The geographical mapping software market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering enhanced accessibility and scalability, the burgeoning need for precise spatial data analysis in urban planning and water resource management, and the escalating use of GIS technology in geological exploration for resource discovery and environmental monitoring. The market's compound annual growth rate (CAGR) is estimated at 8% between 2025 and 2033, projecting significant market expansion. This growth is further supported by the increasing availability of high-resolution satellite imagery and improved data processing capabilities, leading to more accurate and detailed maps for various applications. While the market shows strong potential, certain restraints, including high software licensing costs and the complexity of some GIS software, may impede growth to some extent. However, the overall trend leans towards increased adoption driven by the significant benefits of enhanced spatial analysis across industries. Market segmentation reveals a strong demand for cloud-based solutions due to their flexibility and cost-effectiveness compared to web-based or on-premise software. Geographically, North America and Europe currently hold significant market shares, reflecting established GIS infrastructure and technological advancement. However, Asia-Pacific is expected to witness substantial growth in the coming years driven by rapid urbanization, infrastructure development, and increased government investment in mapping initiatives. This region's expanding market will be fueled by countries like China and India, with significant potential for market penetration. The key players in this competitive landscape continually innovate, releasing new features and functionalities to maintain their market positions. The focus is increasingly on user-friendliness, integration with other software platforms, and advanced analytical capabilities.

This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

The global Business Mapping Software market is experiencing robust growth, driven by the increasing need for data visualization and spatial analysis across diverse sectors. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. The escalating adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting businesses of all sizes. Furthermore, the growing demand for location intelligence in industries such as healthcare (optimizing resource allocation), automotive (supply chain management), and finance (risk assessment) significantly contributes to market growth. The integration of advanced analytics and AI capabilities within business mapping software further enhances its value proposition, enabling better decision-making and strategic planning. While data security concerns and the complexity of implementing such solutions pose some restraints, the overall market outlook remains positive, particularly with the rising adoption of GIS (Geographic Information System) technologies and the increasing availability of high-quality geospatial data. The market segmentation reveals significant opportunities across various applications and deployment types. The healthcare sector is anticipated to be a major driver, followed closely by automotive and financial services. Cloud-based solutions are experiencing faster growth compared to on-premise deployments due to their flexibility and accessibility. Geographically, North America currently holds a substantial market share, benefiting from technological advancements and early adoption. However, the Asia-Pacific region is expected to witness rapid growth in the coming years, fueled by increasing digitalization and infrastructure development in countries like China and India. Key players in the market, including Caliper, Microsoft, IBM, and others, are actively engaged in innovation and strategic partnerships to consolidate their market positions and capitalize on emerging opportunities. This competitive landscape is further characterized by the emergence of specialized solutions catering to niche industry needs.

GIS in Transportation Market Outlook

The global GIS in Transportation market size was valued at USD 9.5 billion in 2023 and is expected to reach USD 21.8 billion by 2032, growing at a CAGR of 9.5%. This rapid growth is driven by advancements in spatial data analytics and the increasing need for efficient transportation management systems across various sectors. The surge in urbanization, coupled with the rising adoption of smart city initiatives, has propelled the demand for geographic information systems (GIS) in transportation, making it an indispensable tool for urban planners and transportation authorities.

One of the primary growth factors in the GIS in Transportation market is the rising need for traffic management solutions. With increasing vehicle ownership and congested road networks, the implementation of GIS-based traffic management systems has become crucial. These systems help in real-time traffic monitoring, congestion management, and route optimization, thereby enhancing overall transportation efficiency. Additionally, the integration of GIS with Internet of Things (IoT) devices and sensors provides valuable data to city planners and traffic authorities, enabling better decision-making and improved traffic flow.

Another significant driver for the market is the growing emphasis on asset management in the transportation sector. GIS technology plays a pivotal role in tracking and managing transportation infrastructure assets such as roads, bridges, and tunnels. By leveraging GIS, transportation agencies can efficiently monitor the condition of these assets, schedule maintenance activities, and allocate resources effectively. This not only extends the lifespan of infrastructure assets but also ensures safety and reduces operational costs, thus driving the adoption of GIS in the transportation sector.

Moreover, the increasing focus on sustainable and eco-friendly transportation solutions is fostering the growth of the GIS in Transportation market. Governments and transportation authorities worldwide are promoting the use of public transit and non-motorized transportation modes to reduce carbon emissions and combat climate change. GIS technology aids in public transit planning and route optimization, ensuring efficient and sustainable transportation systems. Additionally, GIS-based solutions enable the assessment of environmental impacts and support the implementation of green transportation initiatives, further bolstering market growth.

Regionally, North America holds a significant share in the GIS in Transportation market, attributed to the early adoption of advanced technologies and substantial investments in transportation infrastructure. The presence of key market players and the implementation of smart city projects in the United States and Canada further drive the market's growth in this region. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, propelled by rapid urbanization, increasing government initiatives for smart transportation, and the expansion of transportation networks in countries like China and India.

Component Analysis

The GIS in Transportation market is segmented by component into software, hardware, and services. The software segment dominates the market, driven by the rising demand for advanced GIS applications that provide comprehensive spatial analysis, mapping, and visualization capabilities. GIS software solutions, such as geographic information systems for traffic management and route planning, are extensively utilized by transportation authorities and urban planners to improve operational efficiency and decision-making processes. The continuous evolution of GIS software, incorporating advanced features like real-time data integration and predictive analytics, further propels market growth.

Hardware components, although smaller in market share compared to software, play a crucial role in the GIS in Transportation market. Hardware components include GPS devices, sensors, and data collection tools, which are essential for gathering accurate spatial data. The increasing deployment of IoT devices and sensors in transportation infrastructure enhances data collection capabilities, thus supporting the effective implementation of GIS solutions. The integration of GIS hardware with software solutions provides a holistic approach to transportation management, driving the adoption of GIS technology in this sector.

The services segment encompasses a wide range of professional services, including consulting, implementation, and maint

Author: A Lisson, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): gis, geographic thinkingRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.Objectives: Students will be able to:

1. Explain the difference between two types of geospatial technologies - GPS and GIS.
2. Develop basic skills to effectively manipulate and use GPS receivers and ArcGIS software.
3. Explain uses of GPS and GIS.Summary: Students use GPS coordinates to discover geocaches at a local park, and they use ArcGIS to layer maps about the park. Frontenac State park is the example, but any park or area (including school grounds) could be used. Students also investigate careers that use GIS.

The Indoor GIS Software market is experiencing robust growth, driven by the increasing need for precise location-based services within enclosed spaces. The market, valued at approximately $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $5 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of smart buildings and IoT devices provides a wealth of data that Indoor GIS software can effectively leverage for enhanced operational efficiency and improved user experiences. Secondly, the burgeoning e-commerce sector and the consequent demand for optimized warehouse logistics and efficient supply chain management are significantly boosting market demand. Thirdly, the expansion of applications into sectors like healthcare, retail, and security is further diversifying market opportunities. Cloud-based solutions are witnessing higher adoption due to their scalability, cost-effectiveness, and ease of deployment compared to on-premise solutions. However, concerns regarding data security and privacy, as well as the relatively high initial investment costs for implementing Indoor GIS systems, pose challenges to market growth. Segmentation reveals strong demand across various applications. Warehouse logistics and asset management currently dominate the market share due to the clear ROI benefits of improved inventory management and asset tracking. The military and security sectors also present lucrative growth opportunities, driven by the need for sophisticated indoor navigation and situational awareness. Geographically, North America and Europe currently hold the largest market shares, attributed to the high concentration of technologically advanced businesses and early adoption of Indoor GIS technologies. However, Asia-Pacific is expected to show significant growth in the coming years, propelled by rapid urbanization and expanding industrial sectors in countries like China and India. Companies like Mapedin, Esri, and others are key players driving innovation and shaping the competitive landscape. The ongoing development of advanced features such as real-time location tracking, augmented reality integration, and improved data analytics capabilities will further fuel market growth in the coming years.

The Cloud GIS market is experiencing robust growth, projected to reach a substantial value with a Compound Annual Growth Rate (CAGR) of 14% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing need for real-time data processing and analysis across various sectors, including urban planning, environmental management, and logistics, is fueling demand for cloud-based Geographic Information Systems (GIS). The scalability and cost-effectiveness offered by cloud platforms, compared to on-premise solutions, are significant advantages attracting businesses of all sizes. Furthermore, advancements in cloud computing technologies, such as improved storage capacity, enhanced processing power, and advanced analytics capabilities, are accelerating market adoption. The integration of AI and machine learning within Cloud GIS platforms is also a major contributor, enabling sophisticated spatial analysis and predictive modeling. Competition among leading providers like Esri, Hexagon, and Mapbox is intense, focusing on developing innovative solutions, expanding partnerships, and strengthening customer engagement through user-friendly interfaces and comprehensive support services. Geographical expansion, particularly in developing economies with increasing digital infrastructure, further contributes to market growth. However, data security concerns and the reliance on stable internet connectivity remain potential restraints. The market segmentation reveals a diverse landscape. The "Type" segment likely includes various cloud deployment models (e.g., public, private, hybrid), each catering to specific organizational needs and security requirements. The "Application" segment is equally broad, encompassing diverse use cases like smart city initiatives, precision agriculture, disaster response management, and infrastructure development. North America currently holds a significant market share due to early adoption and a mature technological landscape, but the Asia-Pacific region is expected to witness rapid growth driven by increasing urbanization and infrastructure investments. The competitive landscape is dynamic, with companies focusing on strategic partnerships, acquisitions, and continuous product innovation to maintain a leading position. Future growth will be largely influenced by the expansion of 5G networks, the continued advancement of AI/ML in spatial analysis, and the increasing availability of high-resolution geospatial data.

The USDA Long-Term Agroecosystem Research was established to develop national strategies for sustainable intensification of agricultural production. As part of the Agricultural Research Service, the LTAR Network incorporates numerous geographies consisting of experimental areas and locations where data are being gathered. Starting in early 2019, two working groups of the LTAR Network (Remote Sensing and GIS, and Data Management) set a major goal to jointly develop a geodatabase of LTAR Standard GIS Data Layers. The purpose of the geodatabase was to enhance the Network's ability to utilize coordinated, harmonized datasets and reduce redundancy and potential errors associated with multiple copies of similar datasets. Project organizers met at least twice with each of the 18 LTAR sites from September 2019 through December 2020, compiling and editing a set of detailed geospatial data layers comprising a geodatabase, describing essential data collection areas within the LTAR Network. The LTAR Standard GIS Data Layers geodatabase consists of geospatial data that represent locations and areas associated with the LTAR Network as of late 2020, including LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This geodatabase was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. The creation of the geodatabase began with initial requests to LTAR site leads and data managers for geospatial data, followed by meetings with each LTAR site to review the initial draft. Edits were documented, and the final draft was again reviewed and certified by LTAR site leads or their delegates. Revisions to this geodatabase will occur biennially, with the next revision scheduled to be published in 2023. Resources in this dataset:Resource Title: LTAR Standard GIS Data Layers, 2020 version, File Geodatabase. File Name: LTAR_Standard_GIS_Layers_v2020.zipResource Description: This file geodatabase consists of authoritative GIS data layers of the Long-Term Agroecosystem Research Network. Data layers include: LTAR site locations, LTAR site points of contact and street addresses, LTAR experimental boundaries, LTAR site "legacy region" boundaries, LTAR eddy flux tower locations, and LTAR phenocam locations.Resource Software Recommended: ArcGIS,url: esri.com Resource Title: LTAR Standard GIS Data Layers, 2020 version, GeoJSON files. File Name: LTAR_Standard_GIS_Layers_v2020_GeoJSON_ADC.zipResource Description: The contents of the LTAR Standard GIS Data Layers includes geospatial data that represent locations and areas associated with the LTAR Network as of late 2020. This collection of geojson files includes spatial data describing LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This dataset was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. Resource Software Recommended: QGIS,url: https://qgis.org/en/site/

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2032, growing at a CAGR of 12.10% during the forecast period 2026-2032.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth's surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today's interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

The 3D mapping software market is experiencing robust growth, driven by increasing demand across diverse sectors. The convergence of advanced technologies like AI, cloud computing, and improved sensor capabilities is fueling innovation and expanding the applications of 3D mapping. Construction, automobile, and transportation industries are major adopters, leveraging 3D mapping for precise planning, design, and asset management. The on-cloud segment is witnessing rapid expansion due to its scalability, accessibility, and cost-effectiveness compared to on-premise solutions. Furthermore, the growing need for accurate geographic information systems (GIS) and improved visualization in video entertainment is boosting market penetration. While data security concerns and the high initial investment costs for sophisticated software can act as restraints, the overall market trajectory remains positive. Looking ahead, the market is poised for sustained growth, particularly in regions with developing infrastructure and burgeoning technological adoption. Asia-Pacific, driven by significant investments in urban development and infrastructure projects in countries like China and India, is expected to witness the highest growth rate. North America and Europe, while already established markets, will continue to contribute substantially due to continuous technological advancements and the adoption of innovative applications. Key players like Autodesk, ESRI, and Trimble are strategically investing in R&D and partnerships to maintain their market leadership, while smaller companies are focusing on niche applications and specialized solutions. The competitive landscape is dynamic, with companies constantly striving to improve accuracy, processing speed, and user-friendliness to cater to the evolving needs of various industries. The forecast period (2025-2033) anticipates a continued upward trend, propelled by these factors and the ongoing digital transformation across diverse sectors.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This is a raster-based suitability map of landfill sites produced after the February 6, 2023, Türkiye earthquakes centred on Kahramanmaraş - Pazarcık and Kahramanmaraş - Elbistan. In this study, a site selection model was developed using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM), one of the Multi-Criteria Decision-Making Methods, to determine the most suitable landfill areas immediately after the earthquake.The suitability map of the landfill sites can be accessed through the Serverless Cloud-GIS based Disaster Management Portal at https://web.itu.edu.tr/metemu/nominal/deprem.htmlThe pairwise comparison matrix, weight calculation, and sensitivity analysis are also provided in the MS Excel file.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

Land Management Software Market Outlook

The global land management software market size is projected to grow significantly from USD 1.5 billion in 2023 to USD 3.8 billion by 2032, reflecting an impressive compound annual growth rate (CAGR) of 10.5% during this period. This robust growth is driven by multiple factors including advancements in geospatial technologies, the increasing need for efficient land utilization, and heightened regulatory requirements for land management practices.

One of the primary growth factors of the land management software market is the rapid technological advancements in geospatial and remote sensing technologies. These innovations are making it easier to manage land resources more efficiently and accurately. The integration of Geographic Information System (GIS) technologies and remote sensing allows for real-time data collection and analysis, which significantly enhances decision-making processes. Furthermore, the advent of Artificial Intelligence (AI) and Machine Learning (ML) in land management software is expected to optimize land use and improve predictive capabilities, driving the market’s growth.

Another significant growth factor is the increasing global emphasis on sustainable land management practices. As governments and private enterprises become more aware of the environmental impact of land use, there is a growing demand for software solutions that can help monitor, manage, and mitigate these impacts. Policies and regulations aimed at promoting sustainable land use are being enacted globally, compelling landowners and managers to adopt advanced land management software. These regulatory pressures are expected to drive significant adoption of advanced land management solutions over the forecast period.

The rising need for efficient land utilization, particularly in urban areas, is also a crucial growth driver. With global urbanization rates climbing, the need to manage land resources in urban settings has never been more critical. Land management software helps in the optimal allocation and use of land resources, facilitating better urban planning and development. This is particularly vital in densely populated regions where space is at a premium and efficient land use can significantly impact economic and social outcomes.

Regionally, North America is anticipated to dominate the land management software market, attributed to the region's advanced technological infrastructure and high adoption rates of innovative land management solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing investments in smart city projects, and the rising need for efficient land management practices in agriculture and forestry sectors.

Component Analysis

The land management software market is segmented by component into software and services. The software segment is expected to account for the largest market share during the forecast period, driven by continuous advancements in software capabilities and increasing demand for integrated land management solutions. These software solutions offer comprehensive functionalities, including land use planning, property management, and environmental monitoring, which are crucial for efficient land resource management.

Software solutions in land management are increasingly incorporating advanced technologies such as GIS, AI, and ML to provide enhanced functionalities and greater accuracy. These technologies enable real-time data analysis and predictive modeling, which are essential for making informed decisions about land use. The growing adoption of cloud-based land management software is also contributing to the segment’s growth, as it offers greater flexibility, scalability, and cost-effectiveness compared to traditional on-premises solutions.

On the services front, there is a rising demand for consulting, implementation, and maintenance services. As organizations and governments adopt more sophisticated land management software, they require expert guidance to ensure successful deployment and integration with existing systems. Professional services help in customizing the software solutions to meet specific needs, training users, and providing ongoing support, thereby enhancing the overall efficiency and effectiveness of land management practices.

Furthermore, the increasing complexity of land management projects, particularly in urban and environmentally sensitive areas, is driving the demand for comprehensiv

Weekly snapshot of Cleveland City Planning Commission datasets that are featured on the City Planning Zoning Viewer. For the official, most current record of zoning info, use the CPC Zoning Viewer.This file is an open-source geospatial (GIS) format called GeoPackage, which can contain multiple layers. It is similar to Esri's file geodatabase format. Free and open-source GIS software like QGIS, or software like ArcGIS, can read the information to view the tables and map the information.It includes the following mapping layers officially maintained by Cleveland City Planning Commission:Planner Assignment AreasPlanned Unit Development OverlayResidential FacilitiesResidential Facilities 1000 ft. BufferPolice DistrictsLandmarks / Historic LayersLocal Landmark PointsLocal Landmark ParcelsLocal Landmark DistrictsNational Historic DistrictsCentral Business DistrictDesign Review RegionsDesign Review DistrictsOverlay Frontage LinesForm & PRO Overlay DistrictsLive-Work Overlay DistrictsSpecific SetbacksStreet CenterlinesZoningUpdate FrequencyWeekly on Mondays at 4:30 AMContactCity Planning Commission, Zoning & Technology

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $8 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions and the proliferation of readily available geospatial data are lowering the barrier to entry for both individual and corporate users. Furthermore, advancements in mapping technologies, such as 3D mapping capabilities and improved user interfaces, are enhancing the overall user experience and driving wider adoption. The increasing need for effective data visualization in fields like real estate, urban planning, environmental monitoring, and marketing is further bolstering market growth. Segmentation reveals a significant portion of the market is attributed to paid use licenses, reflecting the advanced features and support provided by premium tools. However, the free-use segment is also growing rapidly, driven by the availability of user-friendly open-source tools and freemium models offered by major players. Corporate users constitute a larger portion of the market compared to individual users, primarily due to their higher budget allocations for data visualization and analysis tools. Geographic distribution reveals a concentration of market share in North America and Europe, largely due to higher technological adoption and a well-established digital infrastructure. However, rapid growth is anticipated in Asia Pacific regions like China and India, driven by increasing urbanization and government initiatives promoting digital transformation. Market restraints include the high cost of advanced mapping software, the need for specialized technical skills for complex projects, and the potential for data security and privacy concerns. Nevertheless, ongoing technological innovation, coupled with the increasing accessibility of data and analytical tools, is anticipated to mitigate these challenges and continue to drive significant market expansion throughout the forecast period. Key players like Mapbox, ArcGIS StoryMaps, and Google are actively shaping the market landscape through continuous product development and strategic partnerships, fostering innovation and competitive pricing strategies.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

Tags

   survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES




   Summary


   BES Research, Applications, and Education


   Description


   Geocoded for Baltimore County. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 



   The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 


   Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.


   This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because

These data are digital elevation models (DEMs) of difference (DoD). They are a geospatial dataset created in raster (.tif) format and quantify vertical (z) topographic change between two dates. The data were created to support analysis of landscape change following the 7th February 2021 avalanche-debris flow in Chamoli District, Uttarakhand, India. The data also supported numerical modelling using CAESAR-Lisflood (see related data collection). They are most commonly imported into GIS software, where they can be analysed or support other forms of geospatial analysis.

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GIS In Telecom Sector Market Size 2024-2028

The gis in telecom sector market size is forecast to increase by USD 1.91 billion at a CAGR of 14.68% between 2023 and 2028.

The market is experiencing significant growth due to the increased adoption of Geographic Information Systems (GIS) for capacity planning and network optimization. Telecommunication companies are leveraging GIS technology to analyze and visualize data, enabling them to make informed decisions regarding network expansion, maintenance, and resource allocation. The integration of GIS with big data is a key trend driving market growth, as it allows for real-time analysis of vast amounts of data, leading to improved network performance and customer experience. However, the market is not without challenges. A communication gap exists between developers and end-users, making it essential for telecom companies to invest in user-friendly GIS solutions. This will require collaboration between developers and end-users to ensure that GIS applications meet the specific needs of the telecom industry. By addressing this challenge, companies can capitalize on the market's growth potential and effectively navigate the strategic landscape of the market.

What will be the Size of the GIS In Telecom Sector Market during the forecast period?

Request Free SampleIn the dynamic telecom sector, network intelligence plays a crucial role in driving efficiency and enhancing performance. Wireless network planning relies on key performance indicators to optimize network coverage and capacity. Network security audits ensure the protection of critical infrastructure, while service fulfillment utilizes geospatial databases and network monitoring systems. Telecom infrastructure planning incorporates data modeling and customer relationship management to streamline operations. Packet loss and network outage management are essential components of network availability and performance, with telecom billing systems and order management facilitating seamless business processes. Mobile mapping software and network modeling enable spatial data management, while data integration and data analysis provide valuable network insights. Telecom analytics platforms employ network simulation and capacity planning tools to optimize network performance and ensure network availability. Network optimization algorithms and network performance metrics offer valuable data-driven insights for service interruptions and network insights. Service area optimization and disaster recovery planning are essential elements of a robust telecom strategy, ensuring business continuity and resilience. Data visualization tools facilitate effective decision-making and enable organizations to maintain a competitive edge in the evolving telecom landscape.

How is this GIS In Telecom Sector Industry segmented?

The gis in telecom sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeItalyUKMiddle East and AfricaAPACChinaSouth AmericaRest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the telecom sector, the Global Geographic Information System (GIS) market plays a pivotal role in enhancing network performance and operational efficiency. The software segment, comprising GIS software for desktops, mobiles, cloud, and servers, as well as GIS developers' platforms, experiences significant growth due to the increasing adoption of industry-specific solutions. Telecom companies leverage intelligent maps generated by GIS for strategic decisions on network capacity planning, service improvement, and next-generation enhancements. Moreover, commercial companies provide open-source GIS software to counteract the proliferation of counterfeit products. Network resilience and asset management are crucial aspects of telecom infrastructure management, where GIS software offers valuable insights through location intelligence and spatial analysis. Machine learning and artificial intelligence technologies integrated into GIS software enable predictive network planning and automation, enhancing network reliability and quality. Network operators and service providers invest in wireless network design, 5G network deployment, and edge computing to improve customer experience and network performance. GIS software facilitates network capacity planning, coverage analysis, and tower planning, ensuring optimal network utilization and service level agreements. Network security and infrastructure management are integral components of telecom operations, where GIS software offers data visualization, geospatial data analysis, and