It is important to take human mobilities into consideration at various scales in order to
measure their impact on the spread of epidemics. This is notably the case for vector-borne
diseases such as dengue or Zika. In fact, the main vector of these diseases, the Aedes aegypti
mosquito, is characterized by a very low capacity of autonomous dispersion, which leads us
to assume that human mobilities have an impact on the spread of the virus in urban areas.
Human mobilities are however not always easy to characterize and quantify, especially in
the absence of surveys and census data. Data obtained from mobile telephones is quite
widely used these days to overcome these difficulties. Nevertheless, this data is difficult to
obtain even for research purposes and is mostly subject to confidentiality clauses that
restrict its use and hinder result verification. Data from social networks is thus a new
alternative since it makes it possible to follow users over a long period of time and with
spatial precision obtained from their telephone GPS. This article proposes to explore
potentialities of this type of data obtained from the Twitter social network.
The concept of activity space is used to characterize and quantify daily mobility. Obtained
from time-geography research, activity space takes into account, the moment, the frequency,
the duration and the types of places visited by individuals. This paper thus aims to develop
this concept using (A) a typology of land use obtained from Google Maps for the city of
Bangkok (Thailand) and (B) a database obtained from the social networking platform,
Twitter. These data sources make it possible to characterize (C) the rhythms of daily
mobility in Bangkok, from the perspective of (1) the macroscopic urban pulse and (2) the
rhythms and reasons for individual movement. Finally, we will discuss (D) the advantages
and limitations of this category of data, especially in the specific case of a model for
studying the spread of dengue.