JCSE, vol. 15, no. 1, pp.47-57, March, 2021
DOI: http://dx.doi.org/10.5626/JCSE.2021.15.1.47
A Method to Measure the Degree of the Favorite Location
Visiting of Mobile Objects
Dong Yun Choi and Ha Yoon Song Samsung Electronics, Suwon, Korea
Department of Computer Engineering, Hongik University, Seoul, Korea
Abstract: To understand the mobility of humans or things, it is necessary to measure the degrees of location visits in everyday
mobility. In this paper, we discuss measures that can present human preferences to certain locations based on location
data and analysis. From raw positioning data and the concept of location clusters, which are sets of positioning data representing
location areas, several measures can be deduced. First, the location point and location area can be separated
because visiting a pin point location is different from visiting a certain area. Second, the number of visits to a location
and the duration of a visit to a location have different meanings. Third, the rank of the location visited is sometimes more
meaningful than the absolute counts. In consideration of these aspects, we established six basic measures and two
derived measures. The actual calculation of each measure requires raw positioning data to be processed. The raw positioning
data were collected by volunteers over several years of their everyday lives. All measures for multiple volunteers
were generated and analyzed for verification. The processing of raw positioning data to generate measures requires a vast
number of calculations, like big data processing. As a solution, we implemented a generation process using the programming
language R; GPGPU technology was utilized to derive numerical results within areas on able time limit with considerable
speed-ups, because an undesirably large amount of time was required to process measures with CPU-only
machines.
Keyword:
Human location preference; Location visiting frequency inside area; Rank of location visiting frequency; Rank of area visiting frequency; Location visiting duration inside area; Rank of location visiting duration; Rank of area visiting duration; Positioning data analytics
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