Integrating spatial information with conventional datasets enhances data analysis by uncovering geographic patterns, identifying regional disparities, and detecting spatial clusters. These insights help policymakers in designing targeted interventions through visually intuitive representations. This chapter introduces key steps in spatial data analysis, starting with exploratory techniques such as geo-visualization, choropleth mapping, and statistical distributions. It then examines spatial dependence using global (Moran’s I) and local (Gettis-Ord) correlation measures to identify clusters. The analysis is extended to confirmatory modeling by incorporating spatial dependencies into regression models, improving the explanatory power of traditional linear regression. Using GeoDa software, the chapter illustrates these concepts through a case study on the relationship between child marriage and child malnutrition in India.