Dis­trib­uted pho­to­voltaics (PV) have played a crit­i­cal role in the deploy­ment of solar ener­gy, cur­rent­ly mak­ing up rough­ly half of the glob­al PV installed capac­i­ty. How­ev­er, there remains sig­nif­i­cant unused eco­nom­i­cal­ly ben­e­fi­cial poten­tial. Esti­mates of the total tech­ni­cal poten­tial for rooftop PV sys­tems in the Unit­ed States cal­cu­late a gen­er­a­tion com­pa­ra­ble to approx­i­mate­ly 40% of the 2016 total nation­al elec­tric-sec­tor sales. To best take advan­tage of the rooftop PV poten­tial, effec­tive ana­lyt­ic tools that sup­port deploy­ment strate­gies and aggres­sive local, state, and nation­al poli­cies to reduce the soft cost of solar ener­gy are vital. A key step is the low-cost automa­tion of data analy­sis and busi­ness case pre­sen­ta­tion for struc­ture-inte­grat­ed solar ener­gy. In this paper, the scal­a­bil­i­ty and res­o­lu­tion of var­i­ous meth­ods to assess the urban rooftop PV poten­tial are com­pared, con­clud­ing with sug­ges­tions for future work in bridg­ing method­olo­gies to bet­ter assist pol­i­cy makers.