Our objective was to develop and test a new concept (affinity) analogous to multimorbidity of chronic conditions for individuals at census tract level in Memphis, TN. The use of affinity will improve the surveillance of multiple chronic conditions and facilitate the design of effective interventions. We used publicly available chronic condition data (Center for Disease Control and Prevention 500 Cities project), socio-demographic data (US Census Bureau), and demographic data (Environmental Systems Research Institute). A geo-distinctive pattern of clustered chronic affinity associated with socio-economic deprivation wasobserved. Statistical results confirmed that neighborhoods with higher rates of crime, poverty, and unemploy-ment were associated with an increased likelihood of having a higher affinity among major chronic conditions.With the inclusion of smoking in the model, however, only the crime prevalence was statistically significantlyassociated with the chronic affinity. Chronic affinity disadvantages were disproportionately accumulated in socially disadvantagedareas. We showed links between commonly co-observed chronic diseases at the population level and systemat-ically explored the complexity of affinity and socio-economic disparities. Our affinity score, based on publiclyavailable datasets, served as a surrogate for multimorbidity at the population level, which may assist policy-makers and public health planners to identify urgent hot spots for chronic disease and allocate clinical, medicaland healthcare resources efficiently.