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
0
320
640 Meters
1:25000
Resolution and Scale
Neutral Bay (Sydney)
Landsat 7
20x20 metre
Earlybird
6x6 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
Distance 695 metres
(scale 1:25000 data)
Landsat 7 Image
(20x20 pixels)
Distance 651 metres
Distance 659 metres
Earlybird Image
(6x6 metres)
Cadastral
Visualisation
Cadastral
Streets
Suburbs
National
Local Government Areas
Collection Districts
Statistical Divisions
Relationship between features
Beach?
Relationship
more complex
in the image
Why do we use data captured at different scales?
Because that is the only source of data.
Different features at different scales
Street
No Street
Shoreline for the census
data
Shoreline for the
cadastral 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. a
neighbourhood) then there is limit to the scale you can
go down to. A good example is segregation studies.
2. For some studies the variability in the data sets a limit
to the scale you can work at. For example working on
climatic 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
Dissolve
Eliminate
Simplify Line
Smooth Line
Topic 4: Aggregation
Aggregation as opposed to generalisation
Aggregation is the process of moving to larger geographical units
of space where those spatial units delimit different features.
The most common form of aggregation is moving up from the
individual 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 Areas
3. Indigenous Boundaries
ASGC has 7 different hierarchies, all of which build up from
collection districts (CDs)
Census Geographic Areas. 4 different sets
Commonwealth electoral division
State electoral division
Postal districts
Derived suburbs
Indigenous Areas. 3 different sets
ATSIC 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 what
is 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, plus
the 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 of
the 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
Canterbury
Ashfield
Burwood
Strathfield
Bankstown
Hurstville
Rockdale
Marrickville
Lebanese
11.0
2.0
5.0
5.1
12.2
2.4
5.9
2.7
Population Rank 1 Rank 2
(000s)
130
42
31
29
174
76
91
81
1
4
5
7
2
6
3
3
2
1
7
4
6
5
Sum
4
6
6
14
6
12
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?