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

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

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

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