PROGRESS IN GEOGRAPHY ›› 2016, Vol. 35 ›› Issue (5): 580-588.doi: 10.18306/dlkxjz.2016.05.005

• Reviews • Previous Articles     Next Articles

Progress of studies on indoor positioning data analysis and application

Hua SHU1,2(), Ci SONG1, Tao PEI1,*()   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Accepted:2016-01-01 Online:2016-05-27 Published:2016-05-27
  • Contact: Tao PEI;
  • Supported by:
    National Science Fund for Distinguished Young Scholars of China, No.41525004;Science Fund for Creative Research Groups of the National Natural Science Foundation of China, No.41421001;Classification and Encoding on Aviation Passengers’ Information Service, No.4700003334


Human spatiotemporal behavior increasingly draws attention in modern human geography, and obtaining individuals’ spatiotemporal location data in travel activities is the precondition to study the spatiotemporal behavior of people. Limited by data acquisition technology, previous studies about behavior in space and time mostly focused on behavior in the outdoor space of a city. With the occurrence of technology for indoor positioning, the spatial scale of this type of study has been extended to the indoor space. Although there is a wide range of technologies and methods for indoor positioning, according to the manner that these data are acquired, indoor positioning data can be divided into three categories, that is, geometric location data, fingerprinting location data, and symbolic location data. Currently, studies based on indoor positioning data can be divided into the following categories: (1) spatiotemporal distribution of people in indoor space; (2) movement pattern of people in indoor space; (3) behavioral habit and attribute inference of people in indoor space; and (4) interaction between people and indoor surroundings. However, most of these studies are still at a primary stage and there are no commonly accepted theories and methodologies at present. We believe that studies in the future may pay attention to the following issues: (1) with regard to data acquisition, the positioning system accomplished through “smart phone plus WiFi” that has wider coverage and lower cost, requires no special equipment, and interacts easily compared with Bluetooth, Radio Frequency Identification (RFID), and Infrared Ray (IR),and so on, is the most promising indoor positioning technology; (2) with regard to research contents, behavioral characteristics in space and time will be the basis, individuals’ attribute inference and interaction between individuals and indoor surroundings will be the focus, and analysis of multiscale fused spatiotemporal location data will be the trend of future development; (3) in terms of scientific ethics, privacy issues must be highlighted concerning the recording of individuals’ travel activity data at the micro scale.

Key words: positioning data, indoor positioning, data analysis, spatiotemporal behavior, big data, review