This is the era of Big Data and computational social science. It is an era that requires tools which can do more than visualise data but also model the complex relation between data and human action, and interaction. Agent-Based Models (ABM) - computational models which simulate human action and interaction – do just that. This textbook explains how to design and build ABM and how to link the models to Geographical Information Systems. It guides you from the basics through to constructing more complex models which work with data and human behaviour in a spatial context. All of the fundamental concepts are explained and related to practical examples to facilitate learning (with models developed in NetLogo with all code examples available on the accompanying website). You will be able to use these models to develop your own applications and link, where appropriate, to Geographical Information Systems. All of the key ideas and methods are explained in detail: • geographical modelling; • an introduction to ABM; • the fundamentals of Geographical Information Science; • why ABM and GIS; • using QGIS; • designing and building an ABM; • calibration and validation; • modelling human behaviour; • visualisation and 3D ABM; • using Big Geosocial Data, GIS and ABM. An applied primer, that provides fundamental knowledge and practical skills, it will provide you with the skills to build and run your own models, and to begin your own research projects.
This chapter presents a range of statistics and algorithms that can be used to compare two spatial data sets. These are important for modelling because, at some point, it will be necessary to compare a model outcome to some real world data in order to assess the reliability of the model. This chapter examines the statistics themselves, before Chapter 10 elaborates on how to evaluate the success of a model more broadly, part of which includes making use of the methods discussed here.
Box 9.1 Further resources
The statistics used in this chapter were all calculated in the scripting language R using the code and data available in the accompanying online resources.
Box 9.2 Glossary
The following terms will be used throughout the chapter. ...