Business circle population mobility statistics based on mobile trajectory data

LIU Zhi; LIU Hui-ping; ZHAO Da-peng; WANG Xiao-ling

doi:10.3969/j.issn.1000-5641.2017.04.009

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Jul. 2017

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LIU Zhi, LIU Hui-ping, ZHAO Da-peng, WANG Xiao-ling. Business circle population mobility statistics based on mobile trajectory data[J]. Journal of East China Normal University (Natural Sciences), 2017, (4): 97-113, 138. doi: 10.3969/j.issn.1000-5641.2017.04.009

Citation:

LIU Zhi, LIU Hui-ping, ZHAO Da-peng, WANG Xiao-ling. Business circle population mobility statistics based on mobile trajectory data[J]. Journal of East China Normal University (Natural Sciences), 2017, (4): 97-113, 138. doi: 10.3969/j.issn.1000-5641.2017.04.009

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Business circle population mobility statistics based on mobile trajectory data

doi: 10.3969/j.issn.1000-5641.2017.04.009

Institute for Data Science and Engineering, School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China

Received Date: 2016-07-20
Publish Date: 2017-07-25

Abstract

Abstract

With the advancement of urbanization and continental development of big data technology, smart business has become an important part of smart city construction. The popularity, consumer number scale and consumption level of smart business also become the hot spot in the construction of smart city. However, traditional consumer statistics method is based on traditional survey and sampling, etc. All of these traditional methods are high-cost and inefficient. Fortunately, the fast development of data mining technology makes statistics in business circle by analyzing user behavior trajectory data possible. In this paper, we propose a consumer scale analysis method on business circle using user trajectory data. There are three mainly work parts:① How to determine the real boundary of business circle in trajectory data analysis domain is a primary problem, and we can judge a consumer activity within or outside the business circle based on it. Facing this issue, we raise a new method to delineate business circle using k-Nearest Neighbor(kNN) classification algorithm based on the location of base station within business circle.② How to determine the relationship between user and business circle is also a new problem due to uncertainty of trajectory characteristics. We calculate irregular polygon area to evaluate the weight of each base station and also combine with time threshold in order to analyze consumer scale every day.③ Finally, considering large amounts in trajectory data, we propose a big data computing framework BPDA (Business-Circle Parallel Distributed Algorithm), which is based on Hadoop big data platform and Kafka distributed message system, to implement business circle consumers scale analysis system. Moreover, we take Zhongshan Park business circle as an instance to verify the feasibility of our algorithm.
- mobile trajectory data,
- consumer scale analysis,
- kNN classification algorithm,
- business circle outline

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References(20)

References

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