Emergence of patient-focused measures and sustained interest in large-scale surveys (e.g., Demographic and Health Survey [DHS]) has led to broad use of psychometric models (e.g., Item Response Theory [IRT]) in health policy to assess latent (i.e., unobserved) traits such as quality of life, health-related attitudes, and knowledge. Detection of Differential Item Functioning (DIF), including DIF attributable to geographic location, is relevant in such a context. However, DIF detection techniques often rely on pre-specified groups defined by political borders, which can obscure differences in item functionality that exist on a disaggregate level. Local Item Response Theory (LIRT), a moving-window approach to IRT, provides a flexible framework to identify regional DIF without pre-specified groups. Mappings of spatially-varying item parameters afford visualization that can aid in survey improvement and can assist policy makers in the proposal of targeted, systematic interventions. Using DHS data, current work demonstrates LIRT while exploring regional differences in contraceptive knowledge, HIV/AIDS knowledge, and attitudes toward domestic violence in Lesotho and Malawi.