An analysis of twitter as a relevant human mobility proxy

Abstract

During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatiotemporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period.

Intermunicipal travel networks of Mexico during the COVID-19 pandemic

“…As previously mentioned, human mobility exhibits different characteristics at different scales 42 . In recent work, it has been shown that the granularity in which mobility is analyzed using digital data captures does capture some of the structural differences across scales 40 . Our research group’s work on studying these differences across scales is ongoing 43 .…”

“…Additionally, the data can help refine interventions by providing near real-time information about changes in patterns of human movement, which can inform policy and messaging around social distancing and other interventions. Specific applications using many of the aforementioned data sources, from social networks 40 to cellphone location data 41 .…”

Next Place Prediction Model: A Literature Review

“…Being able to know the path followed is becoming relevant to understand human mobility and consequently developing possible alternatives. Over the years, to overcome the limitation just mentioned various models able to perform path and short-term prediction have been developed [8][9][10][11][12][13][14]. Thanks to these models, it is possible first to identify the path of the users and then to integrate new types of data into mobility studies, generating more accurate and detailed OD matrices from a spatial and temporal perspective [15][16][17].…”

Replacing discontinued Big Tech mobility reports: a penetration-based analysis

“…In general, mobility is affected by global long-term events such as economic crises [15][16][17] and conflicts 18 , and by local short-term events like social unrest and extreme natural events [19][20][21][22] . Mobility data are also used to better assess exposure to health-threatening phenomena 23 .Smartphone mobility data play a key role in estimating mobility patterns [24][25][26][27][28][29][30] . Apple's Mobility Trends Reports and Google's Community Mobility Reports were two global data sets made available to researchers during COVID-19 pandemic [31][32][33][34][35] .…”

“…Smartphone mobility data play a key role in estimating mobility patterns [24][25][26][27][28][29][30] . Apple's Mobility Trends Reports and Google's Community Mobility Reports were two global data sets made available to researchers during COVID-19 pandemic [31][32][33][34][35] .…”