GIS Graduate Seminar: Casey Judge
Spatial-Temporal Interpolation of Survey Point Data: Toolset Development for Multiple Applications using Python 2.7 and ArcGIS 10.4
Casey Judge, GIS Certificate Candidate, Department of Ecology, Evolution, and Organismal Biology
Advisor: Dr. William Crumpton
Spatial survey point data is widely collected for numerous applications (i.e. vegetation, population density, etc). Often, this point data is then interpolated to visualize the spatial distribution of the survey data. However, spatial data can be difficult and costly to gather and is often only collected intermittently. To model changes in the data over time between collection dates, a spatial-temporal interpolation is required. Temporal interpolation is not built in to ArcGIS processing tools, but can be conducted with VBA code by first exporting raster grid cell data to Microsoft Excel. However, this technique is cumbersome and time consuming. The objective of this project is to develop a script set with Python 2.7 to create an ArcGIS toolbox for conducting coupled spatial and temporal interpolations which can be used for a wide array of applications. The contained toolset will provide spatial interpolation using optimized kriging methods with the correct output format required for the temporal interpolation. Subsequent temporal interpolation will allow user specified linear or spline methods to model growth and/or decay between survey dates. An example of the toolset’s utility will be shown using submersed vegetation survey data from three Iowa wetlands collected approximately bi-weekly during the 2016 growing season.