Social Network Foursquare Research Articles

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

We aim to study the temporal patterns of activity in points of interest of cities around the world. In order to do so, we use the data provided by the online location-based social network Foursquare, where users make check-ins that indicate points of interest in the city. The data set comprises more than 90 million check-ins in 632 cities of 87 countries in 5 continents. We analyzed more than 11 million points of interest including all sorts of places: airports, restaurants, parks, hospitals, and many others. With this information, we obtained spatial and temporal patterns of activities for each city. We quantify similarities and differences of these patterns for all the cities involved and construct a network connecting pairs of cities. The links of this network indicate the similarity of temporal visitation patterns of points of interest between cities and is quantified with the Kullback-Leibler divergence between two distributions. Then, we obtained the community structure of this network and the geographic distribution of these communities worldwide. For comparison, we also use a Machine Learning algorithm—unsupervised agglomerative clustering—to obtain clusters or communities of cities with similar patterns. The main result is that both approaches give the same classification of five communities belonging to five different continents worldwide. This suggests that temporal patterns of activity can be universal, with some geographical, historical, and cultural variations, on a planetary scale.

Scoping out urban areas of tourist interest though geolocated social media data: Bucharest as a case study

Social media data has frequently sourced research on topics such as traveller planning or the factors that influence travel decisions. The literature on the location of tourist activities, however, is scarce. The studies in this line that do exist focus mainly on identifying points of interest and rarely on the urban areas that attract tourists. Specifically, as acknowledged in the literature, tourist attractions produce major imbalances with respect to adjacent urban areas. The present study aims to fill this research gap by addressing a twofold objective. The first was to design a methodology allowing to identify the preferred tourist areas based on concentrations of places and activities. The tourist area was delimited using Instasights heatmaps information and the areas of interest were identified by linking data from the location-based social network Foursquare to TripAdvisor’s database. The second objective was to delimit areas of interest based on users’ existing urban dynamics. The method provides a thorough understanding of functional diversity and the location of a city’s different functions. In this way, it contributes to a better understanding of the spatial distribution imbalances of tourist activities. Tourist areas of interest were revealed via the identification of users’ preferences and experiences. A novel methodology was thus created that can be used in the design of future tourism strategies or, indeed, in urban planning. The city of Bucharest, Romania, was taken as a case study to develop this exploratory research.

Exploring the spatial patterns of visitor expenditure in cities using bank card transactions data

ABSTRACT The importance of tourism expenditure is widely recognized, but the spatial patterns of visitor expenditure in cities is so far an unexplored subject. The main aim of this paper is to analyze the spatial distribution of visitor spending using bank card transaction data. Spatial statistics tools are used to identify expenditure clustering patterns and hot spots, distinguishing between national and foreign visitors, and comparing their patterns with those of locals. Our analysis shows that visitor expenditure, and especially that of foreigners, is much more concentrated in the city centre and spatially clustered than local spending. OLS regressions and spatial regression models are used for explaining the spatial distribution of visitor expenditure. Predictors are extracted from old (Cadastre) and new (the social network Foursquare) data sources. Regressions results indicate that socio-symbolic variables have greater explanatory power than physical dimension variables, that expenditure falls sharply with the distance to the city centre, and that the amount of spending in each spatial unit is influenced by the expenditure in the neighbouring spatial units. The paper demonstrates the great potential of this data source to explore with a high level of detail the spatial patterns and drivers of visitor spending within urban destinations.

If You've Got the Money, I've Got the Time: Spatio-Temporal Footprints of Spending at Sports Events on Foursquare

Notable events such as sports games have a highly visible impact both in the media and at the event location itself, but the pre- and post-game activities of attendees - and their spending around such events - are much less evident. In this work, we use social media footprints to analyze and interpret these broader ripples of activity: specifically, we examine the temporal, spatial, and microeconomic patterns of baseball and basketball game attendees' activity around game events for 21 North American cities and 32 teams during 2010--2014, using mobility and price data from the location-based social network Foursquare. We identify some universal spatio temporal patterns that hold across both sports and cities: exploiting additional information about venues' price levels, we find that, in aggregate, users who check in later at the game come from pricier restaurants and bars. We use these insights to predict the costliness of a user's choice of restaurant/bar, based only on a single transition to or from the stadium/arena, using the temporal properties of the transition such as the time of check-in at the game, distance traveled, and speed of transition, with the last being the most important factor. Our analysis carries broad implications for human mobility studies, mobile applications, and sports economics.

La producción de identidad de los nuevos desarrollos urbanos a través del place-based social big data: los crecimientos del área metropolitana de Madrid durante la burbuja inmobiliaria (1990-2012

Este trabajo propone un acercamiento al estudio de la producción de identidad y sentimiento de apropiación espacial en determinados entornos urbanos. Identidad y apropiación son estudiados desde el análisis de hitos y topónimos, utilizando para ello big data desde Foursquare, red social en la cual los usuarios publican sus visitas a lugares. Se analizan varios crecimientos urbanos de la reciente burbuja inmobiliaria en el Área Metropolitana de Madrid (1990-2012), sospechosos de falta de identidad por la reciente y rápida urbanización, así como por posibles mecanismos de simplificación del tejido urbano. El análisis valora diferentes tipos de crecimiento, revelando una correspondencia entre la tipología morfológica de los desarrollos y el tipo de lugares que constituyen hitos en ellas, así como con la aparición de topónimos, destacando ciertos lugares como elementos potenciales generadores de identidad social urbana.

Friend and POI recommendation based on social trust cluster in location-based social networks

Friend and point-of-interest (POI) recommendation are two primary individual services in location-based social networks (LBSNs). Major social platforms such as Foursquare and Instagram are all capable of recommending friends or POIs to individuals. However, most of these social websites make recommendations only based on similarity, popularity, or geographical influence; social trust among individuals has not been considered in those recommendation system. Recently, trust relationship has been proved to be helpful in collaborative recommendation. In this paper, we first propose algorithm to identify trust clusters and then give a trust prediction method based on these trust clusters. Then we combine the trust value and similarity among individuals to recommend friends to the target user. As for the POI recommendation, we devise a hybrid framework that integrates user preference, geographical influence, and trust relationship to improve the recommendation quality. In order to validate the effectiveness and efficiency of our methods, a series of experiments on two real social networks Foursquare and Instagram are conducted. The experiment results show that the trust cluster-based recommendation approach outperforms the baseline recommendation approaches in precision and recall.

Estimation of a Long-Distance Travel Demand Model using Trip Surveys, Location-Based Big Data, and Trip Planning Services

Long-distance trips are less frequent than short-distance urban trips, but contribute significantly to the total distance traveled, and thus to congestion and transport-related emissions. This paper develops a long-distance travel demand model for the province of Ontario, Canada. In this paper, long-distance demand includes non-recurrent overnight trips and daytrips longer than 40 km, as defined by the Travel Survey for Residents in Canada (TSRC). We developed a microscopic discrete choice model including trip generation, destination choice, and mode choice. The model was estimated using travel surveys, which did not provide data about destination attractiveness and modal level of service. Therefore, a data collection method was designed to obtain publicly available data from the location-based social network Foursquare and from the online trip planning service Rome2rio. In the first case, Foursquare data characterized land uses and predominant activities of the destination alternatives, by the number of user check-ins at different venue types (i.e., ski areas, outdoor or medical activities, etc.). In the second case, the use of Rome2rio data described the modal alternatives for each observed trip. Combining data from travel surveys, Foursquare, and Rome2rio, coefficients of the model were estimated econometrically. It was found that the Foursquare data on number of check-ins at destinations was statistically significant, especially for leisure trips, and improved the goodness of fit compared with models that only used population and employment. Additionally, Rome2rio mode-specific variables were found to be significant for mode choice selection, making the resulting model sensitive to changes in travel time, transit fares, or service frequencies.

Emergence of encounter networks due to human mobility.

There is a burst of work on human mobility and encounter networks. However, the connection between these two important fields just begun recently. It is clear that both are closely related: Mobility generates encounters, and these encounters might give rise to contagion phenomena or even friendship. We model a set of random walkers that visit locations in space following a strategy akin to Lévy flights. We measure the encounters in space and time and establish a link between walkers after they coincide several times. This generates a temporal network that is characterized by global quantities. We compare this dynamics with real data for two cities: New York City and Tokyo. We use data from the location-based social network Foursquare and obtain the emergent temporal encounter network, for these two cities, that we compare with our model. We found long-range (Lévy-like) distributions for traveled distances and time intervals that characterize the emergent social network due to human mobility. Studying this connection is important for several fields like epidemics, social influence, voting, contagion models, behavioral adoption and diffusion of ideas.

Using locative social media and urban cartographies to identify and locate successful urban plazas

Locative social media networks as open sources of data allow researchers and professionals to acknowledge which city places are preferred, used and livable. Following this hypothesis, this paper proposes a methodology to identify successful public spaces – plazas – through the location-based social media network Foursquare and to analyze their urban position using morphological and historical cartographies.The overall methodology comprises three stages. First, the most important cities of the province of Alicante were selected. Second, the most relevant plaza of each city was identified using data retrieved from the social network Foursquare. Finally, the location of each plaza is analyzed in relation to the historic center and the main axes of the city. Possible correlations between their urban location and their vibrant character were subsequently identified.Two findings have emerged from this study: (a) a strong spatial relationship exists between the most successful plazas and the historic city center, which reinforces their traditional social character; and (b) all plazas share two similar traits, their location within the urban network and their proximity to the main axes of the city.

Analysing successful public spaces in an urban street network using data from the social networks Foursquare and Twitter.

This paper analyzes success public spaces (specifically plazas) in the urban fabric of the city of Murcia, Spain. Two approaches were adopted. Firstly, the city was visualized as a complex network whose nodes represent plazas. A centrality algorithm was applied to determine the importance of each node. Secondly, data sets were used from social networks Foursquare and Twitter, which provide different types of data as well as user profiles. Foursquare data indicates user preferences of urban public spaces, while in this respect Twitter offers less specific user generated data. Both perspectives have facilitated two rankings based on the most visited plazas in the city. The results enabled a comparative study to determine the potential differences or similarities between both approaches.

Temporal Predictability of Online Behavior in Foursquare

With the widespread use of Internet technologies, online behaviors play a more and more important role in humans’ daily lives. Knowing the times when humans perform their next online activities can be quite valuable for developing better online services, which prompts us to wonder whether the times of users’ next online activities are predictable. In this paper, we investigate the temporal predictability in human online activities through exploiting the dataset from the social network Foursquare. Through discretizing the inter-event times of users’ Foursquare activities into symbols, we map each user’s inter-event time sequence to a sequence of inter-event time symbols. By applying the information-theoretic method to the sequences of inter-event time symbols, we show that for a user’s Foursquare activities, knowing the time interval between the current activity and the previous activity decreases the entropy of the time interval between the next activity and current activity, i.e., the time of the user’s next Foursquare activity is predictable. Much of the predictability is explained by the equal-interval repeat; that is, users perform consecutive Foursquare activities with approximately equal time intervals. On the other hand, the unequal-interval preference, i.e., the preference of performing Foursquare activities with a fixed time interval after another given time interval, is also an origin for predictability. Furthermore, our results reveal that the Foursquare activities on weekdays have a higher temporal predictability than those on weekends and that users’ Foursquare activity is more temporally predictable if his/her previous activity is performed in a location that he/she visits more frequently.

A multilayer approach to multiplexity and link prediction in online geo-social networks.

Online social systems are multiplex in nature as multiple links may exist between the same two users across different social media. In this work, we study the geo-social properties of multiplex links, spanning more than one social network and apply their structural and interaction features to the problem of link prediction across social networking services. Exploring the intersection of two popular online platforms - Twitter and location-based social network Foursquare - we represent the two together as a composite multilayer online social network, where each platform represents a layer in the network. We find that pairs of users connected on both services, have greater neighbourhood similarity and are more similar in terms of their social and spatial properties on both platforms in comparison with pairs who are connected on just one of the social networks. Our evaluation, which aims to shed light on the implications of multiplexity for the link generation process, shows that we can successfully predict links across social networking services. In addition, we also show how combining information from multiple heterogeneous networks in a multilayer configuration can provide new insights into user interactions on online social networks, and can significantly improve link prediction systems with valuable applications to social bootstrapping and friend recommendations.

GeoSRS: A hybrid social recommender system for geolocated data

We present GeoSRS, a hybrid recommender system for a popular location-based social network (LBSN), in which users are able to write short reviews on the places of interest they visit. Using state-of-the-art text mining techniques, our system recommends locations to users using as source the whole set of text reviews in addition to their geographical location. To evaluate our system, we have collected our own data sets by crawling the social network Foursquare. To do this efficiently, we propose the use of a parallel version of the Quadtree technique, which may be applicable to crawling/exploring other spatially distributed sources. Finally, we study the performance of GeoSRSon our collected data set and conclude that by combining sentiment analysis and text modeling, GeoSRSgenerates more accurate recommendations. The performance of the system improves as more reviews are available, which further motivates the use of large-scale crawling techniques such as the Quadtree.

A weighted multi-attribute method for matching user-generated Points of Interest

To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multi-dimensional data sources and not merely its volume. To fully exploit this variety, however, requires conflation. This is a two-step process. First, one has to establish identity relations between information entities across different data sources; and second, attribute values have to be merged according to certain procedures that avoid logical contradictions. The first step, also called matching, can be thought of as a weighted combination of common attributes according to some similarity measures. In this work, we propose such a matching based on multiple attributes of Points of Interest (POI) from the Location-based Social Network Foursquare and the local directory service Yelp. While both contain overlapping attributes that can be used for matching, they have specific strengths and weaknesses that make their conflation desirable. For instance, Foursquare offers information about user check-ins to places, while Yelp specializes in user-contributed reviews. We present a weighted multi-attribute matching strategy, evaluate its performance, and discuss application areas that benefit from a successful matching. Finally, we also outline how the established POI matches can be stored as Linked Data on the Semantic Web. Our strategy can automatically match 97% of randomly selected Yelp POI to their corresponding Foursquare entities.

Being-towards the social: Mood and orientation to location-based social media, computational things and applications

Through an investigation of patterns of use of the location-based social network Foursquare derived from an extensive ethnographic survey of users, this paper focuses on the orientation of users towards location-based social media and mobile computational devices. Utilising Heidegger’s notions of mood and attunement to the world, the paper argues that the towards-which of the user, that is the mood of the user in a phenomenological sense, is critical to their experience of using location-based social media and the revealing of place that emerges from that usage. A contrast between a technological and a poetic or computational revealing of place can then be established based on the phenomenological orientation of user to device, application and world. The emphasis on orientation and attunement has implications for application design and research on user experience.

Foursquare and the parochialization of public space

The mobile social network Foursquare has gained popularity in the last few years among both users and businesses. This article explores how the use of Foursquare changes and impacts people’s sense of place. Drawing on the work of Lofland (1998) on the social production of space, we argue that as new socio–spatial information (i.e., who checks in where) is introduced via the mobile social network, it can change the way people experience a place. Based on qualitative in–depth interviews with active Foursquare users, we explore person–to–person and person–to–place connections and argue that Foursquare promotes parochialization of public space.

Turning life into a game: Foursquare, gamification, and personal mobility

Location-based mobile games (LBMGs) are games that people play while moving through physical spaces. Research has shown that they can impact individuals’ experience of their surrounding space and their mobility decisions. Extending that research, this article looks at the gaming elements of the location-based social network (LBSN) Foursquare, analyzing how Foursquare’s gaming elements can impact people’s mobility decisions. Through an analysis of qualitative interviews, the goal of this study is to draw from the concepts of hybrid space, spatial legibility, and gamification to show how Foursquare’s gaming elements can add a playful layer to physical spaces and discuss the impacts of the application designers’ goal of turning “life into a game.”

A conexão entre lugares e espaços proporcionada pela a rede Foursquare

With the development of mobile communication devices such as smartphones, cell phones, laptops, tablets the contextualization of the spaces in cities can now be connected to the Internet content. One of the clearest exponents of this technological convergence is the system of social networks Foursquare, which is an pioneering way of contextualizing the physical space. Apart from this item, the network has elements of games and services in a synergy provided by the convergence of various technologies. This may be an embryo of a new bridge between the virtual and actual spaces (Levy 1996).