Revision Lecture

Outline

1. Review of Semester 2 Lectures
2. The exam

Lecture 1 - Geocomputation

The Art and Science of Solving Complex Spatial Problems with Computers.

Types of model

Statistical (Quantitative): Regression; multi-level; spatial interaction

GIS based: Map; network analysis; location allocation

Qualitative: Conceptual models, ?

Geocomputation: Agent-based model; cellular automata; microsimulation

Aggregate vs. individual

Lecture 2 - NetLogo

Programming is great!

Computational thinking teaches you how to tackle large problems by breaking them down into a sequence of smaller, more manageable problems. It allows you to tackle complex problems in efficient ways that operate at huge scale. It involves creating models of the real world with a suitable level of abstraction, and focus on the most pertinent aspects. It helps you go from specific solutions to general ones. http://yearofcode.org/

Tools for individual-level modelling

Turtles and Patches, Variables, Flow Control

Writing Nice Code

Lecture 3 - Social Simulation and Agent-Based Modelling

History of social simulation

Club of Rome, microsimulation, AI, ABM

Uses of simulation: predictive vs. explanatory

Agent-based modelling

Examples: SimCity, Playstation, LOTR

Advantages and disadvantages

Lecture 4 - Complexity and Emergence

Emergence

Simple rules → complex outcomes

A new kind of science? (Wolfram)

Cellular Automata and the Game of Life

Schelling's segregation

Non-linear dynamics

Linear: A bunnies tale

Non-linear: wolf-sheep predation. Changes to input values have a non-linear impact on the outcomes.

Understanding the interactions is the key

Seminar 1 - GIS and Geocomputation

Gilbert, Nigel and Klaus G. Troitzsch (2005) Simulation for the Social Scientist. Open University Press

Epstein, J.M., (2009) Modelling to contain pandemics. Nature 460, 687-687.

Lecture 4 - Complexity and Emergence

Non-linear dynamics

Mathematical example: the logistic map

Flocks and swarms

Boids

Simple rules → complex outcomes

Complex Systems: exhibit emergence and non-linearity

Chaos and the butterly effect

ABM great for modelling complex geographical systems

Lecture 5 - Interaction and Behaviour

Interactions

Global and Local

Direct and Indirect / mediated

Examples of modelling interactions in NetLogo

Lecture 5 - Interaction and Behaviour

Behaviour

Humans are not random

Pick a number!

Modelling behaviour

Keep it simple, stupid (KISS)

Keep it descriptive, stupid (KIDS)

Which behaviours are important? Literature, expert opinion, numerical experimentation, rigorous data analysis

Cognitive frameworks

Rule-based, PECS, BDI

Seminar 2: The ethics of individual-level modelling

Students played the ethics committee

Reviewing: "An agent-based model of violent offenders to reduce crime"

The Model Building Process

Preparation, design and building the model

The ODD protocol

Verification, Calibration, Validation

Prediction / Explanation

Seminar 3: Modelling Societal Challenges

Produce the outline for a model (or models) that can help to better understand a societal chalenge.

Challenge 1: Migration

Challenge 2: Disease Spread

Challenge 3: Economic Cycles

Lecture7: ABM and Geography

Reasons to model:

Explanatory

Predictive

The reason for modelling will influence your decisions over the modelling environment

Different types of 'geography'

Grids

Continuous space

Social network?

GIS

Lecture7: ABM and Geography

Loose coupling

The GIS and the model do not interact directly

Tight coupling

The GIS and model are integrated

Loose coupling is more efficient and flexible, but it can make interacting with the model more difficult.

Lecture 8: Simulation for Policy Modelling

Pedestrian modelling

Fluid dynamics / social forces

Magnetic model

Agent-based crowd models

Traffic modelling

Lecture 8: Simulation for Policy

To finish: the exam!

What do you need to know for the exam?

http://www.geog.leeds.ac.uk/courses/level3/geog3150/lectures/revision/