A Deep Learning Approach for Identifying User Communities Based on Geographical Preferences and Its Applications to Urban and Environmental Planning

Abstract

Understanding human mobility plays a vital role in urban and environmental planning as cities continue to grow. Ubiquitous geo-location, localization technology, and availability of big-data-ready computing infrastructure have enabled the development of more sophisticated models to characterize human mobility in urban areas. In this work, our main goal is to extract spatio-temporal features that characterize user mobility and, based on the similarity of these features, identify user communities . To this end, we propose a novel approach that leverages image processing techniques to represent user geographical preferences as images and then apply deep convolutional autoencoders to extract latent spatio-temporal mobility features from these images. These features are then fed to a clustering algorithm that identifies the underlying community structures. We use a diverse urban mobility dataset to validate the proposed framework. Our results show that the proposed framework is able to significantly increase the similarity between intra-community nodes (by up to 107%) as well as dissimilarity between inter-community nodes (up to 54%) when compared against no pre-processing of the datasets, i.e without pre-processing the datasets through any feature fusion method. Moreover, it was also able to reach up to 100% improvement when compared against community identification using Principal Component Analysis (PCA). Our results also show that the proposed approach yields significant increase in contact time amongst users belonging to the same community, by up to 80% when compared to the average contact time when not considering community structures, and by up to 150% when compared to the baseline. To the best of our knowledge, our proposal is the first to consider deep convolutional autoencoding to perform automatic extraction of non-linear spatio-temporal mobility features characterizing individual users from raw mobility datasets with the goal of identifying user communities.

Privacy-Aware Adversarial Network in Human Mobility Prediction

“…1 Similarity of Trajectories. Grounded on the literature on mobility [13,29,38], we mathematically denote the notion of trajectory similarity (𝑆𝐼𝑀 𝑡 ) based on i) the structural similarity index of mobility heatmap images; and ii) the entropy of trajectories.…”

“…Structural Similarity Index Measure (SSIM): SSIM was initially designed to quantify image quality degradation caused by processing, such as data compression or losses in data transmission, which leverages the differences between the reference image and the processed image [40]. To apply SSIM metrics in this work, we construct heatmap images from the raw geo-located data with the methodology proposed by [13]. Figure 1 shows some sample heatmap images with spatial granularity coarsening from 50 meters to 900 meters by the left to right.…”

“…Geolocation and mobility data collected by location-based services (LBS) [32], can reveal human mobility patterns and address various societal research questions [34]. For example, Call Data Records (CDR) have been successfully used to provide real-time traffic anomaly as well as event detection [54,56], and a variety of mobility datasets have been used in shaping policies for urban communities [19] and epidemic management in the public health domain [44,45].…”

“…Understanding human mobility based on collected locations from mobile devices has become a fundamental part of urban and environmental planning in cities [28]. These GPS traces enable the scientific community and policymakers to model citizens' daily mobility patterns (e.g., crowd-sensed car sharing, ride sharing, city bicycles sharing, and RFID-card-based public transportation, or build predictive algorithms to estimate people's flows and community structure [13]. However, location-based traces corresponding to human mobility, even at an aggregate level, have raised numerous privacy concerns [8,38], mainly when the data contains sensitive and revealing insights about people's identity, behaviour, associations, religion, and others [23].…”

Fairness in Federated Learning for Spatial-Temporal Applications

“…Understanding human mobility based on location-based data generated by smartphone devices has become a fundamental part of the urban and environmental planning in cities. Through collection of these geo-traces, it has become possible for the scientific community and policy-makers to model citizens' daily commutes using crowd-sensed carshare data (Ke et al 2017), city bicycles (Li et al 2015) and RFID transportation cards (Silva, Kang, and Airoldi 2015;Mashhadi et al 2016), or to build predictive algorithms to estimate people's flows (Hoang, Zheng, and Singh 2016;Zhang, Zheng, and Qi 2017) and community structure (Ferreira et al 2020;Chuah and Coman 2009).…”