geog 409: advanced spatial analysis & modelling © j.m. piwowar1principles of spatial modelling

© J.M. Piwowar 1Geog 409: Advanced Spatial Analysis & Modelling Principles of Spatial Modelling

Principles of Spatial Modelling


Systems Modelling Approach

The process of breaking down highly complex environments into discrete systems so that it can be more easily studied. Each sub-system will have its own inputs and

outputs and interconnected components. It allows us to focus on that which is of direct

interest; everything else is ignored.

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Environmental Systems Environmental systems

exist over a range of scales: microscopic biota through to the Earth’s climate system

At each scale, the individual components have boundaries (e.g. a

leaf’s surface) but are interconnected with other systems at other scales (e.g. sunlight & moisture).

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Assumptions

1. It is actually possible to subdivide the real world into discrete, contained, functioning systems.

2. It is possible to determine the various inputs and outputs and interrelationships between system components.


Why Model?

A model represents a simplification of reality.The intention is to retain the significant

features and relationships of reality. All models are subjective.

The modeller chooses which real-world elements should be included as well as how they are represented.

Models are used to describe, explore, and analyze how a system works; and to test predictive “what if?” scenarios.


Types of GIS Models

There are many different types of model classifications.

Many models can exist in more than one category.

Some models that are separated in one classification may be joined in another classification.


Types of GIS Models

Purpose Models Descriptive Models

Describe parts or all of a study area. Passive.

• e.g. a map.

Prescriptive Models Prescribe best solutions. Active.

• e.g. best location analysis


Types of GIS Models

Logistic Models Inductive

Builds general models based on individual data. Moves from specific examples to generalized models. Useful if we are unaware of the general conditions or

rules that govern the modelled features. Deductive

Straightforward; easily understood. Logic moves from general to specific. Useful if we already have substantial preliminary

knowledge of what factors are important, how they interact, and which are most important.


Types of GIS Models Methodological Models

Deterministic There is only one output for a given input Unique solutions are obtained The simplest type of relationship between 2 variables is linear

• e.g. a = mx + b

Stochastic There are a range of possible outcomes for any one input; there

is no single answer. Reflects randomness, or uncertainty, in the system Uncertainty is incorporated through probability

• e.g. Markov models: the probability of an event occurring is dependent on the event preceding it.

Monte Carlo simulations: take random samples from a stochastic model; the results are independent of previous states of the system


Types of GIS Models

Deterministic Inductive Descriptive


Types of GIS Models

Stochastic Inductive Prescriptive

Descriptive


Ideal Model Properties Generality RealityPrecision

Only 2 can be adequately represented in any given model.

Analytical or mathematical models focus on generality and precision and predict accurate response within a simplified reality.

Mechanistic or process models are realistic and general and base predictions on functional cause and effect relationships.

Empirical models are precise and realistic and are based on empirical facts.

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The Modelling Process

State ObjectivesThe first step in model

conceptualization is to think about the end results … what do we want the model to produce?Care must be taken to provide an objective

result rather than prescribing the outcome to fit expectations.



Define Model ComponentsDivide the problem into elements and

operators.Recognize spatial patterns.Identify the processes that created the

patterns.Look for linkages.


The Modelling Process Find Data

Find data to work with your model; do not make your model work with your data.

Reasons to ignore existing data:They may not have the necessary scale,

accuracy, classification, etc.They may not have the desired spatial

coverage and/or employed appropriate sampling procedures.

They may have too many themes.Data sets can often bias your thinking.Many data sets are incomplete.



Recognize Spatial PatternsSometimes determining the underlying

processes that created the patterns; sometimes evaluating the effects of existing patterns on on-going processes.

Go beyond pattern recognition into pattern description.

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geog 409: advanced spatial analysis & modelling © j.m. piwowar1principles of spatial modelling

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