Similarity of common land use types among Twitter users and the Travel Tracker survey individuals.

<p>A-C: Violin plots of similarity of common land use types between Twitter users and the Travel survey individuals compared to the control group (a random sample from the land use map of Chicago for rank one (A), rank two (B) and rank three (C). Each sample is made of 10,000 individuals in case of Twitter and the travel survey and 10,000 random land use parcel in the case of the random map sample.</p

Scatter plots of temporal signatures of individual key locations.

<p>A-B: Distribution of individual clusters in a 2D space defined by the temporal activity (percentage of tweets relative to the total number of tweets in the cluster) during different hours of the day. A: morning vs. evening. B: morning vs. afternoon. Clusters with similar land use attributes have a similar distribution of tweets within the twenty-four cycle. Hexagonal binning was used to display the common (mode) land use attribute in each bin.</p

Twitter temporal signatures.

<p>A-D: Twitter users’ temporal signatures aggregated by land use type for all users during weekdays (A-B) and weekends (C-D). Weekdays were defined as Mondays to Fridays while Weekends include Saturdays and Sundays. Signatures were normalized by the total number of tweets counts in a land use class to allow comparisons.</p

Confusion matrix of Twitter land use classification.

<p>Confusion matrix of Twitter land use classification.</p

Semantics of top tweeted-from locations.

<p>A-B: Count of unique users grouped by land use types and ranks of their top ten key locations; absolute count (A) and normalized count (B). C-D: Count of surveyed individuals who reported their stay times at different locations during the day grouped by land use types and ranks (based on the duration of stay); absolute count (C) and normalized count (D). Data were extracted from the travel survey of Chicago and present an estimate of the preferential return of Chicago residents at the time of the survey.</p

Spatial uncertainty.

<p>A: Box plots of the distribution of spatial uncertainty index grouped by rank; an index value of one indicates that all the tweets in a cluster are in the close proximity of a single land use parcel. Notice the strong left-skewed distribution, which indicates that the majority of the parcels are uniquely associated with a particular parcel. B: Log-log distribution of number of parcels per unique users grouped by activity types.</p