Covid-19 has become a significant global public health concern due to its faster rate of mortality and morbidities. Various precautionary measures such as social distancing have been outlined across the globe to reduce its transmission. However, the practicality of implementing the social distancing is a function of multiple factors including; urban morphology, population density and availability of essential socioeconomic services. Using Getis-Ord G* and Spatial-Cluster outlier analysis, we targeted the hotspot centres where effective social distancing will be difficult to achieve within Upper West Region in order to optimize intervention and resource allocation. We also examined factors determining effective Social Distancing using Geographically Weighted Regression (GWR). Ease of achieving social distancing index (ESDI) was acquired from WorldPop Group Research centre at Southampton University, Freshwater availability from Socioeconomic Data and Applications Center (SEDAC) and Average household size from DHIMS-2. Our spatial statistical analysis indicates that ESDI is significantly spatially auto-correlated; with hotspots occurring generally at the urban core. Using both the global (OLS) and local (GWR) regression models, we found that, relationships between ESDI and built-up area, built-up proportion, population density and freshwater availability were statistically significant. We suggest that areas where it is difficult to achieve social distancing due to built-up design or population density, constant surveillance should be adopted for timely detection.