# Predicting Human Location Using Correlated Movements
## 1 Introduction  
Human mobility prediction can be used in many potential and promising applications, such as location-based recommendation services, contagious disease control, patient tracking, geographic profiling, and urban planning [1; 2; 3; 4; 5]. In particular, in order to prevent the outbreak of infectious diseases (e.g., malaria and flu), which usually spread due to people's travels and interactions, estimating the location of persons who are at a high risk of infection is crucial [1; 4]. In geographic profiling, in order to strengthen security, law enforcement agencies need to analyze movements of suspects and need to determine their most likely position [5]. If someone's recent location is known using a global positioning system (GPS) or other localization techniques (e.g., Wi-Fi, cellular-based systems), it is relatively simple to estimate that person's current or next locations. However, there are cases where someone's recent location sequences are not available. For instance, the GPS function is required to turn off due to lack of battery power in a smartphone even though people want to share their positions. In addition, there are some cases where data is sparse [6; 7] (e.g., call detail records, health record of individuals, and credit card spending data). Moreover, in some cases, it is necessary to estimate locations even when someone may not want to share that information (e.g., geographic profiling of criminals). Therefore, in this work, we consider anew problem which is estimating someone's current or next location, particularly when recent location sequence information is not available. Specifically, the mobility information of other Persons with Correlated Movements (PCM) with a given person is used to estimate the person-of-interest's current or future location, based on the fact that the behavior of one individual is largely related to other members. In other words, the current or next location of a given person is estimated using the known position information of other specific people. Although there were several studies on the prediction of human location using social relationships [8; 9; 10], those works focused on finding direct social ties between people (e.g., friendships) using encounters or calling traces between them. However our work, in addition to friendships, considers behavioral similarity between people, which may not be reflected by direct encounters or co-location events between them. In this paper, we propose a two-phase human location prediction model. More specifically, in the first phase, PCMs with a given person are selected. For this phase, two novel PCM selection methods are proposed: community interaction similarity-based (CISB) and behavioral similarity-based (BSB). In the CISB method, interactions with other community members are considered as well as direct encounters between certain individuals, i.e., the strength of the social relationship between people is measured by not only their direct interactions but also their interactions with other members of society. The motivation is that if two people have a close social tie, then they may have similar patterns of meeting other members of the community. In contrast to the CISB method, which attempts to find PCMs with stronger social ties, the objective of the BSB method is to find PCMs with similar behavioral patterns. As a result, the selected PCMs under the BSB method may not have a strong social relationship with the person-of-interest. In the second phase, motivated by the fact that the movement of an individual is highly correlated with other people, the current or next location of a person is estimated using the position information of selected PCMs. A PCM-based location prediction (PLP) model is developed based on a neural network (NN) [11]. For this paper, two different datasets were used to evaluate the proposed prediction model.