LISAStatistiken

LISAStatistiken, also known as Local Indicators of Spatial Association (LISA) statistics, are a set of spatial statistics used to detect and visualize spatial autocorrelation in data. Spatial autocorrelation refers to the tendency of nearby observations to be more similar than those that are further apart. LISA statistics were introduced by Luc Anselin in 1995 as a way to address the limitations of global measures of spatial autocorrelation, which provide a single value for the entire dataset but do not indicate where significant spatial patterns are located.

LISA statistics are based on the concept of local spatial autocorrelation, which examines the relationship between each observation and its neighbors. The most commonly used LISA statistic is the Local Moran's I, which measures the extent to which each observation and its neighbors are similar or dissimilar. The result of a LISA analysis is a map that shows clusters of similar values (high-high or low-low) and outliers (high-low or low-high), providing insights into the spatial structure of the data.

LISA statistics are widely used in various fields, including geography, economics, public health, and environmental science, to identify spatial patterns and hotspots. They are particularly useful for detecting local spatial patterns that may not be apparent from global measures of spatial autocorrelation. LISA statistics can also be used to test for spatial randomness, identify spatial outliers, and assess the robustness of spatial regression models.

In summary, LISAStatistiken are a powerful tool for exploring and understanding the spatial structure of data. By providing a local perspective on spatial autocorrelation, they offer valuable insights into the underlying processes that generate spatial patterns.