Social disorganization theory has significantly contributed to understanding the relationship between community characteristics and crime. It is based on the assumption that the residents of a community define the characteristics of the community. In particular, Shaw and McKay discover that racial heterogeneity could disrupt the local community's social organization and account for crime and delinquency. While residents play the most important role in defining the characteristics of a community, non-residents who frequent a community could also affect the community. The perceived racial composition is likely to be different from the makeup of the residential population. Drawing from the recent big data which could capture residents and non-residents, this paper proposes an innovative racial heterogeneity index based on the ambient population. The effectiveness of the new index is illustrated by using negative binomial models, to explain street crime. The findings show that the ambient population-based racial heterogeneity index outperforms the traditional one in explaining street robbery. Further, the new index positively impacts street robbery in a statistically significant fashion. Conversely, the traditional index shows no statistically significant influence. The study also reveals that the racial heterogeneity of ambient population in an area is mostly contributed by the visitors from the neighboring areas. Overall, the proposed ambient population-based racial heterogeneity is theoretically sound and effective in explaining street crimes such as street robbery. The novel approach and findings add new contributions to social disorganization and routine activity theories.