Mining urban lifestyles: urban computing, human behavior and recommender systems

Abstract

In the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data 1, 2 , phone 3, 4 and email communication 5 to Twitter activity 6 and open-source contributions on Wikipedia and OpenStreetMap 7, 8 . In particular, the study of the shopping and mobility patterns of individual consumers has the potential to give deeper insight into the lifestyles and infrastructure of the region. Credit card records (CCRs) provide detailed insight into purchase behavior and have been found to have inherent regularity in consumer shopping patterns 9 ; call detail records (CDRs) present new opportunities to understand human mobility 10 , analyze wealth 11 , and model social network dynamics 12 .Regarding the analysis of CDR data, there exists a wide body of work characterizing human mobility patterns. As a notable example, 10 describes the temporal and spatial regularity of human trajectories, showing that each individual can be described by a time independent travel distance and a high probability of returning to a small number of locations. Further, the authors are able to model individual travel patterns using a single spatial probability distribution. There has also been work at the intersection of similar datasets, such as the inference of friendships from mobile phone data 13 , or the analysis such data in relation to metrics on spending behavior such as diversity, engagement, and loyalty 14 . Recent work 15 uses the Jaccard distance as a similarity measure on motifs among spending categories, then applies community detection algorithms to find clusters of users. These studies propose models for either mobility or spending behavior, but not in conjunction.The only known paper that incorporates both aspects 16 frames its analysis only on an aggregate scale of city regions. However, the coupled collaborative filtering methods (also known as collective matrix factorization) used in 16 have been successfully applied in a variety of urban computing applications for data fusion and prediction [17][18][19] , from location-based activity recommendations 20, 21 to travel speed estimation on road segments 22 . Recent work includes methods that use Laplacian regularization 23 to leverage social network information, and use geometric deep learning matrix completion methods to model nonlinearities 24 .In this chapter, we jointly model the lifestyles of individuals, a more challenging problem with higher variability when compared to the aggregated behavior of city regions. Using collective matrix factorization, we propose a unified dual view of lifestyles. Understanding these lifestyles will not only inform commercial opportunities, but also help policymakers and nonprofit organizations understand the characteristics and needs of the entire region, as well as of the individuals within that region. The applications of this range from targeted advertisements a...

Changes in the time-space dimension of human mobility during the COVID-19 pandemic

“…Comparison of the number of out-of-home trips in the same periods depicted by Figure S3.4 for the first lockdown (L1), second lockdown (L2) and third lockdown (L3). The results presented in this figure supports the results in FigureS3 4…”

“…2 D) also indicate differences in the response of urban and rural areas to the lockdowns. Due to the characteristics of the geographic distribution of local amenities in rural areas, people tend to have a greater radius of gyration compared to urban areas 4 .…”

“…The places we visit [1][2][3] , the products we purchase [4][5][6] , the people we interact with [7][8][9] , among others activities, produce digital records of our daily activities. Once decoded and analysed, this digital fingerprint provides a new ground to portray urban dynamics 10,11 .…”

COVID-19 is linked to changes in the time–space dimension of human mobility

“…The places we visit [1][2][3] , the products we purchase [4][5][6] and the people we interact with [7][8][9] , among other activities, produce digital records of our daily activities. Once decoded and analysed, this digital fingerprint provides a new ground to portray urban dynamics 10,11 .…”