TY - GEN
T1 - Inferring censored geo-information with non-representative data
AU - Zhang, Yu
AU - Yang, Tse Chuan
AU - Matthews, Stephen A.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84979057671&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-41920-6_17
DO - 10.1007/978-3-319-41920-6_17
M3 - Conference contribution
AN - SCOPUS:84979057671
SN - 9783319419190
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 229
EP - 235
BT - Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings
A2 - Perner, Petra
PB - Springer Verlag
T2 - 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016
Y2 - 16 July 2016 through 21 July 2016
ER -