A human mobility dataset collected via LBSLab

Location-Based Services (LBS) have been prosperous owing to technological advancements of smart devices. Analyzing location-based user-generated data is a helpful way to under-stand human mobility patterns, further fueling applications such as recommender systems and urban computing. This dataset documents user activities of location-based services through LBSLab, a smartphone-based system implemented as a mini-program in the WeChat app. The dataset contains ac-tivity data of multiple types including logins, profile view-ing, weather checking, and check-ins with location informa-tion (latitude and longitude), POI and mood indicated, col-lected from 467 users over a period of 11 days. We also present some temporal and spatial data analysis and believe the reuse of the data will allow researchers to better under-stand user behaviors of LBS, human mobility, and also tem-poral and spatial characteristics of people's moods. (c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license

Automated summary

Location-Based Services (LBS) have thrived due to the technological advancements in smart devices. Analyzing user-generated data based on location is a useful method to understand human mobility patterns, and it has also contributed to the development of applications such as recommender systems and urban computing. This dataset from LBSLab, a smartphone-based system within the WeChat app, documents user activities including logins, profile viewings, weather checks, and check-ins with location information, collected from 467 users over an 11-day period. The dataset also provides temporal and spatial data analysis, offering insights into user behaviors, human mobility, and the temporal and spatial characteristics of people's moods.