STA: A Spatio-Temporal Thematic Analytics Framework for Urban Ground Sensing

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.