Databases

Automating Workflows for Efficiency Benefits of Automation in GIS Automating repetitive GIS tasks and workflows provides multiple advantages for GIS analysts and organizations. Key benefits include improved efficiency, consistency, scalability, and integration. By coding workflows instead of performing manual work, GIS professionals can complete more high-value analysis rather than mundane data tasks. Automated processes also…

Defining the Problem Space The exponential growth of spatial data presents new challenges in managing and analyzing geographic information. With massive volumes of spatial datasets available, users need robust tools to query and select features from complex data structures. However, formulating precise spatial queries across large datasets often involves navigating indexes, simplifying geometries, optimizing performance,…

The Problem of Unwieldy Geospatial Data The rapid accumulation of geospatial data from satellite imagery, aerial photography, surveying, and other sources has led to disorganized and fragmented data management systems. Massive datasets with complex geospatial and temporal components make analysis difficult and time-consuming. Consolidating workflows and leveraging built-in GIS data management tools are key solutions…

Integrating heterogeneous geospatial data from diverse sources is a major challenge. Multiple vector and raster datasets often exist in different formats, coordinate systems, and with inconsistent attributes. Successfully fusing these disparate datasets into an interoperable geospatial data fabric requires leveraging various techniques and technologies. The Challenges of Data Interoperability A major barrier to integrating heterogeneous…

Understanding Large Geospatial Datasets Geospatial datasets containing information about locations on the Earth’s surface along with their associated attributes can quickly become large and complex. Defining what constitutes a “large” geospatial dataset depends greatly on the context – data volume that strains hardware or software capabilities can be considered large. For example, regional lidar point…

Checking the Web Interface To find the Jetty version GeoServer is running on via the web interface, first log in to the GeoServer web admin page using your admin credentials. Once logged in, navigate to the “About & Status” page under the “Server” section. On this page, look for the “Jetty” section which contains information…

The Need for Speed in GIS Joins Geographic information systems (GIS) rely heavily on joining different datasets together to unlock insights. For example, joining parcel data to census demographics can reveal trends about housing prices. Joining point data to boundaries can attach meta-information. The usefulness of GIS relies on fast spatial joins. However, naive implementations…

The Problem of Slow Spatial Joins Spatial joins involve combining geographic data based on their spatial relationship. This can be computationally intensive as the process must compute the spatial relationship between all features across potentially large datasets. A naive implementation results in an O(n^2) algorithm which does not scale well. For example, joining two layers…

The Challenges of Big Geospatial Data Geospatial data such as satellite imagery, aerial photography, LiDAR data, and GIS vector data are growing exponentially in size and complexity. A single satellite can now capture terabytes of high-resolution imagery per day. Lidar surveys easily produce billions of 3D point measurements. High-precision GIS data encodes intricate details of…

Enabling File Geodatabase Support QGIS requires additional components to read and write Esri file geodatabases (.gdb). By default, QGIS builds do not include the FileGDB API libraries required to enable file geodatabase access. Users must verify they have an appropriate QGIS version, install the FileGDB driver, and configure the file geodatabase data source to allow…