Forecasting with Geospacial Data

Geostatistics is a subfield of statistics focused on spatial or spatialtemporial datasets, AKA data with location or longitudinal data with location. Fun fact: Many of the techniques we use today originated from mining engineers who desired to know what minerals might lay beyond a surface given a sample.

In this case, we have a large set of longitudinal data with location, and we want to make guesses about the future in those locations. Not to be confused with kriging which is a gaussian process of estimating a variable at an unobserved location, based on the estimates of nearby or similar locations. 

The following notes aim to provide the fundamentals of creating a network of connected spaces, and forecasting techniques for spatial network data.

Networks

A network is a discrete set of items (referred to as nodes) with some connection between them (referred to as edges). Typically, a graph network is represented mathematically as G = (N, E); with a set of N nodes and E edges.