Human Mobility Mining Using Spatio-Temporal Data
Geospatial technologies have become an integral part of our lives. With technological progress and rapid increase of geospatial information and inexpensive positioning technologies, more space-related data is becoming available at any time. Data is collected using multiple sources such as GPS and mobile computer logs, wireless communication devices, location-aware services and other positioning systems. This gives scientists the opportunity to create new innovative platforms for spatio-temporal data analysis and improve methods for mining and visualization for decision support. In order to provide a good decision support systems, it is vital to understand people’s movement, mobility behaviour and be able to discover hidden patterns and associations in their daily activities. The aim of this thesis is to analyze and discuss spatial data mining techniques by answering questions like what kinds of patterns can be extracted from spatio-temporal data or which methods are best for predicting human mobility behavior. In this work, we verify existing methodologies and theories about spatio-temporal data mining and propose a sequence of algorithms to achieve good human mobility prediction. We evaluate the results and propose a methodology for adaptive data mining of human mobility behavior.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science