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
[1]
WANG J, LI C, XIONG Z, et al Survey of data-centric smart city[J]. Journal of Computer Research and De-velopment, 2014, 51 (2): 239- 259
[2]
YUAN Q, CONG G, MA Z, et al. Time-aware point-of-interest recommendation [C] // Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 363–372.
[3]
吕琳媛 复杂网络链路预测[J]. 电子科技大学学报, 2010, 39 (5): 651- 661 LV Lin-yuan Link Prediction on complex networks[J]. Journal of University of Electronic Science and Technology of China, 2010, 39 (5): 651- 661
[4]
KENNEDY L S, NAAMAN M. Generating diverse and representative image search results for landmarks [C] // Proceedings of the 17th International Conference on World Wide Web. Beijing: ACM, 2008: 297–306.
[5]
LIAN D, ZHAO C, XIE X, et al. GeoMF: joint geograph-ical modeling and matrix factorization for Point-of-Interest recommendation [C] // ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014: 831–840.
[6]
CHENG C, YANG H, LYU M R, et al. Where you like to go next: successive Point-of-Interest recommendation [C] // International Joint Conference on Artificial Intelligence. Beijing: IJCAI. 2013: 2605–2611.
[7]
ZHAO W, LI Q, LI B Extracting hierarchical landmarks from urban POI data[J]. Yaogan Xuebao: Journal of Remote Sensing, 2011, 15 (5): 973- 988
[8]
方斌, 张宪, 杨柳 基于 POI 数据的城市边界变化提取研究——以山西运城市城区为例[J]. 现代测绘, 2017, 40 (5): 20- 22 FANG Bin, ZHANG Xian, YANG Liu Study on urban boundary identification based on POI: a case study of Yuncheng City in Shanxi[J]. Modern Surveying and Mapping, 2017, 40 (5): 20- 22
[9]
禹文豪, 艾廷华, 刘鹏程, 等 设施 POI 分布热点分析的网络核密度估计方法[J]. 测绘学报, 2015, 44 (12): 1378- 1383 YU Wen-hao, AI Ting-hua, LIU Peng-cheng, et al Network kernel density estimation for the analysis of facility POI hotspots[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44 (12): 1378- 1383
[10]
YUAN J, ZHENG Y, ZHANG L, et al. Where to find my next passenger [C] // Proceedings of the 13th International Conference on Ubiquitous Computing. Beijing: ACM, 2011: 109–118.
[11]
YU G, YUAN J, LIU Z. Predicting human activities using spatio-temporal structure of interest points [C] // Proceed-ings of the 20th ACM International Conference on Multimedia. Nara: ACM, 2012: 1049–1052.
[12]
LU X, YU Z, SUN L, et al. Characterizing the life cycle of point of interests using human mobility patterns [C] // Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Heidelberg: ACM, 2016: 1052–1063.
[13]
LU X, YU Z, LIU C, et al. Forecasting the rise and fall of volatile point-of-interests [C] // 2017 IEEE International Conference on Big Data. Boston: IEEE, 2017: 1307–1312.
[14]
BIALEK W, NEMENMAN I, TISHBY N Predictability, complexity, and learning[J]. Neural Computation, 2001, 13 (11): 2409- 2463
[15]
ANG A, BEKAERT G Stock return predictability:Is it there?[J]. The Review of Financial Studies, 2006, 20 (3): 651- 707
[16]
SONG C, QU Z, BLUMM N, et al Limits of predictability in human mobility[J]. Science, 2010, 327 (5968): 1018- 1021
[17]
胡伟.改革开放40年中国工业经济发展的区域特征[J/OL].区域经济评论, 2019(1):1–15[2019-01-24]. Https://doi.org/10.14017/j.cnki.2095-5766.2019.0017. HU Wei. Regional characteristics of China's industrial economic development in the 40 years of reform and openin up [J/OL]. Regional Economic Review, 2019(1): 1–15 [2019-01-24]. Https://doi.org/10.14017/j.cnki.2095-5766.2019.0017.
[18]
QIN S M, VERKASALO H, MOHTASCHEMI M, et al Patterns, entropy, and predictability of human mobility and life[J]. PLoS One, 2012, 7 (12): e51353
[19]
SINATRA R, SZELL M Entropy and the predictability of online life[J]. Entropy, 2014, 16 (1): 543- 556
[20]
NAVET N, CHEN S H. On predictability and profitability: would gp induced trading rules be sensitive to the observed entropy of time series? [M] // Natural Computing in Computational Finance. Berlin Heidelberg: Springer, 2008: 197-210.
[21]
AARONSON J, PARK K K. Predictability, entropy and information of infinite transformations [J]. arXiv preprint arXiv: 0705.2148, 2007.
[22]
DELSOLE T Predictability and information theory. Part I: measures of predictability[J]. Journal of the Atmospheric Sciences, 2004, 61 (20): 2425- 2440
[23]
BOLTZMANN L. Lectures on gas theory [M]. Chelmsford: Courier Corporation, 2012: 34.