Site Selection for the Real Estate industry

Energy companies and utilities face complex decisions about where to invest in infrastructure upgrades, expansions, or new facilities. This use case leverages geospatial analysis combining climate risk, environmental hazard exposure, population growth trajectories, and regulatory boundary data to help energy sector planners identify which assets are most vulnerable and where future demand will concentrate. By overlaying power grid topology with climate risk layers, flood and wildfire hazard zones, and demographic growth signals, organizations can build a data-driven prioritization matrix for capital expenditure. The result is a defensible, spatially explicit investment roadmap that reduces regulatory risk, improves resilience planning, and aligns infrastructure spending with long-term demand forecasts.

Franchise brands and multi-location operators often grant exclusive or protected territories to franchisees and licensees, but as new units open and consumer catchments shift, trade areas can begin to overlap, cannibalizing sales and triggering contractual disputes. This use case combines drive-time and gravity-model trade area modeling with device movement patterns, parcel and building footprint data, and demographic segmentation to precisely define, visualize, and monitor franchisee territories over time. It enables corporate development, legal, and franchise operations teams to proactively identify encroachment risk before a new site is approved, quantify the degree of customer overlap between existing units, and provide defensible, data-backed evidence during territory renegotiations or litigation. Unlike simple radius-based exclusivity clauses, this approach models real-world foot traffic and mobility flows to reflect actual customer behavior rather than static distance assumptions.