Hongjian Wang
ABSTRACT
The proliferation of publicly accessible urban data provide new insights on various urban tasks. A frequently used approach is to treat each region as a data sample and build a model over all the regions to observe the correlations between urban features (e.g., demographics) and the target variable (e.g., crime count). To define regions, most existing studies use fixed grids or pre-defined administrative boundaries (e.g., census tracts or community areas). In reality, however, definitions of regions should be different depending on tasks (e.g., regional crime count prediction vs. real estate prices estimation). In this paper, we propose a new problem of task-specific city region partitioning, aiming to find the best partition in a city w.r.t. a given task. We prove this is an NP-hard search problem with no trivial solution. To learn the partition, we first study two variants of Markov Chain Monte Carlo (MCMC). We further propose a reinforcement learning scheme for effective sampling the search space. We conduct experiments on two real datasets in Chicago (i.e., crime count and real estate price) to demonstrate the effectiveness of our proposed method.
- 2010. United States Census Bureau. Demographics Survey. http://www.census.govGoogle Scholar
- Konstantin Andreev and Harald Racke. 2006. Balanced graph partitioning. Theory of Computing Systems 39, 6 (2006), 929-939. Google ScholarDigital Library
- Christophe Andrieu, Nando De Freitas, Arnaud Doucet, and Michael I Jordan. 2003. An introduction to MCMC for machine learning. Machine learning 50, 1-2 (2003), 5-43.Google Scholar
- Richard Bellman and Robert Roth. 1969. Curve fitting by segmented straight lines. J. Amer. Statist. Assoc. 64, 327 (1969), 1079-1084.Google ScholarCross Ref
- Santo Fortunato. 2010. Community detection in graphs. Physics reports 486, 3-5 (2010), 75-174.Google Scholar
- Santo Fortunato and Marc Barthelemy. 2007. Resolution limit in community detection. Proceedings of the National Academy of Sciences 104, 1 (2007), 36-41.Google ScholarCross Ref
- Yanjie Fu, Yong Ge, Yu Zheng, Zijun Yao, Yanchi Liu, Hui Xiong, and Jing Yuan. 2014. Sparse real estate ranking with online user reviews and offline moving behaviors. In Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, 120-129. Google ScholarDigital Library
- Corina Graif, Andrew S Gladfelter, and Stephen A Matthews. 2014. Urban poverty and neighborhood effects on crime: Incorporating spatial and network perspectives. Sociology compass 8, 9 (2014), 1140-1155.Google Scholar
- Ke Li and Jitendra Malik. 2016. Learning to optimize. arXiv preprint arXiv:1606.01885(2016).Google Scholar
- Yexin Li, Yu Zheng, Huichu Zhang, and Lei Chen. 2015. Traffic prediction in a bike-sharing system. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 33. Google ScholarDigital Library
- Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge, Massachusetts. Google ScholarDigital Library
- Giulio Rossetti, Luca Pappalardo, Riivo Kikas, Dino Pedreschi, Fosca Giannotti, and Marlon Dumas. 2016. Homophilic network decomposition: a community-centric analysis of online social services. Social Network Analysis and Mining 6, 1 (2016), 103.Google ScholarCross Ref
- Malcolm Strens. 2003. Evolutionary MCMC sampling and optimization in discrete spaces. In Proceedings of the 20th International Conference on Machine Learning (ICML-03). 736-743. Google ScholarDigital Library
- Richard S Sutton and Andrew G Barto. 1998. Reinforcement learning: An introduction. Vol. 1. MIT press Cambridge. Google ScholarDigital Library
- Hado Van Hasselt, Arthur Guez, and David Silver. 2016. Deep Reinforcement Learning with Double Q-Learning.. In AAAI, Vol. 16. 2094-2100. Google ScholarDigital Library
- Hongjian Wang, Daniel Kifer, Corina Graif, and Zhenhui Li. 2016. Crime rate inference with big data. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 635-644. Google ScholarDigital Library
- Hongjian Wang, Yu-Hsuan Kuo, Daniel Kifer, and Zhenhui Li. 2016. A simple baseline for travel time estimation using large-scale trip data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 61. Google ScholarDigital Library
- Hongjian Wang and Zhenhui Li. 2017. Region Representation Learningvia Mobility Flow. In In Proceedings of CIKM'17. CIKM'17, 10 pages. Google ScholarDigital Library
- Hongjian Wang, Xianfeng Tang, Yu-Hsuan Kuo, Daniel Kifer, and Zhenhui Li. 2019. A Simple Baseline for Travel Time Estimation Using Large-scale Trip Data. ACM Trans. Intell. Syst. Technol. 10, 2, Article 19 (Jan. 2019), 22 pages. Google ScholarDigital Library
- Hongjian Wang, Huaxiu Yao, Daniel Kifer, Corina Graif, and Zhenhui Li. 2017. Non-Stationary Model for Crime Rate Inference Using Modern Urban Data. IEEE Transactions on Big Data(2017).Google Scholar
- Fei Wu, Hongjian Wang, and Zhenhui Li. 2016. Interpreting traffic dynamics using ubiquitous urban data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 69. Google ScholarDigital Library
- Fengli Xu, Yong Li, Huandong Wang, Pengyu Zhang, and Depeng Jin. 2017. Understanding mobile traffic patterns of large scale cellular towers in urban environment. IEEE/ACM Transactions on Networking 25, 2 (2017), 1147-1161. Google ScholarDigital Library
- Jing Yuan, Yu Zheng, and Xing Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 186-194. Google ScholarDigital Library
- Bin Zhang. 2003. Regression clustering. In Data Mining, 2003. ICDM 2003. Third IEEE International Conference on. IEEE, 451-458. Google ScholarDigital Library
- Yu Zheng, Furui Liu, and Hsun-Ping Hsieh. 2013. U-air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1436-1444. Google ScholarDigital Library
- Yu Zheng, Tong Liu, Yilun Wang, Yanmin Zhu, Yanchi Liu, and Eric Chang. 2014. Diagnosing New York city's noises with ubiquitous data. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 715-725. Google ScholarDigital Library
- Yu Zheng, Xiuwen Yi, Ming Li, Ruiyuan Li, Zhangqing Shan, Eric Chang, and Tianrui Li. 2015. Forecasting fine-grained air quality based on big data. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2267-2276. Google ScholarDigital Library
- Zillow.com. 2017. Real Estate Value in Chicago. https://www.zillow.com/.Google Scholar
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