Ranking the City: The Role of Location-Based Social Media Check-Ins in Collective Human Mobility Prediction

Omid Abbasi,Ali Alesheikh,Mohammad Sharif

doi:10.3390/ijgi6050136

Omid Abbasi, Ali Alesheikh + Show 1 more

Open Access

https://doi.org/10.3390/ijgi6050136

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Abstract

Technological advances have led to an increasing development of data sources. Since the introduction of social networks, numerous studies on the relationships between users and their behaviors have been conducted. In this context, trip behavior is an interesting topic that can be explored via Location-Based Social Networks (LBSN). Due to the wide availability of various spatial data sources, the long-standing field of collective human mobility prediction has been revived and new models have been introduced. Recently, a parameterized model of predicting human mobility in cities, known as rank-based model, has been introduced. The model predicts the flow from an origin toward a destination using “rank” concept. However, the notion of rank has not yet been well explored. In this study, we investigate the potential of LBSN data alongside the rank concept in predicting human mobility patterns in Manhattan, New York City. For this purpose, we propose three scenarios, including: rank-distance, the number of venues between origin and destination, and a check-in weighted venue schema to compute the ranks. When trip distribution patterns are considered as a whole, applying a check-in weighting schema results in patterns that are approximately 10 percent more similar to the ground truth data. From the accuracy perspective, as the predicted numbers of trips are closer to real number of trips, the trip distribution is also enhanced by about 50 percent.

Highlights

Development of new data acquisition techniques has facilitated the study of human mobility patterns
Taking advantages of Global Positioning System (GPS) devices embedded in smartphones, location-based social networks (LBSN) have provided the possibility of studying the relationships between users and places
Similar to the research conducted by Noulas and his colleagues [16], who have presented the rank-based model, Yan et al [17] employed the rank-based model to compare its results with that of their own proposed model, called the Population Weighted Opportunities (PWO) model

Summary

Introduction

Development of new data acquisition techniques has facilitated the study of human mobility patterns. Some reserachers have taken the advantages of LBSN data in context of human mobililty prediction Liu and his colleagues have used social media check-ins to study the inter-urban trip patterns at a collective level. Similar to the research conducted by Noulas and his colleagues [16], who have presented the rank-based model, Yan et al [17] employed the rank-based model to compare its results with that of their own proposed model, called the Population Weighted Opportunities (PWO) model They have used the rank-distance between origins and destinations to compute the model. Liang and his colleagues [18] have overcome the above issue in a sense, by using the population located inside a circle, centered at the origin, with a radius equal to the travel distance They have presented an alternative version of a rank-based model in which the adjustable parameter has been eliminated.

Methodology

1: Let γ40

Findings

Conclusions