Spatial regression is a statistical technique used to analyze data that has a geographical or spatial component. It helps researchers understand how the location of data points affects the relationships between variables. For example, it can be used to study how housing prices vary across different neighborhoods, taking into account factors like proximity to schools or public transportation. This method extends traditional regression analysis by incorporating spatial relationships, allowing for more accurate predictions and insights. By considering the influence of nearby locations, spatial regression can reveal patterns that might be missed in standard analyses, making it valuable in fields like urban planning and environmental science.