Derived from publicly available sources, this dataset contains data on a variety of indicators from the years 2001 and 2011 for India at three levels of administrative geography.

Thematic areas include:

• Business
• Demographics
• Economic Activity
• Education
• Environment
• Finance
• Health
• Information Technology
• Infrastructure
• Jobs
• Living Standards
• Urban Extent

A .docx and .odt showing what the different acronyms mean for BFE, BFC, BGC, BSC, BUC, GRE, BGG.

Spatial Database Market Outlook

According to our latest research, the global spatial database market size reached USD 2.94 billion in 2024, driven by the exponential growth in geospatial data generation and the increasing adoption of location-based services across industries. The market is projected to grow at a robust CAGR of 12.1% from 2025 to 2033, reaching a forecasted value of USD 8.23 billion by 2033. This impressive growth trajectory is primarily fueled by advancements in spatial analytics, the proliferation of IoT devices, and the rising demand for real-time geographic information systems (GIS) in both public and private sectors.

One of the primary growth factors for the spatial database market is the surging demand for advanced geospatial analytics in urban planning and smart city initiatives. As cities across the globe embrace digital transformation, there is an increasing need for sophisticated spatial databases capable of handling complex, multi-dimensional datasets. These databases enable city planners and government agencies to analyze spatial relationships, optimize resource allocation, and improve decision-making processes. The integration of spatial databases with AI and machine learning algorithms further enhances their analytical capabilities, allowing for predictive modeling and real-time visualization of urban dynamics. This has accelerated the adoption of spatial database solutions in both developed and emerging economies, positioning the market for sustained growth over the next decade.

Another significant driver is the rapid expansion of IoT and connected devices, which generate vast volumes of location-based data requiring efficient management and analysis. Industries such as transportation, logistics, and utilities are leveraging spatial databases to track assets, optimize routes, and monitor infrastructure in real time. The ability to process and analyze geospatial data streams from sensors, vehicles, and mobile devices is critical for operational efficiency and risk mitigation. Moreover, the increasing use of spatial databases in environmental monitoring—such as tracking climate change, natural disasters, and resource management—underscores their importance in supporting sustainability initiatives. This trend is further amplified by the growing emphasis on data-driven decision-making across sectors, fueling the demand for scalable and high-performance spatial database solutions.

The adoption of cloud-based spatial database solutions is another pivotal factor contributing to market growth. Cloud deployment offers unparalleled scalability, flexibility, and cost-effectiveness, enabling organizations of all sizes to access and manage spatial data without significant upfront investments in infrastructure. The shift towards cloud-native architectures also facilitates seamless integration with other enterprise applications and data sources, enhancing interoperability and data sharing. This has led to a surge in demand for spatial database-as-a-service (DBaaS) offerings, particularly among small and medium enterprises (SMEs) and organizations with distributed operations. The ongoing advancements in cloud security and data privacy are further encouraging the migration of critical geospatial workloads to the cloud, accelerating the overall expansion of the spatial database market.

From a regional perspective, North America continues to dominate the spatial database market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to the presence of major technology players, a mature IT infrastructure, and significant investments in smart city and defense projects. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid urbanization, government-led digitalization initiatives, and the increasing adoption of advanced GIS technologies in countries such as China, India, and Japan. The region's robust economic growth and expanding industrial base are expected to create substantial opportunities for spatial database vendors, making it a key focus area for future market expansion.

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.

With the help of local and regional natural resource professionals, we developed a broad-scale, spatially-explicit assessment of 146 miles (~20,000 acres) of the Colorado River mainstem in Grand and San Juan Counties, Utah that can be used to support conservation planning and riparian restoration prioritization. For the assessment we: 1) acquired, modified or created spatial datasets of Colorado River bottomland conditions; 2) synthesized those datasets into habitat suitability models and estimates of natural recovery potential, fire risk and relative cost; 3) investigated and described dominant ecosystem trends and human uses, and; 4) suggested site selection and prioritization approaches. Here, we make available to the public spatial data associated with this work. The data include 51 shape files: 6 of these are related to fluvial geomorphology and hydrology; 1 contains riparian vegetation and surrounding land cover types; 30 are related to habitat or conservation element models (including model components and model results); and 14 are related to supplemental models including the relative cost of restoration, site recovery potential, and fire models. The data released here are associated with a publication that describes the project and results in more detail: Rasmussen, C.G., and P.B. Shafroth. 2016. Conservation planning for the Colorado River in Utah. Colorado Mesa University, Ruth Powell Hutchins Water Center, Scientific and Technical Report No. 3. 93p.

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.

Well construction data for 11,917 domestic and 2,390 public-supply wells in the Central Valley were compiled as part of the U.S. Geological Survey (USGS) National Water Quality Assessment Project (NAWQA) and California State Water Resources Control Board (SWRCB) Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP). Data were compiled for wells reported in the USGS National Water Information System (NWIS) database and from well information reported to the SWRCB Department of Drinking Water (SWRCB-DDW). Driller’s log data were transcribed from scanned images of well completion reports filed with California Department of Water Resources (DWR). The wells reported in this data release were filtered by water use to select domestic and public-supply wells and omit other water uses. The compilation was then assumed to be representative of the total population of domestic and public-supply wells in the Central Valley. The wells in the compilation were constructed between 1911 and 2008 but are not grouped or separated by date. The data were used to produce two point data sets containing well location and construction information (depth from land surface to the top and bottom of the well screen, hereafter well-screen tops and bottoms; and screen length), and 12 interpolated GIS raster surfaces created by using Empirical Bayesian Kriging on a 1600 by 1600 meter (1 square-mile) grid. The tables are also included in csv format. The 12 rasters comprise predicted values for well screen tops and bottoms and their 10th and 90th quantile values. The interpolated surfaces may also be used to calculate volumes of water-supply in the Central Valley defined by the well-screen tops and bottoms.

​This dataset contains best endeavours aggregation and depiction of In Force Water Sharing Plan (WSP) – Unregulated River Water Sources & Management Zones derived from WSP’s gazetted under the NSW Water Management Act 2000. PLEASE NOTE: In the case of any discrepancy between this digital dataset and the published Water Sharing Plan (accessible on the www.legislation.nsw.gov.au site) the instrument as made by the Minister remains the authoritative source and should be used to both interpret the intent of the Plan and in subsequent decision making. Best endeavours have been made in collating relevant Water Sharing Plan boundary and attribution contained in this dataset. However, no warranty is provided as to the accuracy or currency of this representation. The department does not warrant and is not liable for the use of this material as per the licenced sharing conditions CC-BY 4.0. Data and Resources

This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS).The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

The Agri-Environmental Spatial Data (AESD) product from the Census of Agriculture provides a large selection of farm-level variables from the Census of Agriculture and uses alternative data sources to improve the spatial distribution of the production activities. Therefore, the AESD database offers clients the possibility to better analyze the impact of agriculture activities on the environment and produce key indicators, or for any applications where accurate location of activities matters. Variables are offered using two types of physical boundaries: by Soil Landscape of Canada polygons and by Sub-sub-drainage areas (watersheds). The focus of the redistribution of the data is on the field crops and land use variables, but the database includes all census variables related to crops, livestock and management practices. This frame can also be used to extract Census of Agriculture data by custom geographic areas. Also, users interested in this version of the Census of Agriculture database using administrative types of regions can request it. In both cases, please contact Statistics Canada. This file was produced by Statistics Canada, Agriculture Division, Remote Sensing and Geospatial Analysis section, 2022, Ottawa.

This dataset contains all the code, notebooks, datasets used in the study conducted to measure the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the dataset measures spatial access for people to hospitals and ICU beds in Illinois. The spatial accessibility is measured by the use of an enhanced two-step floating catchment area (E2FCA) method (Luo & Qi, 2009), which is an outcome of interactions between demands (i.e, # of potential patients; people) and supply (i.e., # of beds or physicians). The result is a map of spatial accessibility to hospital beds. It identifies which regions need more healthcare resources, such as the number of ICU beds and ventilators. This notebook serves as a guideline of which areas need more beds in the fight against COVID-19. ## What's Inside A quick explanation of the components of the zip file * COVID-19Acc.ipynb is a notebook for calculating spatial accessibility and COVID-19Acc.html is an export of the notebook as HTML. * Data contains all of the data necessary for calculations: * Chicago_Network.graphml/Illinois_Network.graphml are GraphML files of the OSMNX street networks for Chicago and Illinois respectively. * GridFile/ has hexagonal gridfiles for Chicago and Illinois * HospitalData/ has shapefiles for the hospitals in Chicago and Illinois * IL_zip_covid19/COVIDZip.json has JSON file which contains COVID cases by zip code from IDPH * PopData/ contains population data for Chicago and Illinois by census tract and zip code. * Result/ is where we write out the results of the spatial accessibility measures * SVI/contains data about the Social Vulnerability Index (SVI) * img/ contains some images and HTML maps of the hospitals (the notebook generates the maps) * README.md is the document you're currently reading! * requirements.txt is a list of Python packages necessary to use the notebook (besides Jupyter/IPython). You can install the packages with python3 -m pip install -r requirements.txt

List of spatial dataset used in the study.

The ORNL DAAC Spatial Data Access Tool (SDAT) is a suite of Web-based applications that enable users to visualize and download spatial data in user-selected spatial/temporal extents, file formats, and projections. SDAT incorporates Open Geospatial Consortium (OGC) standard Web services, including Web Coverage Service (WCS), Web Map Service (WMS), and Web Feature Service (WFS). The SDAT provides ORNL DAAC-archived data sets and additional relevant data products including agriculture, atmosphere, biosphere, climate indicators, human dimensions, land surface, oceans, terrestrial hydrosphere data types, and related model output data sets.

Spatial genomics datasets used in the spaVAE study.ReamME.txt: detailed descriptions of datasetsHuman_DLPFC.zip: LIBD human dorsolateral prefrontal cortex (DLPFC) data151673_151674_151675_151676_samples_union.h5: combined four samples of human DLPFC data, which is used for batch integrating experiment.Mouse_hippocampus_Slide_seq_v2.h5: mouse hippocampus Slide-seq V2 dataMouse_olfactory_bulb_data.zip: mouse olfactory bulb dataHER2_breast_tumor_data.zip: HER2 breast tumor datamouse_brain_10X_dataset.zip: 10X dataset of anterior and posterior of mouse brainMISAR_seq_mouse_E15_brain_data.zip: MISAR-seq mouse E15.5 brain spatial mRNA-seq and spatial ATAC-seqSpatial_CITE_seq_human_tonsil.zip: human tonsil spatial-CITE-seq multiomics dataSpatial_DBiT_seq_mouse_embryo.zip: mouse embryonic brain DBiT-seq multiomics data

We are now providing you an enhanced version of instructions. You can use the script mv.py to update your local instructions if you have already downloaded the dataset.

  We also provide a fps_list.csv for you to get the frame index of each clip using the script get_index.py.

SpatialVID: A Large-Scale Video Dataset with Spatial Annotations

Jiahao Wang1* 
Yufeng Yuan1* 
Rujie Zheng1* 
Youtian Lin1 
Jian Gao1 
Lin-Zhuo Chen1 


Yajie Bao1… See the full description on the dataset page: https://huggingface.co/datasets/SpatialVID/SpatialVID.

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

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.

This is a demonstration layer implementing streamlined INSPIRE data according to the INSPIRE rules for Alternative Encoding. It is provided as a courtesy and should not be used for any purpose other than demonstration.ArcGIS INSPIRE Open Data is a lightweight solution for European public sector organizations implementing the INSPIRE and PSI-2/Open Data Directives. See the Getting to know ArcGIS INSPIRE Open Data story map to learn more.Geodatabase (GDB) templates are available on the ArcGIS INSPIRE Open Data demonstration Hub. INSPIRE Alternative Encoding documentation on GitHub is publicly available per the Implementing Rules on interoperability of spatial data sets and services (Commission Regulation (EU) No 1089/2010). These resources are provided as-is and are freely available.

Dataset includes various regional-scale spatial data layers in geojson format.

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.