Agent-Based Modelling and Geographical Information Systems

Crooks, Andrew|Malleson, Nick|Manley, Ed|Heppenstall, Alison

ISBN 10: 1473958652 / ISBN 13: 9781473958654

Published by SAGE LTD, 2018

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This textbook explains how to design and build Agent Based Models and how to link them to Geographical Information Systems.InhaltsverzeichnisChapter 1 Agent-based Modelling and Geographical Information SystemsChapter 2 Introduct.

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Bibliographic Details

• Title: Agent-Based Modelling and Geographical Information Systems
• Publisher: SAGE LTD
• Publication Date: 2018
• Language: English
• ISBN 10: 1473958652
• ISBN 13: 9781473958654
• Binding: Kartoniert / Broschiert
• Condition: New

About this title

Synopsis

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 behavior.

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.

About the Author

Andrew Crooks is an associate professor in computational social science with a joint appointment between the Departments of Computational and Data Sciences and the Geography and GeoInformation Science at George Mason University. His areas of expertise specifically relate to integrating agent-based modelling and geographic information systems (GIS) to explore human behavior. Moreover, his research focuses on exploring and understanding the natural and socio-economic environments specifically urban areas using GIS, spatial analysis, social network analysis, social media, and agent-based modelling methodologies. More information about this work can be seen at: http://www.gisagents.org.

 

Nick Malleson is an associate professor in geographical information science at the School of Geography, University of Leeds, and a member of the Centre for Spatial Analysis and Policy (CSAP). His research is interdisciplinary and centers on the development and application of spatiotemporal computational models in the social sciences, with a particular focus on crime simulation and modeling. More recently, he has been conducting research that explores the nature of massive "crowd-sourced" data in the social sciences and related modeling approaches. More information about this work can be seen at: http://nickmalleson.co.uk/.

 

Ed Manley is a lecturer at the Centre for Advanced Spatial Analysis (CASA), University College London. His research involves measuring and modelling the individual and collective behaviours that shape urban dynamics, drawing on new streams of data and simulation methods. Ed is particularly interested in advancing data-driven agent-based modelling in order to improve the understanding and prediction of complex social systems. More information about this work can be seen at: http://www.urbanmovements.co.uk.

Alison Heppenstall is professor of geocomputation within the School of Geography, University of Leeds (UK) and an associate of Leeds Institute for Data Analytics. She is an expert in the development of spatial agent-based models (ABMs) with a focus on understanding and simulating behavior. Her current interests are concerned with linking ABMs to artificial intelligence and machine learning methodologies, the role of Big Data in creating more robust ABMs, and the relationship of agent-based modelling to social theory. More information can be found at https://alisonheppenstall.co.uk.