DOI: 10.5281/zenodo.6369319
title: Spatiotemporal checkins with social connections

description: <strong>Introduction</strong> These three datasets are used in the analysis of human mobility research paper [1]. For each dataset, there are checkins info and friendshio info, Brightkite: "brightkite_checkins.csv" and "brightkite_friends.csv". Gowalla: "gowalla_checkins.csv" and "gowalla_friends.csv". Weeplaces: "weeplace_checkins.csv" and "weeplace_friends.csv" <strong>Basic Description </strong> <em>BrightKite</em> [2] is a LBSN service provider that allowed registered users to connect with their existing social ties and also meet new people based on the places that they go. Once a user "checked in" at a place, they could post notes and photos to a location and other users could comment on those posts. The social relationship network was collected using their public API. The raw dataset is from SNAP https://snap.stanford.edu/data/loc-brightkite.html.<br> <em>Gowalla</em> [2] is a LBSN website where users share their locations by checking-in. In early versions of the service, users would occasionally receive a virtual "Item" as a bonus upon checking in, and these items could be swapped or dropped at other spots. Users became "Founders" of a spot by dropping an item there. This incentivises users to create new check-ins, not necessarily to check-in consistently at frequently visited locations. The social relationship network is undirected and was collected using their public API. The raw dataset is from SNAP https://snap.stanford.edu/data/loc-gowalla.html.<br> <br> <em>Weeplaces</em> --This is collected from Weeplaces and integrated with the APIs of other LBSN services, e.g., Facebook Places, Foursquare, and Gowalla. Users can login Weeplaces using their LBSN accounts and connect with their social ties in the same LBSN who have also used this application. Weeplaces visualizes your check-ins on a map. Unlike Gowalla, there is no direct incentive in Weeplaces to alter one's visitation habits or check-ins, so there should be a more accurate representation of a regular person's mobility patterns.<br> The raw dataset is from the website https://www.yongliu.org/datasets/. More details can be found in the data description of paper [1]. <strong>Reference</strong> [1] Chen, Z., Kelty, S., Welles, B.F., Bagrow, J.P., Menezes, R. and Ghoshal, G., 2021. Contrasting social and non-social sources of predictability in human mobility. <em>arXiv preprint arXiv:2104.13282</em>. [2] Cho, Eunjoon, Seth A. Myers, and Jure Leskovec. "Friendship and mobility: user movement in location-based social networks." In <em>Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining</em>, pp. 1082-1090. 2011.
description: {"references": ["Chen, Z., Kelty, S., Welles, B.F., Bagrow, J.P., Menezes, R. and Ghoshal, G., 2021. Contrasting social and non-social sources of predictability in human mobility.\u00a0arXiv preprint arXiv:2104.13282.", "Cho, Eunjoon, Seth A. Myers, and Jure Leskovec. \"Friendship and mobility: user movement in location-based social networks.\" In\u00a0Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1082-1090. 2011."]}