## 2 Materials and Methods
During the weekend, this number falls to less than four million on Saturday and less than 2.5 million on Sunday [49]. Transactions are recorded in a database that contains information about the operator and the time when the transaction was made. Each passenger has to make a transaction by putting the smart-card close to a payment device when entering a bus, bus station, or Metro station. Each payment device has an id, and it is associated with a bus, a Metro station, or a bus station. The information recorded for each transaction includes the smart-card id and type, code of bus or site where the transaction was made, time, date, and amount of money paid. Another database contains geo-coded information about all the buses, such as latitude, longitude, time, date, and instant speed. Each bus is identified by a plate number and an operator code. The position is available every 30 s. Every week of data contains approximately 80 million GPS observations. By matching the Transactions and Positions databases through bus plates or Metro/Bus-station codes and times, it was possible to identify the position where  
Figure 1: Capturing mobility patterns of MR through three sensing methodologies. Panel (**A**) shows around 12,000 bus stations in the MR public transport network. The O-D matrices are estimated by the flow of passengers between these bus stations. Panel (**B**) indicates the network of cell phone antennas that provide coverage in the MR. The mobility index is determined by the flow of mobile phone users and the records they leave on the antennas covering the service while they are moving. Panel (**C**) shows level-6 hexagons covering the MR on a given day. The highest scores/percentile are in blue, while the lowest is yellow, passing through a range of greens. the transaction was made in 98.5% of the cases. The ADATRAP software (developed by the Universidad de Chile and the Metropolitan Public Transport Directory) reconstructs the trip chain of users behind smart-cards by estimating the destination points from the information available, building in this way the origin-destination (O-D) matrices (see [49; 50] for details of this methodology). Around 12,000 bus stations are considered (see Figure 1A). With these data, the trips between communes can be estimated (see Figure 8). The third source of mobility data used in this paper comes from the ubiquitous infrastructure provided by the cell phone network (see Figure 1B). This network is a set of geolocated cell towers (base transceivers stations) that provide cellular phone service to the MR [51]. Each cell tower has a latitude and a longitude--its geolocation--and gives cellular coverage to an area/sector. We assume that each sector is a 2-dimensional non-overlapping polygon that can be geometrically modeled. The exact position of the cell phone user within each sector is unknown. When a cellular device (PHONE_ID) interacts with the network, there is a record of its connection to the antenna (CELL_ID) and a timestamp (EVENT_TM) of when this happened. The passage from one antenna to another is considered a trip. We used publicly available data records from the Telefonica--Motivistar company (according to the Telecommunications Secretary (Subtel) of the Ministry of Transport and Telecommunications, there are 25,731,160 cell phone lines in Chile, and the Telefonica--Motivistar company owns about 28% of them [52]). The available data includes the analysis of records of 1.4 million users. [45]. Two indices were calculated [46; 53], the internal mobility index _MobIn_, which measures only trips within a commune. That is, the changes in the antennas and their respective locations, which give coverage to the cell phone service when the user moves within the limits of the commune. The _MobIn_ corresponds to how many trips (antenna transitions) were made within a specific commune normalized by the number of devices (cell phones) residing in the commune. The external mobility index _MobOut_, for its part, estimates the trips of users crossing the commune border.