Eikosstatistics & data science

Advanced & Applied Topics · Spatial Statistics & Geostatistics

Spatial Statistics: Geographic Information and Spatial Data

Public introduction · full guide in Eikos

Introduction

What this guide covers

Mathematical foundations, mathematical modeling of spatial autocorrelation, theoretical variograms, point processes and kriging theory under a formal approach.

Overview

Topics covered

  • Introduction
  • Spatial Statistics and Geographic Information
  • Fundamentals of Spatial Statistics
  • The Modifiable Area Unit Problem (MAUP)
  • Spatial Edge Effects (Boundary Effects)
  • Representation of Geographic Information

Related concepts

Main connections

  • Time series & ARIMA
  • Linear algebra & matrices
  • Variance & standard deviation
  • Mean & expected value
  • Poisson distribution
  • Calculus & differentiation

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

Cov(Z(si),Z(sj))=E[(Z(si)−E[Z(si)])(Z(sj)−E[Z(sj)])]≠0,for si≠sj
View LaTeX source\text{Cov}(Z(s_i), Z(s_j)) = E\left[(Z(s_i) - E[Z(s_i)])(Z(s_j) - E[Z(s_j)])\right] \neq 0, \quad \text{for } s_i \neq s_j