GEOS 1002/1009 GIS Practical Task
Step-by-step guide
Worth: 20%
Due: Friday 05th November 2021 at 11:59 pm
Content: 5 x maps plus 1000words (+/- 10%, excluding references)Submit: via Canvas
This is a step-by-step guide to help you complete your GIS practical for this course. If you follow
the instructions laid out in here, you will be able to produce the required maps and will learn
about some key socio- spatial relationships in Sydney. Remember that your tutors will be
available during your allocated class time to help you with the task over the coming weeks.
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 recently, in March this year. For this assessment, we will
be focussing on data from 2011.
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, thefore, 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 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.
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In this practical exercise, you will be using local government area boundaries (LGAs) and Statistical Areas
Level 2 boundaries (SA2s).
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 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 structure4.
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.
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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:
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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 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 Sydney to identify spatial
trends and identify equity issues with relation to access to public transport (in this case, the train network).
OK, let’s get started.
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Making your first map: IRSAD
For this unit you will be using ArcGIS Online, a cloud-based GIS. You will be using your Unikey and
password to access the University's ArcGIS Online web site.
1. Open a browser and go to https://sydney-edu.maps.arcgis.com. Create a bookmark for this site in
your browser so that you can easily return to it later.
2. You will be signing in with your Unikey ID. Click on “UNIVERSITY OF SYDNEY”.
3. You will be taken to the University web page where you can enter your Unikey ID and password.
4. You should now be taken to the ArcGIS home page. Click on “Map” (circled in red in the image
below).
[If you are prompted to try the Map Viewer Beta please select 'NOT RIGHT NOW'.]
5. Immediately save your map by clicking the “Save” button then “Save As” (circled in red in the
below image). In the window that opens enter the map name as ‘Socio-spatial mapping’ and
the tag as ‘GEOS’. The Save in folder will automatically be set to your username - do not try
to change this. Click “SAVE MAP”.
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6. NOTE: If you log out of ArcGIS online, you can find your map by clicking “New map” at the top of the
page. This will display a drop-down menu where you should be able to see the title of your saved map,
“Socio-spatial mapping”.
7. Click “base map” (to the right off “Add” in the top menu) and choose the third map across called
“Streets”.
8. We are now going to add some spatial data to this map in the form of a shapefile. Social vulnerability
will be examined using 2011 Australian Bureau of Statistics census data linked to each Local Government
Area (LGA). We will use the Index of Relative Socio-Economic Advantage and Disadvantage or IRSAD.
9. Locate the following zipfile to your computer: LGAs_2011_IRSAD (Note: to import shapefiles into ArcGIS
online, you need to use a zip file)
10.Click the “Add” button and then “Add Layer from File”. Click “Choose File” and go the folder where you
downloaded the LGAs IRSAD.zip file. Select it, leave the Generalize features for web display selected
and then click the “IMPORT LAYER” button.
11.On the left-hand side, the Change Style window will have appeared. In the “Choose an attribute to
show” select Decile. Under the “Select a drawing style” click “select” on the Types (Unique symbols) style
(you may need to scroll down to see this style). Click “DONE”. If you zoom in on Sydney, your map should
look like the one below:
12. Now view the LGAs IRSAD table – click the small arrow to close the change style menu and then click the
“Show table” button (see below image). The table contains a row for each LGA in NSW, however, each
row now has several new fields (columns) related to IRSAD index. We are interested in the 4th column,
Decile. The decile column has values ranging from 1 to 10. The lowest scoring 10% (the most
disadvantaged) of LGAs are given a decile number of 1 whilst the most advantaged LGAs are in the top
10% or decile of 10.
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13.Explore the table by clicking on the Decile column title, sorting in ascending order and having a look at
the top ten and lowest ten LGAs. Even though we have mapped the whole of NSW, we will only be
looking at Sydney for this assessment, so make sure to stay zoomed into the Greater Sydney region.
***Don’t forget to press save regularly***
14.To get these data into an easily digestible visual form, let’s change the colours. You can do this by clicking
open the change style menu (the image of shapes next to the image of the table - see image below),
clicking on the unique styles “options” button, and then clicking on each coloured square.
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15.Using this, you can change the colours so that the difference between advantaged and disadvantaged LGAs is
more apparent. You might wish to do this using a graded scale using one colour (i.e. dark blue for decile 1 to white
for decile 10) or using two different colours (i.e. dark blue to light blue for deciles 1-5 and conversely, light red to
dark red for deciles 6-10). You might also consider adding an outline when changing the colours. Remember to
press ‘Done’ once you have made these changes. In the example below, you can see the colour scheme I have used
and the legend to describe how the colours correlate with the deciles:
16.You have now made a map of the different levels of advantage (IRSAD decile) in Sydney (PRESS SAVE!).
Next, we will compare different variables to these IRSAD data.
Comparing IRSAD to unemployment via LGA
17.Where is unemployment highest in 2011 and does this correlate with the 2011 IRSAD map? To answer
this question, we need to upload a new data layer, however this is non-spatial data in csv form.
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18. Locate this file on your computer: LGAs_2011_LabourForce_NSW. We will have to manipulate the
data in Excel to create a column that represents the unemployment rate for each LGA. To do this, open
the CSV file in Excel and, in column J, put in a new heading “Unemployed % of total”. In the first cell
(J2), type the following formula: =(E2+F2)/I2*100
*Note that I2 is the letter I followed by the number 2. For row two, this has now given you the
unemployment rate. To find the unemployment rate for all rows, simply drag the small square down to
row 153 (see below)
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19. Now we have the unemployment rates and we can overlay these data onto IRSAD to identify
correlations or a lack of correlation between advantage/disadvantage and unemployment.
20. To do this in ArcGIS online, follow the same instructions for importing the IRSAD data: Click the “Add”
button and then “Add Layer” from File. Click the Choose File button and go the folder where you
downloaded the LGAs_2011_LabourForce_NSW csv file. Select it, leave the Generalize features for
web display selected and then click the IMPORT LAYER button.
When “Add CSV Layer” window pops up (see screen shot below), click on the “Addresses or Places”
option at the top. Then find LGA under ‘Field Name’. Next to it in ‘Location Fields’, double click to open
a drop-down menu, and choose “Address or Place”. Once you have pressed Add Layer, if an error
message pops up, please ignore it and press ok.
21. Automatically the “Change Style” menu opens in the left-hand panel. In Choose an Attribute, select
“Unemployed % of total”. In ‘Select a drawing style’, choose ‘Counts and Amounts (colour)”, then click the
options button. In “Divided By” leave it as none and in “Theme” leave it as high to low. The scale below
this will be used to generate colour differentiation for high vs low unemployment rates. As the difference
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between our highest unemployment rate for Sydney (Fairfield, 4.9%) and our lowest unemployment rate
for Sydney (Hunters Hill, 2.1%) is fairly narrow, we might choose to change the scale with the lower point
closer to 2 and the higher point closer to 5 (you can also do this by pressing “zoom in” as this will zoom
in to the data based on the mean and median) – see image below for an example. You can also click
“symbols” to change the shape, colour and size of your symbols, plus you can add an outline to make
them stand out on your map. Furthermore, in this section, you can also set the visibility range for these
symbols – for now, set this to “County” so that they pop-up all-over NSW (even though, as I have
previously mentioned, we are focussed on Sydney for these maps).
22. You can now see whether unemployment correlates with IRSAD at the LGA level of data. This is your
first finished map. Take a screenshot of the map (zoomed in on Sydney), or and insert it into a
Powerpoint slide. To add the finishing features to this map, check out step 38 for more detailed
instructions. We have not provided you with an image of this as this is an assessable output from your
work. Next, we will look at whether some other variables correlate.
***Don’t forget to press save regularly***
From this point on, some instructions have been shortened if they are repetitive
Comparing IRSAD to vehicle ownership
23. Click the tick boxes next to the two layers you have added to turn them off, so that the map is back to
the base streets map.
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24. Going back to your introduction to ABS data on page 4, you will remember that data can be spatially
defined using different types of boundaries. While LGAs provide a useful governance boundary, we will
now look at a that same information (IRSAD and unemployment) via Statistical Area 2 boundaries (SA2),
which is more spatially refined in comparison to LGAs and representative of local socio-economic
interactions.
25. Locate the SA2_2011_Sydney.zip file to your computer. This zipped shapefile contains the SA2 regions
used by the ABS for Sydney only. Click “Add Data” then click “Add Layer from File” and select the zip
file. Leave the Generalize features for web display option selected. Click “import layer”. In the “Change
Style” window, leave the attributes as “SA2_NAME11” and then select ‘Location (Single Symbol)’ for the
drawing style. Click ‘Options’, then ‘Symbol’ to change the colour and outline of the SA2.
26.Locate the SA2_2011_vehicleownership.csv file on your computer. This file contains data on mode of
transport to work for each of the SA2 areas in Sydney. Before you add these data, you will need to
manipulate it in Excel following these steps:
• Delete rows 540 and 541, and then rows 2-28 – these SA2 areas are not included in
the Sydney 2011 SA2 shapefile, and we want these to correlate.
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We want to get rid of the column that states vehicle ownership is “not stated” and the
column which adds this to the total, so delete columns H and I
We now want to transform these raw numbers into percentages of the total number of
dwellings. To do this, add columns alongside the raw numbers with the headings:
• Column H “no vehicle percentage”
• Column I “one vehicle percentage”
• Column J “two vehicles percentage”
• Column K “three vehicles percentage”
• Column L “four vehicles percentage”
Type the following formulas into each column to find the % of the total:
• Column H “=B2/G2*100”
• Column I “=C2/G2*100”
• And so on….
Then drag the corner on each top cell to fill the whole column.
Delete any rows that have “#DIV/0!” in them.
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Save this csv file.
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27.Click “Add” and then “Add Layer” from File. Click the Choose File button and go the folder where you
downloaded the modified SA2 2011 vehicle ownership csv file. Select it then click the IMPORT LAYER
button. You will receive the Add CSV Layer window. You will be asked to choose an option to locate
features by for this file. Because we will be joining this non-spatial data to our existing SA2 layer, choose
‘None, add as a table’, and then click ADD LAYER.
28.(Image provided below to help you complete this step). You will now join this non-spatial table of vehicle
ownership to the SA2 layer you imported earlier. Open the Join Features tool (click the Analysis button
and then open Summarize Data).
Select the target layer as SA2_2011_Sydney and the layer to join as the
SA2_2011_vehicleownership layer. Set the type(s) of join to be Choose the fields to match. Set
the Target field to be SA2_MAIN11 and Join field to be Region_id (both of these fields refer to
the unique SA2 ID number). As there can only be one set of vehicle ownership statistics for each
SA2 area, leave the join operation set to the default of Join one to one. Do not add any statistics
fields.
**Make sure you always append your unikey ID at the end of the Result Layer Name. An example unikey
ID of xyz123 is used in the Result Layer Name circled in red in the image below. Failure to add your
unikey ID to the end of the Result Layer Name will produce an error when you try to run the analysis.**
The Save result in field will be automatically populated with your user name - do not change this. Untick the
Use current map extent. Ensure your settings match the image below and then click RUN ANALYSIS and
wait for the operation to finish.
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29.Click the Change Style button below the new Join Features to SA2 2011 Sydney layer. Choose
percentage columns (i.e. “one vehicle percentage”, “two vehicle percentage” and so on). Select ‘Add
Attribute’ until you have all four of the % categories you created in the Excel sheet. Under Select
Drawing Style, select “Predominant Category and Size” and press options – then press “predominant
category” and press options. Change the colours so that “no vehicle percentage” is a distinct colour and
that all other percentages of ownership are represented by graduated colour. Make these a bit
transparent. Remember to press ‘Done’ once you have made these changes.
30.Click on the text box next to the IRSAD layer to turn it on again. Turn off the SA2 later. Compare the IRSAD layer
to vehicle ownership. Is there a relationship? Describe it.
31.This is your second map – you may have to zoom in a little closer on Sydney to capture the relationship
between IRSAD and vehicle ownership. Screenshot this and paste it into a Powerpoint slide. Format the
map according to Step 38.
Comparing IRSDA to mode of transport to work
32.Now you will compare SA2 2011 mode of transport to work data with IRSAD to determine if there is a
relationship between these variables. Locate the SA2_2011_modeoftransporttowork.csv on your
computer.
33.To do this, you will again need to manipulate the data in the spreadsheet.
• Delete rows 540 and 541 rows 2-28 and – these SA2 areas are not included in the Sydney
2011 SA2 shapefile and we want these to correlate.
• We want to delete the columns that mention two and three modes of transport to work
and the total persons column (Columns 0 to AH). Only keep the columns that mention
one mode of transport. Delete the “one_method_other_persons” column (Column L) as
well.
• We now want to transform these raw numbers into percentages for Public transport, Private
transport and Cycle/Walk. To do this, add columns alongside the raw numbers with the
headings:
o Column N “Public transport only”
o Column O “Public transport percentage”
o Column P “Private transport only”
o Column Q “Private transport percentage”
o Column R “Cycle/walk only”
o Column S “Cycle/walk percentage”
• To find the totals for the “…only” columns, type the following formula into the
o first cell in public transport only =SUM(B2:E2)
o first cell in private transport only =SUM(F2:J2)
o first cell in cycle/walk only =SUM(K2:L2)
o Drag each corner down to fill the column.
• Type the following formulas into each column to find the % of the total:
o First cell in public transport percentage (in column O) type =N2/M2*100
o First cell in private transport percentage (in column Q) type =P2/M2*100
o First cell in cycle/walk percentage (in column S) type =R2/M2*100
o Then drag the corner on each top cell to fill the whole column.
• Delete any rows that have “#DIV/0!” in them.
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Save this csv file.
34.Follow steps 27 and 28 to import and join this csv file in ArcGIS online. Last time you added your unikey,
this time add a different combination of letters and numbers into “result layer name”.In the Change style
menu, include all three percentage columns into attributes, starting with Public transport percentage,
followed by Private transport percentage and Cycle/walk percentage.
Choose “Relationship and size” and click options. You can click on symbols, legend and polygon to change
the way the data is visualised. Remember to press ‘Done’ once you have made these changes.
35.The colours are comparing private to public ratios and the size of the polygon refers to the percentage
of cycle/walk. Where is cycle/walk more dominant? What are some issues with using size to illustrate
this variable?
36.Add in the IRSAD layer to identify relationships between mode of transport to work and
advantage/disadvantage – can you see any relationships? Zoom in on Sydney… This is your third map!
Comparing IRSAD to mode of transport to work to train accessibility
37. Let’s now add in an extra layer of data to identify the train network across Sydney. Find
trainstations_sydney.csv and add it to your map. In Change Style, choose ‘Location (Single Symbol’ as
drawing style and then play with the colour and shape and size of shape so that it does not look too
messy on your map. To prepare your last two maps, first zoom in on the least advantaged LGA in Sydney
and take a screenshot of this (your fourth map) – discuss the dominant mode for getting to work and
access to a train station in your interpretation. To prepare the last map (your fifth map), zoom in on the
advantaged LGAs around Sydney harbour and screenshot this. You can compare to the fourth map in
your discussion.
Making the maps with PowerPoint
38. To create your maps in ArcGIS online, you have to take a screenshot8 of the map, zoomed in
on Sydney and put this into a PowerPoint slide (this will allow you to save each slide as a jpeg
file). In PowerPoint do the following:
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A title (add a text-box)
A North Arrow (find one online and copy and paste) and scale bar (there should be a scale bar
included on your screenshot)
A legend (take screenshots of each legend by clicking on the change style menu for each layer
and screenshotting the legend provided)
A text box that specifies the student’s name, date of map creation and data source
A caption with a succinct description of what your map shows.
Once you have completed these steps, press “save as” and change the file type to “jpeg” before pressing
“save”. Your maps are now images and can be cut and paste into your word document. See Section 3
for guidelines to help you write your assessment.
Remember the data and the spatial relationships that you are trying to illustrate and think about whether
the colours, symbols, and sizes could be improved upon before submitting. You can tinker with your graphs
to make them more visually appealing!
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Content: 5 x maps plus 1000 words (+/- 10%, excluding references)
Submit: via Canvas
Welcome to GIS
Geographic location is fundamental in our lives. From problems so routine in
our day that they may not register in our consciousness (such as using your
smart phone to find the nearest bus stop) to those that have major societal or
environmental consequences (such as predicting the spatial spread of
infectious disease), space and location really matter.
Problems that involve location, either in the information used to address them
or in their end solutions, are termed geographic problems. The ubiquitous
nature of geographic information and the relevance of place to human
behaviour, human-environment interactions and biophysical processes that
operate on the Earth's surface highlight the importance of methods for
geographical analysis and reasoning.
Geographic information is multidimensional in that at least two coordinates
are required to define location (x and y or latitude and longitude) but may also
involve a height (z) or time (t) dimension. The diffusion of mobile devices for
deriving geo-referenced information and increased accessibility of
high-resolution satellite imagery has resulted in the proliferation of
geographic information and 'big data' that can be used in novel ways to
explore and predict trends. However, working with geographic information
and the representation of spatial features and phenomena is often complex
and requires an understanding of the methods that underpin GIS.
Understanding disadvantage,
employment and transport
Understanding spatial patterns of advantage and disadvantage in urban areas
requires an analysis of historical economic changes, specifically shifts in labour
and industry, particularly manufacturing, as well as the housing market
(Pawson et al., 2005). Prior to the 1980s, disadvantage in Sydney existed
largely in the inner city, with the working class living in slums near factories,
warehouses, wharves, shipyards and rail yards. The post-World War 2 period
saw a decline in certain industries and the relocation of others to (what was at
the time) outer Sydney. At the same time an increase in the inner Sydney
housing market coupled with increased provision of public housing in outer
Sydney, ultimately resulted in the “suburbanisation of poverty” (Randolph &
Tice, 2017). Randolph (2004) and Baum et al. (2005) both point to the
emergence of a distinct band of social disadvantage in Sydney’s ‘middle
suburbia’. This has led to spatial income inequality and increasing polarisation
between Sydney’s most advantaged and disadvantaged.
Some factors that are commonly associated with disadvantage include
employment and transport. Disadvantaged suburbs are characterised by low
income, temporary and precarious employment, as well as high rates of
unemployment (Burke & Hulse, 2015). Furthermore, the concentration of
employment, services and recreational opportunities closer to the inner city
limits the ability of people to participate in these opportunities and…
“…forces these populations to travel long distances, and the lack of public
transport options ensures that covering these distances is both difficult and
expensive…Furthermore, distance, and a paucity of infrastructural provision,
limits walking and cycling for transport, as well as other alternatives to private
car ownership such as car sharing” (Ma et al., 2018: 32).
Being ‘forced’ into car ownership in order to generate income can induce
transport disadvantage via the high costs of owning and running a car and the
consequential financial stress (Currie & Delbosc, 2011). Ma et al. (2018) and
Burke et al. (2014) both argue that transport disadvantage contributes to
social exclusion by preventing people “from participating in the economic,
social and political life of the community because of reduced accessibility to
opportunities, services and social networks, due in whole or part to insufficient
mobility in a society built around the assumption of high mobility (Kenyon et
al. 2003: 318 in Ma et al., 2018)”. In summary, there are clear links between
disadvantage, unemployment, car ownership and car dependency in Sydney.
Baum, S., O’Connor, K., and Stimson, R., 2005. Fault lines exposed: advantage
and disadvantage across
Australia’s settlement system. Melbourne: Monash University Press
Burke, T. and Hulse, K., 2015. Spatial disadvantage: why is Australia different?
Melbourne: Australian Housing and Urban Research Institute, AHURI Final
Report
Burke, T. and Stone, J. and Glackin, S. and Scheurer, J. 2014. Transport
disadvantage and low-income rental housing. AHURI Positioning paper. 157:
pp. 1-62
Currie, G. and Delbosc, A. 2011. Transport Disadvantage: A Review, Currie, G.
(Ed.) New Perspectives and Methods in Transport and Social Exclusion Research,
Emerald Group Publishing Limited, pp. 15-25
Kenyon, S., Rafferty, J. and Lyons, G., 2003. Social exclusion and transport in
the UK: a role for virtual accessibility in the alleviation of mobility-related
social exclusion? Journal of Social Policy, 32(3)
Ma, L., Kent, J.L. and Mulley, C., 2018. Transport disadvantage, social exclusion,
and subjective well- being. Journal of transport and land use, 11(1), pp.31-47
Pawson, H., Hulse, K., and Cheshire, L., 2015. Addressing concentrations of
disadvantage in urban Australia. Melbourne: Australian Housing and Urban
Research Institute, AHURI Final Report No. 247
Randolph, B., 2004. The changing Australian city: new patterns, new policies
and new research needs1. Urban policy and research, 22 (4), 481–493
Randolph, B. and Tice, A., 2017. Relocating disadvantage in five Australian
cities: Socio-spatial polarisation under neo-liberalism. Urban policy and
research, 35(2), pp.103-121
Assessment guidelines
Your submission for this assessment is worth 20% of your final grade. For this
assessment we require you to upload Five maps and a 1000
word interpretation of the maps and the issues they illustrate with some
referencing (using Harvard style referencing). We will be using ArcGIS online
to create the maps, a web-based version of ESRI's popular GIS software.
You may be required to use a Virtual Private Network to access University
resources remotely.
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