Analysis on predictability of urban point-of-interest evolution

doi:10.3785/j.issn.1008-973X.2019.09.016

Journal of ZheJiang University (Engineering Science)

2019, Vol. 53

Issue (9): 1768-1778 DOI: 10.3785/j.issn.1008-973X.2019.09.016

Computer Science and Artificial Intelligence

Analysis on predictability of urban point-of-interest evolution

Qi-long WU(

),Zhi-wen YU*(

),Xin-jiang LU,Bin GUO

School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China

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Abstract

A PLCPA model was established to describe the predictability of urban point-of-interest evolution. Based on the change of urban interest points in continuous time window, a definition of the life cycle length and life cycle status was given and the predictability of point-of-interest evolution was analyzed. Fano’s inequality was combined with the life cycle length and status of urban point-of-interest to quantify the predictability of the evolution of urban point-of-interest. And the predictability calculation of life cycle length and status of urban point-of-interest was given based on the information uncertainty measure in the information theory. The predictability of different categories of point-of-interest with different levels of granularity was calculated according to the urban point-of-interest data of seven cities. Results show that the life cycle of urban interest points is predictable; the predictabilities of different categories of point-of-interest are quite different; the predictability of the points-of-interest in the decaying status is higher than that of the stable status and the booming status.

Key words： urban point-of-interest predictability information entropy Fano’s inequality

Received: 17 December 2018 Published: 12 September 2019

CLC:

TP 399

Corresponding Authors: Zhi-wen YU E-mail: 2014302721@mail.nwpu.edu.cn;zhiwenyu@nwpu.edu.cn

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Cite this article:

Qi-long WU,Zhi-wen YU,Xin-jiang LU,Bin GUO. Analysis on predictability of urban point-of-interest evolution. Journal of ZheJiang University (Engineering Science), 2019, 53(9): 1768-1778.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.09.016 OR http://www.zjujournals.com/eng/Y2019/V53/I9/1768

城市兴趣点演化规律的可预测性分析

建立兴趣点生命周期可预测性模型来刻画城市兴趣点演化规律的可预测性. 该模型针对城市兴趣点在连续时间窗口的变化情况，给出其生命周期长度和生命周期状态的定义，分析兴趣点在演化过程中的可预测性. 为量化城市兴趣点演化规律的可预测性，将城市兴趣点的生命周期长度和生命周期状态的信息熵同Fano不等式相结合，基于信息论中信息的不确定性度量方法给出城市兴趣点生命周期长度和生命周期状态的可预测性计算方法，并结合7个城市的城市兴趣点数据，计算出不同粒度、不同类别的兴趣点的可预测性. 结果表明：城市兴趣点的生命周期是可预测的且不同类别兴趣点的可预测性差异较大；相对于稳定状态和爆发状态的兴趣点，处于消亡状态的兴趣点的可预测性更高.

关键词： 城市兴趣点, 可预测性, 信息熵, Fano不等式

Fig.1 Point-of-interest (POI) life cycle predictability analysis model

Fig.2 Collection process of map data for specified area

Tab.1 Number of POIs corresponding to main category of each city

Fig.4 Cumulative distribution of POI life length

Fig.3 Proportional distribution of POI

Fig.5 Cumulative distribution of POI life lengths in different categories in New York City

Fig.6 Distribution of life cycle states

Fig.7 Entropy distribution of POI life cycle length

Fig.8 Predictability of long POI life cycle

Fig.9 Frequency distribution of life cycle length of POI in leisure category

Fig.10 Predictability of subclass POI life cycle length

Fig.11 Distribution of different types of POIs in London

Fig.12 Predictability of POIs life cycle length in primary categories

Fig.13 Proportion of POIs in different life cycle states in Beijing

Fig.14 Proportion of different types of POIs in Beijing

Fig.15 Predictability of POIs in different categories and different states in London

Fig.16 Predictability of POI life state corresponding to different time windows


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