Metro-Wordle: An Interactive Visualization for Urban Text Distributions Based on Wordle

With the development of cities and the explosion of information, vast amounts of geo-tagged textural data about Points of Interests (POIs) have been generated. Extracting useful information and discovering text spatial distributions from the data are challenging and meaningful. Also, the huge numbers of POIs in modern cities make it important to have efficient approaches to retrieve and choose a destination. This paper provides a visual design combing metro map and wordles to meet the needs. In this visualization, metro lines serve as the divider lines splitting the city into several subareas and the boundaries to constrain wordles within each subarea. The wordles are generated from keywords extracted from the text about POIs (including reviews, descriptions, etc.) and embedded into the subareas based on their geographical locations. By generating intuitive results and providing an interactive visualization to support exploring text distribution patterns, our strategy can guide the users to explore urban spatial characteristics and retrieve a location efficiently. Finally, we implement a visual analysis of the restaurants data in Shanghai, China as a case study to evaluate our strategy.