This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

This project serves as a focal point of capability and expertise for integrating remote sensing, satellite telemetry and GIS. Working collaboratively with other principal investigators, this project applies satellite and software technologies to study spatial and temporal interactions between wildlife populations and their environment. There are three primary objectives: 1) develop optimal structures for wildlife distribution databases with emphasis on satellite tracking data; 2) develop environmental thematic databases with emphasis on Arctic regions; and 3) develop GIS algorithms for integrated data analyses. Commensurate with accelerating advances in remote sensing, satellite telemetry, and geographic information system (GIS) technology, the primary objective of this task is to evaluate and apply these state-of-the-art tools for developing or improving the methodologies used in wildlife and ecosystem research. The need for cost-effective techniques to systematically acquire environmental data for remote or inaccessible areas, and locational data for highly mobile or migratory species, crosses bureau, program and issue boundaries. This is especially true in arctic regions, where numerous fish and wildlife populations often range internationally, across extensive landscapes of tundra, boreal forest, polar sea-ice, and aquatic ecosystems. Remote sensing technologies provide alternatives to traditional sampling methods, which are typically too expensive to implement across large spatial scales or severely compromised by hazardous weather conditions and extended winter darkness. Publications: Douglas, D.C., 2010, Arctic sea ice decline: Projected changes in timing and extent of sea ice in the Bering and Chukchi Seas: U.S. Geological Survey Open-File Report 2010-1176, 32 p. Belchansky, G. I., D. C. Douglas, and N. G. Platonov (2005), Spatial and temporal variations in the age structure of Arctic sea ice, Geophys. Res. Lett.,32, L18504, doi:10.1029/2005GL023976 Belchanksy, G. I., D. C. Douglas, I. N. Mordvintsev, and N. G. Platonov (2004), Estimating the time of melt onset and freeze onset over Arctic sea-ice area using active and passive microwave data. Remote Sens. Environ., 92 , 21-39. Belchansky, G. I., D. C. Douglas, and N. G. Platonov (2004), Duration of the Arctic sea ice melt season: Regional and interannual variability, 1979-2001, J. Climate, 17 , 67-80. Belchansky, G. I., D. C. Douglas, I. V. Alpatsky, and N. G. Platonov (2004) , Spatial and temporal multiyear sea ice distributions in the Arctic : A neural network analysis of SSM/I data, 1988-2001, J. Geophys. Res. , 109 (C12), doi:10.1029/2004JC002388. Stone, R. S., D. C. Douglas, G. I. Belchansky, S. D. Drobot, and J. Harris (2005), Cause and effect of variations in western Arctic snow and sea ice cover. 8.3, Proc. Am. Meteorol. Soc. 8 th Conf. on Polar Oceanogr. and Meteorol. , San Diego , CA , 9-13 January. Belchansky, G. I., D. C. Douglas, V. A. Eremeev, and N. G. Platonov (2005), Variations in the Arctic's multiyear sea ice cover: A neural network analysis of SMMR-SSM/I data, 1979-2004. Geophys. Res. Lett. Vol. 32, No. 9, L09605, doi:10.1029/2005GL022395. Stone, R. S., D. C. Douglas, G. I. Belchansky, and S. D. Drobot (2005), Polar climate: Arctic sea ice, Pages 39-41 in D. H. Levinson (ed.), State of the Climate in 2004, Bull. Amer. Meterol. Soc., Vol. 86, No. 6, 86 pp. Stone, R. S., D. C. Douglas, G. I. Belchansky, and S. D. Drobot (2005), Correlated declines in western Arctic snow and sea ice cover. Arctic Res. United States, 19:18-25.

The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

Spatial management tools, such as marine spatial planning and marine protected areas, are playing an increasingly important role in attempts to improve marine management and accommodate conflicting needs. Robust data are needed to inform decisions among different planning options, and early inclusion of stakeholder involvement is widely regarded as vital for success. One of the biggest stakeholder groups, and the most likely to be adversely impacted by spatial restrictions, is the fishing community. In order to take their priorities into account, planners need to understand spatial variation in their perceived value of the sea. Here a readily accessible, novel method for quantitatively mapping fishers’ spatial access priorities is presented. Spatial access priority mapping, or SAPM, uses only basic functions of standard spreadsheet and GIS software. Unlike the use of remote-sensing data, SAPM actively engages fishers in participatory mapping, documenting rather than inferring their priorities. By so doing, SAPM also facilitates the gathering of other useful data, such as local ecological knowledge. The method was tested and validated in Northern Ireland, where over 100 fishers participated in a semi-structured questionnaire and mapping exercise. The response rate was excellent, 97%, demonstrating fishers’ willingness to be involved. The resultant maps are easily accessible and instantly informative, providing a very clear visual indication of which areas are most important for the fishers. The maps also provide quantitative data, which can be used to analyse the relative impact of different management options on the fishing industry and can be incorporated into planning software, such as MARXAN, to ensure that conservation goals can be met at minimum negative impact to the industry. This research shows how spatial access priority mapping can facilitate the early engagement of fishers and the ready incorporation of their priorities into the decision-making process in a transparent, quantitative way.

The Ecological Data Synthesis Tools is a spatially-explicit visualization tool that combines ecological resource layers into a single layer representing relative environmental sensitivity of dredging impacts to provide decision support. The tool incorporates multiple geospatial ecological data layers such as oyster reef habitat and submerged aquatic vegetation, and utilizes existing studies and data to scale the relative risk of each ecological resource to dredging and/or placement activities. The integrated impacts are then weighted across all layers providing an indication of the relative risk of negative project impacts on the environment. The tool was developed as a planning tool to assist Dredged Material Management Plans (DMMP) and preliminary Assessments (PA) project development teams to prioritize efforts and resources in areas of high environmental concern.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

Discover the booming Geographic Information System (GIS) Tools market! Our analysis reveals a $2979.7 million market in 2025, projected to grow at a 5.5% CAGR through 2033. Explore key drivers, trends, regional breakdowns, and leading companies shaping this dynamic industry.

The Geographic Information System (GIS) Tools market is booming, projected to reach $2890.3 million by 2025 with a 5.3% CAGR. Discover key trends, drivers, restraints, and leading companies shaping this dynamic sector. Explore regional market shares and growth forecasts for 2025-2033.

The global Geographic Information System (GIS) Tools market is experiencing robust growth, projected to reach $2979.7 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.5% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing adoption of cloud-based GIS solutions offers scalability, cost-effectiveness, and improved accessibility for businesses of all sizes, particularly SMEs seeking efficient resource management. Secondly, the rising demand for precise location-based data analysis across diverse sectors like urban planning, environmental monitoring, and precision agriculture fuels market growth. Furthermore, technological advancements, including the integration of AI and machine learning capabilities within GIS platforms, enhance analytical power and facilitate more sophisticated spatial decision-making. Finally, government initiatives promoting smart cities and digital transformation worldwide further stimulate market expansion. The market is segmented by application (SMEs, Large Enterprises) and type (Cloud-Based, On-Premises), reflecting the diverse needs of various user groups. Large enterprises, with their extensive spatial data requirements and resources, are expected to drive significant market share, while cloud-based solutions are poised for faster growth due to their flexible deployment models. The regional landscape reveals a dynamic distribution of market share. North America, particularly the United States, holds a prominent position, driven by high technological adoption rates and the presence of major GIS solution providers. Europe follows closely, fueled by increasing government investments in infrastructure development and digitalization initiatives. The Asia-Pacific region is expected to experience significant growth, propelled by rapid urbanization and the expanding adoption of GIS technologies in developing economies like China and India. While the on-premises segment currently dominates, the cloud-based segment is anticipated to exhibit higher growth in the forecast period, driven by its inherent advantages in scalability, accessibility, and cost-efficiency. Competitive dynamics are shaped by both established players like IBM TRIRIGA and emerging technology companies, leading to innovation and diversification of GIS tool offerings. The market's future hinges on continuous technological innovation, the growing adoption of location intelligence across sectors, and the expansion of robust infrastructure supporting data accessibility and management.

Mapping the spatial dynamics of perceived social value across the landscape can help develop a restoration economy that can support ecosystem services in the region. Many different methods have been used to map perceived social value. We used the Social Values for Ecosystem Services (SolVES) GIS tool, version 3.0, which uses social survey responses and various environmental variables to map social value. In the social survey distributed by the Borderlands Restoration Network (BRN) in May 2017, the respondents were asked to consider twelve different social values and map locations on a map where they perceived those social values to be. Additionally, they were asked to weigh each social value using a total of 100 points, and could assign each social value anywhere from 0 to 100 points. A combination of the points, weighted social values, and environmental variables were used within the SolVES tool. The SolVES tool then produced raster outputs that visualize the value index range for each social value assessed using the SolVES tool. This data release consists of two raster products. The first raster (SolVES multi-band raster) product consists of twelve bands, each band representing one of the twelve social values. The twelve total bands in this stacked raster are listed below, with the descriptions provided in the survey. The second raster product is a single band raster (SolVES summed raster) that shows the summed social value index for each pixel for the twelve social value rasters. Both raster products are clipped to the Sonoita Creek Watershed and represent the visual results of the SolVES tool. 1) aesthetic - ... I enjoy the aesthetics - scenery, sights, sounds, smells, etc. - within it, 2) biological diversity - ... it is home to such biological diversity, 3) cultural - ... it is a place of cultural value allowing me to pass down the knowledge, traditions, wisdom and way of life of myself and my ancestors, 4) economic - ... it is a place of economic value where I can earn a living, 5) future generations - ... I want future generations to be able to know, see and experience the watershed, 6) historical - ... it has historic value, with important places and things of natural and human history, 7) intrinsic - ... it has intrinsic value, irrespective of any instrumental value, 8) learning - ... because we can learn a great deal within it, 9) life sustaining - ... because it has life sustaining value through protecting and renewing clean air, soil, water etc., 10) recreational - ... because it provides a place for my favorite outdoor recreation activities, 11) spiritual - ... because it has spiritual value to me in the form of sacred, religious, or spiritual or because I feel reverence and respect for nature there, and 12) therapeutic - ... because it has therapeutic value, making me feel better physically and/or mentally. This data is used in the associated publication in the Air, Soil and Water Research. Petrakis, Roy E., Norman, Laura M., Lysaght, Oliver, Sherrouse, Benson C., Semmens, Darius, Bagstad, Kenneth J., Pritzlaff, Richard. 2020. “Mapping Perceived Social Values to Support a Respondent-Defined Restoration Economy: Case Study in Southeastern Arizona, USA” Air, Soil and Water Research. doi.org/10.1177/1178622120913318. The abstract for the associated publication follows: "Investment in conservation and ecological restoration depends on various socioeconomic factors and the social license for these activities. Our study demonstrates a method for targeting management of ecosystem services based on social values, identified by respondents through a collection of social survey data. We applied the Social Values for Ecosystem Services (SolVES) geographic information systems (GIS)- based tool in the Sonoita Creek watershed, Arizona, to map social values across the watershed. The survey focused on how respondents engage with the landscape, including through their ranking of 12 social values (eg, recreational, economic, or aesthetic value) and their placement of points on a map to identify their associations with the landscape. Additional information was elicited regarding how respondents engaged with water and various land uses, as well as their familiarity with restoration terminology. Results show how respondents perceive benefits from the natural environment. Specifically, maps of social values on the landscape show high social value along streamlines. Life-sustaining services, biological diversity, and aesthetics were the respondents’ highest rated social values. Land surrounding National Forest and private lands had lower values than conservation-based and state-owned areas, which we associate with landscape features. Results can inform watershed management by allowing managers to consider social values when prioritizing restoration or conservation investments."

This is the authors’ version of the work. It is based on a poster presented at the Wageningen Conference on Applied Soil Science, http://www.wageningensoilmeeting.wur.nl/UK/ Cite as: Bosco, C., de Rigo, D., Dewitte, O., Montanarella, L., 2011. Towards the reproducibility in soil erosion modeling: a new Pan-European soil erosion map. Wageningen Conference on Applied Soil Science “Soil Science in a Changing World”, 18 - 22 September 2011, Wageningen, The Netherlands. Author’s version DOI:10.6084/m9.figshare.936872 arXiv:1402.3847

Towards the reproducibility in soil erosion modeling:a new Pan-European soil erosion map

Claudio Bosco ¹, Daniele de Rigo ¹ ² , Olivier Dewitte ¹, Luca Montanarella ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy

Soil erosion by water is a widespread phenomenon throughout Europe and has the potentiality, with his on-site and off-site effects, to affect water quality, food security and floods. Despite the implementation of numerous and different models for estimating soil erosion by water in Europe, there is still a lack of harmonization of assessment methodologies. Often, different approaches result in soil erosion rates significantly different. Even when the same model is applied to the same region the results may differ. This can be due to the way the model is implemented (i.e. with the selection of different algorithms when available) and/or to the use of datasets having different resolution or accuracy. Scientific computation is emerging as one of the central topic of the scientific method, for overcoming these problems there is thus the necessity to develop reproducible computational method where codes and data are available. The present study illustrates this approach. Using only public available datasets, we applied the Revised Universal Soil loss Equation (RUSLE) to locate the most sensitive areas to soil erosion by water in Europe. A significant effort was made for selecting the better simplified equations to be used when a strict application of the RUSLE model is not possible. In particular for the computation of the Rainfall Erosivity factor (R) the reproducible research paradigm was applied. The calculation of the R factor was implemented using public datasets and the GNU R language. An easily reproducible validation procedure based on measured precipitation time series was applied using MATLAB language. Designing the computational modelling architecture with the aim to ease as much as possible the future reuse of the model in analysing climate change scenarios is also a challenging goal of the research.

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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

Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"

The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.

Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:

Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-222, in review, 2021.

https://github.com/jakobjassmann/ecodes-dk-lidar

Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example 7z on Windows, the archiving tool on macOS and bz2 on Linux).

A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 6 in the manuscript, can be found here.

Abstract (from manuscript)

Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~87 GB, compressed 16.4 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.

Acknowledgements (from manuscript)

We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set and Charles Davison for feedback regarding data use and handling. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).

Reinstating connectivity is seen as one way to ameliorate biodiversity loss resulting from agricultural activities. Natural resource management agencies require scientific knowledge to better inform revegetation programmes for increasing connectivity. Concepts of metapopulation theory and landscape ecology have been combined to produce spatially explicit outputs based on fragmentation-sensitive and poor-dispersing woodland species and which are designed to improve the occurrence and persistence of biodiversity. Selected outputs have been incorporated into the operations of a NRM revegetation programme. The results from the research provide alternative management options relevant to variegated and fragmented landscapes. Spatial data, spreadsheets, R scripts

The booming GIS Services market, projected to reach $27.8 billion by 2033 with an 8% CAGR, is transforming industries. Learn about key trends, applications (environmental, utilities, infrastructure), and leading companies shaping this dynamic sector. Explore regional market shares and growth forecasts for North America, Europe, and Asia Pacific.

Mobile GIS Data Collection Software Market Outlook

According to our latest research, the global mobile GIS data collection software market size reached USD 1.64 billion in 2024. The market is experiencing robust expansion, driven by the increasing demand for real-time geospatial data across industries. The market is projected to grow at a CAGR of 14.2% from 2025 to 2033, reaching a forecasted value of USD 4.46 billion by 2033. This growth is primarily fueled by the widespread adoption of mobile GIS solutions for field data collection, asset management, and environmental monitoring, as organizations seek efficient, accurate, and scalable geospatial data collection tools to enhance operational decision-making.

One of the primary growth factors propelling the mobile GIS data collection software market is the rapid digital transformation occurring across multiple sectors, such as utilities, government, agriculture, and transportation. Organizations are increasingly recognizing the value of real-time geospatial data in optimizing workflows, improving resource allocation, and ensuring regulatory compliance. The integration of mobile GIS solutions with Internet of Things (IoT) devices and advanced sensors enables seamless data capture, transmission, and analysis, empowering field teams to make informed decisions on the go. Furthermore, advancements in mobile hardware and connectivity, such as the proliferation of 5G networks, have significantly enhanced the usability and effectiveness of mobile GIS platforms, making them indispensable tools for field operations.

Another significant driver is the growing emphasis on environmental monitoring and sustainability initiatives worldwide. Governments and private organizations are leveraging mobile GIS data collection software to track environmental parameters, monitor land use changes, and support conservation efforts. The ability to collect, visualize, and analyze spatial data in real time is critical for managing natural resources, assessing environmental risks, and responding to emergencies such as natural disasters or hazardous material spills. As climate change concerns intensify and regulatory frameworks become more stringent, the demand for robust and scalable mobile GIS solutions is expected to rise, further boosting market growth.

The market is also benefiting from the increasing adoption of cloud-based mobile GIS solutions, which offer unparalleled scalability, flexibility, and cost-effectiveness. Cloud deployment enables organizations to centralize data storage, streamline collaboration, and ensure data integrity across geographically dispersed teams. The shift towards Software-as-a-Service (SaaS) models is reducing the upfront costs associated with traditional GIS deployments and making advanced geospatial analytics accessible to small and medium-sized enterprises (SMEs) as well as large corporations. This democratization of GIS technology is expanding the addressable market and fostering innovation in application development, user experience, and integration capabilities.

Regionally, North America remains the dominant market, accounting for the largest revenue share in 2024, driven by high technology adoption, a mature IT infrastructure, and the presence of leading GIS software providers. However, Asia Pacific is emerging as the fastest-growing region, supported by rapid urbanization, infrastructure development, and government initiatives promoting digital transformation. Europe also holds a significant market share, particularly in sectors such as utilities management and environmental monitoring. Meanwhile, Latin America and the Middle East & Africa are witnessing increasing investments in GIS technologies, reflecting the global trend toward smarter, data-driven decision-making across industries.

Component Analysis

The mobile GIS data collection software market is segmented by component into software and services, each playing a pivotal role in driving the adoption and effectiveness of GIS solutions. The software segment encompasses a wide array of applications designed for data capture, visualization, editing, and analysis on mobile devices. These software solutions are increasingly equipped with advanced features such as offline data collection, real-time synchronization, customizable workflows, and integration with third-party systems. The evolution of user-friendly interfaces and mobile-first design principles has further acceler

Discover the booming Geographic Information System (GIS) Services market! Explore its $15 Billion (2025 est.) size, 8% CAGR, key drivers, trends, and leading companies. Learn about regional market share and future growth projections in this in-depth analysis.

Advancing Research on Nutrition and Agriculture (AReNA) is a 6-year, multi-country project in South Asia and sub-Saharan Africa funded by the Bill and Melinda Gates Foundation, being implemented from 2015 through 2020. The objective of AReNA is to close important knowledge gaps on the links between nutrition and agriculture, with a particular focus on conducting policy-relevant research at scale and crowding in more research on this issue by creating data sets and analytical tools that can benefit the broader research community. Much of the research on agriculture and nutrition is hindered by a lack of data, and many of the datasets that do contain both agriculture and nutrition information are often small in size and geographic scope. AReNA team constructed a large multi-level, multi-country dataset combining nutrition and nutrition-relevant information at the individual and household level from the Demographic and Health Surveys (DHS) with a wide variety of geo-referenced data on agricultural production, agroecology, climate, demography, and infrastructure (GIS data). This dataset includes 60 countries, 184 DHS, and 122,473 clusters. Over one thousand geospatial variables are linked with DHS. The entire dataset is organized into 13 individual files: DHS_distance, DHS_livestock, DHS_main, DHS_malaria, DHS NDVI, DHS_nightlight, DHS_pasture and climate (mean), DHS_rainfall, DHS_soil, DHS_SPAM, DHS_suit, DHS_temperature, and DHS_traveltime.

de Rigo, D., Corti, P., Caudullo, G., McInerney, D., Di Leo, M., San Miguel-Ayanz, J., 2013. Toward open science at the European scale: Geospatial Semantic Array Programming for integrated environmental modelling. Geophysical Research Abstracts 15, 13245+. ISSN 1607-7962, European Geosciences Union (EGU).

This is the authors’ version of the work. The definitive version is published in the Vol. 15 of Geophysical Research Abstracts (ISSN 1607-7962) and presented at the European Geosciences Union (EGU) General Assembly 2013, Vienna, Austria, 07-12 April 2013http://www.egu2013.eu/

Toward open science at the European scale: Geospatial Semantic Array Programming for integrated environmental modelling

Daniele de Rigo ¹ ², Paolo Corti ¹ ³, Giovanni Caudullo ¹, Daniel McInerney ¹, Margherita Di Leo ¹, Jesús San-Miguel-Ayanz ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy ² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy ³ United Nations World Food Programme,Via C.G.Viola 68 Parco dei Medici, I-00148 Rome, Italy

Excerpt: Interfacing science and policy raises challenging issues when large spatial-scale (regional, continental, global) environmental problems need transdisciplinary integration within a context of modelling complexity and multiple sources of uncertainty. This is characteristic of science-based support for environmental policy at European scale, and key aspects have also long been investigated by European Commission transnational research. Approaches (either of computational science or of policy-making) suitable at a given domain-specific scale may not be appropriate for wide-scale transdisciplinary modelling for environment (WSTMe) and corresponding policy-making. In WSTMe, the characteristic heterogeneity of available spatial information and complexity of the required data-transformation modelling (D-TM) appeal for a paradigm shift in how computational science supports such peculiarly extensive integration processes. In particular, emerging wide-scale integration requirements of typical currently available domain-specific modelling strategies may include increased robustness and scalability along with enhanced transparency and reproducibility. This challenging shift toward open data and reproducible research (open science) is also strongly suggested by the potential - sometimes neglected - huge impact of cascading effects of errors within the impressively growing interconnection among domain-specific computational models and frameworks. Concise array-based mathematical formulation and implementation (with array programming tools) have proved helpful in supporting and mitigating the complexity of WSTMe when complemented with generalized modularization and terse array-oriented semantic constraints. This defines the paradigm of Semantic Array Programming (SemAP) where semantic transparency also implies free software use (although black-boxes - e.g. legacy code - might easily be semantically interfaced). A new approach for WSTMe has emerged by formalizing unorganized best practices and experience-driven informal patterns. The approach introduces a lightweight (non-intrusive) integration of SemAP and geospatial tools - called Geospatial Semantic Array Programming (GeoSemAP). GeoSemAP exploits the joint semantics provided by SemAP and geospatial tools to split a complex D-TM into logical blocks which are easier to check by means of mathematical array-based and geospatial constraints. Those constraints take the form of precondition, invariant and postcondition semantic checks. This way, even complex WSTMe may be described as the composition of simpler GeoSemAP blocks. GeoSemAP allows intermediate data and information layers to be more easily and formally semantically described so as to increase fault-tolerance, transparency and reproducibility of WSTMe. This might also help to better communicate part of the policy-relevant knowledge, often diffcult to transfer from technical WSTMe to the science-policy interface. [...]