Recent baseball scholarship has drawn attention to U.S. professional baseball’s complex twentieth century labor dynamics and expanding global presence. From debates around desegregation to discussions about the sport’s increasingly multicultural identity and global presence, the cultural politics of U.S. professional baseball is connected to the problem of baseball labor. However, most scholars address these topics by focusing on Major League Baseball (MLB), ignoring other teams and leagues—Minor League Baseball (MiLB)—that develop players for Major League teams. Considering Minor League Baseball is critical to understanding the professional game in the United States, since players who populate Major League rosters constitute a fraction of U.S. professional baseball’s entire labor force. As a digital humanities dissertation on baseball labor and globalization, this project uses digital humanities approaches and tools to analyze and visualize a quantitative data set, exploring how Minor League Baseball relates to and complicates MLB-dominated narratives around globalization and diversity in U.S. professional baseball labor. This project addresses how MiLB demographics and global dimensions shifted over time, as well as how the timeline and movement of foreign-born players through the Minor Leagues differs from their U.S.-born counterparts. This project emphasizes the centrality and necessity of including MiLB data in studies of baseball’s labor and ideological significance or cultural meaning, making that argument by drawing on data analysis, visualization, and mapping to address how MiLB labor complicates or supplements existing understandings of the relationship between U.S. professional baseball’s global reach and “national pastime” claims.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

This paper provides an abstract analysis of parallel processing strategies for spatial and spatio-temporal data. It isolates aspects such as data locality and computational locality as well as redundancy and locally sequential access as central elements of parallel algorithm design for spatial data. Furthermore, the paper gives some examples from simple and advanced GIS and spatial data analysis highlighting both that big data systems have been around long before the current hype of big data and that they follow some design principles which are inevitable for spatial data including distributed data structures and messaging, which are, however, incompatible with the popular MapReduce paradigm. Throughout this discussion, the need for a replacement or extension of the MapReduce paradigm for spatial data is derived. This paradigm should be able to deal with the imperfect data locality inherent to spatial data hindering full independence of non-trivial computational tasks. We conclude that more research is needed and that spatial big data systems should pick up more concepts like graphs, shortest paths, raster data, events, and streams at the same time instead of solving exactly the set of spatially separable problems such as line simplifications or range queries in manydifferent ways.

This data was developed to represent city of cape coral citizen action center issues and their associated attributes for the purpose of mapping, analysis, and planning. The accuracy of this data varies and should not be used for precise measurements or calculations.

Within the U.S. Geological Survey (USGS), three-dimensional (3D) geologic models are created as part of geologic framework studies, to support energy, minerals, or water resource assessments, and to inform geologic hazard assessments. Such models are often used within the organization as digital input into process and predictive models. 3D geological modeling typically supports research and project work within a specific part of the USGS – called Mission Areas – and as a result, 3D modeling activities are decentralized and model results are released on a project-by-project basis. This digital data release inventories and catalogs, for the first time, 3D geological models constructed by the USGS across all Mission Areas. This inventory assembles in catalog form the spatial locations and salient characteristics of previously published USGS 3D geological models. This inventory covers the time period from 2004, the date of the earliest published model through 2022. This digital dataset contains spatial extents of the 3D geologic models as polygon features that are attributed with unique identifiers that link the spatial data to nonspatial tables that define the data sources used and describe various aspects of each published model. The nonspatial DataSources table includes full citation and URL address for both published model reports and any digital model data released as a separate publication. The nonspatial ModelAttributes table classifies the type of model, using several classification schemes, identifies the model purpose and originating agency, and describes the spatial extent, depth, and number of layers included in each model. A tabular glossary defines terms used in the dataset. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Excel spreadsheet which only contains numeric data from a set of confusion matrices (one sheet per matrix).It is the same quantitative data stored in a field of a table in the database. Only is provided as a complement to the database in order to access to the quantitative data in a more convenient format.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Under the direction and funding of the National Cooperative Mapping Program with guidance and encouragement from the United States Geological Survey (USGS), a digital database of three-dimensional (3D) vector data, displayed as two-dimensional (2D) data-extent bounding polygons. This geodatabase is to act as a virtual and digital inventory of 3D structure contour and isopach vector data for the USGS National Geologic Synthesis (NGS) team. This data will be available visually through a USGS web application and can be queried using complimentary nonspatial tables associated with each data harboring polygon. This initial publication contains 60 datasets collected directly from USGS specific publications and federal repositories. Further publications of dataset collections in versioned releases will be annotated in additional appendices, respectfully. These datasets can be identified from their specific version through their nonspatial tables. This digital dataset contains spatial extents of the 2D geologic vector data as polygon features that are attributed with unique identifiers that link the spatial data to nonspatial tables that define the data sources used and describe various aspects of each published model. The nonspatial DataSources table includes full citation and URL address for both published model reports, any digital model data released as a separate publication, and input type of vector data, using several classification schemes. A tabular glossary defines terms used in the dataset. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.

Our model is a full-annual-cycle population model {hostetler2015full} that tracks groups of bat surviving through four seasons: breeding season/summer, fall migration, non-breeding/winter, and spring migration. Our state variables are groups of bats that use a specific maternity colony/breeding site and hibernaculum/non-breeding site. Bats are also accounted for by life stages (juveniles/first-year breeders versus adults) and seasonal habitats (breeding versus non-breeding) during each year, This leads to four states variable (here depicted in vector notation): the population of juveniles during the non-breeding season, the population of adults during the non-breeding season, the population of juveniles during the breeding season, and the population of adults during the breeding season, Each vector's elements depict a specific migratory pathway, e.g., is comprised of elements, {non-breeding sites}, {breeding sites}The variables may be summed by either breeding site or non-breeding site to calculate the total population using a specific geographic location. Within our code, we account for this using an index column for breeding sites and an index column for non-breeding sides within the data table. Our choice of state variables caused the time step (i.e. (t)) to be 1 year. However, we recorded the population of each group during the breeding and non-breeding season as an artifact of our state-variable choice. We choose these state variables partially for their biological information and partially to simplify programming. We ran our simulation for 30 years because the USFWS currently issues Indiana Bat take permits for 30 years. Our model covers the range of the Indiana Bat, which is approximately the eastern half of the contiguous United States (Figure \ref{fig:BatInput}). The boundaries of our range was based upon the United States boundary, the NatureServe Range map, and observations of the species. The maximum migration distance was 500-km, which was based upon field observations reported in the literature \citep{gardner2002seasonal, winhold2006aspects}. The landscape was covered with approximately 33,000, 6475-ha grid cells and the grid size was based upon management considerations. The U.S.~Fish and Wildlife Service considers a 2.5 mile radius around a known maternity colony to be its summer habitat range and all of the hibernaculum within a 2.5 miles radius to be a single management unit. Hence the choice of 5-by-5 square grids (25 miles(^2) or 6475 ha). Each group of bats within the model has a summer and winter grid cell as well as a pathway connecting the cells. It is possible for a group to be in the cell for both seasons, but improbable for females (which we modeled). The straight line between summer and winter cells were buffered with different distances (1-km, 2-km, 10-km, 20-km, 100-km, and 200-km) as part of the turbine sensitivity and uncertainty analysis. We dropped the largest two buffer sizes during the model development processes because they were biologically unrealistic and including them caused all populations to go extinct all of the time. Note a 1-km buffer would be a 2-km wide path. An example of two pathways are included in Figure \ref{fig:BatPath}. The buffers accounts for bats not migrating in a straight line. If we had precise locations for all summer maternity colonies, other approaches such as Circuitscape \citep{hanks2013circuit} could have been used to model migration routes and this would have reduced migration uncertainty.

Geospatial ETL Platform Market Outlook

According to our latest research, the global Geospatial ETL Platform market size reached USD 1.68 billion in 2024, demonstrating robust momentum driven by the increasing demand for spatial data integration and advanced analytics across industries. The market is set to expand at a CAGR of 13.7% from 2025 to 2033, with the forecasted market size projected to reach USD 5.23 billion by 2033. This growth trajectory is primarily attributed to the proliferation of location-based services, advancements in geospatial data infrastructure, and the rising importance of real-time decision-making in sectors such as government, utilities, and transportation.

One of the most significant growth factors fueling the Geospatial ETL Platform market is the exponential rise in the volume and variety of geospatial data generated from multiple sources, including satellites, IoT devices, drones, and mobile applications. Organizations are increasingly seeking sophisticated tools to extract, transform, and load (ETL) this data efficiently to derive actionable insights. The need for seamless integration of spatial and non-spatial data has become critical for enterprises aiming to enhance operational efficiency, optimize resource allocation, and improve situational awareness. As businesses realize the value of spatial analytics, investments in geospatial ETL solutions are accelerating, especially for applications such as urban planning, disaster management, and infrastructure monitoring.

Another key driver is the rapid adoption of cloud-based geospatial ETL platforms, which offer scalability, flexibility, and cost-effectiveness compared to traditional on-premises solutions. Cloud deployment enables organizations to process large datasets in real time, collaborate across geographies, and leverage advanced analytics powered by artificial intelligence and machine learning. This shift to the cloud not only reduces infrastructure costs but also empowers organizations to respond quickly to changing business needs. Furthermore, the integration of geospatial ETL platforms with emerging technologies such as 5G, edge computing, and real-time data streaming is unlocking new opportunities for innovation in sectors like smart cities, autonomous vehicles, and precision agriculture.

The increasing focus on regulatory compliance and data governance is also propelling the adoption of geospatial ETL platforms. Governments and regulatory bodies are mandating stringent data management practices, especially for critical infrastructure and public safety applications. Geospatial ETL solutions play a pivotal role in ensuring data quality, lineage, and security, thereby supporting organizations in meeting compliance requirements. Additionally, the growing awareness of the strategic value of location intelligence is encouraging enterprises to invest in advanced ETL solutions that can handle complex spatial data transformations and deliver high-quality, actionable insights for decision-making.

From a regional perspective, North America continues to dominate the Geospatial ETL Platform market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The presence of leading technology providers, strong government initiatives for smart infrastructure, and the high adoption rate of digital transformation strategies are contributing to the region's leadership. Asia Pacific, on the other hand, is witnessing the fastest growth, driven by rapid urbanization, expanding digital infrastructure, and increasing investments in geospatial technologies by governments and private enterprises. Latin America and the Middle East & Africa are also emerging as promising markets, supported by initiatives to modernize infrastructure and enhance public services through spatial data integration.

Component Analysis

The Geospatial ETL Platform market by component is segmented into software and services, each playing a distinct yet complementary role in enabling organizations to harness the power of spatial data. The software segment encompasses a wide array of ETL solutions designed to automate the extraction, transformation, and loading of geospatial data from diverse sources into target systems. These solutions are equipped with advanced features such as data cleansing, schema mapping, spatial data enrichment, and workflow automation, making them indispensable for enterprises seeking to streamline data integration pro

Retail Geospatial Analytics Platform Market Outlook

According to our latest research, the global market size for the Retail Geospatial Analytics Platform Market reached USD 6.2 billion in 2024. Driven by the increasing adoption of location-based intelligence and advanced spatial data analytics in retail, the market is expected to grow at a robust CAGR of 14.2% from 2025 to 2033. By 2033, the market is forecasted to reach USD 18.5 billion. This growth is primarily fueled by the rising demand for real-time insights, the proliferation of IoT and smart devices, and the need for retailers to optimize operations and enhance customer experiences through actionable geospatial data.

The growth of the Retail Geospatial Analytics Platform Market is underpinned by the increasing emphasis on data-driven decision-making across the retail sector. Retailers are leveraging geospatial analytics to gain deeper insights into customer behavior, identify optimal store locations, and streamline supply chain operations. The integration of artificial intelligence and machine learning with geospatial analytics platforms enables more accurate predictions and personalized marketing strategies. As a result, businesses are able to enhance operational efficiency, reduce costs, and improve customer engagement, contributing significantly to the expansion of the market.

Another critical growth factor is the rapid digital transformation occurring within the retail industry. The shift towards omnichannel retailing, combined with the surge in e-commerce activities, necessitates advanced analytics platforms capable of handling vast amounts of spatial and non-spatial data. Retailers are increasingly investing in geospatial analytics to monitor foot traffic, analyze market trends, and assess competitive landscapes. The ability to visualize and interpret complex geographic data in real-time is empowering retailers to make informed, location-specific decisions, thereby driving the adoption of retail geospatial analytics platforms globally.

Furthermore, the proliferation of connected devices and the advent of smart cities are creating new opportunities for the Retail Geospatial Analytics Platform Market. The integration of geospatial data with IoT sensors, mobile applications, and cloud-based services allows retailers to capture granular insights into consumer movements and preferences. This, in turn, facilitates targeted marketing campaigns, efficient inventory management, and optimized supply chain networks. As urbanization accelerates and consumer expectations evolve, the role of geospatial analytics in shaping the future of retail is becoming increasingly prominent, ensuring sustained market growth over the forecast period.

The Geospatial Data Catalog Platform plays a crucial role in the retail sector by providing a centralized repository for managing and accessing diverse geospatial datasets. This platform enables retailers to efficiently organize and retrieve spatial data, facilitating enhanced decision-making processes. By leveraging a geospatial data catalog, businesses can streamline data integration from various sources, ensuring that the most accurate and up-to-date information is available for analysis. This capability is particularly valuable in the context of retail geospatial analytics, where timely insights can drive competitive advantage. As retailers continue to adopt advanced analytics solutions, the importance of a robust geospatial data catalog platform becomes increasingly evident, supporting the seamless integration of spatial data into business operations.

From a regional perspective, North America continues to dominate the Retail Geospatial Analytics Platform Market due to the presence of major technology providers, high adoption rates of advanced analytics, and a mature retail ecosystem. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid urbanization, expanding retail infrastructure, and increasing investments in digital technologies. Europe also holds a significant share, supported by stringent regulations around data privacy and growing awareness of the benefits of geospatial analytics. Latin America and the Middle East & Africa are witnessing gradual adoption, fueled by digital transformation initiatives and the expansion of organized retail.

Spatial data for Bishket urban pollution project data on groundwater pollution

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

A cooperative effort of the Governor’s Office of Administration and Pennsylvania State University Pennsylvania Spatial Data Access (PASDA) is Pennsylvania’s official public access open geospatial data portal. PASDA was developed in 1997 and has severed as the Commonwealth’s geospatial data portal for over 25 years; it is Pennsylvania’s node on the National Spatial Data Infrastructure and is integrated with the National State Geographic Information Council GIS Inventory. Data on PASDA is free to all users and is provided by federal, state local and regional governments, non-profit organizations and academic institutions.

The Referrals Spatial Database - Public records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those who are proposing a development) must supply the maximum extent (location) of any proposed activities that need to be assessed under the EPBC Act through an application process.Referral boundaries should not be misinterpreted as development footprints but where referrals have been received by the Department. It should be noted that not all referrals captured within the Referrals Spatial Database, are assessed and approved by the Minister for the Environment, as some are withdrawn before assessment can take place. For more detailed information on a referral a URL is provided to the EPBC Act Public notices pages. Status and detailed planning documentation is available on the EPBC Act Public notices (http://epbcnotices.environment.gov.au/referralslist/).Post September 2019, this dataset is updated using a spatial data capture tool embedded within the Referral form on the department’s website. Users are able to supply spatial data in multiple formats, review spatial data online and submitted with the completed referral form automatically. Nightly processes update this dataset that are then available for internal staff to use (usually within 24 hours). Prior to September 2019, a manual process was employed to update this dataset. In the first instance where a proponent provides GIS data, this is loaded as the polygons for a referral. Where this doesn't exist other means to digitize boundaries are employed to provide a relatively accurate reflection of the maximum extent for which the referral may impact (it is not a development footprint). This sometimes takes the form of heads up digitizing planning documents, sourcing from other state databases (such as PSMA Australia) features and coordinates supplied through the application forms.Any variations to boundaries after the initial referral (i.e. during the assessment, approval or post-approval stages) are processed on an ad hoc basis through a manual update to the dataset. The REFERRALS_PUBLIC_MV layer is a materialized view that joins the spatial polygon data with the business data (e.g. name, case id, type etc.) about a referral. This layer is available for use by the public and is available via a web service and spatial data download. The data for the web service is updated weekly, while the data download is updated quarterly.

Introduction and Rationale:Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update SPAN. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in the final version of SPAN.Contents:Spatial dataNational rasters of land cover in the conterminous United States: 2012-2021Rasters of pixels mismatched between CDL and NVC: 2012-2021Resources in this dataset:Resource Title: SPAN land cover in the conterminous United States: 2012-2021 - SCINet File Name: KammererNationalRasters.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). Spatial Products for Agriculture and Nature ('SPAN') land cover in the conterminous United States from 2012-2021. This raster dataset is available in GeoTIFF format and was created by joining agricultural classes from the USDA-NASS Cropland Data Layer (CDL) to national vegetation from the LANDFIRE National Vegetation Classification v2.0 ('Remap'). Pixels of national vegetation are the same in all rasters provided here and represent land cover in 2016. Agricultural pixels were taken from the CDL in the specified year, so depict agricultural land from 2012-2021. Resource Title: Rasters of pixels mismatched between CDL and NVC: 2012-2021 - SCINet File Name: MismatchedNational.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). This dataset includes pixels that were classified as agriculture in the NVC but, in the CDL, were not agriculture (or were a conflicting agricultural class). For more details, we refer users to the linked publication describing our geospatial processing and validation workflow.SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node455886/ See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

Cloud GIS Market size was valued at USD 890.81 Million in 2024 and is projected to reach USD 2298.38 Million by 2032, growing at a CAGR of 14.5% from 2026 to 2032.

Key Market Drivers

• Increased Adoption of Cloud Computing: Cloud computing provides scalable resources that can be adjusted based on demand, making it easier for organizations to manage and process large GIS datasets. The pay-as-you-go pricing models of cloud services reduce the need for significant upfront investments in hardware and software, making GIS more accessible to small and medium-sized enterprises.

• Growing Need for Spatial Data Integration: The ability to integrate and analyze large volumes of spatial and non-spatial data helps organizations make more informed decisions. The proliferation of Internet of Things (IoT) devices generates massive amounts of spatial data that can be processed and analyzed using Cloud GIS.

A relational database is a common way way of storing and organising data.In a relational database is typically structured across multiple tables which can be joined together by a common value. Data is stored in several smaller tables rather than one large table, this makes data storage more efficient as not every piece of information needs to be recorded against each record.

GIS Data Management Market Outlook

The global GIS Data Management market size is projected to grow from USD 12.5 billion in 2023 to USD 25.6 billion by 2032, exhibiting a CAGR of 8.4% during the forecast period. This impressive growth is driven by the increasing adoption of geographic information systems (GIS) across various sectors such as urban planning, disaster management, and agriculture. The rising need for effective data management systems to handle the vast amounts of spatial data generated daily also significantly contributes to the market's expansion.

One of the primary growth factors for the GIS Data Management market is the burgeoning demand for spatial data analytics. Businesses and governments are increasingly leveraging GIS data to make informed decisions and strategize operational efficiencies. With the rapid urbanization and industrialization worldwide, there's an unprecedented need to manage and analyze geographic data to plan infrastructure, monitor environmental changes, and optimize resource allocation. Consequently, the integration of GIS with advanced technologies like artificial intelligence and machine learning is becoming more prominent, further fueling market growth.

Another significant factor propelling the market is the advancement in GIS technology itself. The development of sophisticated software and hardware solutions for GIS data management is making it easier for organizations to capture, store, analyze, and visualize geographic data. Innovations such as 3D GIS, real-time data processing, and cloud-based GIS solutions are transforming the landscape of geographic data management. These advancements are not only enhancing the capabilities of GIS systems but also making them more accessible to a broader range of users, from small enterprises to large governmental agencies.

The growing implementation of GIS in disaster management and emergency response activities is also a critical factor driving market growth. GIS systems play a crucial role in disaster preparedness, response, and recovery by providing accurate and timely geographic data. This data helps in assessing risks, coordinating response activities, and planning resource deployment. With the increasing frequency and intensity of natural disasters, the reliance on GIS data management systems is expected to grow, resulting in higher demand for GIS solutions across the globe.

Geospatial Solutions are becoming increasingly integral to the GIS Data Management landscape, offering enhanced capabilities for spatial data analysis and visualization. These solutions provide a comprehensive framework for integrating various data sources, enabling users to gain deeper insights into geographic patterns and trends. As organizations strive to optimize their operations and decision-making processes, the demand for robust geospatial solutions is on the rise. These solutions not only facilitate the efficient management of spatial data but also support advanced analytics and real-time data processing. By leveraging geospatial solutions, businesses and governments can improve their strategic planning, resource allocation, and environmental monitoring efforts, thereby driving the overall growth of the GIS Data Management market.

Regionally, North America holds a significant share of the GIS Data Management market, driven by high technology adoption rates and substantial investments in GIS technologies by government and private sectors. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period. The rapid urbanization, economic development, and increasing adoption of advanced technologies in countries like China and India are major contributors to this growth. Governments in this region are also focusing on smart city projects and infrastructure development, which further boosts the demand for GIS data management solutions.

Component Analysis

The GIS Data Management market is segmented by component into software, hardware, and services. The software segment is the largest and fastest-growing segment, driven by the continuous advancements in GIS software capabilities. GIS software applications enable users to analyze spatial data, create maps, and manage geographic information efficiently. The integration of GIS software with other enterprise systems and the development of user-friendly interfaces are key factors propelling the growth of this segment. Furthermore, the rise of mobile GIS applications, which allow field data collectio