Spatial Regression Models

Spatial regression models analyze data where observations are linked to specific locations, like neighborhoods or regions. Unlike standard models, they account for "spatial dependence," meaning nearby areas often influence each other. For example, property prices in one neighborhood may be affected by prices nearby. These models help identify how location-related factors impact outcomes, improving predictions and understanding of spatial patterns. They are useful in fields like urban planning, environmental science, and public health to better understand complex geographic relationships.