# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 1 Introduction  
In the continuous pursuit of Smart City planning and development, municipalities try to find areas of interest for both residents and visitors, in order to better plan available services. Accordingly, it is necessary to find new _points of interest (Pols)_ that truly meet the cultural or even leisure and entertainment needs of the population. In this context, pervasive computing and pervasive technology play a key role, as pervasive technology can support tracking and learning of Pols in a continuous way, without impact on personal data privacy, and based on the citizen's preferences. Over the last decade, pervasive technology has been emerging with people-centric hardware and systems and has become more efficient and sustainable. Personal mobile IoT devices [1] such as smartphones are carried around by citizens and can assist in inferring trajectories in an anonymous way, without an impact on privacy. Continuously collected anonymous trajectories represent a set of waypoints. Individual trajectories can then be combined and analysed via different _machine learning (ML)_ approaches to provide an estimation of Pols that relate to the social habits of a population. Related work has been addressing the feasibility and accuracy of relying on pervasive technology as a basis for developing large-scale sensing frameworks that can assist Smart City planning [2, 3]. Such frameworks often address planned Pols by relying on offline data analysis, but not a continuous detection of Pols over time, e.g., related to seasonal events, or with collective interest, which may change with time. There are, therefore, Pols that are not implicitly considered so in Smart Cities, and those are usually the ones that are harder todetect. However, they are highly relevant to achieve efficient city planning, as they are derived from citizens' social behaviour and mobility preferences. In the traditional definition, a PoI is a point of interest (a location) that may be of interest to someone. Our definition of PoI attempts to capture such locations, currently unknown to a municipality or to a community, and that is revealed to us by the social trajectories of a large number of people, which cross a specific location. The aim of our work is to consider non-intrusive technology to detect such Pols. To discover them, we use cell phone traces data that reflect the trajectories followed by people in their daily lives. These trajectories reveal their individual stopping points (_stop points_). A cluster in time and in space of stop points can turn out to be a PoI for the local community or for an individual visitor. While trajectories collected from smartphones assist in detecting Pols, they also include _outliers_. By definition, an outlier is a representation of a point that is beyond the limits of a well-defined population [4]. The traces of stored cell phones, which represent the trajectories followed by users, experience several problems, from communication failures to problems with data recording on the cloud. Hence, an outlier is a cluster of points that derive from an error, and not from stop points. It is this discovery of outliers, in the pursuit of data cleansing, that is reflected in our current work. In this context, this work proposes a design for a novel non-intrusive framework for inference of Pols based on mobility trajectories derived from anonymised smartphone data, and performs a performance analysis of DBSCAN and LOF for outlier detection, in the context of a first functional block of such framework, related to outlier detection. We consider that a relevant framework in this context would be a framework that could assist in inferring similarity in individual (and collective) mobility patterns. Data would be obtained via external devices (e.g., smartphones and other embedded devices carried by citizens), and the inference could be done (a) in real time or (b) close to real time.