# Posts tagged spatial

## The Besag-York-Mollie Model for Spatial Data

- 18 August 2023
- intermediate, tutorial

This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.

## Conditional Autoregressive (CAR) Models for Spatial Data

This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.

## Modeling spatial point patterns with a marked log-Gaussian Cox process

- 31 May 2022
- intermediate

The log-Gaussian Cox process (LGCP) is a probabilistic model of point patterns typically observed in space or time. It has two main components. First, an underlying *intensity* field \(\lambda(s)\) of positive real values is modeled over the entire domain \(X\) using an exponentially-transformed Gaussian process which constrains \(\lambda\) to be positive. Then, this intensity field is used to parameterize a Poisson point process which represents a stochastic mechanism for placing points in space. Some phenomena amenable to this representation include the incidence of cancer cases across a county, or the spatiotemporal locations of crime events in a city. Both spatial and temporal dimensions can be handled equivalently within this framework, though this tutorial only addresses data in two spatial dimensions.

## About Conditional Autoregressive models in PyMC

- 14 August 2020
- advanced, explanation

This notebook explains the design principles behind the Conditional Autoregressive (CAR) distribution as implemented in PyMC. For a simple tutorial of why and how to use the CAR distribution, see this notebook.