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Driven by the wave of urbanization in recent decades, the research topicabout migrant behavior analysis draws great attention from both academia andthe government. Nevertheless, subject to the cost of data collection and thelack of modeling methods, most of existing studies use only questionnairesurveys with sparse samples and non-individual level statistical data toachieve coarse-grained studies of migrant behaviors. In this paper, a partiallysupervised cross-domain deep learning model named CD-CNN is proposed formigrant/native recognition using mobile phone signaling data as behavioralfeatures and questionnaire survey data as incomplete labels. Specifically,CD-CNN features in decomposing the mobile data into location domain andcommunication domain, and adopts a joint learning framework that combines twoconvolutional neural networks with a feature balancing scheme. Moreover, CD-CNNemploys a three-step algorithm for training, in which the co-training step isof great value to partially supervised cross-domain learning. Comparativeexperiments on the city Wuxi demonstrate the high predictive power of CD-CNN.Two interesting applications further highlight the ability of CD-CNN forin-depth migrant behavioral analysis.

CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition