smart cities use geospatial data to improve resource management, enhance public safety, and provide better services to citizens. This trend is expected to continue, driving further demand for Cloud GIS solutions.

Regionally, North America is expected to hold the largest market share in the Cloud GIS market during the forecast period. The region's dominance can be attributed to the presence of leading technology companies, high adoption rates of advanced technologies, and substantial investments in infrastructure development. Additionally, Asia Pacific is anticipated to witness the highest growth rate due to rapid urbanization, increasing internet penetration, and government initiatives promoting digitalization and smart city projects.

Component Analysis

The Cloud GIS market is segmented by component into software and services. Within the software segment, cloud-based GIS solutions offer various functionalities, including data storage, data analysis, and visualization tools. These solutions are gaining traction due to their scalability, flexibility, and ability to integrate with other enterprise systems. Cloud GIS software allows organizations to access and analyze geographic data in real-time, facilitating better decision-making and strategic planning. As businesses and governments increasingly rely on geographic data, the demand for advanced GIS software solutions is expected to rise significantly.

On the other hand, the services segment encompasses various offerings such as consulting, integration, maintenance, and support services. These services are crucial for the successful implementation and operation of Cloud GIS systems. Consulting services help organizations understand their specific GIS needs and develop tailored solutions, while integration services ensure seamless integration of GIS with existing IT infrastructure. Maintenance and support services provide ongoing assistance to ensure the smooth functioning of GIS systems. The growing complexity of geospatial data and the need for specialized expertise are driving the demand for professional services in the Cloud GIS market.

Moreover, the shift towards cloud-based solutions has led to the emergence of new service models such as GIS-as-a-Service (GaaS). GaaS allows organizations to access GIS capabilities on a subscription basis, eliminating the need for significant upfront investments in hardware and software. This model is particularly beneficial for small and medium-sized enterprises (SMEs) that may not have the resources to invest in traditional GIS systems. As the adoption of GaaS increases, the services segment is expected to experience substantial growth.

In addition to these core services, many Cloud GIS providers offer value-added services such as data analytics, cus

The global Geographic Information System (GIS) in Telecom market is experiencing robust growth, driven by the increasing demand for precise location-based services, network optimization, and improved infrastructure management within the telecommunications sector. The market's expansion is fueled by the proliferation of 5G networks, the Internet of Things (IoT), and the need for efficient network planning and deployment. Telecom companies are leveraging GIS technologies to optimize their network infrastructure, improve service quality, reduce operational costs, and enhance customer experience. This includes applications such as asset management, site selection, network planning, and emergency response. Furthermore, the integration of GIS with advanced analytics provides valuable insights into network performance, allowing for proactive maintenance and improved resource allocation. Competition is intense, with established players like Autodesk, Esri, and Hexagon competing with specialized telecom GIS providers. Growth is expected across all regions, with North America and Europe maintaining significant market shares due to advanced technological infrastructure and high adoption rates. However, emerging markets in Asia-Pacific and Latin America are showing promising growth potential as telecom infrastructure development accelerates. Challenges remain, including the high initial investment costs for implementing GIS solutions and the need for skilled professionals to manage and utilize these complex systems effectively. The projected Compound Annual Growth Rate (CAGR) suggests a sustained period of expansion for the GIS in Telecom market. Considering a base year of 2025 and a forecast period of 2025-2033, we can anticipate a significant increase in market size over the coming years. The continued advancements in GIS software, including cloud-based solutions and the integration of artificial intelligence (AI) and machine learning (ML), are further strengthening the market’s growth trajectory. This technological evolution enables more efficient data analysis, predictive modeling, and real-time decision-making for telecom operators, enhancing the overall value proposition of GIS solutions. The market's segmentation into various applications and service offerings also contributes to its diversification and continuous growth.

This is not the most current data source. This service contains NPS fuels treatments documented in NFPORS and does NOT contain data after FY23. (September 30, 2023) To access fuels treatments occurring since then please use the IFPRS service or the Integrated Interagency Fuels Treatment (IIFT) service. IFPRSIIFTDocumenting, managing and protecting our lands in the National Park Service, remains fundamental to our understanding of the nationally significant landscapes we steward. The ability to use geographic information systems (GIS) to help manage all aspects of park operations, including wildland fire history and fuel treatments, provides the National Park Service with a powerful tool. In order to take advantage of this tool to adequately plan and maintain wildland fires among other daily activities, we must maintain accurate spatial information for wildland fire history and fuel treatments. This service displays our wildland fire history. It will assist in program direction, reporting and information requests. Purpose and BenefitsThe purpose of creating and utilizing such wildland fire history and fuel treatment spatial data services is to consolidate our wildland fire history and fuel treatment spatial data and integrate the existing feature attribute information into a national database for budgeting, reporting and planning purposes. Visualizing trends in wildland fire history data geographically through a GIS and accessing all available descriptive information at the same time, without needing to physically combine databases creates a powerful management tool. In this way, planners, resource managers, and superintendents can bring all of the various perspectives which may relate to a single wildland fire together via GIS, enabling them to visualize trends and explore how resources of different types may relate to each other and their contexts. Ultimately, use of the wildland fire history service will lead to more comprehensive access to all of our available wildland fire history and fuel treatment data and provide a more integrated approach to wildland fire data management across the NPS and at all levels: park, region and program. As resource specialists and managers continue to move their legacy data into the service and collect new data in the service, it will allow the NPS Fire Program to effectively budget, plan and manage future wildland fires and fuel treatments. LayersThis service is a combination of two feature classes the NPS Fire GIS program uses to store NPS fire program data agency wide. Each polygon feature displayed includes attributes from the shape's corresponding NFPORS Treatment record. Data is updated nightly to reflect any edits from NFPORS, NPS Treatment Inspector and edits made by Regional Fire GIS Specialists. TreatmentA point feature class representing past and future fuel treatments. This feature class is updated on a regular basis from the National Fire Plan Operations and Reporting System (NFPORS). Treatment records can be created for non-NFPORS treatments or for treatments before NFPORS began in 2003. The Treatment feature class has a many to many relationship to the Event feature class - meaning many treatments can have many events and vice versa. Event A polygon feature class representing completed events. This feature class will represent any complete treatments.

The integration of citizen science, volunteered geographic information (VGI), and Web/mobile geographic information systems (GIS) has demonstrated significant potential in enhancing disaster response efforts. However, delivering timely, comprehensive and trustworthy information remains a major challenge, particularly when relying on passive data collection from social media. While researchers have developed specialized platforms for natural hazards and advanced models for data analysis, few studies present a holistic lifecycle from stakeholder-oriented design through development, especially with attention to the design phase. To address this gap, this paper introduces an agile and iterative user-centered framework for designing and developing a participatory mobile GIS application for collecting reliable, first-hand observations. A pilot study conducted during real-world hurricane events demonstrated the application’s ability to operate both in real time and offline, enabling the collection of precise geotagged data, categorized labels, and diverse media formats. The results highlight the potential of this active, stakeholder-centered approach to support intelligent disaster response strategies and complement passive and authoritative data sources. This paper advances the integration of citizen science and mobile GIS by providing a framework that follows user-centered design principles to inform future disaster response applications.

This layer is a high-resolution tree canopy change-detection layer for Baltimore City, MD. It contains three tree-canopy classes for the period 2007-2015: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2007 and 2015 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2007 and 2015 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction. 2006 LiDAR and 2014 LiDAR data was also used to assist in tree canopy change.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

Data for decision making has long been the purpose and design of a geographic information system (GIS). In fact, ‘Data for Decision’ is the title of a documentary video created in 1967 that highlights the pioneering work of Roger Tomlinson, the father of GIS.Much has changed since the early days of government-funded systems designed to guide only the very large public policy discussions. GIS tools and data are fundamentally more accessible and intuitive, a development that has expanded the use of GIS beyond the specialist.Key TakeawaysGovernments at all scales use GIS to plan, measure and improve operations, and guide policy.Location-based applications gather data quickly to inform a live system of actionable information.Esri Maps for Public Policy packages maps, data, case studies, analysis examples, and lessons in themed bundles that address common challenges.

This GIS (Geographic Information System) layer provides detailed information about the sidewalk infrastructure across the city. The layer represents individual sidewalk segments, which are divided at property boundaries and street intersections. Each segment is mapped with precise location data to help understand the layout of sidewalks in the city.PurposeThe dataset supports the City's commitment to maintaining a safe, accessible pedestrian network. It is used by City staff across departments for ADA assessments, maintenance planning, infrastructure upgrades, public inquiries, and capital improvement prioritization.Maintenance & UpdatesThe dataset is maintained by the Transportation Maintenance GIS Specialist. Updates occur as sidewalks change — for example, after new construction, sidewalk repairs, or when previously buried sidewalks are restored. All updates are based on field inspections conducted by trained personnel to ensure data accuracy.While the data was comprehensively reviewed and published in April 2025, it is updated iteratively as changes are reported or observed. There is currently no standardized citywide procedure for notifying the GIS team when sidewalk conditions change, which may result in temporary data gaps.To help keep the dataset accurate, users are encouraged to report discrepancies or recent sidewalk changes using the Cityworks “Street Asset GIS Update” Work Order Template. Known LimitationsThe dataset may not immediately reflect recent sidewalk changes due to delays in reporting and inspection. Coordination with other departments is ongoing to improve update workflows.ContactFor questions about this dataset, please contact the Transportation Maintenance GIS Specialist at isabella.buckley@raleighnc.gov.Key AttributesSidewalk Type – Classification of the sidewalkStreet Name – Name of the adjacent streetFeature Length & Width – Dimensions of the sidewalk segmentSidewalk Material – Surface material

Basic Layers from the MCM-LTER spatial data holdings have been exported and symbolized, and they are available for download here. Most of these layers date from Oct-2007 or earlier, please see the Polar GeoSpatial Center for more updated base and specialized layers. The spatial GIS layers contained in this ZIP file were exported from the 2006 MCM-LTER Spatial Database (now deprecated) in the ESRI Shape File format. For your convenience, Layer Files (.lyr),  which are already symbolized, are also included. The spatial layers contained in the MCM-LTER Spatial Database are accurate (depending on the date the shapefiles in this ZIP file were last exported, they may be out of date). List of layers:  Camp locations.lyr glacier stake locations.lyr glaciers.lyr lakes and ponds.lyr maximum extent.lyr met station locations.lyr ocean.lyr stream gauge locations.lyr streams - monitored.lyr streams - not monitored.lyr topo 50m.lyr Â