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A framework to embed a spatial statistics toolbox in open-source GIS software: kernel density estimation example

Cavur, M.
Düzgün, Hafize Şebnem
It is widely known that geographic information systems (GIS) should include more spatial data analysis (SDA) techniques. The issues of which techniques should be included and how statistical analysis can be integrated with GIS are still widely debated. This paper focuses on the development of a framework that implements R SDA techniques in the uDig. For this purpose, a simple interface is designed between two open-source software applications, uDig and the R statistical software package. The tight coupling strategy is adopted with the RCaller interpreter. Overall, the integration is successfully implemented and tested by users and developers. Fourteen geospatial and four non-geospatial techniques are integrated into uDig successfully, which demonstrates the success of the proposed framework. Among these techniques, the kernel density estimation (KDE) technique is explained with a sample data set to show every step of the implementation. The user tests also prove the success of the integration.