Replicable GPS Data Processing Using KNIME
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Title: Replicable GPS Data Processing Using KNIME
Presentation given by Will Jones at The Symposium on Spatiotemporal Data Science.
Abstract: This workflow was developed for the Out Of Eden Walk, a National Geographic project being conducted by journalist Paul Salopek. An essential component of this project is monitoring Paul’s travel on foot through the use of GPS data. While this workflow is applied to a case study processing specific GPS data, it is intended to serve as a general model that can be easily reproduced for future scenarios using KNIME.
Steps in the workflow include importing “raw” location data straight from a field GPS unit and geospatially enriching it with other important environmental variables. These variables include nearby points of interest (e.g., villages), elevation, slope, area population density, and land cover. This process is achieved using native KNIME nodes, the Harvard Spatial Data Lab’s Geospatial Analytics Extension, and the KNIME Python integration. The data can then be mapped as a line feature, and ultimately appended to the existing line representing the path of Paul’s travel. Through this reproducible workflow, spatial data can be easily enriched, providing improved environmental context.