Measuring urban activities using Foursquare data and network analysis: a case study of Murcia (Spain)

Abstract

Among social networks, Foursquare is a useful reference for identifying recommendations about local stores, restaurants, malls, or other activities in the city. In this paper, we consider the question of whether there is a relationship between the data provided by Foursquare regarding users' tastes and preferences and fieldwork carried out in cities, especially those connected with business and leisure. Murcia was chosen for case study for two reasons: its particular characteristics and the prior knowledge resulting from the fieldwork. Since users of this network establish, what may be called, a ranking of places through their recommendations, we can plot these data with the objective of displaying the characteristics and peculiarities of the network in this city. Fieldwork from the city itself gives us a set of facilities and services observed in the city, which is a physical reality. An analysis of these data using a model based on a network centrality algorithm establishes a classification or ranking of the nodes which form the urban network. We compare the data extracted from the social network with the data collected from the fieldwork, in order to establish the appropriateness in terms of understanding the activity that takes place in this city. Moreover, this comparison allows us to draw conclusions about the degree of similarity between the preferences of Foursquare users and what was obtained through the fieldwork in the city.

An Urban Road-Traffic Commuting Dynamics Study Based on Hotspot Clustering and a New Proposed Urban Commuting Electrostatics Model

“…The quantitative correlation between the number of road grids with specific average velocity N number and the average velocity of vehicles V mean can be very well represented by the following Bi-Gaussian peak function equation: , R 2 = 0.981 (16) The direction in which vehicles are heading is another important index for monitoring traffic flow conditions. We calculated the direction of each vehicle from 1,308,705 individual GPS data records.…”

“…Besides conventional aggregate data, such as censuses, community surveys and questionnaires, the appearance of new technologies offers more types of data about cities. These data are provided by sensors, public or private services, mobile devices, web services, social networks, location-based services and GPS trajectories [16,17]. For instance, Agryzkov et al compared the data extracted from a social network with data collected from the fieldwork in order to establish the appropriateness of understanding the activity that takes place in Murcia, Spain [16].…”

“…These data are provided by sensors, public or private services, mobile devices, web services, social networks, location-based services and GPS trajectories [16,17]. For instance, Agryzkov et al compared the data extracted from a social network with data collected from the fieldwork in order to establish the appropriateness of understanding the activity that takes place in Murcia, Spain [16]. Wan et al tried to simulate traffic commuting patterns in Beijing, China, based on a theory of spatial equilibrium using location-based service (LBS) data [18].…”

“…, 15 • ], (75 • , 105 • ], (165 • , 195 • ], and (255 • , 285 • ] are 269,320, 189,222, 161,221, and 203,597, respectively, accounting for 21.22%, 14.91%, 12.71%, and16.04% of the total, respectively. These figures show that most vehicles in the study area moved in a north-south and east-west direction.…”

Measuring the vibrancy of urban neighborhoods using mobile phone data with an improved PageRank algorithm

“…With the advance of communication technology and prevailing location-aware devices such as mobile phones and other GPS-embedded consumer electronics, massive data, including individual trajectories, can be collected and analyzed. Some researchers use social media data as a supplement to traditional data (Agryzkov, Martí, Tortosa, & Vicent, 2017;Ding & Zhao, 2014;Huang & Li, 2016;Li, Yang, Zhu, & Dai, 2015;Wu et al, 2016). Compared with other data, mobile phone-based individual trajectory data have unique characteristics to help us analyze human mobility in the physical world, as well as the spatial structure of cities (Shi, Chi, Liu, & Liu, 2015).…”