## 4 Analysis and Results
### Human Response at Grid Scale  
In order to explore the differences in human response intensity of different time periods at grid scale, we first divided the whole day into four time periods (08-10, 11-16, 17-20, 21-07), and then map the RRWPs in different time periods by the 4 time periods' dot maps. Figure 10 shows the results. We can find that in the study area, the morning rush hours (08-10) and the evening rush hours (17-20) have the strongest human response intensity. During these two periods, important transportation hubs (Commercial business center, large jobs-housing area) and water ponding point areas have become regions with a high response in the main urban area of Beijing. In addition, there was a phenomenon in which dense points are distributed around the subway and along important roads. The occurrence of rainstorm event has caused great obstacles to traffic operation and delayed human's travel. The points in the second period (11-16) are mainly distributed near the traffic station and the more severely affected areas. The points in the last period are sparsely distributed in the study area. We also found the density distribution of four periods' points in major traffic stations such as airports, railway stations, and bus stations were relatively uniform, while large jobs-housing areas are densely distributed at points of the morning rush hours and the evening rush hours. Figure 11 shows the correlation between the rainfall amount and the number of RRWP at the grid scale. The correlation coefficients vary between \(-0.14\) and \(0.86\) with an average of \(0.22\). The negative or no correlation relationship is mainly found in suburbs, such as the Changping, Huairou and Miyun Districts. By contrast, higher correlation coefficients are mainly found in populated areas within the city, including populated residential communities, important transportation hubs, and areas significantly impacted by the rainstorms as shown in news reports. Figure 9: Quantile regression estimates of the relationship between the RRWP and the rainfall amount at the city scale. Figure 12a shows the _HERI_ across our study area. Only the 2203 grids with at least 10 daily Weibo posts are selected to calculate the _HERI_. At the grid scale, _HERI_ values range between 0 and 9.83 with an average value of 1.23. The regions with a higher _HERI_ value are mainly found in three places in our study area. The first are the areas with more rainstorm-induced damages, including serious house collapse, road blockage and mudslides. These areas are mainly found in the suburbs such as the Fangshan and Mentougou  
Figure 11: Correlation coefficients at grid level across our study area. Figure 10: Dot maps of in different time periods based on grid cells. Districts. The densely populated regions, including Zhongguancun and the CBD, the Tongzhou residential area, an Internet technology parks also have a higher _HERI_ value. Regions with important transportation hubs also have a higher _HERI_ value. The transportation hubs include subway stations, train stations, and airports. Figure 12b shows the _HERI_ hotspot analysis results. Hotspots are mainly located in densely populated areas, important residential and workplaces, such as the Tongzhou residential area, CBD districts, and IT parks. It is worth noting that a large number of hotspots are found in the urban core areas. By contrast, the cold spots are mainly found in the remote suburbs of our study area. Such areas have a low population thus limited human activities. The proportions of POIs within each hotspot identified are shown in Figure 12c. The _HERI_ hotspots in the Beijing Capital Airport, Yizhuang, and Changping-Shahe Districts have the highest percent of transportation POIs. The texts of the RRWP within these hotspots show that the rainstroms may significantly delay the commute in these regions thus stimulate users to publish more RRWP to complain the traffic. The hotspots in the Tongzhou residential area and the Mentougou District show a higher proportion of residential POIs.