Identifying factors that promote active mobility, especially walking, is essential for designing resilient and livable cities and promoting sustainable urban mobility. In spite of recent advances in this direction, available data often remains too spatially and temporally coarse, which constrains analysis. This paper leverages high resolution data from over 200 pedestrian count sensors, placed along Barcelona's commercial areas, providing a detailed understanding of how walking volume has evolved over the past five years, how it varies across neighborhoods, and which socioeconomic and urban attributes influence it. We find that while overall pedestrian traffic has increased, a neighborhood-scale analysis reveals a nuanced picture of fluctuations, including increases, declines, and periodic patterns. The use of global regression models allows us to identify seven key urban factors that shape pedestrian mobility. Subsequently moving the analysis to spatially-aware regression models, we identify the spatial non-stationarity of these factors across the city, indicating the presence of distinct behavioral groups within the urban population. The detailed spatial resolution of our findings provides municipal decision-makers with insights for implementing precise interventions and continually evaluating their effects. Moreover, monitoring pedestrian traffic before and after urban initiatives, while adjusting for seasonal, daily, and time-of-day variations, can yield critical insights for developing pedestrian-oriented urban environments.