Index wouldindex could be 17.961; having said that, the actual geographical concentration index was tration be 17.961; even so, the actual geographical concentration index was consistently greater than this value, this value, revealing that the distribution of index was normally regularly greater than revealing that the distribution of the Baidu the Baidu index was concentrated in the provincial level. frequently concentrated in the provincial level.Land 2021, 10,amongst 0.332 and 0.213 (Figure six), i.e., characterized by a downward trend, indicating that polarization of Baidu index was prominent, despite the fact that it weakened. The average geographic concentration index was 19.638, with values displaying a decreasing trend. In the event the Baidu index had been Ganetespib Activator evenly distributed in all provinces, the geographical concentration index could be 17.961; having said that, the actual geographical concentration index was ten of 21 consistently higher than this value, revealing that the distribution of your Baidu index was typically concentrated at the provincial level.Figure 6. Geographic concentration index and disequilibrium index for public consideration from 2013 to 2020. Figure 6. Geographic concentration index and disequilibrium index for public attention from 2013 to 2020. Around the basis from the preceding analysis, it appears that the pattern and evolution of theBaidu index was characterized by clustering and agglomeration, i.e., the Baidu index of an area was spatially correlated together with the neighboring region to a certain extent. As a result, we examined the autocorrelation and AZD1208 custom synthesis relevancy qualities. The results on the worldwide spatial autocorrelation with the Baidu index for the duration of the period studied are presented in Table 1. The global Moran’s I index was always positive and larger than 0.30, which passed the 0.01 level significance test, indicating that there was a significant and stable spatial autocorrelation within the annual Baidu index. It can be confirmed that the Baidu index on the region was very positively linked with neighboring values.Table 1. Worldwide Moran’s I index of Baidu index concentrations during 2013020. Year 2013 2014 2015 2016 2017 2018 2019 2020 Moran’s I 0.329 0.301 0.305 0.336 0.346 0.384 0.412 0.378 Z-Score four.037 three.721 three.763 four.336 four.428 4.498 four.898 4.408 p-Value 0.001 0.002 0.003 0.002 0.001 0.001 0.001 0.Moreover, the regional spatial autocorrelation evaluation was utilised to detect specific characteristics of Baidu index agglomeration, and also the spatial clustering pattern is shown in the LISA map (Figure 7). The Baidu index primarily formed three categories of clustering inside the regional distribution. The Low ow clusters were situated in Northwest China, for instance in Xinjiang and Qinghai, and in Tibet, which incorporated two, 5, and three provinces in 2015, 2017, and 2019, respectively. The High igh Clusters had been formed in East and Central China in 2015, for example, in Jiangsu, Zhejiang, Hubei, and Hunan. We see that the Higher igh Clusters extended northward in 2017 and 2019 to Hebei and Beijing, which proves that the Baidu index in North China also enhanced to a higher level, and these clusters had eight, 11, and 11 provinces in 2015, 2017, and 2019, respectively. Moreover, there was a “depression area” in Central China, i.e., a Low igh cluster, including Anhui and Jiangxi, whose Baidu index was naturally lower than that from the surrounding locations.Land 2021, ten,such as in Xinjiang and Qinghai, and in Tibet, which integrated two, five, and 3 provinces in 2015, 2017, and 2019,.
