Geospatial extension opens up spatial analytics to non-experts
More and more organizations are using geospatial analysis to improve operations from supply chain logistics, to deployment of equipment in emergency management, to location-based fraud detection, and more. Previously, this type of analysis has required niche expertise and coding skills. Harvard’s Center for Geographic Analysis and KNIME have collaborated to open up spatial analytics to non-expert users. The new Geospatial Analytics Extension for KNIME enables users to access, blend, and analyze geospatial data without the need to code.
This first version of this extension supports the most common vector data types such as points, lines, polygons, and collections of those. Users can download example workflows using the new Geospatial Extension in Harvard’s Geospatial Analytics Examples space from the KNIME Community Hub.
The new Geospatial Extension provides the ability to carry out geospatial analysis within KNIME. In its first version, it will support the most common vector data types such as points, lines, polygons, and collections of those. Major functionality includes: Reading and writing different geospatial files such as shapefiles or GeoJSON files Performing spatial calculations such as computing the area of a polygon, computing distances, performing spatial joins, and other spatial manipulations Spatial Transformation nodes that convert points to lines or harmonize the projection of two different datasets Built-in visualization nodes for interactive views that can be included in KNIME Data Apps, and richly configurable static maps for presentations or reports Nodes to easily access publicly available data such as Open Street Map or the US Census data .
The development of Geospatial Analytics Extension for KNIME is mainly implemented by the joint team from Harvard CGA, KNIME and Wuhan University: Lingbo Liu, Xiaokang Fu, Tobias Koetter, Kevin Sturm.
The Spatial Data Lab is a joint project supported by the Center for Geographical Analysis at Harvard University, Future Data Lab, KNIME, and the NSF Spatiotemporal Innovation Center, which is designed to promote repeatable, replicable, and scalable spatiotemporal data science research, innovation, applications and global collaboration.