Results of the questionnaire on LAKE analysis.

<p>Here we show for each location three sets of keywords and their respective results. Each row is an individual test case. The interrater agreement (Cronbach’s alpha) across all results was <i>α = 0.976</i> suggesting a strong agreement between the raters and the relevance or non-relevance of keywords to all the locations. For most of the cases, respondents agree that the words obtained using LAKE are more relevant to a location than words from random location wordsets or totally random words.</p

Total number of unique devices detected in the city during the study period.

<p>The data demonstrates how over a period of three years the volume of devices doubles, much unlike the population of our city that has grown at more modest rates.</p

Total number of unique devices detected at one location.

<p>The data demonstrates how over a period of 4 weeks in 2009 the number of devices fluctuates on a daily pattern. The first data point in this plot is a Monday. The data also shows that weekends have distinctly less activity.</p

A map showing all the WiFi access point locations.

<p>The map covers an area approximately 20km×20km.</p

Distance affects pedestrian flow correlations.

<p>Correlation in pedestrian flows is affected by distance (in meters) between two locations. Orange dots are pairs of high schools, and blue dots are pairs consisting of the university and high schools. Green dots are pairs of semantically irrelevant locations. Regression lines are included with the colour of the respective category. We identify two trends in this data. With the green colour we show location pairs that are not semantically relevant, which demonstrate an inverse effect between distance and correlation of pedestrian volumes (<i>x_random = </i>–<i>6.285e-05, r²_random = 0.11, p_random = 0.007</i>). In orange we show location pairs that are semantically relevant (pairs of high schools) and in blue we show location pairs that are highly related to each other (pairs consisting of the university and one high school). We find that for both sets of pairs distance has no significant effect on the pair’s correlation of pedestrian flows (<i>x_university = </i>–<i>2.647e-07, r²_university = </i>–<i>0.327, p_university = 0.910; x_highschools = 4.075e-06, r²_highschools = 0.051, p_highschools = 0.172</i>).</p

Pedestrian flow correlation Matrix for all locations.

<p>Each cell <i>C_ij</i> denotes the Pearson’s correlation in daily pedestrian flows between locations L_i and L_j.</p

Distance affects pedestrian flow correlations in a city-scale.

<p>Correlation in pedestrian flows as affected by distance (in meters) between two locations. Approximately 300000 points contained in this figure, and contain all locations in our dataset. Blue dashed line indicates the regression between the two variables, while the red solid line shows the LOWESS smoother that uses locally-weighted polynomial regression. Both contain a confidence interval at p = 0.99. The results confirm a negative relationship between physical distance and pedestrian flow correlation amongst pairwise locations. The scatterplot contains some clustered sets of points at distance = 10, 12 and 15 kilometres, highlighting spatial clusters of WiFi access points and the polycentric nature of the region.</p

Scatterplot matrices showing pedestrian flow.

<p>Here we can see pedestrian flows for (a) various types of locations and (b) high schools. In this figure each scatterplot is for a pair of locations. Each scatterplot data point is for a particular day of our study, and indicates the correlation of pedestrian flows for the two locations on that particular date. Data points are color-coded by season to account for seasonal variations. Reading the scatterplots: to locate the scatterplot for a pair of locations, the row-column intersection cell need to be inspected below the diagonal. Similarly, the correlation values are at the row-column intersection cell above the diagonal.</p

Summary of the linear regression analysis.

<p>Values of the normalization procedure from each dataset in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063980#pone-0063980-g003" target="_blank">Figures 3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063980#pone-0063980-g004" target="_blank">4</a>.</p

Top 10 search engine queries for various locations.

<p>The r-values reported are calculated byGoogle Correlate, and here we report the top-10 results for each location. In brackets are English translations where necessary.</p