Climate and website visit data for multiple cities

The archive contains the following files: -analysis_government_sites.R: code for our analysis of visits to government websites -analysis_lashou.R: code for our analysis of the lash shopping dataset -analysis_transport_sites.R: code for our analysis of visits to public transport websites -data_government_sites (directory): contains number of daily visits to various government websites, as reported by Alexa. -data_transport_sites (directory): contains number of daily visits to various public transport websites, as reported by Alexa -data_weather.csv: daily weather data, analysed in conjunction with the shopping dataset -results_government_sites.csv: table summarising the results of analysing visits to government websites -results_transport_sites.csv: table summarising the results of analysing visits to public transport website

The effect of winter length (number of days) on planning activities across 28 cities.

<p>Each point represents a city in our dataset. For each city we indicate the length of winter defined as the number of days with DIK < = 60 (y-axis), the correlation strength as reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126205#pone.0126205.s001" target="_blank">S1 Text</a> figure 1 (x-axis), and the absolute T-value of the correlation (color scale). Blue points represent the cities reporting no significant correlation. The grey area shows the 95% CIs.</p

Geographic and climatic influence on human planning response.

<p>This map of Mainland China uses a Gaussian process regression (kriging) to visualize the geospatial distribution of the correlation values reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126205#pone.0126205.s001" target="_blank">S1 Text</a> figure 1. We note that cities with a positive correlation are located near the south. The region in low latitude and close to the sea has a warmer climate than the region in high latitude and far from the sea. The black line with numeric value depicts the effect of temperature change to human planning activities.</p

Correlation between average yearly temperature and traffic difference (between comfortable and cold months) for public transport websites.

<p>Each data point represents a city.</p

Correlation between temperature and planning activities.

<p>Scatterplots show the residuals for DIK (x-axis) versus revenue (y-axis) for each day in our dataset. Each scatterplot shows the data for a single city in our dataset, and reports the correlation coefficient. For each regression line we highlight the 95% CIs. The analysis controls for the effect of cloud cover for each data point. The line graphs show a detailed view of the weather and revenue data for the same 2 cities (Guangzhou and Taiyuan) over time.</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

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

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