GEOG5060 GIS & Environment Lecture 4 GEOG5060 - GIS and Environment1 Lecture 4. Grid-based modelling...

Lecture 4 GEOG5060 - GIS and Environment 1

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EnvironmentLecture 4

Lecture 4.Grid-based modelling

•Outline– introduction– linking models to GIS – basics of cartographic modelling– modelling in GRID


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Introduction

• GIS provides:– comprehensive set of tools for

environmental data management– limited spatial analysis functionality– but does provides framework of application

• limited spatial analysis functionality may be addressed by linking models into GIS


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Spatial modelling issues

• Model problems:– most models do not provide tools for data

management and display, etc.– many models are aspatial

• GIS provides:– framework of application– allows user to add spatial dimension (if not

already built into the model)


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GIS-able models

• Types of models applicable to integration with GIS include:– certain aspatial models

• black box models• lumped models

– all spatial models • distributed models

– temporal models


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Linking models to GIS

• Two basic methods of integrating models into the GIS framework:– soft or loose coupling

• models and GIS are linked via file transfer

– hard or tight coupling• models and GIS are linked directly through

sharing common database• model programmed using GIS macros and

functions


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Creating the link

• How models are integrated into a GIS depends on:– the type model itself– the flexibility of the GIS as a modelling

environment– the time and resources available

• Fuzzy boundary between loose and tight coupling


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EnvironmentLecture 4Loose coupling

External data transfe

r

G.I.S

MODEL

GIS database


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EnvironmentLecture 4Tight coupling

GIS database

Internal data

transfer

G.I.S

MODEL


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Example

• GIS-based gas dispersion model– requirements:

• an emergency planning decision support system is required for accident planning involving releases of chlorine gas from chemical plants

• a dense gas dispersion model needs to be linked to a GIS to enable predictions of gas dispersion to be integrated with environmental data to assist in emergency planning procedures

– loose or tight coupling?


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Questions…

• Which model?

• Which GIS?

• Which data?

• What level of coupling?


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Loose coupling approach

1. identify point of release (POR) and conditions of release (COR)

2. input POR and COR variables to model via keyboard input

3. run model

4. pass model results to GIS via file exchange

5. create model results data layer in GIS

6. integrate (overlay) with other data layers


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Tight coupling approach

1. identify POR and COR2. run model

• create POR and COR layers• model accesses GIS database directly for

inputs at every increment of the model run to update basis for predictions

• model creates new data layer in GIS database describing results

3. integrate (overlay) model results with other data layers


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Integrating GASTAR with Arc/Info


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Modelling testing

• Testing models– verifying model output can present certain

problems for the user– especially true if :

• the model is complicated• two or more models are used• the data used is complex or of dubious accuracy or both!• long timescales are involved• the model is of the black box variety or if the user is

unfamiliar with its workings


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Example

• RUNMOD – a lumped catchment model of the hydrological

cycle• lumped input: precipitation• lumped storage: soil store, groundwater store, channel

store• lumped output: evapotranspiration, runoff

– parameters governing infiltration, through flow, percolation, etc. can be altered to improve modelled outputs compared to measured outputs

– this is a process known as calibration


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Questions…

• What are the advantages of model calibration?

• How could this particular model be integrated into a GIS framework?


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Modelling guidelines

• In order to ensure that model results are as close to reality as possible the following guidelines apply:– ensure data quality – beware of making too many assumptions– match model complexity with process complexity – compare predicted results with empirical data

where possible and adjust model parameters and constants to improve goodness of fit

– use results with care!


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Basics of cartographic modelling

• Mathematics applied to raster maps– often referred to as map algebra or

‘mapematics’– e.g. combination of maps by:

• addition• subtraction• multiplication• division, etc.

– operations on single or multiple layers


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A definition

“A generic means of expressing and organising the methods by which

spatial variables and spatial operations are selected and used

to develop a GIS model”


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A simple example…

54

7Input 1

64

4

6

66

6

44

4

4

3

3

33

3 3

23

22

2

2

2

2

1

1

1

1

25

7 7 6 6

7 7 13 56 10 8

5 5 10

Input 2

Output

+

=


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Question…

• How determine topological relationships?i.e. Boolean: AND, NOT, OR, XOR

• What is the arithmetic equivalent?


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Building spatial models

• It is (in theory) surprisingly simple:– algebraic combination of:

• OPERATORS and FUNCTIONS• rules and relationships• inputs (and outputs)

– interfaces• run at the command line/menu interface• batch file• embedded in system macro/script• ‘hard’ programmed into system


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Problems in model building

• knowledge– systems and processes– relationships and rules

• compatability– input data available– outputs required

• quality issues– data quality (accuracy, appropriateness, etc.)– model assumptions and generalisation– confidence and communication


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Modelling in Arc/Info GRID

• Four basic categories of functions in map algebra:– local– focal– zonal– global

• Operate on user specified input grid(s) to produce an output grid, the cell values in which are a function of a value or values in the input grid(s)


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EnvironmentLecture 4Local functions

• Output value of each cell is a function of the corresponding input value at each location– value NOT location determines result– e.g. arithmetic operations and reclassification– full list of local functions in GRID is enormous

• Trigonometric, exponential and logarithmic • Reclassification and selection• Logical expressions in GRID• Operands and logical operators• Connectors• Statistical• Other local functions


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Local functions

2516

54

7

49

input

output = sqr(input)


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Some examples

input

output = tan(input)

output = reclass(input)

output = log2(input)


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Focal functions

• Output value of each cell location is a function of the value of the input cells in the specified neighbourhood of each location

• Type of neighbourhood function– various types of neighbourhood:

• 3 x 3 cell or other– calculate mean, SD, sum, range, max, min,

etc.


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Focal functions

54

7

1611

input

output = focalsum(input)


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Some examples

input

output = focalmean(input, 20)

output = focalstd(input)

output = focalvariety(input)


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Neighbourhood filters

• Type of focal function– used for processing of remotely sensed image

data– change value of target cell based on values of a

set of neighbouring pixels within the filter– size, shape and characteristics of filter?– filtering of raster data

• supervised using established classes• unsupervised based on values of other pixels within

specified filter and using certain rules (diversity, frequency, average, minimum, maximum, etc.)


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Supervised classification

123

45

1

2

Old class New class

1 3 42 4 51 2 4

1 1 21 2 21 1 2


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1 3 42 4 51 2 4

diversity

modal

minimum

maximum

mean

Unsupervised classification

5

4

1

5

3


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Zonal functions

• Output value at each location depends on the values of all the input cells in an input value grid that shares the same input value zone

• Type of complex neighbourhood function– use complex neighbourhoods or zones– calculate mean, SD, sum, range, max, min, etc.


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Zonal functions

54

7

input

output = zonalsum(zone, input)

zoneZone 1

Zone 2

99

99

99

99

7 7 77 7 7

77


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Some examples

input

output = zonalthickness(input_zone)

Input_zone

535.54

766.62

127

160

output = zonalmax(input_zone, input)

output = zonalperimeter(input_zone)

6280

10800


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Global functions

• Output value of each location is potentially a function of all the cells in the input grid– e.g. distance functions, surfaces, interpolation, etc.– Again, full list of global functions in GRID is

enormous• euclidean distance functions• weighted distance functions• surface functions• hydrologic and groundwater functions• multivariate.


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Global functions

54

7input

output = trend(input)

9

87

6

8

76

6

7

65

544

5

6


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Distance functions

• Simple distance functions– calculate the linear distance of a cell from a

target cell(s) such as point, line or area– use different distance decay functions

• linear• non-linear (curvilinear, stepped, exponential,

root, etc.)– use target weighted functions– use cost surfaces


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Some examples

input source

output = eucdistance(source)

output = eucdirection(source)

output = costdistance(source, input)


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COSTPATH example


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Conclusions

• Linking/building models to GIS

• Idea of maths with maps– surprisingly simple, flexible and powerful

technique– basis of all raster GIS

• Fundamental to spatial interpolation, distance and neighbourhood functions


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Workshop

• Constructing models in Arc/Info GRID– Demonstration of GRID functions

• Focal functions• Local functions• Global functions• Zonal functions• AML for GRID


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Practical

• Facilities location using Arc/Info GRID• Task: Locate suitable sites for a wind farm in the

Yorkshire Wolds• Data: The following datasets are provided…

– Digital elevation model (50m resolution 1:50,000 OS Panorama data)

– Contour data (10m interval 1:50,000 OS Panorama data)

– ITE land cover map (25m resolution)– Roads (1:250,000 Meridian data)– Wind speed data


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Practical

• Steps:1. Formulate a location model based on

available data and requirements for a wind farm

2. Pre-process data to create model input layers as required

3. Run model4. Identify best location(s)


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Siting wind turbines


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Practical

• Experience at simple cartographic model building

• Experience with spatial modelling functions within Arc/Info GRID

• Familiarity with locational models and wind farm siting in particular


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Next week…

• Terrain modelling 1: the basics– DEMs and DTMs – Derived variables– Example applications

• Workshop: Terrain modelling in Arc/Info and Grid

• Practical:Using DEMs

GEOG5060 GIS & Environment Lecture 4 GEOG5060 - GIS and Environment1 Lecture 4. Grid-based modelling...

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