Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users
Abstract
:1. Introduction
2. Location-Based Social Network Data
2.1. Travel Representation
2.2. Representativeness
2.3. Experimental Data
- (a)
- “fake” trips: If the speed of the trip, i.e., (distance from previous check-in to next check-in)/(time lapse between previous check-in and next check-in), is greater than 200 km/h, that means the user is traveling at an extremely high speed. Such a speed is greater than all urban transportation modes, including bus, subway, car, etc. Such trips are considered fake trips.
- (b)
- “incomplete” trips: If the trip time, i.e., time lapse between previous check-in and next check-in, is more than 8h, this means that some visits to some locations are very likely to be missing. For instance, a person checks-in at his or her office at 09:00 and further checks-in at his or her apartment at 19:00. The time period between the two consecutive check-ins is 10 hours. Such a pair of check-ins could only reveal a trip from the office to the apartment, implying that the person works or stays in the office for about 10 hours. This is not very reasonable, since a person is not very likely to work or stay in the office for 10 hours. He or she is very likely to visit other places apart from the office and home in the meantime. It is more likely for an individual to leave the office at 17:00 and go to a supermarket before going back home at 19:00. However, the person may not check-in at the supermarket, so his or her visit to the supermarket is not recorded in his or her historical check-ins.
Gender | Number of | ||
---|---|---|---|
Users | Check-ins | Daily trajectories | |
Male | 50 | 9016 | 1944 |
Female | 46 | 9799 | 1790 |
Total | 96 | 18,815 | 3734 |
3. Methodology
3.1. Characteristics of Individual Activity
3.2. Characteristics of Activities at the Aggregate Level
4. Empirical Study
4.1. Gender Differences in Individual Activity Patterns
4.2. Gender Differences in Activity Patterns at the Aggregate Level
Indicator | Mean | Wilcoxon Test | |
---|---|---|---|
Male | Female | p-value | |
Area of SDE (km2) | 135 | 85 | <0.01 |
Ratio of long to short axis | 59 | 53 | 0.25 |
DLC | 4.6 | 5.5 | <0.01 |
DLCC | 4.0 | 4.3 | <0.01 |
4.2.1. Gender Differences in the Spatial Distribution of Activities at the Aggregate Level
(1) Spatial distribution of male and female users’ activities at the aggregate level
(2) Association of clusters and outliers with land use characteristics
(3) are distant from CBD or sub-CBDs.
Land Use Category | Cluster and Outlier (Male-Female) | |||
---|---|---|---|---|
High-High (%) | Low-Low (%) | Low-High (%) | High-Low (%) | |
Residential Land | 26 | 49 | 36 | 53 |
Mixed Residential & Commercial Land | 10 | 8 | 21 | 1 |
Commercial & Office Land | 14 | 7 | 12 | 4 |
Industrial & Manufacturing Land | 14 | 3 | 4 | 2 |
Transportation & Utility | 7 | 4 | 3 | 5 |
Public Facilities & Institutions | 6 | 13 | 15 | 6 |
Open Space & Outdoor Recreation | 18 | 9 | 4 | 18 |
Parking Facilities | 3 | 4 | 2 | 1 |
Vacant Land | 2 | 3 | 2 | 9 |
4.2.2. Gender Differences in Visited Location Categories
Location Category | Visit (check-in) Percentage | Relative Difference | |
---|---|---|---|
Male | Female | ||
Restaurant | 9.84% | 9.73% | 0.01 |
Home | 6.78% | 6.57% | 0.03 |
Subway station | 5.56% | 5.49% | 0.01 |
Food store | 4.29% | 5.31% | 0.21 |
Café | 5.47% | 4.27% | 0.25 |
Sports site | 4.22% | 4.49% | 0.06 |
Bar | 4.60% | 3.79% | 0.19 |
Bus stop | 4.52% | 2.60% | 0.54 |
Clothing, shoes & accessories | 2.48% | 3.84% | 0.43 |
Office | 4.59% | 2.40% | 0.62 |
4.3. Validity of Investigations
Indicator | Mean | Wilcoxon Test | |
---|---|---|---|
Male | Female | p-value | |
Visited location count | 6.9 | 7.1 | <0.01 |
Distinct activity count | 3.2 | 3.4 | <0.01 |
5. Conclusion and Future Work
Acknowledgments
Author Contributions
Conflicts of Interest
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Sun, Y.; Li, M. Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users. ISPRS Int. J. Geo-Inf. 2015, 4, 1512-1529. https://doi.org/10.3390/ijgi4031512
Sun Y, Li M. Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users. ISPRS International Journal of Geo-Information. 2015; 4(3):1512-1529. https://doi.org/10.3390/ijgi4031512
Chicago/Turabian StyleSun, Yeran, and Ming Li. 2015. "Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users" ISPRS International Journal of Geo-Information 4, no. 3: 1512-1529. https://doi.org/10.3390/ijgi4031512