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District Heating Networks have evolved in recent years, moving from transporting high-temperature heat produced in Cogeneration Heat and Power Plants, to nowadays using heat from low-temperature renewable heat sources, driving the sustainable transition in the heating sector. The percentage of heat transported by district heating networks varies widely across Europe, from 1% in Ireland to 64% in Denmark. GIS-based algorithms have emerged as design and decision tools to identify renewable heat sources, heat users, and to determine the district heating networks size to connect them. Most of the current methods assume a constant installation cost for the entire network (Single Cost Area). However, this may not hold true for transmission lines crossing Multiple Cost Areas, such as cities, countryside, rivers, and natural reserves, which have significantly different installation costs. This paper presents a GIS-based methodology developed in Python to adapt the existing methods to lines crossing Multiple Cost Areas. The system's profitability is evaluated in terms of Total Investment Cost, Levelized Cost of Heat, and the Maximum Profitable Distance. The result is that using a constant average cost for a Multiple Cost Area region can lead to an error of 11-71 % on the estimation of the maximum profitable distance. To summarize the results a newly developed concept, the Profitability Map, is introduced to highlight the regions where is profitable to transport the heat. Two case studies (100 MW waste heat, 5 MW low temperature geothermal) are presented to demonstrate the use of Profitability Maps, highlighting the advantages of the new methodology as a decision-support tool.