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.

Integrating Agent-Based Models and GIS

Integrating Agent-Based Models and GIS

Chapter Outline

Previous chapters outlined the fundamentals of GIS and agent-based modelling. In this chapter we will discuss the benefits to linking these approaches and explore the ways that this can be undertaken. We will explain loose and tight coupling, critiquing the relative advantages and disadvantages of both. We then present an overview of open source toolkits that can be used for the creation of geographically explicit agent-based models, before providing a critical look at where and how GIS and ABM should be combined, offering practical advice on best practice.

6.1 Introduction

Consideration of space is often integral to the success of agent-based models, especially those that are applied to geographical systems. For example, in the Schelling model ...

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