Cross-Checking Different Sources of Mobility Information

Abstract

The pervasive use of new mobile devices has allowed a better characterization in space and time of human concentrations and mobility in general. Besides its theoretical interest, describing mobility is of great importance for a number of practical applications ranging from the forecast of disease spreading to the design of new spaces in urban environments. While classical data sources, such as surveys or census, have a limited level of geographical resolution (e.g., districts, municipalities, counties are typically used) or are restricted to generic workdays or weekends, the data coming from mobile devices can be precisely located both in time and space. Most previous works have used a single data source to study human mobility patterns. Here we perform instead a cross-check analysis by comparing results obtained with data collected from three different sources: Twitter, census, and cell phones. The analysis is focused on the urban areas of Barcelona and Madrid, for which data of the three types is available. We assess the correlation between the datasets on different aspects: the spatial distribution of people concentration, the temporal evolution of people density, and the mobility patterns of individuals. Our results show that the three data sources are providing comparable information. Even though the representativeness of Twitter geolocated data is lower than that of mobile phone and census data, the correlations between the population density profiles and mobility patterns detected by the three datasets are close to one in a grid with cells of 2×2 and 1×1 square kilometers. This level of correlation supports the feasibility of interchanging the three data sources at the spatio-temporal scales considered.

Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness

“…The reliability of Twitter data in mobility studies has been validated in the work of Lenormand et al (2014), who compared the data from Twitter, mobile telephony and official data (censuses), and concluded that the three information sources offer comparable results. However, no research has been found in which these new data sources (mobile telephony, social networks and others) have been used as a proxy for the variability in destinations' attractiveness in the study of dynamic accessibility.…”

Dynamic population mapping using mobile phone data

“…Data provided by communication tools are opening up new opportunities for studying sociospatial behaviors (33)(34)(35)(36). MP call detail records were used in the past for studying human mobility patterns at the individual level (37)(38)(39) or for mapping human movements and activities using aggregated data (40)(41)(42)(43)(44). Most of these studies focused on specific cities or city neighborhoods or groups, and were aimed at understanding traffic flows (40), mapping the intensity of human activities at different times (42)(43)(44), or exploring seasonality in foreign tourist numbers and destinations (45,46).…”

“…MP call detail records were used in the past for studying human mobility patterns at the individual level (37)(38)(39) or for mapping human movements and activities using aggregated data (40)(41)(42)(43)(44). Most of these studies focused on specific cities or city neighborhoods or groups, and were aimed at understanding traffic flows (40), mapping the intensity of human activities at different times (42)(43)(44), or exploring seasonality in foreign tourist numbers and destinations (45,46). Population movement analyses based on MP data are particularly promising for improving responses to disasters (47,48) and for planning malaria elimination strategies (49)(50)(51).…”

Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

“…Phone based positioning does not depend on a specific application platform, such as Twitter or Foursquare/Swarm. However, obtaining a cohesive set of spatio-temporal locations from phone records across multiple countries appears to be challenging, given that most studies that analyze mobility from phone records are geographically limited to the city level [60,61] or country level [62], with only a few exceptions that extend beyond national or continental boundaries [63].…”