Название: Agent-Based Modelling and Geographical Information Systems: A Practical Primer
Автор: Andrew Crooks, Nick Malleson, Ed Manley
Издательство: SAGE Publications
Год: 2019
Страниц: 409
Язык: английский
Формат: pdf (true)
Размер: 21.9 MB

his 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.

The last twenty years have witnessed an explosion in computer processing power and storage and micro-level data sets, accompanied by the diffusion of ideas from complexity science. This has led to new ways of thinking about and simulating geographical systems. This is clearly evidenced through the development and uptake of agent-based modelling. This approach puts the individual at the centre of the simulation. Through careful interrogation of new data sources, researchers can construct individuals who are endowed not only with basic characteristics drawn from quantitative data sets, such as age and sex, but also with more qualitative aspects such as opinions and preferences. As well as richer sources of data on individuals, these new forms of data are often spatially referenced, thus giving a greater insight into the interplay between individual and space. This is where technologies such as geographical information systems (GIS) have a role to play. These tools are well established and give the researcher the ability to manipulate and process large quantities of diverse geographical data. However, these systems are largely limited in their inability to handle dynamic processes. Almost all GIS analysis uses snapshots (i.e. static) of data. For the geographical researcher, the holy grail is a system whereby rich representations of individuals can be created and embedded within a rich (realistic) environment. This is where agent-based modelling and GIS come in, and is the motivation behind the writing of this book.

There has been a huge growth in GIS software. Current commercial and open source GIS software systems all contain tools for acquiring, pre-processing, and transforming data. ESRI is the largest of the commercial companies developing GIS software with a large user base, both commercially and within academic institutions. However, ESRI GIS products carry a large license fee and require Windows to run. For Mac users, one needs to use a virtualisation program, such as Parallels or Bootcamp, to run the software. There are several open source GIS packages now available, the most popular being Quantum GIS (QGIS). QGIS offers much of the same basic functionality as ArcGIS - the ability to create, edit, analyse and visualise geographical information (via its plugins) - but without the cost or restriction to a specific operating system. Another open source product is GRASS. This provides similar functionality to QGIS but also offers more functionality for raster data. There are also other tools available for carrying out spatial analysis. While not a fully fledged GIS, GeoDa provides a GUI to carry out exploratory spatial data analysis, including global and local spatial autocorrelation methods, along with tools to create .gal files. There is also a growing body of tools and packages in R and Python (e.g. PySAL (Python Spatial Analysis Library)) which allow for spatial data handling, analysis and the visualisation of geographical information. NetLogo also has an R extension which allows one to use R within a NetLogo model.

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.