Under the dual impact of environmental issues and the energy crisis, new energy vehicles (NEVs) have gradually become a phenomenal emerging industry in China, also essentially becoming a new engine to support the growth of China’s economy. While topics related to the NEV industry have gained widespread attention, there is a lack of studies specifically focusing on the characteristics of its industrial spatial distribution pattern. Based on the data related to NEV-listed companies located in the Yangtze River Delta (YRD) region in 2022, this study constructs the corresponding city network using the method of social network analysis (SNA) and interprets the structural features of this network. The results reveal the following: (1) The network exhibits three fundamental characteristics: low density, short path length, and multiple centers. (2) The NEV industry in the YRD has formed the agglomeration pattern of three major “clubs”, projected on the map in the shape of a “golden bow”. (3) Cities in the YRD show a “pyramid-type” collaboration in the NEV industry. (4) Collaboration between cities in the NEV industry can cross the limits of geographic proximity and even administrative boundaries.

Urban fire response remains largely ground-based, yet the role of building rooftops as distributed UAV infrastructure remains unclear. Here, we develop a spatial planning framework that screens rooftops, models demand-weighted coverage and compares alternative deployment algorithms for Greater London. The framework combines building attributes, fire station proximity, an airspace exclusion layer and nighttime light demand weights. We evaluate NSGA-II, SPEA2, MOEA/D and a greedy baseline and select a representative layout from the observed trade-off between coverage and deployment scale. The selected 14-rooftop layout covers 83.78% of the demand weight reachable from the candidate set. When assessed against historical fire records, the rooftop network complements ground services under independently specified UAV and ground-service spatial scenarios, and their combined modeled coverage reaches 92.00%. These findings provide a case-specific planning reference for evaluating rooftop emergency infrastructure in other dense cities after local data, regulatory and operational adaptation.