Modeling activity spaces using big geo-data: Progress and challenges

Abstract

The growing availability of big geo-data, such as mobile phone data and location-based social media (LBSM), provides new opportunities and challenges for modeling human activity spaces in the big data era. These datasets often cover a large sample size and can be used to model activity spaces more efficiently than traditional travel surveys. However, these data also have inherent limitations, such as the lack of reliable demographic information of individuals and a low sampling rate. This paper first reviews the strengths and weaknesses of various internal and external activity space indicators. We then discuss the pros and cons of using various new data sources (e.g., georeferenced mobile phone data and LBSM data) for activity space modeling. We believe this review paper is a valuable reference not only for researchers who are interested in activity space modeling based on big geo-data, but also for planners and policy makers who are looking to incorporate new data sources into their future workflow.