The csv file contains all participants' scores in each spatial thinking factor.

The R script contains the necessary workflow for reproducing the results reported in the paper.

All statistical analyses have been performed in R (R Core Team, 2021), using: “tidyverse” (Wickham et al., 2019), “DescTools “(Andri et al., 2022), “gdata (Warnes et al., 2022), “ggstatsplot” (Patil, 2021), “corrplot” (Wei & Simko, 2021), “pcal” (Fonseca & Paulo, 2020) and “effectsize” (Ben-Shachar et al., 2020) packages. Andri S. et mult. al. (2022). DescTools: Tools for Descriptive Statistics. R package version 0.99.45, https://cran.r-project.org/package=DescTools Ben-Shachar, M., Lüdecke, D., & Makowski, D. (2020). effectsize: Estimation of Effect Size Indices and Standardized Parameters. Journal of Open Source Software, 5(56), 2815. https://doi.org/10.21105/joss.02815 Fonseca, P. & Paulo, R. (2020). pcal: Calibration of P-values for Point Null Hypothesis Testing, R package version 1.1.0. https://pedro-teles-fonseca.github.io/pcal/ Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach. Journal of Open Source Software, 6(61), 3167. https://doi.org/10.21105/joss.03167 R Core Team: R (2021). A language and environment for statistical computing. R Foundation for Statistical Computing, https://www.R-project.org Warnes, G. R. et mult. Al (2022). ‘gdata’: Various R Programming Tools for Data Manipulation Version 2.18.0.1, https://CRAN.R-project.org/package=gdata Wei, T., Simko, V. (2021). R package 'corrplot': Visualization of a Correlation Matrix. (Version 0.92), https://github.com/taiyun/corrplot Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L.D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T.L., Miller, E., Bache, S.M., Müller, K., Ooms, J., Robinson, D., Seidel, D.P., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K., Yutani, H. (2019). Welcome to the tidyverse, Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686.

The development of an operational sea ice mapping system. This metadata record refers to the development and testing of an prototype system, ICEMAPPER, to interpret NOAA AVHRR imagery on a semi-automatic basis, off the Southern Ocean near to the Antarctic coast.

From the abstract of one of the referenced papers:

This paper reports work towards the development of a semi-automated technique for creating sea-ice and cloud maps from Advanced Very High Resolution Radiometer (AVHRR) images of the Southern Ocean near to the Antarctic coast. The technique is implemented as a computer-based system which applies a number of classification rules to the five bands of an AVHRR image and classifies each pixel in the image as representing open water, low cloud, high cloud or one of several different sea ice concentration categories. The map produced by the system is then displayed and an experienced sea ice forecaster evaluates the result. If it is deemed satisfactory the map is saved on disk. If not, the expert can alter various parameters within the classification rules to produce a satisfactory map. Experience so far has shown that judicious, but reasonably minor, changes to the rule parameters can produce a satisfactory sea-ice map relatively quickly in most cases. The system is also capable of effectively distinguishing cloudy from clear pixels but it does not accurately distinguish high cloud from low cloud in some of the images. Current work is being undertaken to improve the cloud classification rules.

Geographic Information System (GIS) Analytics Market Outlook

The global Geographic Information System (GIS) Analytics market size is projected to grow remarkably from $9.1 billion in 2023 to $21.7 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.2% during the forecast period. This substantial growth can be attributed to several factors such as technological advancements in GIS, increasing adoption in various industry verticals, and the rising importance of spatial data for decision-making processes.

The primary growth driver for the GIS Analytics market is the increasing need for accurate and efficient spatial data analysis to support critical decision-making processes across various industries. Governments and private sectors are investing heavily in GIS technology to enhance urban planning, disaster management, and resource allocation. With the world becoming more data-driven, the reliance on GIS for geospatial data has surged, further propelling its market growth. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) with GIS is revolutionizing the analytics capabilities, offering deeper insights and predictive analytics.

Another significant growth factor is the expanding application of GIS analytics in disaster management and emergency response. Natural disasters such as hurricanes, earthquakes, and wildfires have highlighted the importance of GIS in disaster preparedness, response, and recovery. The ability to analyze spatial data in real-time allows for quicker and more efficient allocation of resources, thus minimizing the impact of disasters. Moreover, GIS analytics plays a pivotal role in climate change studies, helping scientists and policymakers understand and mitigate the adverse effects of climate change.

The transportation sector is also a major contributor to the growth of the GIS Analytics market. With the rapid urbanization and increasing traffic congestion in cities, there is a growing demand for effective transport management solutions. GIS analytics helps in route optimization, traffic management, and infrastructure development, thereby enhancing the overall efficiency of transportation systems. The integration of GIS with Internet of Things (IoT) devices and sensors is further enhancing the capabilities of traffic management systems, contributing to the market growth.

Regionally, North America is the largest market for GIS analytics, driven by the high adoption rate of advanced technologies and significant investment in geospatial infrastructure by both public and private sectors. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to the rapid urbanization, infrastructural developments, and increasing government initiatives for smart city projects. Europe and Latin America are also contributing significantly to the market growth owing to the increasing use of GIS in urban planning and environmental monitoring.

Component Analysis

The GIS Analytics market can be segmented by component into software, hardware, and services. The software segment holds the largest market share due to the continuous advancements in GIS software solutions that offer enhanced functionalities such as data visualization, spatial analysis, and predictive modeling. The increasing adoption of cloud-based GIS software solutions, which offer scalable and cost-effective options, is further driving the growth of this segment. Additionally, open-source GIS software is gaining popularity, providing more accessible and customizable options for users.

The hardware segment includes GIS data collection devices such as GPS units, remote sensing instruments, and other data acquisition tools. This segment is witnessing steady growth due to the increasing demand for high-precision GIS data collection equipment. Technological advancements in hardware, such as the development of LiDAR and drones for spatial data collection, are significantly enhancing the capabilities of GIS analytics. Additionally, the integration of mobile GIS devices is facilitating real-time data collection, contributing to the growth of the hardware segment.

The services segment encompasses consulting, implementation, training, and maintenance services. This segment is expected to grow at a significant pace due to the increasing demand for professional services to manage and optimize GIS systems. Organizations are seeking expert consultants to help them leverage GIS analytics for strategic decision-making and operational efficiency. Additionally, the growing complexity o

The Geographic Information System (GIS) Analytics market is experiencing robust growth, projected to reach a market size of $2979.7 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.6% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of cloud-based GIS solutions offers scalability and cost-effectiveness, attracting both large enterprises and smaller organizations. Furthermore, the rising demand for location intelligence across various sectors, including urban planning, environmental management, and logistics, significantly drives market growth. Advancements in data analytics techniques, such as machine learning and artificial intelligence, are enhancing the capabilities of GIS analytics, leading to more accurate predictions and insightful decision-making. The integration of GIS with other technologies, like IoT and Big Data, further amplifies its value proposition across diverse applications. Competitive pressures among established players like ESRI, Hexagon, Pitney Bowes, SuperMap, Bentley Systems, GE, GeoStar, and Zondy Cyber Group are driving innovation and fostering market expansion. However, market growth might face certain challenges. The complexity of GIS analytics software and the need for specialized expertise can hinder widespread adoption, particularly among smaller businesses with limited resources. Data security and privacy concerns related to handling sensitive location data also pose a significant restraint. Despite these challenges, the long-term outlook remains positive, driven by continuous technological innovation, increasing data availability, and growing awareness of the strategic value of location intelligence across various industries. The market's segmentation, while not explicitly provided, can reasonably be assumed to include software, services, and hardware components, further contributing to its multifaceted growth trajectory.

In the Mediterranean region, land systems have been shaped gradually through centuries. They provide services to a large and growing population in a region that is among the most vulnerable to future global change. The spatial extent and distribution of Mediterranean land systems was, until now, unknown. We present a new, expert-based classification of Mediterranean land systems, representing landscapes as integrated social-ecological systems. We combined data on land cover, management intensity and livestock available on the European and global scale in a geographic information system based approach. We put special emphasis on agro-silvo-pastoral mosaic systems: multifunctional Mediterranean landscapes hosting different human activities that are not represented in common land cover maps.The resulting land systems typology can be used to prioritize and protect landscapes of high cultural and environmental significance. The map 'medi_LS' presents the spatial distribution of Mediterranean land system, and can be imported in a GIS. The map is created in a Lambert Azimuthal Equal Area projection with a resolution of 2 x 2 km (custom projection with more details in the file "readme_projection").

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 has been superseded by the dataset "Flood Defence Structures" (NRW_DS125266).

This dataset shows flood defences that have been built to protect against flooding from rivers and the sea. The defences shown provide different levels of flood protection and this is recognised in the risk classification shown in the Flood Risk Assessment Wales (FRAW) map.

Flood defences that are not yet shown, and the areas that benefit from them, will be gradually added.

Flood defences do not remove the risk of flooding however and can be overtopped or fail in extreme weather conditions.

Market Overview and Drivers: The global Virtual Space Service market is experiencing significant growth, with a current market size of approximately XXX million and a projected CAGR of XX% over the forecast period of 2025-2033. This growth is primarily driven by the increasing adoption of remote work and collaboration tools due to the COVID-19 pandemic, as well as the need for businesses to create immersive and engaging virtual experiences for customers and employees. Key segments within this market include cloud-based and web-based solutions, with applications across small and medium enterprises (SMEs) and large enterprises. Competitive Landscape and Trends: The Virtual Space Service market is highly competitive, with a diverse array of players ranging from established tech giants like Spatial Systems and Cisco to niche specialists such as oVice and Wurkr. Companies are focusing on innovation and the development of user-friendly platforms that offer features such as virtual reality (VR) integration and spatial audio for enhanced immersion. Emerging trends include the use of artificial intelligence (AI) to personalize experiences and automate tasks, as well as the adoption of hybrid and augmented reality technologies for seamless transitions between physical and virtual spaces.

Dear users,this dataset contains the 500m base grid for Central Germany as an ARCGIS shapefile in which all pixels have a uniqui ID.Please use this ID to link the spatial data (=grid) to the scenario tables, which are provided in the EXCEL file. Please read the read_me in the EXCEL file for more details on the data provided therein.Scenarios have been calculated with the SITE model using among other inputs 2 other figshare dadatsets:Population scenarios: https://figshare.com/articles/Population_scenarios_for_Central_Germany/3082183Spatial data Central Germany: https://figshare.com/articles/Central_Germany_GIS_dataset/1318765 Metadata to spatial data: https://figshare.com/articles/Centra_Germany_Dataset_Metadata/1319479Any questions welcome: joerg.priess@ufz.de (or the authors of the other datasets)

How to cite

When you use the datasets or maps, please also cite to the following paper introducing the whole of process from data collection, harmonization and visualization until releasing the data:

Rantanen, T., Tolvanen, H., Roose, M., Ylikoski, J. & Vesakoski, O. (2022) “Best practices for spatial language data harmonization, sharing and map creation - A case study of Uralic” PLoS ONE 17(6): e0269648. https://doi.org/10.1371/journal.pone.0269648.

Overview

The Geographical database of the Uralic languages consists of past and current distributions of the Uralic languages both as the original digital spatial datasets and as finalized maps. The database has been collected by the interdisciplinary BEDLAN (Biological Evolution and Diversification of LANguages) research team in collaboration with experts of Uralic languages. The work has been financed by the University of Turku (UTU–BGG), Kone Foundation (UraLex, AikaSyyni), the Academy of Finland (URKO), UiT – The Arctic University of Norway and the University of Oulu, as well as the Finno-Ugrian Society. The data have been compiled for the purposes of doing spatial linguistic and multidisciplinary research, and to visually present the state-of-the-art knowledge of the Uralic languages and their dialects. Geographic distributions are visualized as vector data primarily by using polygon objects (speaker areas or language areas), and in some rare cases, by using points. Based on the language distributions, coordinates for the languages and their dialects (point locations) have also been defined.

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.

GIS Mapping Software Market Outlook

The global GIS Mapping Software market size was valued at approximately USD 8.5 billion in 2023 and is projected to reach around USD 17.5 billion by 2032, growing at a CAGR of 8.3% from 2024 to 2032. This robust growth is driven by the increasing adoption of geospatial technologies across various sectors, including urban planning, disaster management, and agriculture.

One of the primary growth factors for the GIS Mapping Software market is the rising need for spatial data analytics. Organizations are increasingly recognizing the value of geographical data in making informed decisions, driving the demand for sophisticated mapping solutions. Furthermore, advancements in satellite imaging technology and the increasing availability of high-resolution imagery are enhancing the capabilities of GIS software, making it a crucial tool for various applications.

Another significant driver is the integration of GIS with emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT). These integrations are facilitating real-time data processing and analysis, thereby improving the efficiency and accuracy of GIS applications. For instance, in urban planning and disaster management, real-time data can significantly enhance predictive modeling and response strategies. This synergy between GIS and cutting-edge technologies is expected to fuel market growth further.

The growing emphasis on sustainable development and smart city initiatives globally is also contributing to the market's expansion. Governments and private entities are investing heavily in GIS technologies to optimize resource management, enhance public services, and improve urban infrastructure. These investments are particularly evident in developing regions where urbanization rates are high, and there is a pressing need for efficient spatial planning and management.

In terms of regional outlook, North America holds a significant share of the GIS Mapping Software market, driven by robust technological infrastructure and high adoption rates across various industries. However, Asia Pacific is expected to witness the highest growth rate during the forecast period. This growth is attributed to rapid urbanization, increasing government initiatives for smart cities, and rising investments in infrastructure development.

The Geographic Information Systems Platform has become an integral part of modern spatial data management, offering a comprehensive framework for collecting, analyzing, and visualizing geographic data. This platform facilitates the integration of diverse data sources, enabling users to create detailed maps and spatial models that support decision-making across various sectors. With the increasing complexity of urban environments and the need for efficient resource management, the Geographic Information Systems Platform provides the tools necessary for real-time data processing and analysis. Its versatility and scalability make it an essential component for organizations looking to leverage geospatial data for strategic planning and operational efficiency.

Component Analysis

The GIS Mapping Software market is segmented by component into software and services. The software segment dominates the market, primarily due to the continuous advancements in GIS software capabilities. Modern GIS software offers a range of functionalities, from basic mapping to complex spatial analysis, making it indispensable for various sectors. These software solutions are increasingly user-friendly, allowing even non-experts to leverage geospatial data effectively.

Moreover, the software segment is witnessing significant innovation with the integration of AI and machine learning algorithms. These advancements are enabling more sophisticated data analysis and predictive modeling, which are crucial for applications such as disaster management and urban planning. The adoption of cloud-based GIS software is also on the rise, offering scalability and real-time data processing capabilities, which are essential for dynamic applications like transport management.

The services segment, although smaller than the software segment, is also experiencing growth. This includes consulting, implementation, and maintenance services that are critical for the successful deployment and operation of GIS systems. The increasing complexity of GIS applications nec

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.

Database created for replication of GeoStoryTelling. Our life stories evolve in specific and contextualized places. Although our homes may be our primarily shaping environment, our homes are themselves situated in neighborhoods that expose us to the immediate “real world” outside home. Indeed, the places where we are currently experiencing, and have experienced life, play a fundamental role in gaining a deeper and more nuanced understanding of our beliefs, fears, perceptions of the world, and even our prospects of social mobility. Despite the immediate impact of the places where we experience life in reaching a better understanding of our life stories, to date most qualitative and mixed methods researchers forego the analytic and elucidating power that geo-contextualizing our narratives bring to social and health research. From this view then, most research findings and conclusions may have been ignoring the spatial contexts that most likely have shaped the experiences of research participants. The main reason for the underuse of these geo-contextualized stories is the requirement of specialized training in geographical information systems and/or computer and statistical programming along with the absence of cost-free and user-friendly geo-visualization tools that may allow non-GIS experts to benefit from geo-contextualized outputs. To address this gap, we present GeoStoryTelling, an analytic framework and user-friendly, cost-free, multi-platform software that enables researchers to visualize their geo-contextualized data narratives. The use of this software (available in Mac and Windows operative systems) does not require users to learn GIS nor computer programming to obtain state-of-the-art, and visually appealing maps. In addition to providing a toy database to fully replicate the outputs presented, we detail the process that researchers need to follow to build their own databases without the need of specialized external software nor hardware. We show how the resulting HTML outputs are capable of integrating a variety of multi-media inputs (i.e., text, image, videos, sound recordings/music, and hyperlinks to other websites) to provide further context to the geo-located stories we are sharing (example https://cutt.ly/k7X9tfN). Accordingly, the goals of this paper are to describe the components of the methodology, the steps to construct the database, and to provide unrestricted access to the software tool, along with a toy dataset so that researchers may interact first-hand with GeoStoryTelling and fully replicate the outputs discussed herein. Since GeoStoryTelling relied on OpenStreetMap its applications may be used worldwide, thus strengthening its potential reach to the mixed methods and qualitative scientific communities, regardless of location around the world. Keywords: Geographical Information Systems; Interactive Visualizations; Data StoryTelling; Mixed Methods & Qualitative Research Methodologies; Spatial Data Science; Geo-Computation.

The Geographic Information System (GIS) Analytics market is experiencing robust growth, projected to reach $15.10 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.41% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing adoption of cloud-based GIS solutions enhances accessibility and scalability for diverse industries. The growing need for data-driven decision-making across sectors like retail, real estate, government, and telecommunications is a significant catalyst. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) integrated with GIS analytics are revolutionizing spatial data analysis, enabling more sophisticated predictive modeling and insightful interpretations. The market's segmentation reflects this broad adoption, with retail and real estate, government and utilities, and telecommunications representing key end-user segments, each leveraging GIS analytics for distinct applications such as location optimization, infrastructure management, and network planning. Competitive pressures are shaping the market landscape, with established players like Esri, Trimble, and Autodesk innovating alongside emerging tech companies focusing on AI and specialized solutions. The North American market currently holds a significant share, driven by early adoption and technological advancements. However, Asia-Pacific is expected to witness substantial growth due to rapid urbanization and increasing investment in infrastructure projects. Market restraints primarily involve the high cost of implementation and maintenance of advanced GIS analytics solutions and the need for skilled professionals to effectively utilize these technologies. However, the overall outlook remains extremely positive, driven by continuous technological innovation and escalating demand across multiple sectors. The future trajectory of the GIS analytics market hinges on several factors. Continued investment in research and development, especially in AI and ML integration, will be crucial for unlocking new possibilities. Furthermore, the simplification of GIS analytics software and the development of user-friendly interfaces will broaden accessibility beyond specialized technical experts. Growing data volumes from various sources (IoT, remote sensing) present both opportunities and challenges; efficient data management and analytics techniques will be paramount. The market's success also depends on addressing cybersecurity concerns related to sensitive geospatial data. Strong partnerships between technology providers and end-users will be vital in optimizing solution implementation and maximizing return on investment. Government initiatives promoting the use of GIS technology for smart city development and infrastructure planning will also play a significant role in market expansion. Overall, the GIS analytics market is poised for sustained growth, driven by technological advancements, increasing data availability, and heightened demand for location-based intelligence across a wide range of industries.

The Geospatial Analytics Market size was valued at USD 79.06 USD billion in 2023 and is projected to reach USD 202.74 USD billion by 2032, exhibiting a CAGR of 14.4 % during the forecast period. The growing adoption of location-based technologies and the increasing need for data-driven decision-making in various industries are key factors driving market growth. Geospatial analytics captures, produces and displays GIS (geographic information system)-maps and pictures that may be weather maps, GPS or satellite photos. The geospatial analysis as a tool works with state of art technology in every formats namely; the GPS, sensors that locates, social media, mobile devices, multi of the satellite imagery to produce data visualizations that are facilitating trend-finding in complex relations between people and places as well are the situations' understanding. Visualizations are depicted through the use of maps, graphs, figures, and cartograms that illustrate the entire historical picture as well as a current changing trend. This is why the forecast becomes more confident and the situation is anticipated better. Recent developments include: February 2024: Placer.ai and Esri, a Geographic Information System (GIS) technology provider, partnered to empower customers with enhanced analytics capabilities, integrating consumer behavior analysis. Additionally, the agreement will foster collaborations to unlock further features by synergizing our respective product offerings., December 2023: CKS and Esri India Technologies Pvt Ltd teamed up to introduce the 'MMGEIS' program, focusing on students from 8th grade to undergraduates, to position India as a global leader in geospatial technology through skill development and innovation., December 2023: In collaboration with Bayanat, the UAE Space Agency revealed the initiation of the operational phase of the Geospatial Analytics Platform during its participation in organizing the Space at COP28 initiatives., November 2023: USAID unveiled its inaugural Geospatial Strategy, designed to harness geospatial data and technology for more targeted international program delivery. The strategy foresees a future where geographic methods enhance the effectiveness of USAID's efforts by pinpointing development needs, monitoring program implementation, and evaluating outcomes based on location., May 2023: TomTom International BV, a geolocation technology specialist, expanded its partnership with Alteryx, Inc. Through this partnership, Alteryx will use TomTom’s Maps APIs and location data to integrate spatial data into Alteryx’s products and location insights packages, such as Alteryx Designer., May 2023: Oracle Corporation announced the launch of Oracle Spatial Studio 23.1, available in the Oracle Cloud Infrastructure (OCI) marketplace and for on-premises deployment. Users can browse, explore, and analyze geographic data stored in and managed by Oracle using a no-code mapping tool., May 2023: CAPE Analytics, a property intelligence company, announced an enhanced insurance offering by leveraging Google geospatial data. Google’s geospatial data can help CAPE create appropriate solutions for insurance carriers., February 2023: HERE Global B.V. announced a collaboration with Cognizant, an information technology, services, and consulting company, to offer digital customer experience using location data. In this partnership, Cognizant will utilize the HERE location platform’s real-time traffic data, weather, and road attribute data to develop spatial intelligent solutions for its customers., July 2022: Athenium Analytics, a climate risk analytics company, launched a comprehensive tornado data set on the Esri ArcGIS Marketplace. This offering, which included the last 25 years of tornado insights from Athenium Analytics, would extend its Bronze partner relationship with Esri. . Key drivers for this market are: Advancements in Technologies to Fuel Market Growth. Potential restraints include: Lack of Standardization Coupled with Shortage of Skilled Workforce to Limit Market Growth. Notable trends are: Rise of Web-based GIS Platforms Will Transform Market.

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.

This layer is a high-resolution land-cover dataset for Virginia Beach, Virginia. Nine land-cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads; (7) other paved surfaces; (8) forested wetlands; and (9) non-forested wetlands. The primary sources used to derive this land cover layer were 2012 LiDAR and 2013 3-band orthoimagery. Ancillary data sources included GIS data provided by Virginia Beach (e.g., county boundary, building footprints, water, parking lots, roads, utility poles, bridges, airfields) or developed by the UVM Spatial Analysis Laboratory (bare soils, utility lines, modified National Wetland Inventory wetlands polygons). This land cover dataset is considered current as of 2012. Object-based image analysis (OBIA) was used to extract land-cover information using the best available remotely-sensed and vector GIS datasets. 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 used to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to thorough manual quality control. More than 50,000 corrections were made to the classification

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Spatial Genomics And Transcriptomics Market Size 2025-2029

The spatial genomics and transcriptomics market size is forecast to increase by USD 732.3 million, at a CAGR of 12% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of spatial genomics in drug discovery. This innovative approach allows for a more precise understanding of the spatial organization of cells, enabling the identification of new targets and biomarkers for disease diagnosis and treatment. Furthermore, the use of spatial omics is gaining traction in biomarker identification, offering potential for personalized medicine and improved patient outcomes and in therapeutic areas like neurological disorders, infectious diseases, neuroscience, immunology, genomics, and proteomics. However, the market faces challenges, including the lack of workforce expertise in spatial genomics. As this field continues to evolve, there is a pressing need for skilled professionals to drive research and development efforts.
Companies seeking to capitalize on the opportunities in this market must invest in workforce development and collaborate with academic institutions and industry partners to build a strong foundation for future success. The ability to navigate these challenges and harness the power of spatial genomics will be crucial for companies looking to gain a competitive edge in the life sciences industry.

What will be the Size of the Spatial Genomics And Transcriptomics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in technologies and applications across various sectors. Cell signaling, confocal microscopy, RNA extraction, and sample preparation are integral components of this dynamic landscape. Ethical considerations are increasingly becoming a focus, as the use of high-throughput sequencing and data visualization tools uncovers new insights into genomic data. In situ sequencing and software solutions facilitate pathway analysis and data integration, enabling a more comprehensive understanding of biological processes. RNA extraction and sample preparation techniques play a crucial role in the market, ensuring accurate and reliable data. High-throughput sequencing technologies, such as next-generation sequencing (NGS), have revolutionized genome editing and disease modeling by providing vast amounts of genomic data.

Data repositories and machine learning algorithms facilitate data interpretation and gene regulatory network analysis. The continuous unfolding of market activities includes the development of spatial transcriptomics platforms, which offer three-dimensional genome organization insights. Microfluidic devices and protein-DNA interactions are also gaining attention, as they enable precise manipulation of biological samples. Quantitative PCR (qPCR) and chromatin conformation capture techniques complement these advancements, providing additional layers of information. The integration of various technologies, such as microarray technology, fluorescence microscopy, and data visualization tools, offers a more holistic approach to understanding complex biological systems. Spatial genomics and transcriptomics applications extend to drug discovery and gene expression analysis, providing valuable insights into cellular processes and biological pathways.

In conclusion, the market is characterized by continuous innovation and evolving patterns. The integration of various technologies, including cell signaling, confocal microscopy, RNA extraction, sample preparation, ethical considerations, high-throughput sequencing, data visualization, in situ sequencing, software solutions, pathway analysis, data integration, microfluidic devices, protein-DNA interactions, next-generation sequencing, gene regulatory networks, and more, offers a more comprehensive understanding of biological systems. This knowledge drives progress in personalized medicine, biomarker discovery, genome editing, disease modeling, and other sectors.

How is this Spatial Genomics And Transcriptomics Industry segmented?

The spatial genomics and transcriptomics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.ProductConsumablesInstrumentsEnd-userTranslational researchAcademic customersDiagnostic customersPharmaceutical manufacturerApplicationDrug Discovery & DevelopmentDisease Research (Oncology, Neuroscience)Biomarker IdentificationTechniqueSpatial Transcriptomics (e.g., Visium, MERFISH)Spatial GenomicsProteomics (Spatial Proteomics)GeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKMiddle East and Afr

The Geographic Information System (GIS) Services market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, we can infer substantial expansion based on the identified market drivers and trends. The burgeoning adoption of GIS technology in environmental management, infrastructure development, and precision agriculture is fueling market expansion. The integration of GIS with advanced analytics, such as AI and machine learning, is further enhancing its capabilities and broadening its applications. This leads to increased efficiency, improved decision-making, and cost optimization across various industries. The market's segmentation, encompassing diverse application areas like environmental agencies, utility companies, and telecommunications, highlights its widespread utility. Furthermore, the geographical distribution across North America, Europe, Asia Pacific, and other regions underscores a global market with significant growth potential in both developed and emerging economies. Given the rapid technological advancements and increasing data availability, the GIS services market is projected to maintain a strong growth trajectory in the coming years, surpassing previous estimates for market size. We estimate the market size in 2025 to be approximately $15 Billion, with a conservative CAGR of 8% projected through 2033. This growth will be fueled by continued technological advancements and increasing reliance on data-driven decision making in various sectors. The competitive landscape is marked by a mix of established players and emerging technology providers. Companies like Intellias, EnviroScience, and Infosys BPM are leading the charge, leveraging their expertise in GIS technology and data analytics. The presence of numerous regional players also reflects the market's geographically diverse growth. The market's future growth will likely hinge on factors such as the development of more sophisticated GIS software and analytics tools, the increased adoption of cloud-based GIS solutions, and the continuous integration of GIS with other technologies like IoT and blockchain. Addressing potential restraints, such as high initial investment costs for some organizations, will be crucial for sustained market growth.