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.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This is a full-day training, developed by UNEP CMB, to introduce participants to the basics of GIS, how to import points from Excel to a GIS, and how to make maps with QGIS, MapX and Tableau. It prioritizes the use of free and open software.

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.

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.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This web map references the live tiled map service from the OpenStreetMap project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information such as free satellite imagery, and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in Esri products under a Creative Commons Attribution-ShareAlike license.Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

To do more than the very basics of GIS you will need to sign up for a FREE Schools ArcGIS Online subscription. To sign up for a subscription contact gisinschools@eagle.co.nz

The Cumberland County GIS Data Viewer provides the general public with parcel, zoning, hydrology, soils, utilities and topographic data. You can search for a specific address, street name, parcel number (PIN), or by the owner's name.

Click here to open the ArcGIS Online 3D Map Viewer and work through the examples shown belowTo add 3D data to ArcGIS Online you will need a login for an ArcGIS Online account. We would recommend that you use a free schools subscription (full functionality) or the free public account (reduced functionality).Login to ArcGIS OnlineChoose a location that you would like to visit and a create a 3D tour

How do you ensure your data is free of errors? While you may already leverage ArcGIS Data Reviewer for its automated validation capabilities, you might ocassionally encounter problems with certain challenging subsets of features. For example, think about a situation in which you expected an automated data check to return a certain error but it did not. You tried configuring the check over and over again, but did not figure out a method of automatically detecting the error.Visual review can help. Manually reviewing your data provides a way to find errors that are difficult to detect using automated methods, such as features that are missing, misplaced, miscoded, or redundant.The following graphic shows the topics that will be covered throughout the course. You will learn the associated workflows that take advantage of ArcGIS Data Reviewer functionality.After completing this course, you will be able to:Determine situations in which visual review is appropriate.Analyze a statistically significant sample.Create a QC grid and perform a systematic visual review.Indicate missing, misplaced, miscoded, or redundant features.Recognize how to find changes between versions.

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 Biogeographic 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. Currently, the primary features of the MedOBIS application are its offline GIS data formatting capabilities and its online Java and JavaScript enabling data server with taxon-based search, mapping and data downloading capabilities. In its completion, the MedOBIS online marine biological data system (http://www.iobis.org/OBISWEB/ObisDynPage1.jsp?content=meta/42.html) 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).

    INTRODUCTION

    The international and interdisciplinary nature of the biological degradation issue as well as the technological advances of the Internet capabilities allowed the development of a considerable number of interrelated online databases. The free dissemination of valuable historical and current biological, environmental and genetic information has contributed to the establishment of an interdisciplinary platform targeted towards information integration at regional and also at global scales and to the development of information-based management schemes about our common interest.

    The spatial component of these data has led to the integration of the information by means of the Geographic Information System (GIS) technology. The latter is widely used as the natural framework for spatial data handling (Wright & Bartlett 1999, Valavanis 2002). GIS serves as the basic technological infrastructure for several online marine biodiversity databases available on the Internet today. Developments like OBIS (Ocean Biogeographic 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/fishery/figis) facilitate the study of anthropogenic impacts on threatened species, enhance our ability to test biogeographic and biodiversity models, support modelling 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 (Schalk 1998, Decker & O'Dor 2002, Tsontos & Kiefer 2002).

    Towards this end, a development of a new online marine biological information system is presented here in its initial phase. MedOBIS (Mediterranean Ocean Biogeographic 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.

    MedOBIS DESCRIPTION

    MedOBIS current development consists of four main phases (Fig. 1). The data assembly phase is based on the free contribution of biological data from various national and international scientific surveys in the region. The data formatting phase is based on a GIS (ESRI, 1994), under which the geographic location of data stations is used to convert station data and their attributes to GIS shapefiles. The data analysis phase is based on data integration through GIS and spatial analyses (e.g. species distribution maps, species-environment relations, etc). Finally, the dissemination phase is based on ALOV Map, a free portable Java application for publication of vector and raster maps to the Internet and interactive viewing on web browsers. It supports navigation and search capabilities and allows working with multiple layers, thematic maps, hyperlinked features and attributed data.

    During the on-going data assembly phase, a total number of 776 stations with surveyed benthic biological data was employed. These data include mainly benthic species abundance (for nearly 3000 benthic organisms), benthic substrate types and several environmental parameters. Currently, 100 stations have been assembled for the Ionian Sea, 570 stations for the Aegean Sea and 106 stations for the Black Sea. The temporal resolution of these data extends for the period 1937-2000 while most data cover the period 1986-1996. Additionally, monthly satellite images of sea surface temperature (SST) and chlorophyll (Chl-a) were assembled for the period 1998-2003. Satellite data were obtained from the Advanced Very High Resolution Radiometer (AVHRR SST) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS Chl-a). 

    During the data formatting phase, all assembled surveyed stations were converted to a GIS shapefile (Fig. 2). This GIS information layer includes the geographic coordinates of the stations as well as stations' identification number. Station data attributes were organised in an MS Access Database while satellite data were embedded in a GIS database as GIS regular grids. The MedOBIS data analysis phase is still at the initial stage. Several off line analytical published efforts (e.g. Arvanitidis et al. 2002, Valavanis et al. 2004a,b,c) will be included in the MedOBIS development, which mainly focus on species distribution maps, mapping of productive oceanic processes and species-environment interactions. 

    The MedOBIS dissemination phase ("http://www.medobis.org/") is based on ALOV Map ("http://www.alov.org/"), a joint project of ALOV Software and the Archaeological Computing Laboratory, University of Sydney, Australia. ALOV Map is a Java-based application for publication of GIS data on the Internet and interactive viewing on web browsers. ALOV Map is designed to display geographical information stored in shapefiles or in any SQL database or even in an XML (Extensible Markup Language) document serving as a database. MedOBIS uses ALOV Map's full capabilities and runs in a client-server mode (Fig. 3). ALOV Map is connected to an MS Access database via a servlet container. This architecture was needed to connect the biological data with the spatial data and facilitate search options, such as, which species are found at which stations. Additionally, a JavaScript code is invoked, which searches the data, pops up a window with the results and then shows the relevant stations on the map.

    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 SQL (Structured Query Language) 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 (Fig. 4). 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 (Fig. 5). 

    CURRENT LIMITATIONS AND FUTURE PLANS

    A disadvantage of embedding information from the database as a layer is the relatively long download time due to the current MedOBIS-ALOV Map client-server architecture. An appropriate solution would be a direct search on the server side, which will allow partial data downloading to the client side. This work will be embedded in the MedOBIS application in the future (client-side architecture), when the size of assembled data becomes relatively 'heavy' for the current client-server architecture. This is an on-going process, since the MedOBIS initiative has been endorsed by the "Excellence of the Institute of Marine Biology of Crete (IMBC) in Marine Biodiversity", a Hellenic National Project that has been evaluated and approved by European experts. 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 modelling/mapping of species-environment interactions.

    Size reference: 2953 species; 776 stations

    [Source: The information provided in the summary was extracted from the MarBEF Data System at "http://www.marbef.org/data/eurobisproviders.php"]

Total file size: about 367M in zip format and about 600M after extracted. (To download: click the Download button at the upper right area of this page)Alternatively, you can download the data by chapters:- Go to https://go.esri.com/gtkwebgis4- Under Group Categories on the left, click each chapter, you will see the data file to download for that chapter.

World Countries Generalized represents generalized boundaries for the countries of the world as of August 2022. The generalized political boundaries improve draw performance and effectiveness at a global or continental level. This layer is best viewed out beyond a scale of 1:5,000,000.This layer's geography was developed by Esri and sourced from Garmin International, Inc., the U.S. Central Intelligence Agency (The World Factbook), and the National Geographic Society for use as a world basemap. It is updated annually as country names or significant borders change.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).

The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.

Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.

An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset contains 4 …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset contains 4 different scale GEODATA TOPO series, Geoscience Australia topographic datasets. 1M, 2.5M, 5M and 10M with age ranges from 2001 to 2004. 1:1 Million - Global Map Australia 1M 2001 is a digital dataset covering the Australian landmass and island territories, at a 1:1 million scale. Product Specifications -Themes: It consists of eight layers of information: Vector layers - administrative boundaries, drainage, transportation and population centres Raster layers - elevation, vegetation, land use and land cover -Coverage: Australia -Currency: Variable, based on GEODATA TOPO 250K Series 1 -Coordinates: Geographical -Datum: GDA94, AHD -Medium: Free online -Format: -Vector: ArcInfo Export, ESRI Shapefile, MapInfo mid/mif and Vector Product Format (VPF) -Raster: Band Interleaved by Line (BIL) 1:2.5 Million - GEODATA TOPO 2.5M 2003 is a national seamless data product aimed at regional or national applications. It is a vector representation of the Australian landscape as represented on the Geoscience Australia 2.5 million general reference map and is suitable for GIS applications. The product consists of the following layers: built-up areas; contours; drainage; framework; localities; offshore; rail transport; road transport; sand ridges; Spot heights; and waterbodies. It is a vector representation of the Australian landscape as represented on the Geoscience Australia 1:2.5 million scale general reference maps. This data supersedes the TOPO 2.5M 1998 product through the following characteristics: developed according to GEODATA specifications derived from GEODATA TOPO 250K Series 2 data where available. Product Specifications Themes: GEODATA TOPO 2.5M 2003 consists of eleven layers: built-up areas; contours; drainage; framework; localities; offshore; rail transport; road transport; sand ridges; spot heights; and waterbodies Coverage: Australia Currency: 2003 Coordinates: Geographical Datum: GDA94, AHD Format: ArcInfo Export, ArcView Shapefile and MapInfo mid/mif Medium: Free online - Available in ArcInfo Export, ArcView Shapefile and MapInfo mid/mif 1:5 Million - GEODATA TOPO 5M 2004 is a national seamless data product aimed at regional or national applications. It is a vector representation of the Australian landscape as represented on the Geoscience Australia 5 million general reference map and is suitable for GIS applications. Offshore and sand ridge layers were sourced from scanning of the original 1:5 million map production material. The remaining nine layers were derived from the GEODATA TOPO 2.5M 2003 dataset. Free online. Available in ArcInfo Export, ArcView Shapefile and MapInfo mid/mif. Product Specifications: Themes: consists of eleven layers: built-up areas; contours; drainage; framework; localities; offshore; rail transport; road transport; sand ridges, spot heights and waterbodies Coverage: Australia Currency: 2004 Coordinates: Geographical Datum: GDA94, AHD Format: ArcInfo Export, ArcView Shapefile and MapInfo mid/mif Medium: Free online 1:10 Million - The GEODATA TOPO 10M 2002 version of this product has been completely revised, including the source information. The data is derived primarily from GEODATA TOPO 250K Series 1 data. In October 2003, the data was released in double precision coordinates. It provides a fundamental base layer of geographic information on which you can build a wide range of applications and is particularly suited to State-wide and national applications. The data consists of ten layers: built-up areas, contours, drainage, Spot heights, framework, localities, offshore, rail transport, road transport, and waterbodies. Coverage: Australia Currency: 2002 Coordinates: Geographical Datum: GDA94, AHD Format: ArcInfo Export, Arcview Shapefile and MapInfo mid/mif Medium: Free online Dataset History 1:1Million - Vector data was produced by generalising Geoscience Australia's GEODATA TOPO 250K Series 1 data and updated using Series 2 data where available in January 2001. Raster data was sourced from USGS and updated using GEODATA 9 Second DEM Series 2, 1:5 million, Vegetation - Present (1988) and National Land and Water Resources data. However, updates have not been subjected to thorough vetting. A more detailed land use classification for Australia is available at www.nlwra.gov.au. Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_48006 1:2.5Million - Data for the Contours, Offshore, and Sand ridge layers was captured from 1:2.5 million scale mapping by scanning stable base photographic film positives of the original map production material. The key source material for Built-up areas, Drainage, Spot heights, Framework, Localities, Rail transport, Road transport and Waterbodies layers was GEODATA TOPO 2.5M 2003 Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_60804 1:5Million - Offshore and Sand Ridge layers have been derived from 1:5M scale mapping by scanning stable base photographic film positives of the various layers of the original map production material. The remaining layers were sourced from the GEODATA TOPO 2.5M 2003 product. Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_61114 1:10Million - The key source for production of the Builtup Areas, Drainage, Framework, Localities, Rail Transport, Road Transport and Waterbodies layers was the GEODATA TOPO 250K Series 1 product. Some revision of the Builtup Areas, Road Transport, Rail Transport and Waterbodies layers was carried out using the latest available satelite imagery. The primary source for the Spot Heights, Contours and Offshore layers was the GEODATA TOPO 10M Version 1 product. A further element to the production of GEODATA TOPO 10M 2002 has been the datum shift from the Australian Geodetic Datum 1966 (AGD66) to the Geocentric Datum of Australia 1994 (GDA94). Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_60803 Dataset Citation Geoscience Australia (2001) Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/310c5d07-5a56-4cf7-a5c8-63bdb001cd1a.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

This Guide is designed to assist you with using ArcGIS Online (AGOL)'s Map Viewer.An ArcGIS web map is an interactive display of geographic information. Web maps are made by adding and combining layers. The layers are made from data, they are logical collections of geographic data.Map Viewer can be used to view, explore and create web maps in ArcGIS Online.

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 photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore Coal Oil Point map area data layers. Data layers are symbolized as shown on the associated map sheets.