Mapping population and mobile pervasive datasets into individual behaviours for urban ecosystems

Abstract

Mobile phone network data routinely collected by providers possess very valuable and encoded information about human behaviours. In order to obtain the information it is necessary to carry out an arduous extraction process. Nevertheless, this information would be of fundamental importance for a successfully building and operating smart urban ecosystem understood as a self-organized and open system gathering and using knowledge about smart city environment. Intensive tourist activities in urban spaces bring smartness via mobile phone fingerprints into urban ecosystems and municipal services. This paper provides a unified approach comprising both informal (use cases) and formal (algorithms) elements to obtain a common framework which after ignoring redundant information maps pervasive datasets into a collection of individual patterns and anonymized tourist behaviours in urban spaces. They strongly influence municipal services to understand urban context and operate more effectively to support tourist activities to become more safe and comfortable.