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Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems. [ABSTRACT FROM AUTHOR]

Activity space studies are beneficial for discovering meaningful activity patterns and providing a deeper understanding of human behaviors. There is insufficient research, however, on how reliable location-based social media (LBSM) is as a new data source for discovering user activity spaces. To this end, this research calculates four external and three internal activity space indicators based on Weibo data from three Chinese cities. We compared the strengths and weaknesses of these indicators for approximating user activity spaces from LBSM data with a low sampling resolution. We also tested how different amounts of check-in data affect the calculation of these activity space indicators. The results provide a useful reference for future experimental design in human activity modeling based on social media data. [ABSTRACT FROM AUTHOR]

Social networking sites (SNS), such as Facebook and Twitter, have attracted users worldwide by providing a means to communicate and share opinions and experiences of daily lives. When empowered by pervasive location acquisition technologies, location-based social media (LBSM) has become a potential resource for smart city applications to characterize social perceptions of place and model human activities. There is a lack of systematic examination of the representativeness of LBSM data, though. If LBSM data are applied to decision making in smart city services, such as emergency response or transportation, it is essential to understand their limitations to implement better policies or management practices. This study formalizes the sampling biases of LBSM data from various perspectives, including sociodemographic, spatiotemporal, and semantic. This article examines LBSM data representativeness issues using empirical cases and discusses the impacts on smart city applications. The results provide insights for understanding the limitations of LBSM data for smart city applications and for developing mitigation approaches. Key Words: data quality, location-based social media, sampling biases, smart city. [ABSTRACT FROM AUTHOR]