District heating and cooling networks connect and distribute thermal energy resources within a network of sources and demands. While individual networks have been extensively studied, the scaling up of this technology requires the interconnection of larger sets of networks. This poses the problem of the optimal allocation of thermal resources across a spatially distributed network. Addressing this problem guarantees the efficient uti-lization of thermal resources and assists achieving carbon-neutral energy systems; however, previous studies have not addressed this issue. This work contributes to filling this gap by presenting an optimal spatial allocation method combining an existing spatial clustering method, transportation theory, and linear programming to maximize the allocable resources under spatial constraints. A case study shows that the proposed method is effective at handling large-scale problems. The method enables large-scale analysis of a broad range of geo-spatially bounded resources, especially in the application of mapping renewable energy sources to supply district heating and cooling.