## 4 Result
### Spatializing the Nighttime Economy
#### 4.1.2. Spatializing the Nighttime Economy Vitality  
We classified the NEVI of the YRD urban agglomeration for 2020 using the natural discontinuity method, as shown in Figure 3, which indicates that nighttime human activity in Shanghai is more active; the NEVI is the highest there. Hefei, Nanjing, Wuxi, Suzhou, Hangzhou, Ningbo, and Hangzhou fall under the second category; the NEVI is between 0.1141 and 0.5766. Yancheng, Yangzhou, Taizhou, Nantong, Zhenjiang, Changzhou, Jiaxing, Shaoxing, Taizhou, and Jinhua belong to the third category; the NEVI is between 0.0631 and 0.1140. Chuzhou, Wuhu, Huzhou, and Zhoushan comprise the fourth category; the NEVI is between 0.0297 and 0.0630. Anqing, Tongling, Ma'anshan, Xuancheng, and Chizhou fall under the fifth category; the NEVI is between 0.0027 and 0.0296. In general,  
Figure 1: Percentage of nighttime merchants in the YRD region in 2019. Figure 2: Compound growth rate of nighttime trading among new businesses in the YRD. the level of nighttime economic growth in municipalities directly under the central government, eastern coastal cities, and provincial capital cities is higher than that of other regions. Shanghai's level of nighttime economic growth is higher than that of Zhejiang and Jiangsu provinces, and the levels of Zhejiang and Jiangsu Province are higher than that of Anhui Province. We calculated the total NTL intensity and average NTL intensity and used the natural discontinuity method to classify the total NTL intensity and average NTL intensity of the YRD urban agglomeration in 2020, as shown in Figure 4. The coefficients in Tables 3 and 4 represent the fitted coefficients from the OLS linear regression. In Table 3, the independent variables include the total light intensity and average light intensity. The dependent variable is the economic vitality index at night. In Table 4, the independent variables are total light intensity, average light intensity, and the number of PO  
The spatial distribution trend of the total NTL intensity is similar to that of the NEVI. The total NTL intensity of Shanghai and Suzhou is between 219,806.7039 and 426,214.2813. The total NTL intensity in Nanjing, Wuxi, Hangzhou, and Ningbo is between 144,451.5565 and 219,806.7038, which falls under the second category. The total NTL intensity in the cities of Yancheng, Nantong, Hefei, Changzhou, Jiaxing, and Jinhua is between 88,754.27363 and 144,451.5564, which belongs to the third category. The rest of the cities fall under the fourth and fifth categories; the total NTL intensity is between 8484.660156 and 88,754.27362. The spatial distributions of the average NTL intensity, total NTL intensity, and the NEVI differ slightly. Shanghai and Suzhou belong to the first category; the average NTL intensity is between 8.164186577 and 15.18127441. The cities of Nanjing, Zhenjiang, Changzhou, Wuxi, Jiaxing, Ningbo, and Zhoushan belong to the second category; the average NTL intensity is between 4.71411839 and 8.164186576. The cities of Yangzhou, Taizhou, Nantong, Hefei, Huzhou, Hangzhou, and Shaoxing fall under the third category; the average NTL intensity is between 2.6674677 and 4.714118389. The rest of the cities fall under the fourth and fifth categories, respectively; the total intensity of NTL is between 0.269962758 and 2.667467769. The total and average NTL intensities from 2011 to 2020 are dimensionless and compared with the NEVI of each city during the same time span. The fitting results are listed in Table 3. All pass the significance test at the 1% level. The R\({}^{2}\) value of the total NTL intensity is 0.7974, and the R\({}^{2}\) value of the average NTL intensity is 0.5847. The fitting effect of the