Retail is a dynamic, evolving function that reflects or drives shifts in the social and functional dimensions of cities. The transformation of retail in the 19th and 20th centuries has been widely studied, yet the rise of the internet and mobile commerce has introduced new patterns of change in urban retail presence. However, research on retail transformation at the local scale remains limited, particularly due to the lack of microscale datasets with temporal data. This study proposes a geoprocessing protocol to identify and analyze retail functional changes over time in urban environments. Using spatiotemporal point pattern analysis over the street network, the approach provides a robust framework for identifying and describing the nature of shifts in the retailscape. We test this methodology to official time-series datasets from some metropolitan areas in France, spanning 2008–2022. Preliminary results demonstrate the protocol’s effectiveness in detecting retail transformations in terms of nature, intensity, and spatial distribution across urban areas. This research contributes to a deeper understanding of urban retailscape evolution, offering insights into retail dynamics, urban planning, and the resilience of urban systems in response to changing economic and social contexts.