Objectives
- Conduct a detailed spatio-temporal analysis of human mobility during the lockdown.
- Automatically infer land use and classify each zone in Paris as either residential, activity, or outlier area, allowing the observation of how the usage of any given area changes once the lockdown is established.
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Model mobility flows using a time-varying weighted mobility graph and study three
different types of graph centrality, which quantify the importance of each zone in the
city according to the habits in mobility of the daily habits in mobility of the
population::
- the betweenness centrality captures the paths preferences of people, e.g., shortest or less congested paths
- the closeness centrality captures the locality of people movement
- the degree centrality captures topologically central hubs
- Combine the three centralities and the daily human density of an area into one metric, named impact factor, quantifying the global importance of zones in terms of frequentation and occupation, according to the mobility behavior of the population.
- Study the correlation between the impact factor and the type of area (residential, activity, other).