Fragmented and manual spatial data analysis leads to underutilized land and slow regulatory processing. Automated spatio-temporal fusion reduces administrative latency and identifies hidden land availability.

Incomplete or fragmented trajectory and population data prevents accurate behavioral modeling. Overcoming this limitation allows for reliable predictive analytics in urban planning and mobility.