In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This dataset holds all materials for the Inform E-learning GIS course

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

This layer shows number of Recognized Educational Institutions in India.Data source: https://www.indiabudget.gov.in/economicsurvey/doc/stat/tab84.pdfThis web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

Lab 4 GIST 603A Introductin to ArcGIS Online University of Arizona MS GIST programLab 4 – ArcGIS OnlineArcGIS Online is a simple cloud-based utility for producing, editing, and sharing geospatial data. Designed by ESRI, the makers of the popular ArcGIS software suite, ArcGIS Online is meant to act as a Web-based mapping solution for everyone from GIS professionals to those with no formal GIS training.ArcGIS Online allows you to:Upload and manipulate dataMap points, lines and areasCreate point, cloropleth, and other thematic mapsEmbed maps in Web sitesShare maps in a multitude of waysView maps on mobile devices

This research study considers one such urban technology, namely utilising digital twins in cities. Digital twin city (DTC) technology is investigated to identify the gap in soft infrastructure data inclusion in DTC development. Soft infrastructure data considers the social and economic systems of a city, which leads to the identification of socio-economic security (SES) as the metric of investigation. The study also investigated how GIS mapping of the SES system in the specific context of Hatfield informs a soft infrastructure understanding that contributes to DTC readiness. This research study collected desk-researched secondary data and field-researched primary data in GIS using ArcGIS PRO and the Esri Online Platform using ArcGIS software. To form conclusions, grounded theory qualitative analysis and descriptive statistics analysis of the spatial GIS data schema data sets were performed.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

Lands approved or conveyed to the State of Alaska for a variety of reasons such as general purpose, expansion of communities, University of Alaska, and recreation.

This shape file characterizes the geographic representation of land parcels within the State of Alaska contained by the Ownership - State Owned, Managed - State Tentatively Approved or Patented category. It has been extracted from data sets used to produce the State status plats. This data set includes cases noted on the digital status plats up to one day prior to data extraction.

Each feature has an associated attribute record, including a Land Administration System (LAS) file-type and file-number which serves as an index to related LAS case-file information. Additional LAS case-file and customer information may be obtained at: http://dnr.alaska.gov/projects/las/ Those requiring more information regarding State land records should contact the Alaska Department of Natural Resources Public Information Center directly.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

The Kansas Master Ground-water Well Inventory (MWI) is a central repository that imports and links together the State's primary ground-water well data sets- KDHE's WWC5, KDA-DWR's WIMAS, and KGS' WIZARD into a single, online source. The most "accurate" of the common source fields are used to represent the well sites, for example- GPS coordinates if available are used over other methods to locate a well. The MWI maintains the primary identification tags to allow specific well records to be linked back to the original data sources.This mapper is managed by the Kansas Geological Survey. For more information about the data, please see the Groundwater Master Well Inventory page.

This file provides the raw data of an online survey intended at gathering information regarding remote sensing (RS) and Geographical Information Systems (GIS) for conservation in academic education. The aim was to unfold best practices as well as gaps in teaching methods of remote sensing/GIS, and to help inform how these may be adapted and improved. A total of 73 people answered the survey, which was distributed through closed mailing lists of universities and conservation groups.

These are the main layers that were used in the mapping and analysis for the Santa Monica Mountains Local Coastal Plan, which was adopted by the Board of Supervisors on August 26, 2014, and certified by the California Coastal Commission on October 10, 2014. Below are some links to important documents and web mapping applications, as well as a link to the actual GIS data:

Plan Website – This has links to the actual plan, maps, and a link to our online web mapping application known as SMMLCP-NET. Click here for website. Online Web Mapping Application – This is the online web mapping application that shows all the layers associated with the plan. These are the same layers that are available for download below. Click here for the web mapping application. GIS Layers – This is a link to the GIS layers in the form of an ArcGIS Map Package, click here (LINK TO FOLLOW SOON) for ArcGIS Map Package (version 10.3). Also, included are layers in shapefile format. Those are included below.

Below is a list of the GIS Layers provided (shapefile format):

Recreation (Zipped - 5 MB - click here)

Coastal Zone Campground Trails (2012 National Park Service) Backbone Trail Class III Bike Route – Existing Class III Bike Route – Proposed

Scenic Resources (Zipped - 3 MB - click here)

Significant Ridgeline State-Designated Scenic Highway State-Designated Scenic Highway 200-foot buffer Scenic Route Scenic Route 200-foot buffer Scenic Element

Biological Resources (Zipped - 45 MB - click here)

National Hydrography Dataset – Streams H2 Habitat (High Scrutiny) H1 Habitat H1 Habitat 100-foot buffer H1 Habitat Quiet Zone H2 Habitat H3 Habitat

Hazards (Zipped - 8 MB - click here)

FEMA Flood Zone (100-year flood plain) Liquefaction Zone (Earthquake-Induced Liquefaction Potential) Landslide Area (Earthquake-Induced Landslide Potential) Fire Hazard and Responsibility Area

Zoning and Land Use (Zipped - 13 MB - click here)

Malibu LCP – LUP (1986) Malibu LCP – Zoning (1986) Land Use Policy Zoning

Other Layers (Zipped - 38 MB - click here)

Coastal Commission Appeal Jurisdiction Community Names Santa Monica Mountains (SMM) Coastal Zone Boundary Pepperdine University Long Range Development Plan (LRDP) Rural Village

Contact the L.A. County Dept. of Regional Planning's GIS Section if you have questions. Send to our email.

The contemporary globalized world characterizes the rapid population growth, its significant concentration in cities, and an increase in the urban population. Currently, many socio-cultural, economic, environmental, and other challenges are arising in modern cities, negatively affecting the state of the urban environment, health, and quality of life. There is a need to study these problems in order to solve them. Urban Green Areas (UGAs) are a part of the social space and a vital part of the urban landscape. They act as an environmental framework of the territory and a factor ensuring a more comfortable environment of human life. This study aims at substantiating the importance of the UGAs, identifying the spatiotemporal dynamics of their functioning, and transforming changes in their infrastructure given the expansion of their functions. This research was carried out as a case study of the second city in Ukraine, Kharkiv. The authors developed and used an original integrated approach using urban remote sensing (URS) and GIS for changes detection to evaluate the current state and monitor spatial transformations of the UGAs. We used several GIS platforms and online resources to overcome the lack of digital cadastre of the thematic municipal area of Kharkiv. This resulted in the present original study. The study analyses the dynamics of the spatial and functional organization of the UGAs according to the Master Plans, plans, maps, and functional zoning of the city for the period from 1867 to 2019. The peripheral green areas became important after the large-scale development of the extensive residential areas during the rapid industrial development in remote districts of the city. They provide opportunities for population recreation near living places. Central UGAs are modern, comprehensively developed clusters with multidisciplinary infrastructure, while the peripheral UGAs are currently being developed. The use of URS/GIS tools in the analysis of the satellite images covering 2000–2020 allowed identifying the factors of the UGAs losses in Kharkiv and finding that UGAs were not expanding and partially shrinking during the study period. It is caused by the intensive construction of the residential neighborhoods, primarily peripheral areas, infrastructure development, and expansion of the city transport network. Nonetheless, some sustainable trends of UGA functioning without more or less significant decrease could be proved as existing in a long-term perspective. The authors analyzed and evaluated changes and expansion of the UGAs functions according to modern social demand. The research value of this is the usage of different approaches, scientific sources, URS/GIS tools to determine the UGAs transformation in the second-largest city in Ukraine (Kharkiv), to expand and update the main functions of UGAs and their role in the population’s recreation. The obtained scientific results can be used to update the following strategies, programs, and development plans of Kharkiv.

This site provides free access to Iowa geographic map data through an on-line map viewer and through Web Map Service (WMS) connections for GIS. The site was developed by the Iowa State University Geographic Information Systems Support and Research Facility in cooperation with the Iowa Department of Natural Resources, the USDA Natural Resources Conservation Service, and the Massachusetts Institute of Technology. This site was first launched in March 1999. Resources in this dataset:Resource Title: Iowa Geographic Map Server. File Name: Web Page, url: http://ortho.gis.iastate.edu/#MapLayers Online access to Iowa geographic map data through an on-line map viewer and through Web Map Service (WMS) connections for GIS, as well as a full featured ArcGIS web app.

Esri ArcGIS Online (AGOL) Imagery Layer which includes the MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid geospatial data product.MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 10% annual chance event (10-Year Storm) scenario for coastal areas throughout the State of Maryland in year 2050. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2050.MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/07/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/MDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

Pleiades Datasets 3.2 (3 November 2023)

This is a package of data derived from the Pleiades gazetteer of ancient places. It is used for archival and redistribution purposes and is likely to be less up-to-date than the live data at https://pleiades.stoa.org. We encourage use and citation of these numbered releases for scholarly work that will be published in static form.

Updates and additions to published content of the Pleiades gazetteer of ancient places between 1 August 2023 and 3 November 2023

What's new since 3.1 (1 August 2023):

• 108 new and 1,629 updated place resources reflecting work by Erin Walcek Averett, Jeffrey Becker, Catherine Bouras, Anne Chen, Niels Christoffersen, Peter Cobb, Tom Elliott, Jonathan Fu, Greta Hawes, Carolin Johansson, Noah Kaye, Brady Kiesling, Thomas Landvatter, Stanisław Ludwiński, Ingrid Luo, Stephan Maurer, Colin McCaffrey, Gabriel McKee, David Meadows, Gabriel Moss, John Muccigrosso, Gifford Quinn, Rune Rattenborg, Enrico Regazzoni, Rosemary Selth, R. Scott Smith, Richard Talbert, Georgios Tsolakis, and Scott Vanderbilt (see data/changelogs/release.html for details).
• Included experimental JSON index of links extracted from Pleiades place resources to "toponym" entries in Veronique Chankowski et al. Chronique Des Fouilles En Ligne = Archaeology in Greece Online. Athens: Ecole française d'Athènes and British School at Athens, 2018, together with links to the associated Chronique archaeological reports. See data/indexes/pids2chronique.json.

About Pleiades

Pleiades is a community-built gazetteer and graph of ancient places. It publishes authoritative information about ancient places and spaces, providing unique services for finding, displaying, and reusing that information under open license. It publishes not just for individual human users, but also for search engines and for the widening array of computational research and visualization tools that support humanities teaching and research.

Pleiades is a continuously published scholarly reference work for the 21st century. We embrace the new paradigm of citizen humanities, encouraging contributions from any knowledgeable person and doing so in a context of pervasive peer review. Pleiades welcomes your contribution, no matter how small, and we have a number of useful tasks suitable for volunteers of every interest.

Credits

Pleiades is brought to you by:

• Our volunteer content contributors (see data/rdf/authors.ttl for complete list and associated identifiers or data).
• Pleiades has received significant, periodic support from the National Endowment for the Humanities since 2006. Grant numbers: HK-230973-15, PA-51873-06, PX-50003-08, and PW-50557-10. Any views, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect those of the National Endowment for the Humanities.
• Additional support has been provided since 2000 by the Ancient World Mapping Center at the University of North Carolina at Chapel Hill. * Development hosting and other project incubation support was provided between 2000 and 2008 by Ross Scaife and the Stoa Consortium.
• Web hosting and additional support has been provided since 2008 by the Institute for the Study of the Ancient World at New York University.

The Mediterranean Ocean Biodiversity Information System (MedOBIS) is a distributed system that allows you to search multiple datasets simultaneously for biogeographic information on marine organisms. AccConID=21 AccConstrDescription=This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials. AccConstrDisplay=This dataset is licensed under a Creative Commons Attribution 4.0 International License. AccConstrEN=Attribution (CC BY) AccessConstraint=Attribution (CC BY) AccessConstraints=None Acronym=MedOBIS added_date=2004-12-17 13:02:51 BrackishFlag=0 CDate=2004-12-07 cdm_data_type=Other CheckedFlag=1 Citation=Hellenic Centre For Marine Research, MedOBIS - Mediterranean Ocean Biodiversity Information System. Hellenic Centre for Marine Research; Institute of Marine Biology and Genetics; Biodiversity and Ecosystem Management Department, Heraklion, Greece. Http://www.medobis.org/ Comments=None ContactEmail=arvanitidis@her.hcmr.gr Conventions=COARDS, CF-1.6, ACDD-1.3 CurrencyDate=None DasID=481 DasOrigin=Data collection DasType=Data DasTypeID=1 DateLastModified={'date': '2025-08-22 01:33:40.264606', 'timezone_type': 1, 'timezone': '+02:00'} DescrCompFlag=0 DescrTransFlag=0 Easternmost_Easting=35.38 EmbargoDate=None EngAbstract=The Mediterranean Ocean Biodiversity Information System (MedOBIS) is a distributed system that allows you to search multiple datasets simultaneously for biogeographic information on marine organisms. EngDescr=An attempt to collect, format, analyse and disseminate surveyed marine biological data deriving from the Eastern Mediterranean and Black Sea region is currently under development at the Hellenic Center for Marine Research (HCMR, Greece). The effort has been supported by the MedOBIS project (Mediterranean Ocean Biodiversity Information System) and has been carried out in cooperation with the Aristotelian University of Thessaloniki (Greece), the National Institute of Oceanography (Israel) and the Institute of Biology of the Southern Seas (Ukraine). The aim is to develop a taxon-based biogeography database and online data server with a link to survey and provide satellite environmental data. In its completion, the MedOBIS online marine biological data system (http://www.medobis.org/) will be a single source of biological and environmental data (raw and analysed) as well as an online GIS tool for access of historical and current data by marine researchers. It will function as the Eastern Mediterranean and Black Sea node of EurOBIS (the European node of the International OBIS initiative, part of the Census of Marine Life).

The spatial component of data has led to the integration of datasets by means of the Geographic Information System (GIS) technology. The latter is widely used as the natural framework for spatial data handling. GIS serves as the basic technological infrastructure for several online marine biodiversity databases available on the Internet today. Developments like OBIS (Ocean Biodiversity Information System, http://www.iobis.org/), OBIS-SEAMAP (Spatial Ecological Analysis of Megavertebrate Populations, http://seamap.env.duke.edu) and FIGIS (FAO Fisheries Global Information System, http://www.fao.org/fi/figis) facilitate the study of anthropogenic impacts on threatened species, enhance our ability to test biogeographic and biodiversity models, support modeling efforts to predict distribution changes in response to environmental change and develop a strong potential for the public outreach component. In addition, such online database systems provide a broader view of marine biodiversity problems and allow the development of management practices that are based on synthetic analysis of interdisciplinary data.

Towards this end, a new online marine biological information system is developed. MedOBIS (Mediterranean Ocean Biodiversity Information System) intends to assemble, formulate and disseminate marine biological data for the Eastern Mediterranean and Black Sea regions focusing on the assurance and longevity of historical surveyed data, the assembly of current and new information and the dissemination of raw and integrated biological and environmental data and future products through the Internet.

To provide a taxon-based search capability to the MedOBIS development, the sampling data as well as the relevant spatial data are stored in the database, so taxonomic data can be linked with the geographical data by queries. To reference each species to its location on the map, the database queries are stored and added to the applet as individual layers. A search function written in JavaScript searches the attribute data of that layer, displays the results in a separate window and marks the matching stations on the map. Finally, selecting several stations by drawing a zooming rectangle on the map provides a list with predefined themes from which the user may select more information.

As more data will be assembled in time-series databases, an additional future work will include the development of MedOBIS data analysis phase, which is planned to include GIS modeling/mapping of species-environment interactions. FreshFlag=0 GBIF_UUID=83bede10-f762-11e1-a439-00145eb45e9a geospatial_lat_max=45.7 geospatial_lat_min=31.89 geospatial_lat_units=degrees_north geospatial_lon_max=35.38 geospatial_lon_min=12.3 geospatial_lon_units=degrees_east infoUrl=None InputNotes=S:\datac\original datasets\Marbef\Europe\EurOBIS\MedOBIS[481]\medobis_masterbase.mdb institution=HCMR License=https://creativecommons.org/licenses/by/4.0/ Lineage=Prior to publication data undergo quality control checked which are described in https://github.com/EMODnet/EMODnetBiocheck?tab=readme-ov-file#understanding-the-output MarineFlag=1 modified_sync=2021-02-04 00:00:00 Northernmost_Northing=45.7 OrigAbstract=None OrigDescr=None OrigDescrLang=None OrigDescrLangNL=None OrigLangCode=None OrigLangCodeExtended=None OrigLangID=None OrigTitle=None OrigTitleLang=None OrigTitleLangCode=None OrigTitleLangID=None OrigTitleLangNL=None Progress=In Progress PublicFlag=1 ReleaseDate=Dec 17 2004 12:00AM ReleaseDate0=2004-12-17 RevisionDate=None SizeReference=2953 species; 776 stations sourceUrl=(local files) Southernmost_Northing=31.89 standard_name_vocabulary=CF Standard Name Table v70 StandardTitle=Mediterranean Ocean Biodiversity Information System StatusID=1 subsetVariables=ScientificName,aphia_id TerrestrialFlag=0 UDate=2025-03-26 VersionDate=Dec 7 2004 12:00AM VersionDay=7 VersionMonth=12 VersionName=1 VersionYear=2004 VlizCoreFlag=1 Westernmost_Easting=12.3

This feature has added links to State DOT GIS Portals and Office websites.The States dataset was updated on October 31, 2023 from the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

The Eel River CZO operates on several spatial scales from a zero order hillslope to the entire Eel River on the north coast of California. Rivendell, Angelo, Sagehorn, South Fork, and Eel River GIS boundaries. GIS polygon shapefiles. All files are in geographic projection (Lat/Long) with a datum of WGS84.

The watershed boundaries are from USGS Watershed Boundary Dataset (WBD) http://nhd.usgs.gov/wbd.html. Rivendell and Angelo boundaries are created from LiDAR by the CZO. Sagehorn Ranch is a privately held, active commercial ranch with no public access. Please contact the CZO if you are interested in data from Sagehorn Ranch.

Shapefiles

Eel River Watershed (drainage area 9534 km^2): Entire eel river. Greatest extent of CZO research.

South Fork Eel Watershed (drainage area 1784 km^2).

Angelo Reserve Boundary (30.0 km^2): Angelo Coast Range Reserve is a University of California Natural Reserve System protected land. It is the central focus of CZO research. http://angelo.berkeley.edu

Sagehorn Ranch Boundary (21.1 km^2): Sagehorn Ranch is a private ranch with active cattle raising. The owners have allowed the CZO to place instrumentation on their lands. Access is only by explicit agreement by owners.

Rivendell Cachement (0.0076 km^2): Rivendell is a small, heavily instrumented hillslope within the Angelo Reserve. It has roughly 700 instruments deployed as of 2016. Data is online at http://sensor.berkeley.edu

This is a GIS of Gede, Coast, Kenya. GIS (Geographic Information System) is a digital toolset used to map, analyze, and visualize spatial data related to a site, enabling the documentation, preservation, and interpretation of archaeological features and historical environments.The information in this site description is provided for contextual purposes only and should not be regarded as a primary source.Gede is a Swahili archaeological site comprising coral stone structures, including mosques, houses, and tombs arranged within a walled town layout. Architectural features such as mihrabs, water cisterns, and decorative niches reflect Islamic influence and urban planning. Excavations have revealed trade goods and domestic artifacts, indicating participation in Indian Ocean commerce. Gede provides insights into Swahili cultural identity, religious practice, and economic networks.Gede is listed as the UNESCO World Heritage Site, 'The Historic Town and Archaeological Site of Gedi'.The Zamani Project seeks to increase awareness and knowledge of tangible cultural heritage in Africa and internationally by creating metrically accurate digital representations of historical sites. Digital spatial data of cultural heritage sites can be used for research and education, for restoration and conservation, and as a record for future generations. The Zamani Project operates as a non-profit organisation within the University of Cape Town.Special thanks to the Saville Foundation, and the Andrew W. Mellon Foundation, among others, for their contributions to the digital documentation of this heritage site.If you believe any information in this description is incorrect, please contact the repository administrators.