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

The Flood Inundation Mapping (FIM) Visualization Deck is a web-based application designed to display and compare flood extent and depth information across various temporal and scenario conditions. It provides a front-end interface for accessing geospatial flood data and interacting with mapped outputs generated from hydraulic modeling.

Core Functions: • Flood Extent Mapping: Visualizes flood extents from modeled scenarios (e.g., 2-year, 10-year, 100-year events) and real-time conditions based on streamflow observations or forecasts. • Flood Depth Visualization: Displays depth rasters over affected areas, derived from hydraulic simulations (e.g., HEC-RAS). • Scenario Comparison: Allows side-by-side viewing of multiple FIM outputs to support calibration or decision analysis. • Layer Management Toolbox: Users can toggle basemaps, adjust layer transparency, load datasets, and control map extents.

Data Inputs: • Precomputed flood inundation extents (raster/tile layers) • Depth grids • Stream gauge metadata • Associated hydraulic model outputs

Technical Stack: • Front-end: Built with JavaScript, primarily using Leaflet.js for interactive map rendering. • Back-end Services: Uses GeoServer to serve raster tiles and vector layers (via WMS/WFS). Uses OGC-compliant services and REST endpoints for data queries. • Data Formats: Raster layers (e.g., GeoTIFF, PNG tiles), vector layers (GeoJSON, shapefiles), elevation models, and model-derived grid outputs. • Database: Integrates with a PostgreSQL/PostGIS backend or similar spatial database for hydrologic and geospatial data management. • Deployment: Hosted via University of Iowa infrastructure, with modular UI elements tied to specific watersheds or study areas.

The dataspatial extension for CKAN enhances the platform's ability to handle and search geospatial data stored in the datastore. It equips CKAN with capabilities to perform geospatial searches within datasets, determine the spatial extent of searches, and integrate with PostGIS and Solr for spatial data management, it is important to note the extension is not currently maintained and may require syntax updates to work with current CKAN versions. While unmaintained, it provides valuable geospatial functionalities. Key Features: Geospatial Searching: Enables users to search datastore data based on geospatial criteria defined by a Well-Known Text (WKT) string, supporting POLYGON or MULTIPOLYGON geometries. Spatial Extent Calculation: Allows determining the geospatial extent (bounding box) of search results, providing a summary of the geographic area covered by the data. PostGIS Integration: Supports storing and querying geospatial data using PostGIS, a spatial database extension for PostgreSQL. Requires creating specific PostGIS columns on resources. Solr Integration: If ckanext-datasolr is also installed, enables indexing and searching geospatial data using Solr, a search platform. Requires configuring Solr to index spatial data. Solr support is enabled via the ckanext-datasolr extension. creategeomcolumns Action: Creates the necessary PostGIS columns on a specified resource, populating them and creating an index if desired. updategeomcolumns Action: Updates the geospatial column for a resource, enabling continual synchronization between the column and data rows. Technical Integration: The dataspatial extension integrates with CKAN by adding plugins that enable geospatial searching and extent calculations. It requires configuration settings in the CKAN .ini file to define the backend (PostGIS or Solr), specify the names of spatial fields, and configure the PostGIS and Solr settings, including required extensions. Commands like create-columns, create-index, and populate-columns manage PostGIS columns. The datastore_search action is extended to support geospatial filters. Benefits & Impact: By implementing the dataspatial extension, organizations can make their datastore data spatially aware, leading to richer discovery and utilization of data based on geographic location. It supports use cases such as environmental monitoring, urban planning, and location-based services by allowing users to find, analyze, and visualize datasets tied to specific geographic areas. Geospatial awareness increases the ability to discover and utilize data effectively.

The ckanext-tsbsatellites extension appears to be designed to enhance CKAN's capabilities, primarily focusing on geospatial data management, potentially within the context of a project or organization named "TSB Satellites". The extension leverages other CKAN extensions like ckanext-spatial and ckanext-harvest to provide a more comprehensive solution. Set up involves Solr for spatial search, GeoNetwork integration, and proper configuration parameters. Key Features: Leverages ckanext-spatial: Relies on the ckanext-spatial extension to provide geospatial search and display functionalities within CKAN, enabling users to search for datasets based on location. Employs ckanext-harvest: Uses ckanext-harvest to automatically import or synchronize metadata from external sources, potentially including GeoNetwork, through a Redis backend. GeoNetwork Integration: Supports integration with GeoNetwork, a metadata catalog, ostensibly for importing and managing geospatial metadata, with instructions for installing and configuring GeoNetwork with PostgreSQL and PostGIS. Instructions are for GeoNetwork v2.10.3 running on Ubuntu 12.04 64bit.