We propose to design a working prototype Geospatial Decision Support Toolkit (GeoKit) that will enable scientists, agencies, and stakeholders to configure and deploy their own web based applications containing maps, forms, algorithms, and a rich set of functionality related to visualization, analysis, querying, and publication of geospatial data and information. GeoKit will focus on development of a suite of tools that will operate on data, to create rule-based applications for risk analysis, risk mitigation, operations management, and science research support. GeoKit will enhance the the use of data from NASA and other sources, provide a tool for non-software developers to create a website with custom functions and tools that operate on geospatial data, and provide a framework for development of new tools to support risk assessment, risk management, and operational analysis of spatially- explicit data from NASA platforms, climate reanalyses, and user-defined sources, as well as allow real-time publication of results in standard geobrowser compatible formats.

Geographic Information System (GIS) Tools Market Outlook

The global Geographic Information System (GIS) tools market size was valued at approximately USD 10.8 billion in 2023, and it is projected to reach USD 21.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2032. The increasing demand for spatial data analytics and the rising adoption of GIS tools across various industries are significant growth factors propelling the market forward.

One of the primary growth factors for the GIS tools market is the surging demand for spatial data analytics. Spatial data plays a critical role in numerous sectors, including urban planning, environmental monitoring, disaster management, and natural resource exploration. The ability to visualize and analyze spatial data provides organizations with valuable insights, enabling them to make informed decisions. Advances in technology, such as the integration of artificial intelligence (AI) and machine learning (ML) with GIS, are enhancing the capabilities of these tools, further driving market growth.

Moreover, the increasing adoption of GIS tools in the construction and agriculture sectors is fueling market expansion. In construction, GIS tools are used for site selection, route planning, and resource management, enhancing operational efficiency and reducing costs. Similarly, in agriculture, GIS tools aid in precision farming, crop monitoring, and soil analysis, leading to improved crop yields and sustainable farming practices. The ability of GIS tools to provide real-time data and analytics is particularly beneficial in these industries, contributing to their widespread adoption.

The growing importance of location-based services (LBS) in various applications is another key driver for the GIS tools market. LBS are extensively used in navigation, logistics, and transportation, providing real-time location information and route optimization. The proliferation of smartphones and the development of advanced GPS technologies have significantly increased the demand for LBS, thereby boosting the GIS tools market. Additionally, the integration of GIS with other technologies, such as the Internet of Things (IoT) and Big Data, is creating new opportunities for market growth.

Regionally, North America holds a significant share of the GIS tools market, driven by the high adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to increasing investments in infrastructure development, smart city projects, and the growing use of GIS tools in emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also expected to contribute to market growth, driven by various government initiatives and increasing awareness of the benefits of GIS tools.

Component Analysis

The GIS tools market can be segmented by component into software, hardware, and services. The software segment is anticipated to dominate the market due to the increasing demand for advanced GIS software solutions that offer enhanced data visualization, spatial analysis, and decision-making capabilities. GIS software encompasses a wide range of applications, including mapping, spatial data analysis, and geospatial data management, making it indispensable for various industries. The continuous development of user-friendly and feature-rich software solutions is expected to drive the growth of this segment.

Hardware components in the GIS tools market include devices such as GPS units, remote sensing devices, and plotting and digitizing tools. The hardware segment is also expected to witness substantial growth, driven by the increasing use of advanced hardware devices that provide accurate and real-time spatial data. The advancements in GPS technology and the development of sophisticated remote sensing devices are key factors contributing to the growth of the hardware segment. Additionally, the integration of hardware with IoT and AI technologies is enhancing the capabilities of GIS tools, further propelling market expansion.

The services segment includes consulting, integration, maintenance, and support services related to GIS tools. This segment is expected to grow significantly, driven by the increasing demand for specialized services that help organizations effectively implement and manage GIS solutions. Consulting services assist organizations in selecting the right GIS tools and optimizing their use, while integration services ensure seamless integr

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

Geospatial Solutions Market: Definition/ Overview

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

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

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033, reaching approximately $39 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of cloud-based GIS solutions offers enhanced accessibility, scalability, and cost-effectiveness, particularly appealing to smaller organizations. Secondly, the burgeoning need for precise spatial data analysis in various applications, including urban planning, geological exploration, and water resource management, significantly contributes to market growth. Thirdly, advancements in technologies such as AI and machine learning are integrating into GIS tools, leading to more sophisticated analytical capabilities and improved decision-making. Finally, the increasing availability of high-resolution satellite imagery and other geospatial data further fuels market expansion. However, market growth is not without challenges. High initial investment costs associated with implementing and maintaining sophisticated GIS systems can pose a barrier to entry for smaller businesses. Furthermore, the complexity of GIS software and the need for specialized skills to operate and interpret data effectively can limit widespread adoption. Despite these restraints, the market’s overall trajectory remains positive, with the cloud-based segment projected to maintain a dominant market share due to its inherent advantages. Growth will be geographically diverse, with North America and Europe continuing to be significant markets, while Asia-Pacific is expected to experience the fastest growth due to rapid urbanization and infrastructure development. The continued development of user-friendly interfaces and increased integration with other business intelligence tools will further accelerate market expansion in the coming years.

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of cloud-based GIS solutions offering enhanced accessibility and scalability, the escalating need for precise spatial data analysis in urban planning and resource management, and the expanding application of GIS in geological exploration for efficient resource discovery and extraction. Furthermore, advancements in location-based services (LBS) and the integration of GIS with other technologies such as IoT and AI are creating new opportunities and driving market expansion. While the market size in 2025 is estimated at $15 billion (a reasonable assumption considering similar market sizes for related technologies), the Compound Annual Growth Rate (CAGR) is projected to remain strong, likely exceeding 8% through 2033. This sustained growth indicates a highly promising market outlook for vendors and investors. However, market growth is not without challenges. High initial investment costs for sophisticated GIS software and the requirement for skilled personnel to operate and maintain these systems can pose barriers to entry, particularly for smaller organizations. Additionally, data security concerns and the need for robust data management strategies are critical factors impacting market adoption. Despite these constraints, the continued integration of GIS tools into various business processes and the growing availability of user-friendly, affordable solutions are expected to mitigate these challenges and propel the market towards sustained and significant growth in the coming years. Segmentation reveals a strong preference for cloud-based solutions due to their flexibility and cost-effectiveness, with the geological exploration and urban planning applications exhibiting the highest growth rates. Key players such as Esri, Autodesk, and Hexagon are strategically positioned to capitalize on these trends.

This data set archives all inputs, outputs and scripts needed to reproduce the findings of W.H. Farmer and G.F. Koltun in the 2017 Journal of Hydrology Regional Studies article entitled “Geospatial Tools Effectively Estimated Nonexceedance Probabilities of Daily Streamflow at Ungauged and Intermittently Gauged Locations in Ohio”. Input data includes observed streamflow values, in cubic feet per second, for 152 streamgages in and around Ohio from 01 January 2009 through 31 August 2015. Data from the Ohio Environmental Protection Agency on where and when water quality samples were taken are also provided. Geospatial locations are provided for all streamgages and sampling sites considered. ESRI ArcGIS shapefiles are available for all maps produced in the original publication. Comma-separated-value files contain the output data required to reproduce every figure in the report. This archive also includes an R script capable of reading the input files and producing output files and figures. See the README.txt file for a full description of model application.

Reliance on groundwater during drought cycles is a common cause of overdraft conditions, particularly in regions dominated by irrigated agriculture. Groundwater overdraft is evidenced by declining water table levels, widespread well failure, and land subsidence. Given the severity of these outcomes, natural resource managers are under increasing pressure to create economic and equitable sustainability plans in response to human water demands and climate change impacts. This work describes the development of a novel toolkit (software) designed to support multicriteria decisions centered around restoring groundwater sustainability in overdrafted regions. The toolkit was developed collaboratively with participants in California’s Multibenefit Land Repurposing Program (MLRP), which aims to repurpose irrigated agricultural land to reduce groundwater extraction while providing multiple benefits. The toolkit integrates existing spatial data layers using a Web-based, open-source package (Shiny R) to assess the suitability of land for repurposing. We used fuzzy logic to create six land repurposing suitability indices for (1) enhancing groundwater recharge, (2) minimizing negative impacts to the agricultural economy, (3) increasing renewable energy production, (4) increasing wildlife habitat restoration and conservation, (5) mitigating local flood risk, and (6) reducing environmental health risks in disadvantaged communities. These indices (or subsets) can be combined as weighted averages to create user-specified multibenefit scenarios. The resulting output can be inspected locally to screen prospective land parcels based on their repurposing potential, or holistically to prioritize specific areas in the context of regional land repurposing strategies. We illustrate the development, application, and possible uses of the toolkit in the context of two critically overdrafted groundwater subbasins, Tule and Kaweah, both located in California’s San Joaquin Valley. The methods described are transferable to other overdrafted regions assuming that adequate geospatial data is available. Given its Web-accessibility and user-controlled weighting scheme, the MLRP toolkit can facilitate regional coordination of resource agencies and stakeholders and help to maximize multiple benefits of land repurposing while achieving groundwater sustainability.

Geospatial Analytics Software Market Outlook

The global geospatial analytics software market size is projected to grow from USD 50.1 billion in 2023 to USD 114.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 9.5%. This remarkable growth is largely driven by the increasing adoption of geospatial technologies across various sectors, including urban planning, agriculture, transportation, and disaster management. The surge in the utilization of geospatial data for strategic decision-making, coupled with advancements in technology such as artificial intelligence (AI) and big data analytics, plays a pivotal role in propelling market growth.

One of the key growth factors of the geospatial analytics software market is the rapid digital transformation occurring globally. Governments and enterprises are increasingly recognizing the value of geospatial data in enhancing operational efficiency and strategic planning. The rise in smart city initiatives across the world has bolstered the demand for geospatial analytics, as cities leverage these technologies to optimize infrastructure, manage resources, and improve public services. Additionally, the integration of AI and machine learning with geospatial analytics has enhanced the accuracy and predictive capabilities of these systems, further driving their adoption.

Another significant driver is the growing need for disaster management and climate change adaptation. As the frequency and intensity of natural disasters increase due to climate change, there is a heightened demand for geospatial analytics to predict, monitor, and mitigate the impact of such events. Geospatial software aids in mapping hazard zones, planning evacuation routes, and assessing damage post-disaster. This capability is crucial for governments and organizations involved in disaster management and mitigation, thereby boosting the market growth.

The transportation and logistics sector is also a major contributor to the growth of the geospatial analytics software market. The advent of autonomous vehicles and the continuous evolution of logistics and supply chain management have heightened the need for precise geospatial data. Geospatial analytics enables real-time tracking, route optimization, and efficient fleet management, which are critical for the smooth operation of transportation systems. This trend is expected to continue, driving the demand for geospatial analytics solutions in transportation and logistics.

On a regional level, North America is anticipated to dominate the geospatial analytics software market, driven by technological advancements and substantial investments in geospatial technologies. The presence of major market players and the high adoption rate of advanced technologies in sectors such as defense, agriculture, and urban planning contribute to this dominance. However, the Asia Pacific region is expected to witness the highest growth rate, fueled by rapid urbanization, government initiatives for smart cities, and increasing investments in infrastructure development.

GIS Software plays a crucial role in the geospatial analytics software market, offering powerful tools for data visualization, spatial analysis, and geographic mapping. As organizations across various sectors increasingly rely on geospatial data for strategic decision-making, GIS Software provides the necessary infrastructure to manage, analyze, and interpret this data effectively. Its integration with other technologies such as AI and machine learning enhances its capabilities, enabling more accurate predictions and insights. This makes GIS Software an indispensable component for industries like urban planning, agriculture, and transportation, where spatial data is pivotal for optimizing operations and improving outcomes. The growing demand for GIS Software is a testament to its importance in driving the geospatial analytics market forward.

Component Analysis

The geospatial analytics software market is segmented into software and services when considering components. The software segment includes comprehensive solutions that integrate various geospatial data types and provide analytical tools for mapping, visualization, and data processing. This segment is expected to hold the largest market share due to the increasing adoption of these solutions in various industries for efficient data management and decision-making. The continuous advancements in software capabilities, such as the inclusion of AI and machine learning algorithms

SONG, Carol X., Rosen Center for Advanced Computing, Purdue University, 155 South Grant Street, Young Hall, West Lafayette, IN 47907

Science gateways are becoming an integral component of modern collaborative research. They find widespread adoption by research groups to share data, code and tools both within a project and with the broader community. Sustainability beyond initial funding is a significant challenge for a science gateway to continue to operate, update and support the communities it serves. MyGeoHub.org is a geospatial science gateway powered by HUBzero. MyGeoHub employs a business model of hosting multiple research projects on a single HUBzero instance to manage the gateway operations more efficiently and sustainably while lowering the cost to individual projects. This model allows projects to share the gateway’s common capabilities and the underlying hardware and other connected computing resources, and continued maintenance of their sites even after the original funding has run out allowing time for acquiring new funding. MyGeoHub has hosted a number of projects, ranging from hydrologic modeling and data sharing, plant phenotyping, global and local sustainable development, climate variability impact on crops, and most recently, modeling of industry processes to improve reuse and recycling of materials. The shared need to manage, visualize and process geospatial data across the projects has motivated the Geospatial Data Building Blocks (GABBs) development funded by NSF DIBBs. GABBs provides a “File Explorer” type user interface for managing geospatial data (no coding is needed), a builder for visualizing and exploring geo-referenced data without coding, a Python map library and other toolkits for building geospatial analysis and computational tools without requiring GIS programming expertise. GABBs can be added to an existing or new HUBzero site, as is the case on MyGeoHub. Teams use MyGeoHub to coordinate project activities, share files and information, publish tools and datasets (with DOI) to provide not only easy access but also improved reuse and reproducibility of data and code as the interactive online tools and workflows can be used without downloading or installing software. Tools on MyGeoHub have also been used in courses, training workshops and summer camps. MyGeoHub is supporting more than 8000 users annually.

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This growth is fueled by several key factors. Firstly, the burgeoning adoption of cloud-based solutions offers scalability, cost-effectiveness, and enhanced accessibility to a wider user base, including small and medium-sized enterprises (SMEs). Secondly, the escalating need for precise spatial data analysis in various applications, such as urban planning, geological exploration, and water resource management, is significantly boosting market demand. The increasing integration of GIS with other technologies like AI and IoT further amplifies its capabilities, leading to more sophisticated applications and increased market penetration. Finally, government initiatives promoting digitalization and smart city development across the globe are indirectly fueling this market expansion. However, certain restraints limit market growth. The high initial investment cost for advanced GIS software and the requirement for skilled professionals to operate these systems can be a barrier, especially for smaller organizations. Additionally, data security and privacy concerns related to the handling of sensitive geographical information pose challenges to wider adoption. Market segmentation reveals strong growth in the cloud-based GIS segment, driven by its inherent advantages, while applications in urban planning and geological exploration lead the application-based segmentation. North America and Europe currently hold significant market shares, with strong growth potential in the Asia-Pacific region due to increasing infrastructure development and government investments. Leading companies like Esri, Hexagon, and Autodesk are shaping the market landscape through continuous innovation and competitive pricing strategies, while the emergence of open-source options like QGIS and GRASS GIS provides alternative, cost-effective solutions.

The market for GIS Mapping Tools is projected to reach a value of $XX million by 2033, growing at a CAGR of XX% during the forecast period (2025-2033). The market growth is attributed to the increasing adoption of GIS mapping tools by various industries, including government, utilities, and telecom, for a wide range of applications such as geological exploration, water conservancy projects, and urban planning. The convergence of GIS with other technologies such as artificial intelligence (AI) and the Internet of Things (IoT) is further driving market growth, as these technologies enable GIS mapping tools to provide more accurate and real-time data analysis. The market is segmented by type (cloud-based, web-based), application (geological exploration, water conservancy projects, urban planning, others), and region (North America, Europe, Asia Pacific, Middle East & Africa). North America is expected to remain the largest market for GIS mapping tools throughout the forecast period, due to the early adoption of these technologies and the presence of leading vendors such as Esri, MapInfo, and Autodesk. Asia Pacific is expected to experience the highest growth rate during the forecast period, due to the increasing adoption of GIS mapping tools in emerging economies such as China and India. Key industry players include Golden Software Surfer, Geoway, QGIS, GRASS GIS, Google Earth Pro, CARTO, Maptive, Shenzhen Edraw Software, MapGIS, Oasis montaj, DIVA-GIS, Esri, MapInfo, Autodesk, BatchGeo, Cadcorp, Hexagon, Mapbox, Trimble, and ArcGIS.

Climate forecasts, both experimental and operational, are often made by calibrating Global Climate Model (GCM) outputs with observed climate variables using statistical and machine learning models. Often, machine learning techniques are applied to gridded data independently at each gridpoint. However, the implementation of these gridpoint-wise operations is a significant barrier to entry to climate data science. Unfortunately, there is a significant disconnect between the Python data science ecosystem and the gridded earth data ecosystem. Traditional Python data science tools are not designed to be used with gridded datasets, like those commonly used in climate forecasting. Heavy data preprocessing is needed: gridded data must be aggregated, reshaped, or reduced in dimensionality in order to fit the strict formatting requirements of Python's data science tools. Efficiently implementing this gridpoint-wise workflow is a time-consuming logistical burden which presents a high barrier to entry to earth data science. A set of high-performance, easy-to-use Python climate forecasting tools is needed to bridge the gap between Python's data science ecosystem and its gridded earth data ecosystem. XCast, an Xarray-based climate forecasting Python library developed by the authors, bridges this gap. XCast wraps underlying two-dimensional data science methods, like those of Scikit-Learn, with data structures that allow them to be applied to each gridpoint independently. XCast uses high-performance computing libraries to efficiently parallelize the gridpoint-wise application of data science utilities and make Python's traditional data science toolkits compatible with multidimensional gridded data. XCast also implements a diverse set of climate forecasting tools including traditional statistical methods, state-of-the-art machine learning approaches, preprocessing functionality (regridding, rescaling, smoothing), and postprocessing modules (cross validation, forecast verification, visualization). These tools are useful for producing and analyzing both experimental and operational climate forecasts. In this study, we describe the development of XCast, and present in-depth technical details on how XCast brings highly parallelized gridpoint-wise versions of traditional Python data science tools into Python's gridded earth data ecosystem. We also demonstrate a case study where XCast was used to generate experimental real-time deterministic and probabilistic forecasts for South Asian Summer Monsoon Rainfall in 2022 using different machine learning-based multi-model ensembles.

This record contains data that accompanies the Cloud-native geospatial data cube workflows with open-source tools tutorial. This data pertains to tutorial 2, which demonstrates working with Sentinel-1 RTC imagery processed by Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (HyP3). Users have the option to follow the tutorial on their own machine using the entire dataset (103 scenes, 47 GB) or a subset of the dataset (5 scenes, ~ 2.2 GB). Both are contained in this record.

The Crisis Mapping Toolkit (CMT) is a collection of tools for processing geospatial data (images, satellite data, etc.) into cartographic products that improve understanding of large-scale crises, such as natural disasters. The cartographic products produced by CMT include flood inundation maps, maps of damaged or destroyed structures, forest fire maps, population density estimates, etc. CMT is designed to rapidly process large-scale data using Google Earth Engine and other geospatial data systems.

Geospatial Tools for the Georgia Classroom. Maps, links, data and more!

This paper performs, describes, and evaluates a comparison of seven software tools (ArcGIS Pro, GRASS GIS, SAGA GIS, CitySim, Ladybug, SimStadt and UMEP) to calculate solar irradiation. The analysis focuses on data requirements, software usability, and accuracy simulation output. The use case for the comparison is solar irradiation on building surfaces, in particular on roofs. The research involves collecting and preparing spatial and weather data. Two test areas - the Santana district in S ̃ao Paulo, Brazil, and the Heino rural area in Raalte, the Netherlands - were selected. In both cases, the study area encompasses the vicinity of a weather station. Therefore, the meteorological data from these stations serve as ground truth for the validation of the simulation results. We create several models (raster and vector) to meet the diverse input requirements. We present our findings and discuss the output from the software tools from both quantitative and qualitative points of view. Vector-based simulation models offer better results than raster-based ones. However, they have more complex data requirements. Future research will focus on evaluating the quality of the simulation results on vertical and tilted surfaces as well as the calculation of direct and diffuse solar irradiation values for vector-based methods.

This is not a specific dataset, but rather the Good for People Toolkit is a spatial modelling framework developed to aid the bringing together and combining of data on a modelling platform producing key evidence for use by Natural Resources Wales (NRW).

This metadata entry can be supplied alongside data outputs although more specific metadata may be written if required for specific outputs.

Outputs from the model could be in the form of spreadsheets, spatial layers or maps, but all require some form of interpretation and knowledge for them to be understood by users.

About the Model: In 2014, NRW commissioned AECOM to develop a GIS based toolkit that would help to inform decisions around the use and targeting of NRW resources in order to best deliver priorities and outcomes that are "Good for People".

The Good for People GIS Toolkit is a spatial tool designed to help the Good for People programmes and the associated Enabling Plans: Outdoor Access and Recreation; Communities and Regeneration; and Education and Skills.

The toolkit addresses 18 research questions specified by NRW in relation to these plans by allowing data to be viewed in combination. These cover: Q01 - Deprivation; Q02 - Physical and Mental Ill-health; Q03 - Unemployment; Q04 - Education; Q05 - Community Facilities; Q06 - ESTYN (annual inspection report for individual education settings and for educational authorities); Q07 - NEET and Pre-NEET (Not in Education Employment or Training); Q08 - Access to Natural Green Space; Q09 - Urban Tree Canopy Cover; Q10 - Air Quality; Q11 - Flooding (criteria not mapped at time); Q12 - Access to NRW Land; Q13 - Recreation Provision; Q14 - Crime and Anti-Social Behaviour; Q15 - Tourism; Q16 - Active Travel; Q17 - Slope; Q18 - NRW and Partner Managed Land.

The toolkit can be used in a number of different ways. It has been designed to allow users to select the data layers that are most relevant to their purposes. Users can be confident that as an NRW-wide tool all NRW managers will be using the same evidence in a visual format so that plans for different areas can be compared fairly. The Good for People GIS Toolkit should be used where possible to support strategic and operational decision making within NRW in particular to strengthen decisions involving the needs of local communities and the benefits they derive from using and engaging with the natural environment.

This dataset was developed by the National Renewable Energy Laboratory (NREL) for the U.S. Agency for International Development's (USAID) South Asia Regional Initiative for Energy Cooperation (SARI/E). The dataset contains graphical files of seasonal and diurnal data from over 50 surface weather stations in .pdf format in Afghanistan. The data were output in Geographic Information Systems (GIS) format and incorporated into a Geospatial Toolkit (GsT). The GsT allows the user to examine the resource data in a geospatial context along with other key information relevant to renewable energy development, such as transportation networks, transmission corridors, existing power facilities, load centers, terrain conditions, and land use.

License Info

DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

This dataset was developed by the National Renewable Energy Laboratory (NREL) for the U.S. Agency for International Development's (USAID) South Asia Regional Initiative for Energy Cooperation (SARI/E). The dataset contains Wind Power Density at 50-m Above Ground Level in the form of a GIS shapefile. The data were output in Geographic Information Systems (GIS) format and incorporated into a Geospatial Toolkit (GsT) which is provided in data resources. The GsT allows the user to examine the resource data in a geospatial context along with other key information relevant to renewable energy development, such as transportation networks, transmission corridors, existing power facilities, load centers, terrain conditions, and land use.

Geospatial applications are systems and tools that use geographical and spatial data to collect, analyze, and interpret information that is tied to a location on Earth. This data can include anything from geographic coordinates and street addresses to satellite imagery and demographic information.