- freely available
Sensors 2016, 16(7), 1098; https://doi.org/10.3390/s16071098
- We present “Antenna Virtual Placement” (AVP), a method to geolocate mobile devices connected to network antennas, based on the technology and orientation of the antenna, and a post-processing using Voronoi tessellation. This method decouples the antennas from the cell towers, which is the common spatial unit in the literature.
- We present a method to estimate two important places for a person using a mobile device: home and work. This method works reliably with the information of one day, and has potential to improve accuracy by considering more days.
- We present a method to cluster areas of the city based on floating population patterns measured through mobile connectivity. We use crowdsourced information to explain and characterize those clusters according to land use.
- Given a set of antennas A and a network event e for a device m in a CDR dataset D, estimate a geographical position based on the corresponding antenna a (Section 3.1).
- Given a designed zoning Z, and the set of geographical positions P estimated during a day for all mobile devices, estimate the home and work zones and for a mobile device in (Section 3.2).
- Given a designed zoning Z, and the CDR dataset D, estimate the set of land-usage clusters C, where each cluster c contains a set of zones . Then, characterize each according to the Points of Interest (POIs) located in those zones (Section 3.3).
3.1. Antenna Virtual Placement
- Define the set of antennas A and towers , where each tower τ consists in a subset of antennas and a single antenna belongs to a unique tower. All antennas belonging to the same tower τ have the same geographical position of the tower, denoted . Using the same notation, we have for all .
- For each antenna , obtain the azimuth α, downtilt d and height h (see Figure 1a).
- Obtain the projection of the antenna a, denoted , in the ground using the parameters with simple trigonometric rules. The projection will have the new position .
- Optionally, a relocation of the new positions can be obtained by moving the position to the centroid of the Voronoi polygon generated with the positions : . The relocated position is denoted Each position will belong to a unique Voronoi polygon, so the relocation can be viewed as a bijective map.
- For each network event e, where the active antenna is a, set the position of the mobile device m as .
3.2. Important Places at Individual Level
- Define time windows during the day that are likely to be related to home/work. For instance, to model home location we consider two time windows: one in the range 6:00 A.M. to 8:00 A.M., and one during 8:00 P.M. to 10:00 P.M.
- In all defined time windows, we weight network events using the exponential distribution:
- For each mobile antenna a, we estimate as the sum of the weights of its corresponding records in a specific time window t.
- To determine the regularity of citizen behavior in the different time windows, we use an intersection metric converted to a distance. To calculate this distance, we define a as an antenna and as its weight in the time window t:
- The device home/work locations are the weighted interpolations of the antenna positions (, estimated with AVP at Section 3.1) the mobile device was connected to in the corresponding time windows.
3.3. Determining Land-Usage Patterns
- We analyze network events at zone level. For each zone z (which may or not may be designed), we build a time-series :
- For each time-series , we build a smoothed time-series using LOWESS (Locally Weighted Scatterplot Smoothing) interpolation. This allows us to smooth noise and drastic changes in the number of connections in consecutive intervals of time, as well as to interpolate the number of network events between minutes. This is needed because CDR data is sparse.
- To quantify how near (or similar) the time-series are we build a pairwise distance matrix M. Each element contains the correlation distance (as in ) between time-series u and v, defined as follows:
- Having the pairwise distance matrix between all smoothed zone time-series, we estimate agglomerative clustering using Ward variance minimization . As result, we have a dendrogram of locations.
- We flatten the dendrogram of locations. If the number of desired clusters is known, the flattening can be performed based on cophenetic distance  between locations. This distance is the height of the dendrogram where the corresponding location branches merge into a single branch.
4.1. Santiago 2012 Travel Survey
4.2. Mobile Antennas
4.3. Call Detail Records
5. Case Study: Santiago, Chile
5.1. Antenna Virtual Placement
5.2. Important Places
5.3. Land-Use Results
6. Discussion and Conclusions
6.2. Limitations and Future Work
6.3. Concluding Remarks
Conflicts of Interest
Antenna Virtual Placement
Base Transceiver Station
Cumulative Distribution Function
Call Detail Records
Kernel Density Estimation
Pointwise Mutual Information
Point of Interest
Appendix A. Data Events in Call Detail Records
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|Cluster ID||% of City Surface||Top-15 Venues||Label|
|Cluster 1||13.34||turning_circle (0.32)||Transition|
|Cluster 2||46.60||fire_hydrant (0.54), neighbourhood (0.34), kindergarten (0.19), bus_stop (0.15), |
school (0.13), turning_circle (0.12)
|Cluster 3||24.66||waste_basket (1.40), bureau_de_change (1.40), bench (1.39), surveillance (1.35), |
computer (1.29), company (1.28), hotel (1.27), clothes (1.17), embassy (1.16),
post_office (1.11), bank (1.08), memorial (1.07), hostel (1.06), cafe (1.04), bicycle_parking (1.03)
|Cluster 4||5.96||museum (1.25), bicycle (1.08), motorcycle (1.03), fire_hydrant (0.87), mall (0.66), |
station (0.47), bar (0.07), bus_stop (0.06), school (0.05)
|Leisure Activities After Working Hours|
|Cluster 5||4.43||brewery (2.36), motorcycle (1.67), government (1.41), commercial (1.30), cinema (1.03), |
turning_circle (1.02), hostel (0.93), department_store (0.81), tower (0.76), books (0.66),
museum (0.57), memorial (0.50), mall (0.49), pub (0.46), university (0.41)
|Civic Districts and Recreation |
Activities Before Working Hours
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