dataesr/scientific-paragraphs-categorization huggingface dataset:
Although providing coarse-grained mobility information, CDR datasets, when available, allow the mobility investigation in large metropolitan areas. Additionally, we note that mobility investigations in the literature relate to one or another category of dataset, usually dictated by the dataset availability (Section 2). We claim that human mobility investigation in the context of datasets providing different scales and mobility information brings much stronger certitudes on identified features. Therefore, our contributions to this are is two-fold. We first present an extensive human mobility analysis from several fine-grained and one additional large-scale coarse-grained urban datasets. Our datasets represent human mobility from 8 cities in 3 different continents around the world, namely London, Moscow, New York, Paris, San Francisco, Tokyo, Mexico, and Beijing.
For each of them, we first model urban scenario with GPS-or CDR-based trajectories and points of interest (Section 4). Our points of interest represent real venues. We have collected information regarding more than 1. 5 million unique venues distributed among the studied cities. Our human mobility evaluation comprises visit, temporal and spatiotemporal aspects (Section 5). From our analysis we show that human mobility presents three main characteristics: tendency to use shortest-path, confinement and a strong repetitive behavior relative to few locations. From that, our second contribution is to unveil consistent human mobility characteristics regardless of the dataset representation. It is not evident that human mobility characteristics seen in one type of dataset will also be found in a different one, which, to the best of our knowledge, no previous work in the literature has assessed.
Related work The understanding of mobility and its modeling started with animals such as monkeys  [14] , jackals  [15] , and albatrosses  [16]. Such works indicated that animal mobility follows a random walk for which their displacement is power-law distributed, i. e. , Lévy flight  [17]. Early human mobility studies used tracking methods such as bank notes dispersion  [4]. Latterly, the lower cost of GPS devices increased the possibility of collecting mobility datasets. In  [18] , the authors evaluate GPS traces of 44 volunteers in various outdoor scenarios including two different college campuses, a metropolitan area, a theme park and a state fair. The analysis shows that human mobility resembles Lévy flight within a scale of less than 10 km, which corroborates the findings from  [4]. Authors then create a Lévy flight model that captures the mobility from those individuals.
More recently, easier methods for collecting human mobility in large scale such as mobile phones open new horizons for deeper human mobility investigations. Through extensive analysis,  [19]  presents a seminal study on human mobility using a CDR dataset of 100,000 subscribers. Authors show that human trajectories show a high degree of temporal and spatial regularity, in disagreement with the aforementioned random trajectories predicted by the prevailing Lévy flight random walk models. Besides, each individual is characterized by a specific travel distance that is time-independent and a significant probability to return to a few highly frequented locations. The return to a previously visited location occurs with a frequency proportional to the ranking in popularity of the location with respect to other locations.
This means, that humans have a strong tendency to return to locations that they visited before, due to the recurrence and temporal periodicity inherent to human mobility. [20]  presents an extension of this work using two CDR datasets totalizing 3 million subscribers focusing on the visiting time, i. e. , the period of time spent at one location. The resulting curve shows a truncated power-law with a cutoff of 17 hours, which authors link to the typical awake period of humans. In  [21]  authors analyze a CDR dataset of 97,000 subscribers in Los Angeles and 71,000 in New York aiming to identify important locations in peoples' lives. Using ground-truth data of home and work location from 19 subscribers, authors were able to identify home and work locations with about 1 and 21 miles of error, respectively.
In  [22]  authors evaluate a dataset of CDRs with information for about 450,000 subscribers to capture city dynamics. More specifically, authors want to discover two main groups in a city, one active during the day (laborish) and another during the night (partyish). Their grouping strategy relies on a set of fixed rules, e. g. , a subscriber is set to laborish group if he makes 4 calls (or send 4 SMSs) during business hours using city cell towers, at least, twice per week. This algorithm correctly matched 81% of the individuals to their corresponding groups using the US Census dataset as ground-truth. In  [23]  authors analyze a subset of Lausanne mobile phones dataset  [7]  with 38 participants in order to understand how temporal and personal factors, e. g. , occupation and age, affect individual mobility patterns.
Temporal analysis indicate that people are less active during workdays and night than during weekends and daytime. Occupational analysis shows that among full-workers and students, the former are more prone to shorter displacement during the day due to the stricter time rules imposed on companies compared to universities. Finally, age analysis shows higher nightly mobility of younger people compared to older counterparts, which is the result of nightlife attractions being more interesting for younger people. In  [24] , a study was made using a CDR dataset containing information of 180 subscribers, which presented similar temporal findings. Due to the routinary behavior of individuals, human mobility is highly predictable. [12]  presents a study using a CDR dataset with 50,000 subscribers aiming to measure how predictable human mobility is.
Authors measure the predictability of subscribers' next whereabouts by using three entropy measures:  (1)  uniform probability among all locations the subscriber visited,  (2)  probabilities given by the frequency of the visited locations, and (3) probabilities based on frequency, time spent and the order of the visits. As a result, for the typical subscriber, the uncertainty of the next location (i. e. , the cell tower the subscriber will be connected to) resides, on average, in a set of less than two locations. Moreover,  [19]  shows that individuals are found at their first two preferred locations on 40% of the time. In  [25, 26]  authors analyse check-in 1 -like datasets as source of mobility, wherein a check-in represents a location point, and one can infer user's mobility by a sequence of check-ins.
However, our intuition says that people's mobility presents other characteristics such as tendency to use shortest-paths. Besides, no large scale evaluation of fine-grained datasets was performed to verify this intuition nor the aspects previously assessed in the literature. We complement thus the literature by providing insights about people's mobility behavior  (1)  collected from several scenarios presenting different cultural habits, time and space granularities 2 , physical infrastructure organization, and geographical size factors that may impact people's routine;  (2)  coming from different sources/natures of datasets (GPS and CDR). to CDRs, only a few number of geographic points are present per user each day due to the time sparsity of the calls. Moreover, due to the routinary behavior, people tend to make calls using the same antenna.
Background Each of the considered mobility datasets represents a fairly real urban scenario composed by people and their mobility (i. e. , a set of timestamped geographic coordinates). Although all the dataset files represent mobility in a similar way, i. e. , through a set of trajectories (Section 3), there are conceptual differences between their mobility representation. For instance, GPS and CDR datasets differ in spatial and temporal information: mobility described by CDR dataset is sparse in time and coarse in space (Section 1). This makes the analysis of common human mobility behavior extracted from datasets with different natures and collected in different cities a challenging task: the main problem tackled in our work. Besides such datasets, we perform the collection of data describing more than 1.
5 million real points of interest spread in the cities of the considered datasets. We extract and use three major entities: people, trajectories, and points of interest. The first two are directly represented in the datasets of Table  1. As described in Section 3, recreating the user mobility from the dataset requires chaining user's trajectory points in a temporal order by their timestamp attribute. Doing so for every user in a specific dataset results in the mobility representation of all the users in a respective city. As discussed in the introduction, due to the nature of each of the datasets (i. e, CDR or GPS), GPS datasets allow us to more precisely recreate user mobility due to its high frequent positioning sampling rate (Figure  1  do not possess precise user positioning and the sampling rate depends on the user's call frequency (Figure  1(d) ).
Thus, analysis related to the shortest path comparison could not be performed for the Telco datasets, the only exception in terms of mobility analysis in this paper. Points of interest (or PI) are the third entity of our system model. They represent geolocalized physical venues spread in a real urban scenario. Those PIs describe more than mere locations in the map, they reflect routinary aspects (e. g. , repetitiveness, confinement) of human's mobility behavior involving people and PIs in a city: e. g. , students are frequently going to meet their colleagues in a coffeehouse close to the university they attend. People move and build their trajectories. While moving around, they "interact" with many PIs (e. g. , shops, touristic places, bus stations, etc. ) and sometimes may stop by. We model this interaction as a visit: A visit is considered to happen when an user enters in the interaction range of a point of interest and lasts as long as the user is inside its interaction range.
Trajectories A trajectory represents how people move around and it is described as a set of geolocalized points periodically collected. Regardless the dataset, each trajectory point has latitude, longitude, and timestamp, to indicate when the position was recorded. Hereafter, we detail trajectories described in the considered datasets. GeoLife and OpenStreetMap. In the GeoLife dataset, trajectories are made up of ten different transportation modes: taxi, bike, run, bus, walk, train, subway, car, boat, and motorcycle. The transportation mode is given by labels set by the users being tracked. In order to improve the precision of our shortest path analysis when comparing users' path lengths with the benchmark's path lengths collected from Google Directions API (e. g. , Figure  8 (a)), we divided each trajectory into legs.
A leg is a contiguous set of geolocalized points traveled using a unique transportation mode. Figure  1 (c) illustrates a trajectory composed by "walk" (cf. red dots) and "bike" (cf. blue dots) legs. It may represent a situation in which a person went to a bicycle-sharing station, took a bicycle, and continued on it. Not all trajectories in the GeoLife dataset, however, have been labeled. That is due to the fact that labeling was not mandatory for people participating on the GeoLife experiment. In order to label all the trajectories, we have created a simple inference strategy that labels legs by their speed compared against known average speeds for transportation modes (Table  2 ). Consider we have an unlabeled leg traveled with average speed of 5 m/s. Such speed falls on a range describing average speeds of three transportation modes: "taxi", "motorcycle", and "car".
In order to keep the proportion of legs that were originally labeled by the users in the experiment, we have calculated the percentage of legs (shown between parentheses) on each overlapping range. Therefore, the unlabeled leg will be labeled either as "taxi", "motorcycle", or "car" with 39%, 0. 1%, and 60% of chance, respectively. Contrarily to GeoLife, OpenStreetMap trajectories are not labeled at all. To overcome this limitation, we have used our inference strategy described here above: we infer the transportation mode for a given leg based on the average speed of the user along this leg (Table  2 ). Nevertheless, due to the lack of a sample with labeled trajectories from the original dataset, the probabilities are equally divided, e. g. , "taxi", "motorcycle", or "car" have 33. 3% of chance of being assigned to a leg.
Telco. As is usual for mobility traces based on CDRs, we use the antennas used by the subscriber as a proxy for identifying geographic locations along a users trajectory. For each subscriber, the identified geographic locations are ascendantly sorted by time of the day. In our dataset, about 70% of the antennas are inside Mexico City urban area and the median pairwise distance between sequential locations per subscriber is 1. 5 km. It is much more coarse-grained than the 16 m from GeoLife or the 7 to 18 m granularity from OpenStreetMap. Furthermore, no transportation mode is available or could be inferred from the trajectory locations in this dataset. Figure  2 (a) shows the total number of users and the trajectories they have performed for each day over a three month period in the city of Beijing.
As expected, there is a strong temporal correlation between the daily number of users and trajectories. The shape similarity of these two time series is remarkable: the appearance (cf. disappearance) of a new user in a day of the dataset also implies the appearance (cf. disappearance) of trajectories that day, resulting in a proportional relationship among number of users and number of trajectories on each day. Indeed, Pearson's correlation between number of users and number of trajectories is 92%. Similarly, this correlation is 72% in Moscow, 70% in London, and 60% in Beijing. In Figure  2 (b), we present the average number of users and trajectories per week day in Beijing. The day-wise difference for the number of users slightly decreases as the week progresses. On average, the highest difference is 12% more users on Monday than on Sunday.
People and Points of Interest (PI) As people move along their trajectories being recorded via their mobile devices, they may also visit or stop by different points of interest (PIs), e. g. , bar, bus station, supermarket, etc. In order to work with real PIs, we have collected information from different databases of places (e. g. , Google Places  4  ). Such databases are growing and are the most accurate source of public information about points of interest. To avoid possible biases given by a particular database, e. g. , some types of interests might be over-represented in a given database, we have collected data from multiple sources, namely Google Places, Nokia Maps  5  , and Foursquare  6  , adding up to more than 1. 5 million distinct points of interest with their respective IDs, latitudes and longitudes.
While Google Places and Nokia Maps databases provide information about points of interest collected from city hall's records, by the respective owner of the venues, Foursquare provides only information from places where its users checked in, generally places related with leisure and social relationships. For each set of places collected from a source, repeated points of interest were removed by keeping an unique occurrence of each latitude and longitude pairs, i. e. , there will be only one point of interest per geographical position throughout all the datasets. Table  3  describes the characteristics of the sets of collected PIs per source in 98 categories, e. g. , market, library, school, etc. Figure  2 (c) shows the distribution of points of interest by their distance to each of the cities' downtown.
Mobility Dynamics So far we have shown isolated characteristics of the dataset such as number of users, trajectories and how PIs are arranged within the studied cities. This section complements this analysis looking at people's space and time dynamics for different urban scenarios and PIs, e. g. , time spent on shopping areas during weekdays and weekends. In order to understand some of the people's routinary mobility and interaction within their urban environment, we have aggregated the location data by time period of the day or by day of the week. For instance, the curve labeled Monday on Figure  3 (a) shows the cumulative distribution for the average number of unique users per PI for all Mondays. This also applies to the periods of the day, whose data represents all occurrences of the respective measurement for each of the periods in all days of the week.
New York presents similar results, for up to 75% of users, the most active period of the day, which is from 12:00 to 17:59, has 27%, 72%, and 21% more visits to PIs than from 00:00 to 05:59, from 06:00 to 11:59, and from 18:00 to 23:59, respectively. It is important to note that this behavior slightly varies in certain cities, while it presents extremely similar results in others. Aside from New York, Tokyo presents very similar results. On the other hand, cities such as London and San Francisco still had high number of visits during daylight periods, but similar number of PI visits during the night. In such cases it is difficult to indicate a single reason. It might be due to the city context, i. e. , people are as active during the day as during the night, or to dataset's characteristics, which contain a more balanced number of users during the day and night.
Contrarily, from 00:00 to 05:59 people are more stationary performing at most short trajectories and likely at home, sleeping, etc. Median radius of gyration per user in the period from 00:00 to 05:59, is 92% shorter than the radius of gyration from 06:00 to 11:59 in Beijing. Considering all cities, except Mexico, the radius of gyration from 00:00 to 05:59, is 57% shorter than the radius of gyration from 06:00 to 11:59. For instance, the average radius of gyration is 759 meters from 00:00 to 05:59 and about 10 km from 06:00 to 11:59 in Beijing. Due to the sparsity of the Telco dataset, the radius of gyration tends to be larger on Mexico than on the other cities. Therefore, we have made a separate analysis for Mexico dataset on displacement aspects. Figure  7 (a) shows the radius of gyration for subscribers per period of the day on weekdays and weekends in Mexico.
9 and 2. 5 km, respectively in Beijing and 1. 9 and 2. 2 km for all cities. Figure  7 (b) shows the same results for Mexico. The behavior present on the GeoLife and OpenStreetMap is also present on the Telco dataset, i. e. , peak on Friday and Saturday, 69 and 71 km, respectively. Moreover, the average radius of gyration grows as the week passes by. Figure  6 (c) shows the CDF of the final radius of gyration per user in Beijing. The steady and constant increase on the CDF curve shows that users are almost equally distributed by their radius of gyration. To further analyze the radius of gyration, we have grouped users by their final radius of gyration into four groups: r g ≤ 10 4 , 10 4 < r g ≤ 10 5 , 10 5 < r g ≤ 10 6 , and r g > 10 6 meters. Figure  6 (d) shows the average radius of gyration for each of the groups up to the hour on the x-axis.
g, people coming back to work from lunchtime). Moreover, it is possible to see that the length ratio changes in function of the transportation mode. For instance, walk and taxi modes present trajectories that are closer to the shortest path. That is probably due to the human capacity of being able to identify the trajectories, mainly when one has the knowledge of the neighborhood, which is the case of the participants of GeoLife experiment. Taxis tend to be equipped with GPS-enable devices and route planning software in order to find the addresses and the better (shorter in time and/or cost) routes. Besides, Table  4  shows the percentage of trajectories half longer than the shortest-path for all cities. We conclude that regardless of the transportation mode, people tend to be oriented by the shortest paths.
We have also analyzed the length ratio grouped per weekdays and weekends and per transportation mode. For Beijing, the median length ratio is 1. 1 and 1. 3 on weekdays and weekends, respectively. Those values are consistent for all cities, on average 1. 1 and 1. 2, respectively. Additionally, on all cities, bike, car, taxi, and walk presented median length ratio of 1. 05, 1. 07, 1. 12, and 1. 39 for weekdays and 1. 06, 1. 2, 1. 33, and 1. 72 for weekends, respectively. It shows that people on our datasets were presenting more routes closer to the shortest ones on weekdays than on weekends. That is interesting because it measures a difference in people's behavior on weekdays and weekends. People's mobility is generally confined. Even if people are not using the shortest routes, they are at least not going far from their home location.
To check how that premise occurs in our scenario, we have measured how confined the trajectories are by their maximum displacement. Maximum displacement is the distance between a trajectory's initial and farthest point (not necessarily the last point). Figure  8 (b) presents the CDF of the maximum displacement for all trajectories grouped by period of the day in Beijing. It shows that 90% of the trajectories per period of the day have, at most, 10 km maximum displacement in Beijing. Considering all cities, except Mexico, this values is 75%. Similar findings are present on the analysis of maximum displacement per day. For Mexico, the displacement is generally larger than 10 km due to the coarse-grained nature of the CDR dataset. Figure  8 (c) presents the maximum displacement per period of the day for Mexico City.
57% of the trajectories per period of the day have, at most, 10 km. This value is lower than the other cities because of the sparsity of the dataset. For instance, 75% of the trajectories have, at most, 35 km. Additionally, there is a significant difference between the maximum displacement from 00:00 to 05:59 to the other periods, which is not observed nor on (refer to Sec. 4. 3) in 9 classes. This classification aims to group together, on the same class, PIs whose categories carry similar meaning. For example, class Education (which we abbreviate as Edu) groups together all PIs with category "school", "university", and "library". Similarly, the remaining 95 categories were classified into more 8 classes. Table  5  describes the classes and some of the categories they contain. Figure  9 (a) shows the amount of coverage time each of classes provided per period of the day in Beijing.
It is interesting to see the periodical behavior that matches with the diurnal activities on both cities. It means that people tend to crowd some popular areas and their neighborhoods on certain hours of the day. It is particularly true around lunch time on metropolitan areas when people go to common areas of restaurants. Moreover, the right end of the curve represents the weekend on both curves, in which the autocorrelation is higher for Beijing and slightly lower on Mexico. The difference lays in the nature of both datasets. First, Mexico dataset has slightly less users making calls during weekends, i. e. , there are less people sharing the antennas, thus spatiotemporal correlation is lower than on weekdays. On the other hand, on a mobility dataset as GeoLife, people increase their mobility during weekends, and, as consequence they gather on common leisure areas more than weekdays.