spatial_autocorrelation
Performing Moran's I to conduct correlation analysis on topological/geometrical relationship.
Moran's I, developed by Patrick Alfred Pierce Moran [1], measures spatial autocorrelation globally based on the feature locations and values. It quantifies the relationship how clustered the values of data points geometrically are, i.e. the spatial lagged.
Requirements
This module is expected to compile for 'python 3.7-3.9'
Usage
You have to customly define the spatial weighted matrix for describing the topogical/geometrical relationship. You may want to refer to example/Spatial Autocorrelation.ipynb.
For Moran's I (global metric)
Moran's I is within-1 and 1.
- -1 represents perfectly dispersed
- 0 represents randomness
- 1 represents perfectly clustered
For calculating the global Moran's I, you can execute
from spatial_autocorrelation import global_moransI
You are also able to visualize the global relationship on a plot
from spatial_autocorrelation import moransI_scatterplot
Since it is a inferential statistics, the Moran's I value can be converted into Z score for conducting statistical hypothesis testing
from spatial_autocorrelation import hypothesis_testing
For LISA (local metric)
You can retrieve a dataframe containing local Moran's I, Z score of each individual data point by using
from spatial_autocorrelation import get_localMoransI
You can also visualize the high-high, high-low, low-high, low-low clusters on a plot
from spatial_autocorrelation import LISA_scatterplot
References:
