Abstract
Urban planning has always involved getting feedback from various stakeholders and members of public, to inform plans and evaluation of proposals. A lot of rich information comes in textual forms, which traditionally have to be read manually. With advancements in machine learning capabilities, there is potential to tap on it to aid planners in synthesizing insights from large amount of textual feedback data more efficiently. In this paper, we developed a more general urban-centric feedback analysis framework, which encompasses the spatio-temporal thematic of ground sensing. Three essential methods: geotagging, topic modeling, and trend analysis are proposed and a prototype has been implemented. The results of experiments indicate that the proposed framework could not only accurately extract precise geospatial information, but also efficiently analyze the semantic themes based on a probabilistic topic modeling with Latent Dirichlet Allocation. Importantly, the spatial and temporal trends of detected topics indicate the effectiveness of our proposed algorithm and then benefit domain experts in their routine work and reveal many interesting insights on ground sensing matters.
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References
Peter, Q.: Exploit Technology for Smart Urban Planning. Urban Redevelopment Authority, Singapore (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Chang, J.: Collapsed Gibbs sampling methods for topic models (2012)
Bolelli, L., Ertekin, Ş., Giles, C.L.: Topic and trend detection in text collections using latent dirichlet allocation. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 776–780. Springer, Heidelberg (2009). doi:10.1007/978-3-642-00958-7_84
Broder, A.Z., Glassman, S.C., Manasse, M.S., Zweig, G.: Syntactic clustering of the web. Comput. Netw. ISDN Syst. 29(8), 1157–1166 (1997)
Compton, R., Jurgens, D., Allen, D.: Geotagging one hundred million twitter accounts with total variation minimization. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 393–401. IEEE (2014)
Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)
Voutilainen, A.: Part-of-speech tagging. In: The Oxford Handbook of Computational Linguistics, pp. 219–232, Oxford University Press, New York (2003)
Liaw, A., Matthew, W.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)
Thijs, G., Marchal, K., Lescot, M., Rombauts, S., De Moor, B., Rouzé, P., Moreau, Y.: A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes. J. Comput. Biol. 9(2), 447–464 (2002)
Schulz, A., Hadjakos, A., Paulheim, H., Nachtwey, J., Mühlhäuser, M.: A multi-indicator approach for geolocalization of tweets. In: ICWSM (2013)
Crandall, D.J., Backstrom, L., Huttenlocher, D., Kleinberg, J.: Mapping the world’s photos. In: Proceedings of the 18th International Conference on World Wide Web, pp. 761–770. ACM (2009)
Quercia, D., Ellis, J., Capra, L., Crowcroft, J.: Tracking gross community happiness from tweets. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 965–968. ACM (2012)
Acknowledgments
This work was partially supported by the A*STAR Science and Engineering Research Council (SERC) Grant No. 1524100032.
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Chen, G., Yu, L., Ng, W.S., Wu, H., Kunasegaran, U.N. (2017). STA: A Spatio-Temporal Thematic Analytics Framework for Urban Ground Sensing. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_56
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DOI: https://doi.org/10.1007/978-3-319-69179-4_56
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