Abstract
This research sheds light on the relationship between the presence of location-based social network (LBSN) data and other economic and demographic variables in the city of Valencia (Spain). For that purpose, a comparison is made between location patterns of geolocated data from various social networks (i.e., Google Places, Foursquare, Twitter, Airbnb and Idealista) and statistical information such as land value, average gross income, and population distribution by age range. The main findings show that there is no direct relationship between land value or age of registered population and the amount of social network data generated in a given area. However, a noteworthy coincidence was observed between Google Places data-clustering patterns, which represent the offer of economic activities, and the spatial concentration of the other LBSNs analyzed, suggesting that data from these sources are mostly generated in areas with a high density of economic activities.
Highlights
IntroductionRepresentativeness andThe field of research that deals with the analysis of urban dynamics through locationbased social networks (LBSNs) has led to the development of new methods and techniques that provide valuable insights from a wide range of qualitative and quantitative approaches.These methods and tools aim to unlock the great potential of the information provided by these sources about urban activities and human behavior in city spaces [1].A great deal of scholarship in the field of location-based social network (LBSN) data applied to the study of urban phenomena focuses on large dense urban areas with important amounts of data [2,3].These include metropolitan areas with a high population density [4]; areas where urban activities are more likely to happen, such as commercial areas; or areas where points of interest are concentrated, as opposed to predominantly residential areas [5,6] where the amount of available data may not be considered sufficiently representative to draw meaningful conclusions
The latter is often achieved by thickening the samples by various methods, such as combining the features of different complementary datasets to leverage the strengths of each, or by triangulating between traditional and new data sources, which would allow for a more complete understanding of urban phenomena [8]. This study supports these two assertions and builds upon existing literature that aims to bridge the gap in the debate about whether data from location-based social network (LBSN) is representative of the demographic and socio-economic profile in urban areas from which these data are generated and that have an impact on the reliability of these data for urban studies [9]
From the comparison between population density and LBSN data density it was found that areas with lower population density in all age groups—except for some areas of Ensanche where the density is slightly higher—match those with the highest data density from Foursquare, Google Places, Twitter and Airbnb (Figures 5–9)
Summary
Representativeness andThe field of research that deals with the analysis of urban dynamics through locationbased social networks (LBSNs) has led to the development of new methods and techniques that provide valuable insights from a wide range of qualitative and quantitative approaches.These methods and tools aim to unlock the great potential of the information provided by these sources about urban activities and human behavior in city spaces [1].A great deal of scholarship in the field of LBSNs data applied to the study of urban phenomena focuses on large dense urban areas with important amounts of data [2,3].These include metropolitan areas with a high population density [4]; areas where urban activities are more likely to happen, such as commercial areas; or areas where points of interest are concentrated, as opposed to predominantly residential areas [5,6] where the amount of available data may not be considered sufficiently representative to draw meaningful conclusions. The field of research that deals with the analysis of urban dynamics through locationbased social networks (LBSNs) has led to the development of new methods and techniques that provide valuable insights from a wide range of qualitative and quantitative approaches These methods and tools aim to unlock the great potential of the information provided by these sources about urban activities and human behavior in city spaces [1]. Analyzing LBSN data requires strategies to keep the size of the datasets manageable, whilst there must be sufficient gathered data to obtain rigorous findings [7] The latter is often achieved by thickening the samples by various methods, such as combining the features of different complementary datasets to leverage the strengths of each, or by triangulating between traditional (administrative and field studies) and new data sources, which would allow for a more complete understanding of urban phenomena [8]. This study supports these two assertions and builds upon existing literature that aims to bridge the gap in the debate about whether data from LBSN is representative of the demographic and socio-economic profile in urban areas from which these data are generated and that have an impact on the reliability of these data for urban studies [9]
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