Abstract
Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors’ opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source’s main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors’ research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data.
Highlights
In December 2019, the coronavirus disease 2019 (COVID-19) was first detected in human beings, which quickly developed into a global pandemic
While the whole world is looking forward to going back to normal life, it is becoming important to advance the understanding of the relationship between the dynamics of human mobility and the spread of COVID-19
Studies have demonstrated that effectively monitoring the human mobility dynamics during the pandemic could benefit modeling the spread and size of epidemics, assessing the effectiveness of ongoing movement-controlling measures, but more importantly, help the government and health authorities to decide whether ease or tighten the mobility restrictions
Summary
In December 2019, the coronavirus disease 2019 (COVID-19) was first detected in human beings, which quickly developed into a global pandemic. Many of these products are cloud-scale applications developed using the state-of-art computing and storage paradigm (e.g., mobile edge computing, fog computing, and distributed data stores), providing intuitive web-style interfaces and visual dashboards to allow users to search, discover, explore, and perform analytics (through machine learning and visualizations) on various IoT-connected data sources in near-real-time with minimum programming efforts An example of these products would be the Elastic Stack, a combination of open-source web-based data science products (Elasticsearch, Logstash, and Kibana) from Elastic (Fakhir, 2018), which is designed to allow users without intensive big-data and coding expertise, through an end-to-end workflow, to discover data from any type and format through Elasticsearch engine, processing collected data using Logstash pipeline, and analyze and visualize that data in real-time through the Kibana online platform, which is powered by a variety of modular data analytics and visualization libraries. As for the data uncertainty associated with the crowdsourced mobility data, it can be managed and analyzed by imposing system-based metadata standards that could help data scientists identify records retrieved using devices without reliable GPS or under poor network quality
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