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Activity Number: 204 - Experimental Design
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313651
Title: Satellite Images and Deep Learning to Indentify Discrepency in Mailing Addresses with Applications to Census 2020 in Houston
Author(s): Zhaozhuo Xu* and Beidi Chen and Alan Ji and Anshumali Shrivastava
Companies: Rice University and Rice University and Rice University and Rice University
Keywords: Houston Census 2020; hidden multi-family (HMF) households; satellite imagery

The accuracy and completeness of population estimation would significantly impact the allocation of public resources. However, the current census paradigm experiences a non-negligible level of under-counting. Existing solutions to this problem by Census Bureau is to increase canvassing efforts, which leads to expensive and inefficient usage of human resources. In this work, we argue that the existence of hidden multi-family households is a major cause for under-counting. Accordingly, we introduce a low-cost but high accuracy method that combine satellite imagery and deep learning technologies to identify all hidden multi-family (HMF) households. With comprehensive knowledge on the HMF households, the efficiency and effectiveness of the decennial census could be largely improved. An extensive experiment demonstrates that our approach can discover over 1800 undetected HMF in single zipcode of the Houston area.

Authors who are presenting talks have a * after their name.

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