The Young Scholar distinction is awarded to Mr Mayrhofer to highlight his ongoing implementation work of a specific classifier leading to an ArcMap Add-In using ArcObjects. (Proceedings AGIT Symposium - | ArcMap Add-In | see also paper).
- Christoph Mayrhofer states: “Conventional methods for cartographic classification are often solely based on underlying attribute values. There are numerous algorithms to determine the resulting classes, such as Jenks Optimal classification, but they do not account for the spatial patterns that are inherent to spatial data. This can cause a visual disruption of areas that would normally be considered a cluster, thus making the overall message of a map harder to grasp. With a method called “Autocorrelation-Based Regioclassification” (TRAUN, C. & M. LOIDL, 2012. Autocorrelation-Based Regioclassification – A self-calibrating classification approach for choropleth maps explicitly considering spatial autocorrelation. International Journal of Geographical Information Science: iFirst 1-17.) an alternative approach was introduced that takes spatial properties into account and classifies data values in respect to their statistical and spatial properties. My work builds upon their method and shows how their approach has been implemented for ArcMap using ArcObjects in C#. The main objectives of the Add-in are to (a) decrease visual noise, (b) emphasize statistically relevant outliers, (c) deal with overlapping classes that result from the method and (d) provide visual tools to aid the understanding and interpretation of the classification. Distinct time steps of a dataset can be additionally considered to extend the concept of “neighborhood” with the temporal dimension. This allows to apply aforementioned benefits to time-enabled visualizations in ArcMap.”
Research on this advanced and highly innovative cartographic method is currently continuing, with Christoph again leading the parallel software development efforts. His track record includes multiple other contributions to significant geospatial research efforts (e.g. a Python Tool to automatically calibrate Landsat 8 imagery into Top of Atmosphere reflectance values, including automatic parsing of different Landsat metadata files, calibration and stacking of the different bands).
Christoph’s work at the interface of advanced geospatial methods and solid implementations serve as a role model for Geoinformatics students, helping us to emphasize and illustrate the need for computational competences in Z_GIS’ study programs.