*4.1. Analysis of Output Multi-Convolutional Neural Network*

We divide the haze prediction results of Beijing in 2014 into nine regions. Further, to visually observe the spatial distribution of haze and the temporal evolution characteristics at the seasonal scale, we divide the results into four seasons and plot the frequency histograms of the haze level. We analyze the overall frequency of haze, some areas with obvious haze characteristics in different seasons and the similarity between different re-

gions. The frequency histograms of haze levels in winter, spring, summer, and autumn are respectively shown in Figure 5a–d.

**Figure 5.** The frequency histograms of haze levels in (**a**) winter. (**b**) spring. (**c**) summer. (**d**) autumn.

Then the results are analyzed in three aspects:

	- Figure 5a shows that:

The frequency histograms of haze levels in spring show:


Analyzing the frequency histograms in autumn, we can draw the following conclusions:


From the degree of haze pollution at a seasonal scale, Beijing's pollution intensity is in the order: autumn > winter > spring > summer.

If the generation of data is related to the geographical location, the spatial distribution of the data is also location-dependent, and the correlation is positive to the distance. There are three distribution forms: clustering, random distribution, and rule distribution. Haze is a kind of data related to location. We conducted a spatial autocorrelation analysis of the prediction results to analyze the distribution of haze in different geographies and the correlation between different regions. We used Global Moran's *I* and Local Moran's *I* to analyze the global and local spatial characteristics of haze, respectively.
