Exploratory spatial data analysis (ESDA) is an extension of exploratory data analysis which focuses on the spatial or geographical data explicitly.
Data exploration can be performed using the Exploratory Spatial Data Analysis (ESDA) tools. These tools allow you to examine the data in more quantitative ways than mapping it and let you gain a deeper understanding of the phenomena you are investigating so that you can make more informed decisions on how the interpolation model should be constructed (source: https://desktop.arcgis.com/en/arcmap/latest/extensions/geostatistical-analyst/exploratory-spatial-data-analysis-esda-.htm).
We know that the conventional Exploratory data analysis does not investigate the location component of the dataset explicitly but instead deals with the relationship between variables and their correlation.
Whereas, Exploratory Spatial Data Analysis (ESDA) correlates a specific variable to a location, considering the values of the same variable in the neighbourhood (Spatial Autocorrelation methods).
Spatial autocorrelation describes the presence (or absence) of spatial variations in a given variable. Spatial autocorrelation has positive and negative values. Positive spatial autocorrelation is when areas close to each other have similar values (High-high or Low-low). On the other hand, negative spatial autocorrelation indicates that neighborhood areas to be different (Low values next to high values).
Before performing any spatial autocorrelation, the spatial weights and spatial lag should be determined. Spatial weights are how we determine the area’s neighbourhood while spatial lag is the weighted average of the neighbouring values for that variable (based on spatial similarity). There are different statistical methods that are used for determining spatial weights (E.g. Queen Contiguity Matrix)
There are mainly two methods of Exploratory Spatial Data Analysis (ESDA): global and local spatial autocorrelation. The global spatial autocorrelation focuses on the overall trend in the dataset and tells us if the degree of clustering int eh dataset. In contrast, The local spatial autocorrelation detects variability and divergence in the dataset, which helps us identify hot spots and cold spots in the data.
source:https://towardsdatascience.com/what-is-exploratory-spatial-data-analysis-esda-335da79026ee