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Advanced Wireless Solutions (Case Studies on Application Scenarios)
Published in Abid Hussain, Garima Tyagi, Sheng-Lung Peng, IoT and AI Technologies for Sustainable Living, 2023
Land mobile radio/professional mobile radio dPMROpenSkyEDACSDMRTETRAP25
Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges
Published in International Journal of Digital Earth, 2021
Tao Hu, Siqin Wang, Bing She, Mengxi Zhang, Xiao Huang, Yunhe Cui, Jacob Khuri, Yaxin Hu, Xiaokang Fu, Xiaoyue Wang, Peixiao Wang, Xinyan Zhu, Shuming Bao, Wendy Guan, Zhenlong Li
The quality of mobility data depends on data types, and this makes it challenging to assess data quality without careful comparison studies. For example, the OpenSky-Network provides freely accessible airline flight data since 2019; however, it does not provide every global flight movement but only those ADS-B-equipped aircraft seen within the coverage (The OpenSky Network 2020). Additionally, the monitored ADS-B-equipped aircraft data are mostly concentrated only in North America and Europe (Iacus et al. 2020). The smart-card-based public transit data usually contain a certain amount of missing data due to human errors made by smart card users (e.g. forgetting to tap in/out) or due to the malfunction of collection devices (e.g. tapping machines not working) (Liu, Wang, and Xie 2019). Furthermore, the producers of social activity data and index-based mobility data (e.g. telecommunication and IT companies, research institutes, and universities) consider that their data quality is reasonably controlled through data validation and calibration. However, most of these mobility data suffer from representativeness issues as the data are limited to only mobile phone users or app users whose locational function is turned on. For example, geotagged tweets only account for a small portion of the entire tweets (Jurdak et al. 2015; Martín et al. 2020). The accuracy of the geographic locations in geotagged tweets also varies as users may only geotag their posts at the city or state level rather than specific GPS coordinates. Therefore, more efforts are needed to refine, clean, anonymize, combine, and compare multiple mobility data types to ensure data quality and reliability.