Abstract
This study aims at revealing how commercial hotness of urban commercial districts (UCDs) is shaped by social contexts of surrounding areas so as to render predictive business planning. We define social contexts for a given region as the number of visitors, the region functions, the population and buying power of local residents, the average price of services, and the rating scores of customers, which are computed from heterogeneous data including taxi GPS trajectories, point of interests, geographical data, and user-generated comments. Then, we apply sparse representation to discover the impactor factor of each variable of the social contexts in terms of predicting commercial activeness of UCDs under a linear predictive model. The experiments show that a linear correlation between social contexts and commercial activeness exists for Beijing and Shanghai based on an average prediction accuracy of 77.69% but the impact factors of social contexts vary from city to city, where the key factors are rich life services, diversity of restaurants, good shopping experience, large number of local residents with relatively high purchasing power, and convenient transportation. This study reveals the underlying mechanism of urban business ecosystems, and promise social context-aware business planning over heterogeneous urban big data.
- J. Blumenstock, G. Cadamuro, and R. On. 2015. Predicting poverty and wealth from mobile phone metadata. Science 350, 6264 (2015), 1073--1076.Google Scholar
- J. Chen, S. Yang, W. Wang, and M. Wang. 2015. Social Context Awareness from Taxi Traces: Mining How Human Mobility Patterns Are Shaped by Bags of POI. In the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015). ACM, Osaka, Japan, 97--100. Google ScholarDigital Library
- L. Chen, D. Zhang, G. Pan, X. Ma, D. Yang, Kostadin. Kushlev, and etc. 2015. Bike Sharing Station Placement Leveraging Heterogeneous Urban Open Data. In the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015). ACM, Osaka, Japan, 571--575. Google ScholarDigital Library
- S. Chen, D L. Donoho, and M A. Saunders. 1998. Atomic decomposition by basis pursuit. SIAM journal on scientific computing 20, 1 (1998), 33--61. Google ScholarDigital Library
- B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. 2004. Least angle regression. The Annals of statistics 32, 2 (2004), 407--499.Google ScholarCross Ref
- Y. Jing, Y. Zheng, and X. Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. In the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, Beijing, China, 186--194. Google ScholarDigital Library
- D. Karamshuk, A. Noulas, S. Scellato, V. Nicosia, and C. Mascolo. 2013. Geo-spotting: Mining online location-based services for optimal retail store placement. In In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). Chicago, USA, 793C801. Google ScholarDigital Library
- De Nadai. M, Staiano. J, Larcher. R, Sebe. N, Quercia. D, and Lepri. B. 2016. The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective. In Proceedings of the 25th International Conference on World Wide Web. Montreal, Canada, 413--423. Google ScholarDigital Library
- E. M. 2010. Sparse and redundant representations-From theory to application in signal and image processing. Springer. Google ScholarDigital Library
- V. K. Singh, L. Freeman, B. Lepri, and A. Pentland. 2013. Predicting Spending Behavior using Socio-Mobile Features. In International Conference on Social Computing IEEE. Washington D.C, USA, 174--179. Google ScholarDigital Library
- F. Wang, Y. Li, and X. Gao. 2016. A SP survey-based method for evaluating environmental performance of urban commercial districts: A case study in Beijing. Habitat International 53 (2016), 284--291.Google ScholarCross Ref
- M. Wang, S. Yang, Y. Sun, and J. Gao. 2016. Predicting Human Mobility from Region Functions. In IEEE Cyber, Physical and Social Computing (CPSCom). Chengdu, China, 540--547.Google Scholar
- Z. Yu, D. Zhang, and D. Yang. 2013. Where is the Largest Market: Ranking Areas by Popularity from Location Based Social Networks. In In Proceedings of the 2013 IEEE 10th International Conference on Autonomic and Trusted Computing (UIC/ATC). TBD, Italy, 157C162. Google ScholarDigital Library
- Y. Zhang, B. Li, and J. Hong. 2016. Understanding User Economic Behavior in the City Using Large-scale Geotagged and Crowdsourced Data. In Proceedings of the 25th International Conference on World Wide Web. Montreal, Canada, 205--214. Google ScholarDigital Library
- Y. Zheng, F. Liu, and Hsun-Ping Hsieh. 2013. U-Air: When urban air quality inference meets big data. In the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, Chicago, I L, USA, 1436--1444. Google ScholarDigital Library
Index Terms
- Predicting Commercial Activeness over Urban Big Data
Recommendations
Urban Perception of Commercial Activeness from Satellite Images and Streetscapes
WWW '18: Companion Proceedings of the The Web Conference 2018People can percept social attributes from streetscapes such as safety, richness, and happiness by means of visual perception, which inspires the research in terms of urban perception. To the best of our knowledge, this is the first work focused on ...
Urban Informatics beyond Data: Media Architecture, Placemaking, and Citizen Action
UCUI '15: Proceedings of the ACM First International Workshop on Understanding the City with Urban InformaticsSince 2006, we have been conducting urban informatics research that we define as "the study, design, and practice of urban experiences across different urban contexts that are created by new opportunities of real-time, ubiquitous technology and the ...
Urban: crowdsourcing for the good of London
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide WebFor the last few years, we have been studying existing social media sites and created new ones in the context of London. By combining what Twitter users in a variety of London neighborhoods talk about with census data, we showed that neighborhood ...
Comments