Kriging Toolkit for Python

Core components of Python Spatial Analysis Library

Spatial econometric regression in Python

Spatial modeling using machine learning concepts

Pieces of code that have appeared on my blog.

SParse Generalized Linear Models (spglm)

Compute structure factor of stationary and isotropic point processes

Iterative heuristics for endogenous spatial regimes delineation (IJGIS 2023)

Code for ridesharing paper @ WWW 2018.

Spatial effects in network measures of spatially embedded networks

A Python wrapper for Fragstats.

A package for fast bayesian analysis of single season spatial occupancy models.

This is an interactive app (run on local computer) to visualize neural likelihood surfaces from the paper "Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods"

Sample a repelled point process, compute a Monte Carlo estimation for the integral of a function using various variants of the Monte Carlo method including the Monte Carlo with a repelled point process, and visualize gravitational allocations 2D.

Provide functions for computing multi-scale, multi-tapers estimator of the hyperuniformity exponent and associated asymptotic confidence intervals.

Module for Kernel-Density Estimation (KDE) on sphere

A simple implementation for creating a Meta-learning rule using spatial statistical parameters of a dataset

Intyearpolator is, as its name suggests, a spatial interpolator designed for predicting years of a random field.

Python package to perform Tan et al. (2014)'s analysis of distance