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Inferring Censored Geo-Information with Non-Representative Data

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 9729)


The goal of this study is to develop a method that is capable of inferring geo-locations for non-representative data. In order to protect privacy of surveyed individuals, most data collectors release coarse geo-information (e.g., tract), rather than detailed geo-information (e.g., street, apt number) when sharing surveyed data. Without the exact locations, many point-based analyses cannot be performed. While several scholars have developed new methods to address this issue, little attention has been paid to how to correct this issue when data are not representative. To fill this knowledge gap, we propose a bias correction method that adjusts for the bias using a bias factor approach. Applying our method to an empirical data set with a known bias associated with gender, we found that our method could generate reliable results despite the non-representativeness of the sample.


  • Bias correction
  • Non-representative data
  • Censored geo-location

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Correspondence to Yu Zhang .

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© 2016 Springer International Publishing Switzerland

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Zhang, Y., Yang, TC., Matthews, S.A. (2016). Inferring Censored Geo-Information with Non-Representative Data. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41919-0

  • Online ISBN: 978-3-319-41920-6

  • eBook Packages: Computer ScienceComputer Science (R0)