The collection of traditional administrative unit-based gross domestic product (GDP) data is time-consuming and laborious, and the data lacks accurate spatial information. Long-term series nighttime light (NTL) data can provide effective spatiotemporal GDP change information, which can be used to analyze economies’ spatial distributions and development trends. In this study, we generated a spatial model of the relationship between NTL indices and GDP parameters, based on NPP-VIIRS-like NTL data for the period 2001 to 2020, conducted a multitemporal and multilevel connectivity analysis of the GDP spatialization data, and constructed a tree structure for horizontal and vertical analysis. Standard deviation ellipses and economic centers of the first-level economic connected components at the provincial and municipal levels were generated, and the economic center distribution range and development direction at the provincial and municipal levels were analyzed. The results show that GDP spatialization data, based on NPP-VIIRS-like NTL data, can intuitively reflect the GDP spatial distribution. In Henan Province, the economic levels of different regions vary, and the economic regions represented by Zhengzhou have developed rapidly, driving surrounding regional economic rapid development. Henan Province’s development trend from single-city economic centers to multicity economic centers is obvious, and the economic center has shifted to the southeast.