Analysis of spatial features and determination of metropolitan area boundaries in the Guanzhong region affected by intercity rail transit
摘要
This study explores how intercity rail transit reshapes spatial structures, which underpins the optimization of regional resource allocation. Taking 54 county-level units in the Guanzhong region as research subjects, we constructed a dual-model analytical framework combining “field potential” and “hydrological network” models. A counterfactual simulation approach was adopted to compare spatial field potential distributions under scenarios with and without rail transit, while topological relationships of river networks were used to delineate metropolitan areas. Key findings include: (1) Under scenarios with and without intercity rail transit, the mean field potential values of county-level units in Guanzhong were 9.59 and 8.50, respectively, with standard deviations of 205.65 and 170.45. This indicates that rail transit significantly enhances field potential while amplifying spatial heterogeneity. (2) The “layered-corridor” composite structure in Guanzhong exhibits stability. However, intercity rail transit introduces coexisting “corridor effects” and “shielding effects” in non-corridor areas, enabling potential leapfrog development in metropolitan areas. (3) Fractal differences in county-level field potential distributions help identify the spatial boundaries of three metropolitan areas: Xi’an, Baoji, and Weinan. The Xi’an metropolitan area already dominates Guanzhong, whereas the Linwei District of Weinan exhibits a field potential attenuation slope toward Xi’an that is 2.3 times faster than its local attenuation, suggesting future integration of Weinan into Xi’an’s framework. (4) The study concludes that intercity rail networks reconstruct regional energy fields through space-time compression effects. Future planning should leverage rail transit’s positive role in spatial regulation and resource allocation, adopting a “layered-corridor” organizational model to promote integration and structural optimization.