Abstract
Abstract. In recent decades, the growing availability of location-aware devices, such as Global Positioning System (GPS) receivers and smart phones, has provided new challenges and opportunities for policy makers to analyze, model, and predict human mobility patterns. However, previous studies on Bluetooth technologies have mainly focused on applying Bluetooth data to analyzing traffic and optimizing transportation networks or deploying new Bluetooth devices in civil engineering. The use of such datasets in understanding urban dynamics and real-time land use patterns is rather limited. This study develops an extendable workflow to explore urban dynamics from Bluetooth data based on a case study in Austin, Texas. We identified similar mobility patterns in different areas of Austin during various study periods, including the Memorial Day long weekend in 2016 and a national musical festival (South by Southwest). Our main goal is to prove the efficacy of this specific workflow and methodology to understand urban dynamics based on real-time Bluetooth data. The hypothesis is that Bluetooth data is sensitive to the daily patterns of human interactions and movements on the individual level, therefore it can capture detailed dynamic patterns. The proposed research also validates new concepts such as “human sensing” and “social sensing” in the field of geography and spatial sciences, which introduces new opportunities to monitor the human aspects of social life.
Highlights
In recent decades, the growing availability of locationaware devices, such as Global Positioning System (GPS) receivers and smart phones, has provided new challenges and opportunities for policy makers to analyze, model, and predict human mobility patterns (Chen et al 2016, Salganik 2018, Shi et al 2018, Poorthuis and Zook 2017)
This study proposes to identify outlier urban functional regions from Bluetooth data based on a case study in Austin, Texas
Our main objective is to prove the efficacy of this specific workflow and methodology to analyze urban dynamics based on Bluetooth data
Summary
The growing availability of locationaware devices, such as Global Positioning System (GPS) receivers and smart phones, has provided new challenges and opportunities for policy makers to analyze, model, and predict human mobility patterns (Chen et al 2016, Salganik 2018, Shi et al 2018, Poorthuis and Zook 2017). But are not limited to, georeferenced mobile phone records, location-based social media, GPS floating-car data, and Bluetooth tracking data (Delafontaine et al 2012, Yuan and Raubal 2016, Yang et al 2018, Costa et al 2018). Among these data sources, information collected through Bluetooth sensors are effective at capturing intra-urban mobility patterns across street networks due to their high precision and sampling frequency. Because previous studies have demonstrated that various urban regions can be characterized by their activity levels at different times of day (Ahas et al 2015, Calabrese, Ferrari and Blondel 2015), this research adopts time series data to characterize urban dynamics
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
