Article

Downscaling human mobility data based on demographic, socioeconomic, and commuting characteristics using interpretable machine learning methods

2025-10-13

Abstract excerpt

<title>Abstract</title> <p>Understanding urban human mobility patterns at various spatial levels is essential for social science. This study presents a machine learning framework to downscale origin-destination (OD) taxi trips flows in New York City from a larger spatial unit to a smaller spatial unit. First, correlations between OD trips and demographic, socioeconomic, and commuting characteristics are developed...

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Literature Corpus work: e4a3f478-5180-54ae-9b0c-17af9aeef605
DOI: 10.21203/rs.3.rs-7707829/v1

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