Spatial data mining is a highly emerging field as a consequence of tremendous growth in spatial data collection. Such growth has been made possible through various applications, such as: remote sensing, GIS, environmental assessment, planning, web-based spatial data sharing, and location-based services. Through advanced spatial data mining methods and analysis, valuable knowledge can be extracted. The gained knowledge is used to support decision making based on spatial data. As data based decision making is becoming more and more important and a large proportion of data includes significant spatial components the use of spatial algorithms is becoming an important part of modern data mining. For this thesis the used dataset is based on user data of a smartphone application for indoor navigation. This smartphone application was developed and designed for a fashion trade show in Copenhagen. This thesis evaluates, if it is possible to analyse this movement data to gain beneficial knowledge with the provided toolset of commercial GIS software. The functions that were provided by this software were embedded and adjusted in several scripts to automatically process datasets in post-processing. By testing the feasibility of these methods in post-processing the possibility of future real-time analysis can be evaluated as well. Furthermore, a comparison shall be made how processing large amounts of data differ from smaller datasets and if the use of cloud computing can improve possible issues. In conclusion the study found that it is possible to extract valuable knowledge from the provided movement data despite certain limitations. However, such limitations are primarily related to the aspects of data acquisition rather than the data analysis methods. Firstly, in order to analyse some phenomena, for example detecting movement patterns, large amounts of data are necessary in a dense temporal structure. The weight of this limitation is even more severe for real-time applications. Secondly, a relatively high spatial accuracy is necessary in order to yield high quality results. Lastly, some issues related to pre-processing tasks could be observed, especially concerning coordinate transformations.