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.
Fig.1Point-of-interest (POI) life cycle predictability analysis model
Fig.2Collection process of map data for specified area
类别
amenity
building
historic
leisure
shop
sport
tourism
北京
3 992
24
0
130
1 609
0
747
柏林
23 308
19
0
2 002
18 617
64
1 210
伦敦
24 364
42
0
982
14 905
61
981
纽约
13 191
27
0
1 848
4 167
24
368
巴黎
18 065
28
0
422
12 741
23
1 291
上海
3 092
18
0
279
1 432
12
322
东京
37 485
20
1
1 024
22 665
14
832
Tab.1Number of POIs corresponding to main category of each city
Fig.4Cumulative distribution of POI life length
Fig.3Proportional distribution of POI
Fig.5Cumulative distribution of POI life lengths in different categories in New York City
Fig.6Distribution of life cycle states
Fig.7Entropy distribution of POI life cycle length
Fig.8Predictability of long POI life cycle
Fig.9Frequency distribution of life cycle length of POI in leisure category
Fig.10Predictability of subclass POI life cycle length
Fig.11Distribution of different types of POIs in London
Fig.12Predictability of POIs life cycle length in primary categories
Fig.13Proportion of POIs in different life cycle states in Beijing
Fig.14Proportion of different types of POIs in Beijing
Fig.15Predictability of POIs in different categories and different states in London
Fig.16Predictability of POI life state corresponding to different time windows
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