How Do Cities Flow in an Emergency? Tracing Human Mobility Patterns during a Natural Disaster with Big Data and Geospatial Data Science
2.1. Hurricane Matthew
2.2. The Use of Social Media Data to Study Human Mobility Patterns
2.3. Twitter Data Characteristics Related to Users’ Locations
3. Research Design and Method
3.1. Step 1: Data Collection
3.2. Step 2: Locating Tweets
3.3. Step 3: Identifying Evacuation Zones and Tweets Related to Evacuation Travel
3.4. Step 4: Exploring Temporal Patterns of Evacuation Travel
3.4.1. Visualization of Temporal Patterns of the Frequency of Tweets Created Inside and Outside of Evacuation Zones by Evacuation Zone Residents (EZR)
3.4.2. Separating Noise from Tweets Created by Human Users
3.5. Step 5: Exploring Spatiotemporal Patterns of Evacuation Travel
3.5.1. Visualization of Daily Changing Spatial Patterns of Tweets created by Evacuation Zone Residents (EZR)
3.5.2. Visualization of the Direction of Travel during the Evacuation Period
3.5.3. Modeling Evacuation Travel Patterns of EZR
4. Findings and Interpretation
4.1. The Result of Data Preprocessing and Noise Filtering
4.2. Effects of Noise Removal on Temporal Tweeting Patterns Inside and Outside of Evacuation Zones
4.3. Spatiotemporal Patterns of Evacuation Travels of Twitter Users
4.4. Travel Directions of Evacuation Zone Residents among Twitter Users during the Hurricane Evacuation
4.5. Modeling Human Movements during the Hurricane Evacuation
5. Conclusion and Limitation
Conflicts of Interest
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|(A) The Diagonal Length of Bounding Boxes (km)||(B) The Number of Tweets||(C) Percentage||(D) Cumulative Percentage|
|Sources (Platforms through which Tweets Are Created)||Tweets (Text)|
|Ebb Tide Bot||@ebbtideapp Tide in Summit Bridge, Delaware 09/12/2016 Low 1:38am 0.5 High 7:41am 3.4 Low 1:38pm 0.4 High 7:59pm 3.9|
|@ebbtideapp Tide in Hobcaw Point, South Carolina 09/12/2016 Low 10:51pm 1.1 High 4:44am 5.2 Low 10:52am 0.7 High 5:33pm 6.0|
|@ebbtideapp Tide in Quonset Point, Rhode Island 09/12/2016 Low 10:23pm 0.8 High 4:24am 3.3 Low 10:13am 0.7 High 4:53pm 3.7|
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|South Carolina (SC)||Georgia (GA)||Florida (FL)|
|(a) Tweets created by humans||32,735||33,019||63,642|
|(b) human users||923||504||1179|
|(d) users creating noise||131||79||125|
|(e) total tweets||379,078||280,023||407,177|
|(f) total users||1054||583||1304|
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Han, S.Y.; Tsou, M.-H.; Knaap, E.; Rey, S.; Cao, G. How Do Cities Flow in an Emergency? Tracing Human Mobility Patterns during a Natural Disaster with Big Data and Geospatial Data Science. Urban Sci. 2019, 3, 51. https://doi.org/10.3390/urbansci3020051
Han SY, Tsou M-H, Knaap E, Rey S, Cao G. How Do Cities Flow in an Emergency? Tracing Human Mobility Patterns during a Natural Disaster with Big Data and Geospatial Data Science. Urban Science. 2019; 3(2):51. https://doi.org/10.3390/urbansci3020051Chicago/Turabian Style
Han, Su Yeon, Ming-Hsiang Tsou, Elijah Knaap, Sergio Rey, and Guofeng Cao. 2019. "How Do Cities Flow in an Emergency? Tracing Human Mobility Patterns during a Natural Disaster with Big Data and Geospatial Data Science" Urban Science 3, no. 2: 51. https://doi.org/10.3390/urbansci3020051