The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

The Southeast Texas Urban Integrated field lab’s Co-design team captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through autonomous flight, and models were processed through the DroneDeploy engine. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point Cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset will support researchers' decision-making processes under uncertainties.

This dataset provides locations and values of water quality parameters from a four-day survey conducted between August 23, 2016 and August 26, 2016 using an Autonomous Underwater Vehicle (AUV) in Great South Bay, New York. Measured parameters include bottom dissolved oxygen (DO), salinity, specific conductance, water temperature, and pH. During the four day period, data was collected along 15 transects of the Great South Bay, totaling 60,480 observation points. From these point data, rasters showing the spatial distribution of bottom dissolved oxygen were generated using an interpolator in a GIS. A unique raster is provided for each day of the survey. All data files for download are available within 'Child Items' below. Observation point data are available as shapefiles while DO rasters are available as TIFFs. Both point data and DO rasters are made available as web mapping services. This allows for use in ArcGIS for Desktop, ArcGIS Online, and other web applications. For additional information on how to use web mapping services please visit http://server.arcgis.com/en/server/10.3/publish-services/linux/what-is-a-map-service.htm. Please note that the .sd files included are not meant to be open as standalone files, but rather were uploaded to generate the online web mapping service links provided.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Proportional change in effective area of similar ecological environments for Mammals as a function of land clearing within the present long term (30 year average) climate (1990 centred) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover.

This metric describes the effects of land clearing on the area of similar environments to each grid cell as a proportion. Each cell is compared with a sample of 60,000 points in both uncleared landscape and degraded landscape (pairwise similarities summed (e.g. a completely similar cell will contribute 1, a dissimilar cell 0, with a range of values in between). The contribution of each cell is then multiplied by a 0 (cleared) to 1 (intact) condition index based on the natural areas layer. By dividing the test area by the current area, we are able to quantify the reduction in area as a function of land use/climate change. Values less than one indicate a reduction, values of 1 no change, and values greater than 1 (rare cases in the north) show an increase in similar environments.

This metric was developed along with others for use in an assessment of the efficacy of the protected area system for biodiversity under climate change at continental and global scales, presented at the IUCN World Parks Congress 2014. It is described in the AdaptNRM Guide “Implications of Climate Change for Biodiversity: a community-level modelling approach”, available online at: www.adaptnrm.org.

Data are provided in two forms: 1. Zipped ESRI float grids: Binary float grids (.flt) with associated ESRI header files (.hdr) and projection files (.prj). After extracting from the zip archive, these files can be imported into most GIS software packages, and can be used as other binary file formats by substituting the appropriate header file. 2. ArcGIS layer package (.lpk): These packages contain can be unpacked by ArcGIS as a raster with associated legend.

Additionally a short methods summary is provided in the file 9sMethodsSummary.pdf for further information.

Layers in this 9s series use a consistent naming convention: BIOLOGICAL GROUP _ FROM BASE_ TO SCENARIO_ ANALYSIS e.g. A_90_CAN85_S or R_90_MIR85_L where BIOLOGICAL GROUP is A: amphibians, M: mammals, R: reptiles and V: vascular plants

Lineage: Proportional change in the area of similar ecological environments was calculated using the highly parallel bespoke CSIRO Muru software running on a LINUX high-performance-computing cluster, taking GDM model transformed environmental grids as inputs. Proportional change was calculated by taking the area of baseline ecological environments similar to each present cell as the denominator and the area of present cells with their contribution scaled by the natural areas condition index (0 degraded to 1 intact) as the numerator. More detail of the calculations and methods are given in the document “9sMethodsSummary.pdf” provided with the data download. GDM Model: Generalised dissimilarity model of compositional turnover in reptile species for continental Australia at 9 second resolution using ALA data extracted 28 February 2014 (GDM: REP_r3_v2) Climate data. Models were built and projected using: a) 9-second gridded climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment b) 9-second gridded climatology for continental Australia 2036-2065 CanESM2 RCP 8.5 (CMIP5): Summary variables with elevation and radiative adjustment Natural Areas Layer (intact to degraded land) Australian Government Department of the Environment (2014) Natural areas of Australia - 100 metre (digital dataset and metadata). Available at http://www.environment.gov.au/metadataexplorer/explorer.jsp and up to date information for Western Australia were provided at 25m Albers projection were reprojected to GDA94, merged and aggregated to a continuous measure of proportion of intact area per grid cell at 9s.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including: I. Beaumont Soccer Club II. Corps’ Port Arthur Resident Office III. Halbouty Pump Station comprises its vicinity IV. Lamar University (Including Exxon Power Plants close to Lamar Univ.) V. MLK Boulevard for aerial images of the industry and the ship channel VI. Salt Water Barrier (include some aerial images about the Big Thicket) Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location. The processed data package including 3D models, geospatial data, mappings, point clouds, and the animation video of Halbouty Pump Station has various file types: The Adobe Suite gives you great software to open .Tif files. You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.

The Southeast Texas Urban Integrated field lab’s Co-design team captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through autonomous flight, and models were processed through the DroneDeploy engine. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point Cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: The Adobe Suite gives you great software to open .Tif files. You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.

GRASS is a Geographic Information System (GIS) used for geospatial data management and analysis, image processing, graphics/maps production, spatial modeling, and visualization. GRASS is used in academic and commercial settings around the world, as well as by many governmental agencies and environmental consulting companies. GRASS is official project of the Open Source Geospatial Foundation and available from the Internet as Open Source software. It runs on MS-Windows, Linux, MacOSX and other operating systems.

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.

Configure the Map Tour template to show a feature photo tour in a native app that will run on your chosen device. Nominate either a single web map, a map gallery query, or an existing web based story map tour to create your app. Add your own branding – icons, colors and images – to make the Map Tour app your own. Windows, Android, iOS, Mac and Linux apps can be generated from this template. For more information on configuring this app, see the Map Tour documentation.

EMODnet Vessel Density Map were created by Cogea in 2019 in the framework of EMODnet Human Activities, an initiative funded by the EU Commission. The maps are based on AIS data purchased by CLS and show shipping density in 1km*1km cells of a grid covering all EU waters (and some neighbouring areas). Density is expressed as hours per square kilometre per month. A set of AIS data had to be purchased from CLS, a commercial provider. The data consists of messages sent by automatic tracking system installed on board ships and received by terrestrial and satellite receivers alike. The dataset covers the whole 2017 for an area covering all EU waters. A partial pre-processing of the data was carried out by CLS: (i) The only AIS messages delivered were the ones relevant for assessing shipping activities (AIS messages 1, 2, 3, 18 and 19). (ii) The AIS DATA were down-sampled to 3 minutes (iii) Duplicate signals were removed. (iv) Wrong MMSI signals were removed. (v) Special characters and diacritics were removed. (vi) Signals with erroneous speed over ground (SOG) were removed (negative values or more than 80 knots). (vii) Signals with erroneous course over ground (COG) were removed (negative values or more than 360 degrees). (viii) A Kalman filter was applied to remove satellite noise. The Kalman filter was based on a correlated random walk fine-tuned for ship behaviour. The consistency of a new observation with the modeled position is checked compared to key performance indicators such as innovation, likelihood and speed. (ix) A footprint filter was applied to check for satellite AIS data consistency. All positions which were not compliant with the ship-satellite co-visibility were flagged as invalid.The AIS data were converted from their original format (NMEA) to CSV, and split into 12 files, each corresponding to a month of 2017. Overall the pre-processed dataset included about 1.9 billion records. Upon trying and importing the data into a database, it emerged that some messages still contained invalid characters. By running a series of commands from a Linux shell, all invalid characters were removed. The data were then imported into a PostgreSQL relational database. By querying the database it emerged that some MMSI numbers are associated to more than a ship type during the year. To cope with this issue, we thus created an unique MMSI/shyp type register where we attributed to an MMSI the most recurring ship type. The admissible ship types reported in the AIS messages were grouped into macro categories: 0 Other, 1 Fishing, 2 Service, 3 Dredging or underwater ops, 4 Sailing, 5 Pleasure Craft, 6 High speed craft, 7 Tug and towing, 8 Passenger, 9 Cargo, 10 Tanker, 11 Military and Law Enforcement, 12 Unknown and All ship types. The subsequent step consisted of creating points representing ship positions from the AIS messages. This was done through a custom-made script for ArcGIS developed by Lovell Johns. Another custom-made script reconstructed ship routes (lines) from the points, by using the MMSI number as a unique identifier of a ship. The script created a line for every two consecutive positions of a ship. In addition, for each line the script calculated its length (in km) and its duration (in hours) and appended them both as attributes to the line. If the distance between two consecutive positions of a ship was longer than 30 km or if the time interval was longer than 6 hours, no line was created. Both datasets (points and lines) were projected into the ETRS89/ETRS-LAEA coordinate reference system, used for statistical mapping at all scales, where true area representation is required (EPSG: 3035).The lines obtained through the ArcGIS script were then intersected with a custom-made 1km*1km grid polygon (21 million cells) based on the EEA's grid and covering the whole area of interest (all EU sea basins). Because each line had length and duration as attributes, it was possible to calculate how much time each ship spent in a given cell over a month by intersecting line records with grid cell records in another dedicated PostgreSQL database. Using the PostGIS Intersect tool, for each cell of the grid, we then summed the time value of each 'segment' in it, thus obtaining the density value associated to that cell, stored in calculated PostGIS raster tables. Density is thus expressed in hours per square kilometre per month. The final step consisted of creating raster files (TIFF file format) with QuantumGIS from the PostgreSQL vessel density tables. Annual average rasters by ship type were also created. The dataset was clipped according to the National Marine Planning Framework (NMPF) assessment area. None

The Nearby template provides a simple native app experience to discover what’s nearby, and includes filters to narrow your search by distance or feature category. Start by creating an Nearby web application and then add your own branding – icons, colors, and splash screen images – to make the Nearby native app your own. Use this template to create mobile apps (iOS, Android) that can be published to the Apple App Store and Google Play Store; or distributed in your others in your organization. Builds build for Windows, Mac, and Linux can also be generated from this template. For more information on configuring this app, see the Nearby documentation.

This is release 2.0 of geoOttaWOW, a Minecraft world based on data sets used in geoOttawa. This version was created with Minecraft PC version 1.7.10, using software that supports version 1.7.*, with NBT version 19133. This smaller, improved version of the original release requires around 494Mb of disk space, so it will run on most common devices where Minecraft has been installed. The zip file is a folder that you will need to un-zip in your saves directory.We have given Ottawa a new spin, allowing us to have flat-sided buildings. Be creative and build the Ottawa you want. We have provided you with the base structure, and done all the heavy lifting. Now it’s up to you to fill in the blocks and add some new structures to Ottawa, or tear down the old. Build and explore Roads, Rails, Rivers, and Ottawa’s buildings in this Minecraft world. This version focuses in on the most popular areas of Ottawa in 2017 and those that will change the most in the year ahead. Starting at Ottawa City Hall users can explore the city, and create an Ottawa the way they want it to be. We have even added a few surprises, celebrating Canada 150. Enjoy your fireworks.Most popular locations

Location

 X      Y       Z

Ottawa City Hall
-320 53.0000 -360

Parliament Hill
-744 57.000 -878

National Gallery of Canada -278
50.0000
-1314

Byward Market
-90 49.000 -1084

Rails

1557 48.000 1635

How to Load geoOttaWOW

1. Locate the Minecraft Saves directory

You will first need to locate the saved file in your Minecraft "saves" folder, as that is where downloaded game files like maps are generally stored.

What is the Minecraft "saves" folder, and how do you locate it? The folder is in your directory of Minecraft files. There are a few ways to locate it:

Using the Minecraft Launcher:

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a. Open the Launcher, and select Edit Profile.

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b. Click the Open Game Dir option. "Dir" is short for "Directory."

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c. Your "saves" folder will be in the .minecraft directory.

Using Windows

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a. Open the Start menu and select Run.

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b. Type (without quotes) "%appdata%.minecraft\saves" and hit Enter.

Using Mac OS

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a. Open the Finder.

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b. Select Go and Go to Folder...

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c. When prompted, enter (without quotes) "~/Library/Application Support/minecraft/saves".

Using Ubuntu Linux

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a. Open the File Manager in your Home directory

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b. In the top menu select GO and Open Location

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c. Type (without quotes) “/.minecraft/saves”

2. Store the Minecraft Map Files

Having located the "saves" folder, you can copy the folder to the "saves" folder.

You can also rename your downloaded map if you like by renaming the folder.

3. Launch Your Downloaded Minecraft Map

If your downloaded map has been saved in the Minecraft "saves" folder, you should be able to select it when you play Minecraft when asked to select a World from your Worlds list.

Update Frequency: Updates should be bi-annual, but will be posted as needed.Contact: GIS Team

Configure the Quick Report template to create a citizen engagement native app that allows you to capture an observation and submit it to an online service. Nominate a publicly shared or secured point, polyline, or polygon feature service where your citizen created features will be saved to. Add your own branding – icons, colors and splash screen images – to make the Quick Report app your own. Windows, Android, iOS, Mac and Linux apps can be generated from this template. For more information on configuring this app, see the Quick Report documentation.

The Attachment Viewer template provides a simple experience to review locations, feature information, and media attachments in a native app. Start by creating an Attachment Viewer web application and then add your own branding – icons, colors, and splash screen images – to make the Attachment Viewer native app your own. Use this template to create mobile apps (iOS, Android) that can be published to the Apple App Store and Google Play Store; or distributed in your others in your organization. Builds build for Windows, Mac, and Linux can also be generated from this template. For more information on configuring this app, see the Attachment Viewer documentation.

Configure the 3D Scene Viewer template to display your own scenes in a native app that will run on your chosen device. Nominate either online 3D web scenes or offline scene packages to define which scenes will display in your app gallery. Add your own branding – icons, colors and splash screen images – to make the 3D Scene Viewer app your own. Windows, Android, iOS, Mac and Linux apps can be generated from this template. For more information on configuring this app, see the 3D Scene Viewer documentation.

The Attachment Viewer template provides a simple experience to review locations, feature information, and media attachments in a native app. Start by creating an Attachment Viewer web application and then add your own branding – icons, colors, and splash screen images – to make the Attachment Viewer native app your own. Use this template to create mobile apps (iOS, Android) that can be published to the Apple App Store and Google Play Store; or distributed in your others in your organization. Builds build for Windows, Mac, and Linux can also be generated from this template. For more information on configuring this app, see the Attachment Viewer documentation.