This study explores how sustainable retail locations can be achieved within the urban areas vis-a-vis the applications of GeoAI. The study develops conceptual framework on the integration of relevant datasets, tools, algorithms, and techniques for developing smart geo-spatial tools capable of aiding smart decisions on various activities including, retail real estate development, investment, occupation, and efficient urban centre planning. The study rests on three (3) broad underlying principles. That is: (1) retail consumers (directly or indirectly) control the retail property markets and retail property location performance (2) spatial behaviour of retail consumers can be scientifically assessed and scored based on interconnectedness of streets (that is, space syntax theory) through spatial configuration analysis and (3) historical data on retail property performance could help in predicting retail location performance and resilience index using predictive machine learning algorithms. The study argues that a rethinking of the distribution of physical retail spaces would be appropriate to ensure various classes of retail real estate are optimally positioned in locations that best meet the present needs of the stakeholders whilst considering the future implications of their actions.
ORCID: 
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)