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Interactive Visualization of Urban Areas of Interest: A Parameter-Free and Efficient Footprint Method

Published: 30 January 2019 Publication History

Abstract

Understanding urban areas of interest (AOIs) is essential to decision making in various urban planning and exploration tasks. Such AOIs can be computed based on the geographic points that satisfy the user query. In this demo, we present an interactive visualization system of urban AOIs, supported by a parameter-free and efficient footprint method called AOI-shapes. Compared to state-of-the-art footprint methods, the proposed AOI-shapes (i) is parameter-free, (ii) is able to recognize multiple regions/outliers, (iii) can detect inner holes, and (iv) supports the incremental method. We demonstrate the effectiveness and efficiency of the proposed AOI-shapes based on a real-world real estate dataset in Australia. A preliminary version of the online demo can be accessed at http://aoishapes.com/.

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Yingjie Hu, Song Gao, Krzysztof Janowicz, Bailang Yu, Wenwen Li, and Sathya Prasad. 2015. Extracting and understanding urban areas of interest using geotagged photos . Computers, Environment and Urban Systems, Vol. 54 (2015), 240--254.
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Mingzhao Li, Zhifeng Bao, Farhana Choudhury, Hanan Samet, Timos Sellis, and Bang Zhang. 2019. AOI-shapes: supporting interactive visualization of urban areas of interest in an incremental way {Draft}. 'http://aoishapes.com/research_paper/'.
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Mingzhao Li, Zhifeng Bao, Timos Sellis, Shi Yan, and Rui Zhang. 2018. HomeSeeker: a visual analytics system of real estate data. Journal of Visual Languages & Computing, Vol. 45 (2018), 1--16.
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Hanan Samet. 2006. Foundations of multidimensional and metric data structures .Morgan Kaufmann.
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  • (2021)AOI-shapes: An Efficient Footprint Algorithm to Support Visualization of User-defined Urban Areas of InterestACM Transactions on Interactive Intelligent Systems10.1145/343181711:3-4(1-32)Online publication date: 3-Sep-2021

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cover image ACM Conferences
WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
January 2019
874 pages
ISBN:9781450359405
DOI:10.1145/3289600
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Publication History

Published: 30 January 2019

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Author Tags

  1. aoi-shapes
  2. area of interest
  3. footprint
  4. geographic visualization
  5. visual search

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WSDM '19 Paper Acceptance Rate 84 of 511 submissions, 16%;
Overall Acceptance Rate 498 of 2,863 submissions, 17%

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  • (2021)AOI-shapes: An Efficient Footprint Algorithm to Support Visualization of User-defined Urban Areas of InterestACM Transactions on Interactive Intelligent Systems10.1145/343181711:3-4(1-32)Online publication date: 3-Sep-2021

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