This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

Learn how to visualize an E.coli outbreak by importing a spreadsheet of data. In this Learn GIS PDF lesson you will:Build a spreadsheet in the CSV format and import it into a map. Mark a location using a Map Note. Use a proximity tool to generate lines illustrating data and origin points linesThe lesson takes approximately 30 minutes to complete._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The mapping component of the BRCA project used a combination of methods to interpret and delineate vegetation and land use polygons. The USGS applied an electronic segmentation method (e-Cognition software) to create preliminary linework on features with high-contrast photo-signatures. Using the preliminary linework as a baseline starting point, the primary photointerpreter drew polygons directly on screen through heads-up digitizing using ArcGIS editing tools. Additionally, trained photointerpreters assisting the primary photointerpreter drew polygons on Mylar overlays covering 1m resolution, 1:12,000-scale, 9 x 9-inch true-color aerial photographs. This process enabled the photointerpreters to view the landscape in stereo in order to identify finer details. The linework drawn on Mylar overlays was then transferred into digital media by heads-up digitizing using ArcGIS software. The park and environs were interpreted and mapped to the same level of detail.

A Certificate of Convenience and Necessity (CCN) is issued by the Public Utility Commission of Texas (PUCT), and authorizes a utility to provide water and/or sewer service to a specific service area. The CCN obligates the water or sewer retail public utility to provide continuous and adequate service to every customer who requests service in that area. The maps and digital data provided in the Water and Sewer CCN Viewer delineate the official CCN service areas and CCN facility lines issued by the PUCT and its predecessor agencies. This dataset is a Texas statewide polygon layer of water CCN service areas. The CCNs were digitized from Texas Department of Transportation (TxDOT) county mylar maps. The mylar maps were the base maps on which the CCNs were originally drawn and maintained. CCNs are currently created and maintained using digitizing methods, coordinate geography or imported from digital files submitted by the applicant. TxDOT digital county urban road files are used as the base maps on which the CCNs are geo-referenced. It is best to view the water CCN service area data in conjunction with the water CCN facility line data, since these two layers together represent all of the retail public water utilities in Texas.*Important Notes: The CCN spatial dataset and metadata were last updated on: October 1, 2018The official state-wide CCN spatial dataset includes all types of CCN services areas: water and sewer CCN service areas; water and sewer CCN facility lines. This CCN spatial dataset is updated on a quarterly, or as needed basis using Geographic Information System (GIS) software called ArcGIS 10.3.1.The complete state-wide CCN spatial dataset is available for download from the following website: http://www.puc.texas.gov/industry/water/utilities/gis.aspxThe Water and Sewer CCN Viewer may be accessed from the following web site: http://www.puc.texas.gov/industry/water/utilities/map.htmlIf you have questions about this CCN spatial dataset or about CCN mapping requirements, please e-mail CCN Mapping Staff: water@puc.texas.govTYPE - This numeric field indicates whether a CCN is considered a water or a sewer system. If the CCN number begins with a '"1", the CCN is considered a water system (utility). If a CCN number begins with a "2", the CCN is considered a sewer system (utility).CCN_NO - This five-digit alphanumeric field contains a unique number assigned to each CCN when it is created and approved by the Commission.UTILITY - This alphanumeric field contains the name of the utility which owns the CCN.TX_CNTY - This three-character alphanumeric field contains the Texas county code assigned to the Texas county by the TxDOT. This code represents the county which completely contains the CCN. If the CCN crosses county lines, then an "M" is used to indicate multiple counties. COUNTY - This alphanumeric field contains the name(s) of the county(ies) referenced in TX_CNTY.

This dataset comes from the Annual Community Survey questions about satisfaction with Community Service Programs. The Community Survey question relating to the Community Services Programs performance measure: "Please rate your level of satisfaction with each of the following: a) Quality of Before & After School (Kid Zone) programs; b) Quality of City library programs & services; c) Quality of City recreation programs & services; d) Quality of Tempe Center for the Arts programs." Respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (responses of "don't know" are excluded).The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This page provides data for the Community Services Programs performance measure.The performance measure dashboard is available at 3.17 Community Services Programs.Note: Kid Zone is being removed from the measure as it no longer resides in Community Services.Additional InformationSource: Community Attitude Survey (Vendor: ETC Institute)Contact: Wydale HolmesContact E-Mail: wydale_holmes@tempe.govData Source Type: Excel and PDF ReportPreparation Method: Extracted from Annual Community Survey resultsPublish Frequency: AnnualPublish Method: ManualData Dictionary

Location Intelligence and Location Analytics Market Outlook

The global market size for Location Intelligence (LI) and Location Analytics is projected to grow from $XX billion in 2023 to $XX billion by 2032, exhibiting a CAGR of XX%. This remarkable growth is driven by the increasing adoption of geospatial data in business operations and the rising demand for location-based services in various industries.

One of the primary growth factors for the Location Intelligence and Location Analytics market is the proliferation of Internet of Things (IoT) devices. These devices generate vast amounts of location-based data that can be analyzed to provide valuable insights. Companies are increasingly recognizing the importance of leveraging this data to enhance operational efficiency, improve customer experience, and drive strategic decision-making. The integration of artificial intelligence (AI) and machine learning (ML) with Location Analytics further enhances the ability to process and analyze large datasets, providing more accurate and actionable insights.

Another significant driver is the growing need for real-time location-based services. In sectors such as retail, transportation, and logistics, real-time location analytics enable businesses to track assets, monitor workforce movements, and manage facilities more effectively. This real-time data helps in optimizing routes, reducing fuel consumption, and improving overall productivity. Additionally, the COVID-19 pandemic has accelerated the adoption of location-based services for contact tracing, social distancing monitoring, and ensuring workplace safety, further propelling market growth.

Advancements in geographic information systems (GIS) and the increasing availability of high-resolution satellite imagery are also contributing to market expansion. Modern GIS platforms offer sophisticated tools for spatial analysis, mapping, and visualization, enabling organizations to derive meaningful insights from complex geospatial data. The integration of location analytics with business intelligence (BI) tools allows for comprehensive analysis and visualization of data, leading to better strategic planning and decision-making.

Regionally, North America is expected to hold the largest market share, driven by the presence of major technology companies and early adoption of advanced technologies. The Asia Pacific region is anticipated to witness the highest growth rate, fueled by rapid urbanization, increasing investments in smart city projects, and the expanding e-commerce sector. Europe, Latin America, and the Middle East & Africa are also expected to contribute significantly to the market growth, with various industries adopting location-based services to enhance operational efficiency and customer engagement.

Component Analysis

The Location Intelligence and Location Analytics market is segmented into two main components: Software and Services. The Software segment dominates the market, driven by the increasing demand for sophisticated analytics tools that can process and visualize geospatial data. Advanced software solutions offer capabilities such as spatial analysis, mapping, and real-time data processing, enabling businesses to gain deeper insights into their operations and customer behavior. The integration of AI and ML with location analytics software further enhances its analytical capabilities, making it a crucial component for businesses seeking to leverage geospatial data.

Within the Software segment, geographic information systems (GIS) and business intelligence (BI) tools play a pivotal role. GIS platforms provide extensive functionalities for spatial data analysis, mapping, and visualization, allowing organizations to derive actionable insights from complex datasets. The integration of BI tools with location analytics enables businesses to perform comprehensive analyses and generate interactive dashboards, facilitating informed decision-making. The increasing adoption of cloud-based software solutions is also driving market growth, offering scalability, flexibility, and cost-effectiveness to businesses of all sizes.

The Services segment encompasses various professional and managed services that support the deployment and utilization of location analytics solutions. Consulting services assist organizations in identifying their specific needs and developing customized solutions, while implementation services ensure seamless integration of location analytics tools with existing systems. Managed services provide ongoing support, maintenance, and optimization of location analy

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.

References [1] Rusco, E., Montanarella, L., Bosco, C., 2008. Soil erosion: a main threats to the soils in Europe. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 37-45 [2] Casagrandi, R. and Guariso, G., 2009. Impact of ICT in Environmental Sciences: A citation analysis 1990-2007. Environmental Modelling & Software 24 (7), 865-871. DOI:10.1016/j.envsoft.2008.11.013 [3] Stallman, R. M., 2005. Free community science and the free development of science. PLoS Med 2 (2), e47+. DOI:10.1371/journal.pmed.0020047 [4] Waldrop, M. M., 2008. Science 2.0. Scientific American 298 (5), 68-73. DOI:10.1038/scientificamerican0508-68 [5] Heineke, H. J., Eckelmann, W., Thomasson, A. J., Jones, R. J. A., Montanarella, L., and Buckley, B., 1998. Land Information Systems: Developments for planning the sustainable use of land resources. Office for Official Publications of the European Communities, Luxembourg. EUR 17729 EN [6] Farr, T. G., Rosen, P A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., Alsdorf, D., 2007. The Shuttle Radar Topography Mission. Review of Geophysics 45, RG2004, DOI:10.1029/2005RG000183 [7] Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M., 2008. A European daily high-resolution gridded dataset of surface temperature and precipitation. Journal of Geophysical Research 113, (D20) D20119+ DOI:10.1029/2008jd010201 [8] Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., and Yoder, D. C., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture handbook 703. US Dept Agric., Agr. Handbook, 703 [9] Bosco, C., Rusco, E., Montanarella, L., Panagos, P., 2009. Soil erosion in the alpine area: risk assessment and climate change. Studi Trentini di scienze naturali 85, 119-125 [10] Bosco, C., Rusco, E., Montanarella, L., Oliveri, S., 2008. Soil erosion risk assessment in the alpine area according to the IPCC scenarios. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 47-58 [11] de Rigo, D. and Bosco, C., 2011. Architecture of a Pan-European Framework for Integrated Soil Water Erosion Assessment. IFIP Advances in Information and Communication Technology 359 (34), 310-31. DOI:10.1007/978-3-642-22285-6_34 [12] Bosco, C., de Rigo, D., Dewitte, O., and Montanarella, L., 2011. Towards a Reproducible Pan-European Soil Erosion Risk Assessment - RUSLE. Geophys. Res. Abstr. 13, 3351 [13] Bollinne, A., Laurant, A., and Boon, W., 1979. L’érosivité des précipitations a Florennes. Révision de la carte des isohyétes et de la carte d’erosivite de la Belgique. Bulletin de la Société géographique de Liége 15, 77-99 [14] Ferro, V., Porto, P and Yu, B., 1999. A comparative study of rainfall erosivity estimation for southern Italy and southeastern Australia. Hydrolog. Sci. J. 44 (1), 3-24. DOI:10.1080/02626669909492199 [15] de Santos Loureiro, N. S. and de Azevedo Coutinho, M., 2001. A new procedure to estimate the RUSLE EI30 index, based on monthly rainfall data and applied to the Algarve region, Portugal. J. Hydrol. 250, 12-18. DOI:10.1016/S0022-1694(01)00387-0 [16] Rogler, H., and Schwertmann, U., 1981. Erosivität der Niederschläge und Isoerodentkarte von Bayern (Rainfall erosivity and isoerodent map of Bavaria). Zeitschrift fur Kulturtechnik und Flurbereinigung 22, 99-112 [17] Nearing, M. A., 1997. A single, continuous function for slope steepness influence on soil loss. Soil Sci. Soc. Am. J. 61 (3), 917-919. DOI:10.2136/sssaj1997.03615995006100030029x [18] Morgan, R. P C., 2005. Soil Erosion and Conservation, 3rd ed. Blackwell Publ., Oxford, pp. 304 [19] Šúri, M., Cebecauer, T., Hofierka, J., Fulajtár, E., 2002. Erosion Assessment of Slovakia at regional scale using GIS. Ecology 21 (4), 404-422 [20] Cebecauer, T. and Hofierka, J., 2008. The consequences of land-cover changes on soil erosion distribution in Slovakia. Geomorphology 98, 187-198. DOI:10.1016/j.geomorph.2006.12.035 [21] Poesen, J., Torri, D., and Bunte, K., 1994. Effects of rock fragments on soil erosion by water at different spatial scales: a review. Catena 23, 141-166. DOI:10.1016/0341-8162(94)90058-2 [22] Wischmeier, W. H., 1959. A rainfall erosion index for a universal Soil-Loss Equation. Soil Sci. Soc. Amer. Proc. 23, 246-249 [23] Iverson, K. E., 1980. Notation as a tool of thought. Commun. ACM 23 (8), 444-465. DOI:10.1145/358896.358899 [24] Quarteroni, A., Saleri, F., 2006. Scientific Computing with MATLAB and Octave. Texts in Computational Science and Engineering. Milan, Springer-Verlag [25] The MathWorks, 2011. MATLAB. http://www.mathworks.com/help/techdoc/ref/ [26] Eaton, J. W., Bateman, D., and Hauberg, S., 2008. GNU Octave Manual Version 3. A high-level interactive language for numerical computations. Network Theory Limited, ISBN: 0-9546120-6-X [27] de Rigo, D., 2011. Semantic Array Programming with Mastrave - Introduction to Semantic Computational Modeling. The Mastrave project. http://mastrave.org/doc/MTV-1.012-1 [28] de Rigo, D., (exp.) 2012. Semantic array programming for environmental modelling: application of the Mastrave library. In prep. [29] Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P.: Modelling Soil Erosion at European Scale. Towards Harmonization and Reproducibility. In prep. [30] R Development Core Team, 2005. R: A language and environment for statistical computing. R Foundation for Statistical Computing. [31] Stallman, R. M., 2009. Viewpoint: Why “open source” misses the point of free software. Commun. ACM 52 (6), 31–33. DOI:10.1145/1516046.1516058 [32] de Rigo, D. 2011. Multi-dimensional weighted median: the module "wmedian" of the Mastrave modelling library. Mastrave project technical report. http://mastrave.org/doc/mtv_m/wmedian [33] Shakesby, R. A., 2011. Post-wildfire soil erosion in the Mediterranean: Review and future research directions. Earth-Science Reviews 105 (3-4), 71-100. DOI:10.1016/j.earscirev.2011.01.001 [34] Zuazo, V. H., Pleguezuelo, C. R., 2009. Soil-Erosion and runoff prevention by plant covers: A review. In: Lichtfouse, E., Navarrete, M., Debaeke, P Véronique, S., Alberola, C. (Eds.), Sustainable Agriculture. Springer Netherlands, pp. 785-811. DOI:10.1007/978-90-481-2666-8_48

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands†from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Coal Oil Point map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and... Visit https://dataone.org/datasets/acc7efe8-36b7-4e7a-925b-6724d91f3ade for complete metadata about this dataset.

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. [...]

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of location-based services (LBS) across various sectors, including transportation, logistics, and e-commerce, is a primary driver. Furthermore, the proliferation of smartphones and connected devices, coupled with advancements in GPS technology and mapping software, continues to fuel market growth. The rising demand for high-resolution, real-time mapping data for autonomous vehicles and smart city initiatives also significantly contributes to market expansion. Competition among established players like Google, TomTom, and ESRI, alongside emerging innovative companies, is fostering continuous improvement in map accuracy, functionality, and data accessibility. This competitive landscape drives innovation and lowers costs, making digital maps increasingly accessible to a broader range of users and applications. However, market growth is not without its challenges. Data security and privacy concerns surrounding the collection and use of location data represent a significant restraint. Ensuring data accuracy and maintaining up-to-date map information in rapidly changing environments also pose operational hurdles. Regulatory compliance with differing data privacy laws across various jurisdictions adds another layer of complexity. Despite these challenges, the long-term outlook for the digital map market remains positive, driven by the relentless integration of location intelligence into nearly every facet of modern life, from personal navigation to complex enterprise logistics solutions. The market's segmentation (although not explicitly provided) likely includes various map types (e.g., road maps, satellite imagery, 3D maps), pricing models (subscriptions, one-time purchases), and industry verticals served. This diversified market structure further underscores its resilience and potential for sustained growth. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

Estimates of evapotranspiration (ET) are an essential component for understanding the water budget of the High Plains aquifer. The amount of ET that occurs is a function of crop type, crop management practices, soil characteristics, and climate conditions. These raster ET data represent monthly actual ET of the High Plains aquifer from April 2000 to December 2009. These data were developed using the Simplified-Surface-Energy- Balance (SSEB) model (Senay and others, 2007) and then processed using a Geographic Information System (GIS). The GIS software used to process these data was Environmental Systems Research Institute (ESRI, Inc.) ArcDesktop 9.3.1.

This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Buena Vista 30 x 60 minute quadrangle in Virginia and West Virginia. The source data used to construct this imagery consists of 1-meter lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2017 and 2021. The data were processed using geographic information systems (GIS) software. The data is projected in WGS 1984 Web Mercator. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief according to a topographic position index (TPI) calculation.

A Certificate of Convenience and Necessity (CCN) is issued by the PUCT, and authorizes a utility to provide water and/or sewer service to a specific service area. The CCN obligates the water or sewer retail public utility to provide continuous and adequate service to every customer who requests service in that area. The maps and digital data provided in the Water and Sewer CCN Viewer delineate the official CCN service areas and CCN facility lines issued by the PUCT and its predecessor agencies.This dataset is a Texas statewide polyline layer of water CCN facility lines. The CCNs were digitized from Texas Department of Transportation (TxDOT) county mylar maps. The mylar maps were the base maps on which the CCNs were originally drawn and maintained. CCNs are currently created and maintained using digitizing methods, coordinate geography or imported from digital files submitted by the applicant. TxDOT digital county urban road files are used as the base maps on which the CCNs are geo-referenced.This dataset is a Texas statewide polyline layer of water Certificates of Convenience and Necessity (CCN) facility lines. This type of CCN may either be a Facilities Only (F0), a CCN Facility line (point of use) service area that covers only the customer connections at the time the CCN was granted, or Facilities plus a specified number of feet (usually 200 feet buffer) around the facility line. It is best to view the water CCN facility lines in conjunction with the water CCN service areas, since these two layers together represent all of the retail public water utilities in Texas.*Important Notes: The CCN spatial dataset and metadata were last updated on: October 4, 2022The official state-wide CCN spatial dataset includes all types of CCN services areas: water and sewer CCN service areas; water and sewer CCN facility lines. This CCN spatial dataset is updated on a quarterly, or as needed basis using Geographic Information System (GIS) software called ArcGIS 10.8.2.The complete state-wide CCN spatial dataset is available for download from the following website: http://www.puc.texas.gov/industry/water/utilities/gis.aspxThe Water and Sewer CCN Viewer may be accessed from the following web site: http://www.puc.texas.gov/industry/water/utilities/map.htmlIf you have questions about this CCN spatial dataset or about CCN mapping requirements, please e-mail CCN Mapping Staff: water@puc.texas.govTYPE - Indicates whether a CCN is considered a water or a sewer system. If the CCN number begins with a '"1", the CCN is considered a water system (utility). If a CCN number begins with a "2", the CCN is considered a sewer system (utility).CCN_NO - A unique five-digit number assigned to each CCN when it is created and approved by the Commission. *CCN number starting with an ‘N’ indicates an exempt utility.UTILITY - The name of the utility which owns the CCN.COUNTY - The name(s) of the county(ies) in which the CCN exist.CCN_TYPE –One of three types:Bounded Service Area: A certificated service area with closed boundaries that often follow identifiable physical and cultural features such as roads, rivers, streams and political boundaries. Facilities +200 Feet: A certificated service area represented by lines. They include a buffer of a specified number of feet (usually 200 feet). The lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.Facilities Only: A certificated service area represented by lines. They are granted for a "point of use" that covers only the customer connections at the time the CCN is granted. Facility only service lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.STATUS – For pending dockets check the PUC Interchange Filing Search

This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Front Royal 30 x 60 minute quadrangle in West Virginia and Virginia. The source data used to construct this imagery consists of 1-meter lidar-derived digital elevation models (DEMs) and lidar point cloud (LPC). The lidar source data were compiled from different acquisitions published between 2014 and 2021. The data were processed using geographic information systems (GIS) software. The data is projected in WGS 1984 Web Mercator. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief according to a topographic position index (TPI) calculation.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands†from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Refugio Beach map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and ... Visit https://dataone.org/datasets/3c8df94c-24b5-4b57-a9ae-5cdfa8ab64b5 for complete metadata about this dataset.

This dataset contains the results of Land Use/Land Cover changes (LULC) analysis within the Bagno Chlebowo peatland (52°43'54''N, 16°44'7''E). Data was created as a result of analysis of archival and contemporary cartographic materials (Ur-messtischblatt maps, sheet 1713 from 1832; Messtischblatt from 1892, Polajewo sheet) and the BDOT10k geospatial database (from 2020).The data was created in GIS software and may be displayed, validated, and edited in an open software, e.g., QGIS.The dataset contains the following LULC classes: wetland, forest, open water, open area, and streams.The shx, dbf, cpg, prj, and qmd are supported by the open QGIS software and are auxiliary files for the correct operation of the shp file. The final results of the spatial analysis are displayed in LULC1823-2020.pdf.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands†from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Santa Barbara map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and ... Visit https://dataone.org/datasets/54fddf57-aa76-4823-b91e-e94d7b6528d8 for complete metadata about this dataset.

The Evaporation (E) data component is the actual evaporation of the soil surface. The value of each pixel represents the total annual evaporation for that specific year.

Data publication: 2024-01-31

Supplemental Information:

No data value: -9999

Unit : mm/year

Scale Factor : 0.1

Map code : L3-E-A.ODN

Scale factor: The pixel value in the downloaded data must be multiplied by

New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later.

Citation:

FAO WaPOR database, License: CC BY-NC-SA 4.0, [Date accessed: Day/Month/Year]

Contact points:

Resource Contact: WaPOR

Metadata Contact: WaPOR

Data lineage:

The calculation is based on the WaPOR-ETLook model described in the Wapor methodology document.

The annual total is obtained by taking the E in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. See the methodology of the evapotranspiration data components (E, T and I) for further information.

Data component are developed through collaboration with eLEAF. More information can be found on the WaPOR Website.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Online resources:

Download the data from Google Cloud Storage

Download the data from File-Browser

For visualizing renewable energy Candidate Project Areas (CPAs) using GIS software. A growing number of pledges are being made by major corporations, municipalities, states, and national governments to reach net-zero emissions by 2050 or sooner. This dataset provides granular guidance on what getting to net-zero really requires and on actions needed to translate these pledges into tangible progress.

This data set contains the GIS data for solar, land-based wind, and offshore wind Candidate Project Areas (CPAs) under base and constrained land use assumptions (BLUA, CLUA). Each record in this dataset represents a “Candidate Project Area” with attributes such as nameplate capacity, annual generation, model region, distance to transmission, etc. The Lawrence Berkeley National Lab MAPRE tools (https://mapre.lbl.gov/gis-tools/) were used to create this dataset, along with input assumptions adapted from Wu et al 2020 (Grace C Wu et al 2020 Environ. Res. Lett. 15 074044). A full description of the processes used to generate this dataset can be found in Annex D of the main NZA report. The main report and report annexes can be found at https://netzeroamerica.princeton.edu/.

What's new in this version:

1. Reverted to earlier version of CPA dataset, prior to removal of densely populated areas, and prior to removal of existing and planned facilities. CPAs now include areas with population density up to 100 person/km2, and they have an attribute indicating the population density. Users can apply their own population density filters and thresholds.
2. Added attributes indicating the following, in separate columns for each CPA: Human Modification Index (HMI), prime farmland, land cover type, presence of existing facility, presence of planned facility

Data sources:

Population density: Rose, Amy N., McKee, Jacob J., Sims, Kelly M., Bright, Edward A, Reith, Andrew E., and Urban, Marie L. “LandScan 2019.” Oak Ridge National Laboratory, 2020. https://landscan.ornl.gov/landscan-datasets

HMI: Theobald, David et al. “Detailed Temporal Mapping of Global Human Modification from 1990 to 2017.” Dryad, 2020. https://doi.org/10.5061/dryad.n5tb2rbs1.

Prime farmland: “USA Soils Farmland Class.” USDA NRCS, Esri, October 1, 2019. https://landscape11.arcgis.com/arcgis/rest/services/USA_Soils_Farmland_Class/ImageServer.

Land cover: NLCD 2016. https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover&f%5B1%5D=region%3Aconus

Homer, Collin G., Dewitz, Jon A., Jin, Suming, Xian, George, Costello, C., Danielson, Patrick, Gass, L., et al. “Conterminous United States Land Cover Change Patterns 2001–2016 from the 2016 National Land Cover Database: ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184–199, At.” ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184–199, April 2020. https://doi.org/10.1016/j.isprsjprs.2020.02.019.

Existing solar arrays: Carr, N.B., Fancher, T.S., Freeman, A.T., and Battles Manley, H.M. “Surface Area of Solar Arrays in the Conterminous United States: U.S. Geological Survey Data Release,” 2016. http://dx.doi.org/10.5066/F79S1P57.

Existing wind turbines: Hoen, B.D., Diffendorfer, J.E., Rand, J.T., Kramer, L.A., Garrity, C.P., and Hunt, H.E. “United States Wind Turbine Database (Ver. 3.3, January 14, 2021).” U.S. Geological Survey, American Clean Power Association, and Lawrence Berkeley National Laboratory, 2018. https://doi.org/10.5066/F7TX3DN0.

Planned wind and solar facilities: “EIA (Last) (2019). Preliminary Monthly Electric Generator Inventory (Based on Form EIA-860M as a Supplement to Form EIA-860).” U.S. Energy Information Administration (EIA), n.d. https://www.eia.gov/electricity/data/eia860m/.

New-ID: NBI44

Ocean mask for Africa.

Integrated Elevation and Bathymetry Dataset Documentation

File: AFELBA.IMG Code: 100048-001

Raster Member This IMG file is in IDRISI format

Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet)

References:

Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master"'"s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106.

Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004.

NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado.

Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review.

Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231.

Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois.

Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI