Section C: Management of the Built Environment GIS As A Tool: Technical Aspects of Basic GIS

Transcription

1 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)

2 A list of the sorts of questions we might ask: hy 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?

3 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?

4 Topic 1: Scale. Scale as a scale bar and as a representative fraction cadastral Meters 1:25000

5 Resolution and Scale Neutral Bay (Sydney) Landsat 7 20x20 metre Earlybird 6x6 metre

6 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)

7 Why is scale important in terms of data? 1. Accuracy of features 2. Accuracy of measurement. 3. Visualisation. 4. Relationship between features.

8 Accuracy of features

9 Accuracy of measurement Distance 639 metres (scale 1:25000 data) Distance 695 metres Landsat 7 Image (20x20 pixels)

10 Distance 651 metres Earlybird Image (6x6 metres) Distance 659 metres Cadastral

11 Visualisation Cadastral Streets

12 Suburbs

13 National Local Government Areas Collection Districts Statistical Divisions

14 Relationship between features Beach? Relationship more complex in the image

15 Why do we use data captured at different scales? Because that is the only source of data.

16 Different features at different scales Street No Street

17 Shoreline for the census data Shoreline for the cadastral data

18 Changing the form of features as scale increases Post Office as a polygon Post Office as a point

19 Generalising boundaries as scale increases Detail of the shoreline

20 Crowding of features as scale increases

21 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.

22 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.

23 Topic 2: Framework Data Use cadastral data

24 Street centrelines and cadastral data

25 Census collection districts and cadastral data

26 Cadastral data and imagery (Not Orthorectified)

27 Topic 3: Generalisation ArcToolbox s 4 Tools Dissolve Eliminate Simplify Line Smooth Line

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33 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.

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35 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

36 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.

37 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

38 Using Dissolve to Aggregate Data (Multipart checked)

39 Results

40 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

41 Percent Population Rank 1 Rank 2 Sum Lebanese (000s) Canterbury Ashfield Burwood Strathfield Bankstown Hurstville Rockdale Marrickville 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)

42 First Law of Geography Issues: Spatial Autocorrelation Spatial Inference

43 A list of the sorts of questions we might ask: hy 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?

44 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?