The population proliferation of Latinos in the U.S. has propelled them into the new majority-minority. Mexicans make up more than half of all Latinos/as. Social scientists have long known that accounting for social environment is crucial in deciphering how social structures interact with individual human behavior. Academic discourse needs to explicitly delineate the logic and best practices for measuring social contexts. Standardizing how contexts are geographically boundarized and subsequently measured could provide multilevel and spatial modeling researchers a more solid theoretical foundation for nesting individuals and measuring their environment. Context measuring standardization would make cross study comparisons more readily available. This project seeks to contribute to this endeavor by employing and advancing the "Saenzian" logic for regionalizing Mexican origin Latinos/as. The proposed solution applies to social research that uses U.S. Census Bureau microdata to investigate the Mexican population. By using Saenzian concepts, this study explores and proposes three alternatives for geographically regionalizing the Mexican population. Maps are utilized to present the logic for the classical, new, and clustered Saenzian regional classification schemes. Findings comparing the classical and new approach reveal that smaller geographical units reveal important insights that are typically hidden by large polygon conglomerations. Findings from the clustered analysis reveal that regions are more tightly and well defined. A discussion is offered in closing posing basic theoretical questions on what constitutes a region.
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