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GEOS 1002/1902 GIS Practical Task

创建日期：2024-06-06 14:42:10

所属分类：管理Management

标签： GEOS 1002

GIS Practical Task

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GEOS 1002/1902 GIS Practical Task

Step-by-step guide
Worth: 20%
Due: Friday 03rd November 2023 at 11:59 pm
Content: 5 x maps plus 1200words (+/- 10%, excluding references)
This is a step-by-step guide on how to create your maps for the GIS practical
assignment for this course. If you carefully follow the instructions in this document,
you should be able to create high-quality maps using QGIS software and gain an
understanding of the distribution of levels of advantage and disadvantage across
Sydney and some of the factors that influence this. This task will mostly be done in
your own time, but your tutors will help you in your practical classes in the final few
weeks of semester.
Data you are using
Understanding Australian Bureau of Statistics (ABS) census data:
The ABS is where all census data, as well as other population level statistical data,
are stored and analysed. Census data is collected from households and workplaces
on a particular night every 5 years; the last two occurred in 2016 and just last year in
2021. For this assessment, we will be focussing on data from 2016.
Census data paints a picture of who we are as Australians and highlights the
characteristics – in particular, what is different and what has changed – that make up
our big, diverse community. These data – about whom we are, where we have come
from, where we live and work – is underpinned by a strong foundation in geographic
location. It is important, therefore, to understand the basics of this geography before
tackling your Census data questions head-on.
Before using ABS data, it is important to understand the geography of how the data
is organised. All census data is collected and coded to specific household addresses
and then aggregated in ‘Meshblocks’. Meshblocks are aggregated to form Statistical
Area level 1 (SA1) data. There are usually about 200 hundred households per SA1.
This means that, as population density of an area diminishes, the size of the SA1
increases. There are also other factors affecting the size of the SA1 (such as
topography – SA1 boundaries are often defined by boundaries in the landscape).
Meshblocks and SA1s can be combined to form a range of other ABS and non-ABS
geographies. For example, a collection of SA1s form SA2s, all the way up to SA4
then States. SA1s can also be aggregated to form other non-ABS boundaries,
including suburbs, electoral divisions, local government areas (LGAs) and so on. The
diagram on the next page provides a schematic account of the hierarchy of
2
geography. It is important to understand how the basic hierarchy of geography works
before attempting to map out census data. The diagram below shows the boundaries
up to SA3 for the inner Sydney area.
Note that data are not always published at the Meshblock scale, because of
confidentiality and privacy issues. It is important to think about what scale is best
suited to representing the information you want to display.
In this practical exercise, you will be using data at a range of different scales,
including local government area boundaries (LGAs) and Statistical Area level 2 to
level 4 boundaries (SA2 to SA4) depending on availability of data and the
effectiveness of its display.
Local Government Areas (LGAs) approximate officially gazetted LGAs as defined by
each State and Territory Local Government Department. These are good for
understanding characteristics of an individual LGA at a point in time. Because these
3
boundaries sometimes change between Census years, SA2s or SA3s might be
better alternatives if you’re interested in trends or comparisons over time. Local
Government Areas cover incorporated areas of Australia only. Incorporated areas
are legally designated parts of a State or Territory over which incorporated local
governing bodies have responsibility. The major areas of Australia not administered
by incorporated bodies are the northern parts of South Australia, and all the
Australian Capital Territory and the Other Territories. These regions are identified as
‘Unincorporated’ in the ASGS Local Government Areas structure.
Statistical Areas Level 2 boundaries (SA2s) are medium-sized general-purpose
areas built up from whole Statistical Areas Level 1. Their purpose is to represent a
community that interacts together socially and economically. There are 2,310 SA2
regions covering the whole of Australia without gaps or overlaps. These include 18
non-spatial SA2 special purpose codes, comprising Migratory–Offshore– Shipping
and No Usual Address codes for each State and Territory. SA2s will be the most
common data distribution we will be working with in this task.
Understanding SEIFA
To map advantage and disadvantage, we are going to use SEIFA data (Socio-
Economic Indexes for Areas). The four indexes of SEIFA each capture a slightly
different concept of socio-economic advantage and disadvantage.
The ABS broadly defines relative socio-economic advantage and disadvantage in
terms of people's access to material and social resources, and their ability to
participate in society.
The four indexes included in SEIFA are:
• the Index of Relative Socio-economic Disadvantage (IRSD)
• the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD)
• the Index of Economic Resources (IER)
• the Index of Education and Occupation (IEO)
Each index aims to capture a slightly different aspect of relative advantage and/or
disadvantage and is constructed using different variables. It is therefore likely that
the same area will have different rankings on each index. For example, it is possible
for an area to rank relatively lowly in the Disadvantage index but not in the
Advantage and Disadvantage index, because these indexes include different
variables.
The Index of Relative Disadvantage identifies and ranks areas in terms of their
relative socio-economic disadvantage. The Index of Relative Advantage and
Disadvantage broadly measures both advantage and disadvantage (IRSAD), while
4
the Index of Education and Occupation and the Index of Economic Resources both
measure aspects of socio-economic advantage and disadvantage. It is therefore
important to clarify what is meant by relative socio-economic advantage and
disadvantage, as this is the concept SEIFA aims to summarise from the numerous
Census variables available for analysis.
While income is the strongest variable in IRSAD, employment status and car
ownership are also key indicators in this index. For the purposes of this practical
exercise, we will be focusing on IRSAD to map advantage and disadvantage and
consequently compare these to these key variables related to employment and
mobility. We will also look at mode of transport to work and map this across
Sydneyto identify spatial trends and identify equity issues with relation to access to
public transport (in this case, the train network).

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