This paper is presented as how data-enabled principles can be applied for the field of urban geography. It shows the fundamentals and examples for researchers in other fields to be considered. Specifically, we examine the link between three types of job concentrations and the site's spatial and nonspatial factors using OnTheMap 2011 data. We empirically analyzed spatial and non-spatial attributes, respectively and combined. The logistic and OLS regression analyses indicate that overall job concentrations locate with access to large employment areas, major roads, freight facilities, and White labor, while proximate local roads and Black labor force deter jobs which confirms selective race-based business location decisions. Employment size reflects economic health of an area and is a predictor of growth. We modeled employment magnitude by spatial and non-spatial factors among various employment concentrations. For small and medium job concentrations, areal employment benefits from proximity to Memphis Aerotropolis which offers excellent transportation opportunities. Employment in medium-sized concentrations is associated with experienced older workers as well as female resident workers. Employment size in large job areas is inversely related to the distance to the CBD and is positively associated with available high-earning resident workers. The paper contributes to the field of data-enabled science where real-world systems are modeled quantitatively using extensive economic activities data sets. Although a single-time period data was analyzed, the principles demonstrated in the paper may be considered for analysis of time series disciplinespecific problems of urban environment and those arising in other fields of endeavor.