Section C: Management of the Built Environment

GIS As A Tool: Technical Aspects of Basic GIS

This lecture covers five topics:

1.Scale,2.Framework data,3.Generalisation,4.Aggregation,5.Modifiable unit area problem (MAUP)

A list of the sorts of questions we might ask:

Why is the concept of scale so important in the generation of information from a GIS?

Why do we not mix data that was captured at different scales?

What are the issues we face with overlaying features and what alternative approaches could we use?

What is framework data and in what sorts of application areas is it used?

What do we mean by generalization and how do we undertake that task using ArcGIS software?

How do we undertake the aggregation of data using the ArcGIS software and what are some of the issues we face?

What is meant by the term MAUP, and what concepts is MAUP linked to in GIS?

Topic 1: Scale. Scale as a scale bar and as a representative fraction

cadastral

1:25000

´0 640320 Meters

Resolution and Scale

Neutral Bay (Sydney)

Earlybird6x6 metre

Landsat 720x20 metre

Two different units used to describe resolution.

High Resolution (small scale)

6 x 6 metre resolution

Low Resolution (large scale)

1 degree (200x200 kilometres)0.25 (50x50 kilometres)

Why is scale important in terms of data?

1. Accuracy of features

2. Accuracy of measurement.

3. Visualisation.

4. Relationship between features.

Accuracy of features

Accuracy of measurement

Distance 639 metres

(scale 1:25000 data)

Distance 695 metres

Landsat 7 Image (20x20 pixels)

Distance 659 metres

Cadastral

Distance 651 metres

Earlybird Image(6x6 metres)

Visualisation

StreetsCadastral

Suburbs

National

Local Government Areas

Statistical Divisions

Collection Districts

Relationship between features

Relationshipmore complexin the image

Beach?

Why do we use data captured at different scales?

Because that is the only source of data.

Different features at different scales

No StreetStreet

Shoreline for the censusdata

Shoreline for thecadastral data

Changing the form of features as scale increases

Post Office as a polygon Post Office as a point

Generalising boundaries as scale increases

Detail of the shoreline

Crowding of features as scale increases

General rules of scale

1. Where possible use data captured at the same scale.

2. Work with data at the smallest scale.

3. Always be aware that the information produced is scale dependent.

Exceptions to the general rules of scale

1. For some studies where the focus is on a spatial unit (i.e. aneighbourhood) then there is limit to the scale you cango down to. A good example is segregation studies.

2. For some studies the variability in the data sets a limitto the scale you can work at. For example working onclimatic change probably means the scale has to be large,in that the climate data has to modelled at a large scale.

Topic 2: Framework Data

Use cadastral data

Street centrelines and cadastral data

Census collection districts and cadastral data

Cadastral data and imagery (Not Orthorectified)

Topic 3: Generalisation

ArcToolbox’s 4 Tools

DissolveEliminateSimplify LineSmooth Line

Topic 4: Aggregation

Aggregation as opposed to generalisation

Aggregation is the process of moving to larger geographical unitsof space where those spatial units delimit different features.

The most common form of aggregation is moving up from theindividual to different spatial units of administration.

Example from Census Collection Districts, to Local Government Areas, to Statistical Areas, to States, to Countries, to regions, The world.

Hierarchical Structure Census Data

1. Australian Standard Geographical Classification Areas (ASGC)2. Census Geographic Areas3. Indigenous Boundaries

ASGC has 7 different hierarchies, all of which build up from collection districts (CDs)

Census Geographic Areas. 4 different setsCommonwealth electoral divisionState electoral divisionPostal districtsDerived suburbs

Indigenous Areas. 3 different setsATSIC Region, Indigenous Area, Indigenous Location

Aggregation and the human ecological fallacy

The argument is that you cannot infer individual information from aggregate data.

Homogeneity of characteristics or behaviour cannot be assumed.

The reverse is also true, that you cannot necessarily infer whatis best for an administrative area on the basis of a set of individual’s behaviour.

Aggregation in GIS

We use the command dissolve for this task and select both the field we want to aggregate on, plusthe attributes we want to aggregate

Using Dissolve to Aggregate Data (Multipart checked)

Results

Topic 5 Modifiable Unit Problem (MAUP)

MAUP With aggregation if you change the location ofthe boundaries you will change the results of the analyses.

Classic case is the political gerrymander.

Related concepts:

1. Thomlinson’s first law of geography

2. Spatial Autocorrelation

3. Spatial Inference

Percent Population Rank 1 Rank 2 SumLebanese (000s)

Canterbury 11.0 130Ashfield 2.0 42 1 3 4Burwood 5.0 31 4 2 6Strathfield 5.1 29 5 1 6Bankstown 12.2 174 7 7 14Hurstville 2.4 76 2 4 6Rockdale 5.9 91 6 6 12Marrickville 2.7 81 3 5 8

Suggests Canterbury might be amalgamated with Ashfield,Burwood, Strathfield and maybe Hurstville.

That would result in a LGA with 6.5 percent Lebanese (311k pop)

First Law of Geography

Issues:

Spatial Autocorrelation

Spatial Inference

A list of the sorts of questions we might ask:

Why is the concept of scale so important in the generation of information from a GIS?

Why do we not mix data that was captured at different scales?

What are the issues we face with overlaying features and what alternative approaches could we use?

What is framework data and in what sorts of application areas is it used?

What do we mean by generalization and how do we undertake that task using ArcGIS software?

How do we undertake the aggregation of data using the ArcGIS software and what are some of the issues we face?

What is meant by the term MAUP, and what concepts is MAUP linked to in GIS?