Active transportation primarily walking and cycling has become a central pillar of sustainable urban mobility, yet its adoption remains uneven across cities due to disparities in infrastructure quality, accessibility, and socio-spatial conditions. As municipalities pursue climate goals, congestion reduction, and public-health improvements, the ability to accurately model active transportation demand has become essential for targeted planning and equitable policy design. Geographic Information Systems (GIS) now serve as powerful platforms for integrating multimodal infrastructure data, land-use patterns, demographic distributions, and environmental variables, enabling a comprehensive spatial understanding of mobility behavior. However, traditional statistical models often fail to capture the inherently spatial nature of travel decisions, including neighborhood clustering, geographic spillover effects, and the influence of adjacent infrastructure networks. Spatial econometric methods such as spatial lag, spatial error, and geographically weighted regression offer a robust analytical framework to address these complexities by explicitly modeling spatial dependence and heterogeneity. When combined with GIS-based accessibility metrics, these techniques allow researchers to quantify how sidewalk continuity, cycling-network density, intersection safety, and transit interconnectivity shape active travel outcomes across heterogeneous urban landscapes. This integration also supports the evaluation of equity implications, revealing whether marginalized communities face disproportionate barriers to safe, connected, and sustainable mobility infrastructure. By linking spatially explicit infrastructure supply with observed or forecasted demand, GIS-integrated spatial econometrics enables planners to identify high-impact investment zones, prioritize low-access neighborhoods, and assess long-term sustainability benefits. Ultimately, this modeling approach moves beyond traditional mobility analyses by providing a multidimensional view of accessibility, environmental performance, and social fairness. It equips decision-makers with a data-driven foundation to design resilient, inclusive, and future-oriented active transportation systems that align with broader urban sustainability goals.