Abstract
Abstract Spatial data analysis allows for a better understanding of environmental effects on the performance of an organization’s activities. One of the first steps required to process such an analysis is to gather all of the spatialized data corresponding to the elements that might influence the activities. Then, a series of treatments must be processed on those datasets to make them ready to be used in classical data mining tools. Those pre-processing steps are complex and time consuming tasks that may require advanced Geographic Information System (GIS) skills. Moreover, the choices involved in this process influence the quality of analysis results. With the aim of addressing those issues, we developed a tool that automatizes several steps of spatial data pre-processing tasks. To allow for reproducibility, the specifications of our approach, tools, architectures and techniques required are presented in detail. To support the effectiveness of our approach, a case study is presented that focuses on an evaluation of the processing time that is saved and the improvement of the quality of analysis.
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