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GEOS1002/1902-gis代写
时间：2023-10-23
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
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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
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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
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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).
OK, let’s get started.
Making your first map: IRSAD vs unemployment
For this unit you will be using QGIS, an open source GIS software. It’s quite similar
in functionality to ArcGIS that you would have used if you took GEOS1001 last
semester, though it still will probably take some time to become familiar with the
layout of this software. The advantage of using QGIS over ArcGIS is that it’s free for
anyone to download and use, and it works for both PC and Mac computers. If you
haven’t yet installed the software on your computer, go back to the Canvas page for
this assignment and follow the linked instructions to download and install it. You will
also need to download the “GIS data payload” zip file from the Canvas page and
extract the files to your computer.
We suggest creating a new map file for each map as otherwise your map window will
get very crowded and it becomes easier to lose information. If you have the software
installed and running on your computer, you can begin making your first map using
the instructions below.
1. Run QGIS and click on the “New Project” button in the top left corner
2. Next we will add a basemap, this will provide a background to the shapefiles
that you will be displaying and manipulating. In the browser panel on the left
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side of the screen, expand the “XYZ Tiles” tab and double click
“OpenStreetMap”. This will open up a global street map.
3. Zoom in on the Sydney region on the east coast of Australia. This is the
region we are working with for all of our maps. You don’t have to zoom in too
far, just enough so you can see the greater Sydney area and some of the
regions and water around it.
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4. Now is a good time to save your work. I will add some reminders to save
throughout these instructions, but it’s a good idea to regularly save your work
as you go as the software can occasionally crash. Call your file “Map 1” and
save it to the hard drive if you are using your own computer, or to OneDrive or
a USB stick if you are using a university computer.
5. Next we will add the first shapefile to your map.
NOTE: If you haven’t yet unzipped your “GIS data payload.zip” file, you will
not be able to add it to QGIS. To extract the file in Windows, right click on it
and click “Extract All”. On a Mac, double-click the zip file and it should
automatically extract the files. If you look through the files you will be using,
you will notice many files with the same name and a different filename
extension to it – this is how GIS shapefiles are stored. They typically have 5 or
6 files associated with each GIS file.
To add your first shapefile, click the “Layer” menu at the top of the screen and
then navigate down to “Add Layer” and click “Add Vector Layer”
Browser panel
Layers panel
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When the “Data Source Manager” window pops up click on the button
(next to “Source”) and navigate to the “GIS data payload” folder and open it
up. Go to the “Sydney_boundary” folder. In there, you will find 6 files, all
named “2016_GreaterSydney”. The file you need to add is called
“2016_GreaterSydney.shp”. When adding any shapefiles, the “.shp” file will
always be the one you add – it can also be easily identified if your computer
shows the size of each file, as it will always be the largest file in the folder by
a big margin. Once you have selected the .shp file, click “Open”, and then in
the Data Source Manager window, click “Add”. A window may pop up asking
you to select the transformation for this file, just click “Ok”, close the Data
Source Manager and the file will display in your QGIS map window. Note: the
colour of the shapefile is randomised when added, so yours may be a
different colour to the one in my screenshot.
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6. This shapefile is displaying most of Greater Sydney, and this is the area we
will be working with. How much you see will depend on your zoom level and
your screen size, but as long as most of this shapefile is visible, this is okay.
Obviously as a solid shape it doesn’t allow us to see much, so we are going to
edit it so that it is just an outline. Double-click the “2016_GreaterSydney” file
in the layers panel on the left side of the window and go to the “Symbology”
section. You will see that it is currently displayed as a single symbol with a
simple fill of one colour. If you click on “Simple Fill” near the top of this
window, more details on the type of fill will appear. We want to change this so
this file is just displayed by its outline. Next to “Symbol Layer Type”, click
“Simple Fill”. In the drop down menu, change it to “Outline: Simple Line”.
Next change the Stroke width to 0.5 millimetres and press Ok.
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7. The shapefile should now display as an outline with the map of Sydney visible
beneath it.
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8. Next we will add the Index of Relative Advantage and Disadvantage shapefile
to our map. Using the instructions from step 5, click the Layer → Add Layer
→ Add Vector Layer menu item, click the 3 dots, navigate to the IRSAD
folder and add the “IRSAD_LGA.shp” file. You’ll notice that this map is much
larger than the Greater Sydney one, it covers all of New South Wales. You’ll
also notice that this shapefile is broken up into smaller pieces (called
polygons), this is because it has been broken up into LGAs. Stay focused on
Sydney though.
9. This map looks quite simple, but contains a lot more information than it initially
seems. We can view this information using the attribute table. Before you
can view the attribute table, you first need to activate the plugin. Open
the Plugins menu at the top of the screen and then click on Manage and
Install Plugins. When the window pops up, start typing in
RasterAttributeTable and it should appear, then you need to click “Install
Plugin”. NOTE: if you are doing this task on your own computer, once you
have installed this plugin, it will work every time you run the software. If you
are doing this task on a university computer, you will need to install this plugin
every time you run the QGIS software.
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Once you have installed the Raster Attribute Table plugin, close this window
and right click on the IRSAD_LGA file in the layers panel and click “Open
Attribute Table”. You’ll see that for each LGA, this file has the data on the
name, area, population (in 2016) and some other categories – Score, Rank,
Decile and Percentile. “Decile” is the category we are interested in, as this is
the IRSAD ranking. Once you are finished looking at the table, you can close
it.
10. What we want to do is display the map according to the IRSAD decile of each
LGA. To do this, double click on the IRSAD_LGA file and open up the
Symbology section, same as what you did for the Greater Sydney shapefile
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in step 6. This time, click on Single Symbol at the top of the screen and
change it to Categorised. Under “Value”, we want to select Decile, then near
the bottom of the window click “Classify”. The values of 1 - 10 and “all other
values” will pop up.
The first thing to do is remove the “all other values” category. We don’t need
it. Click the “all other values” text and click the button near the bottom of
the screen. The rest of the categories we will change from random colours to
a meaningful colour scheme. Under “Colour Ramp”, click on the down arrow
to the right of where it says “Random colours” and select a colour scheme that
is a spectrum (that is, gradually transforming from one colour to another). In
the image below, I chose the “Cividis” colour scheme. Feel free to use this or
choose a colour scheme that you like.
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11. If you followed the steps above, your map should look something like the
above image (with whichever colour scheme you chose). To see what these
colours mean, click the little arrow next to “IRSAD_LGA” in the layers panel
on the left and a legend will pop up (you can see this in the above image).
What this is showing is that each colour is associated with an IRSAD value,
where an LGA with a value of 1 is ranked as the most disadvantaged and an
LGA with a value of 10 is the most advantaged.
Take a moment to look at your map. What trends can you see about the
spatial distribution of the more advantaged and disadvantaged regions of
Sydney?
12. Now is probably a good time to save your work again!
13. You will notice that with the IRSAD_LGA shapefile on display, you can no
longer see the 2016_GreaterSydney boundary. This is because QGIS uses a
top-down display where the files that are highest on the list in the Layers
panel are on top of the files below them. If you untick the IRSAD_LGA file in
this panel, it will disappear and you can see the greater Sydney boundary
again. Tick the IRSAD shapefile so it is on display again and make the
2016_GreaterSydney shapefile visible by right clicking it and clicking “Move
to Top”. This will move the file to the top of the list and make it visible again.
14. Next we will add another shapefile to compare the rate of unemployment with
the levels of advantage and disadvantage in Sydney. Click Layer → Add
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Layer → Add Vector Layer again, navigate to the “Unemployment” folder
and add the “SA2_unemployment.shp” file. This will look quite similar to the
IRSAD shapefile when you first added it, though divided into smaller areas –
this is because this file is categorised according to Statistical Area Level 2
(SA2) rather than LGA. If you view the attribute table for this file (step 9) you
will see another column – “PER_UNEMPL”, this column is giving us the
percentage of residents that were unemployed in each SA2 region in 2016.
15. Close the attribute table and double click on the SA2_unemployment file and
open up Symbology. We are now going to change the symbology of this
shapefile in a similar way to what we did with the IRSAD file in step 10 but
with a few changes. In the Symbology window, click “Single Symbol” and this
time change it to “Graduated”. Under “Value” choose “Per_UNEMPL” and
click classify at the bottom. The unemployment rate data should pop up, but
this time in ranges (0 - 4.1%, 4.1 - 5.3% etc.). The colour scheme should
automatically be a spectrum for this shapefile, so click on the down arrow next
to “Colour ramp” and choose a colour scheme you would like to use to display
levels of unemployment. Click Ok and have a look at the map you’ve created.
Can you see any trends of which areas of Sydney have higher levels of
unemployment than others?
16. What would be useful would be to see if there is any relationship between
levels of unemployment and levels of advantage and disadvantage in Sydney,
but unfortunately your shiny new unemployment map is now in the way of
your IRSAD map. To fix this, we are going to change the way unemployment
is displayed so that the IRSAD levels are also visible.
Go back to the symbology settings for the unemployment shapefile. Near the
top of this window is a category called “Symbol” with a solid block of a
random colour next to it. Either click on this block of colour or click the arrow
next to it and then select “Configure Symbol”. The Symbol Settings window
will pop up you need to click on the text that says “Simple Fill”. In the section
that appears below, under “Symbol layer type” you need to click on “Simple
Fill” and change it to “Centroid Fill”. This means that instead of displaying the
unemployment rate as shaded polygons, it will instead be displayed as a
smaller symbol, allowing you to see both layers. Genius!
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In the new section that appears, untick “Draw markers on every part of multi-
part features” and then click “Simple Marker” to edit the marker you will use.
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At this point you probably don’t need to change much, but you can come back
to this window if you want to use a different shape or change the shape size.
For now, press Ok and close the Symbology window. Your map should now
look something like this, according to whichever colour scheme you chose:
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Make sure that the two colour schemes you have chosen are different enough
from each other to see the trends! You’ll find that a red circle on a red
background is rather difficult to follow.
17. Save your work again
18. Now that we have the shapefiles we need for our first map, we are going to try
and put them all together to make a polished map worthy of your GIS
assignment. But before this, we want to move the Greater Sydney shapefile to
the top of the list so that it is on display again (right click → move to top).
Once you’ve done this, click the Project menu and click New Print Layout. A
window will pop up asking you to give this map a name, call it something
meaningful such as “IRSAD vs Unemployment” and press Ok. A New blank
window will appear.
19. The first thing we need to do is add the map to this page. Click the Add item
menu and choose Add map. Initially it will look like nothing has happened but
you will notice that the cursor has changed. Try drawing a box on the blank
sheet and see what happens. Your map is back! Click and drag from the
corners of the map so that it fills up the whole sheet.
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Your map should cover an area similar to this. If you need to move it or zoom
in/out, click on the map and in the Item Properties panel that appears on the
right side and click the button. This will allow you to click and drag your
map so that it is focused on the right area. You can also use your mouse
wheel to zoom in and out. If you find that it zooms in and out too much, you
can manually change the zoom by altering the Scale value in the Item
Properties panel (hint, I find that a scale of somewhere between 300,000 and
500,000 is the best for most of my maps of Sydney, but play around with it to
see what works for you). Once you are satisfied with the display area, click
the button on the left side (or possibly top) of the screen.
20. Now we need to add some display elements to your map. Try to remember
that every GIS map you make needs to have a title, scale bar, north
arrow and legend. This is important and is an easy way to lose marks if you
forget.
21. The first thing we will add is a legend. This is probably the trickiest thing to
add, but I know you can do this. Click Add Item → Add Legend and draw a
box somewhere on your map. Try to not block too much of your data, so the
best place to have your legend is probably going to be over the Pacific Ocean.
22. Your legend will probably look something like this:
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While this legend does convey some meaning, it’s not very descriptive. It
would be useful to change the title of your shapefiles so that they are easier to
understand and there is no reason for your legend to include OpenStreetMap.
Go to the Item Properties panel at the side of the screen. The first thing to do
is to title your legend. So in the title section, simply write “Legend”. Next we
will remove OpenStreetMap. In the Item Properties panel, scroll down to the
Legend Items section (if this isn’t visible, click on your legend and it should
appear). First, untick Auto update, then find the OpenStreetMap text, click on
it and press the button to remove it.
23. Great! Now we will change the shapefile names so that the person viewing
your map understands what the colours mean. The first thing to do is either
minimise the map window or close it. Don’t worry, if you close the map
window, you can get it back by clicking Project → Layouts → IRSAD vs
Unemployment in the main QGIS window. Now that we are back on the main
QGIS page, what we need to do is rename the shapefiles so that even a
person who knows nothing about geography or this assignment can
understand it. First right-click on the IRSAD_LGA file and click Rename
Layer. Then give it a meaningful name, such as “Levels of advantage and
disadvantage”. Next, rename the SA2_unemployment layer to
“Unemployment rate %” and rename 2016_GreaterSydney to “Greater
Sydney boundary”. Remember to include the units whenever you rename
these files.
24. Now that this is done, go back to your map layout window and just like that,
your legend should be looking much nicer!
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25. The next thing we want to add a scale bar. This part is easy. Go to Add Item
→ Add Scale Bar and draw a box on the screen. A basic scale bar should
appear. It can be edited in the Item Properties panel on the side if you want a
different style or scale. Feel free to change it as you like.
26. Then we will add a north arrow. Go to Add Item → Add North Arrow and
draw a box. Adjust the size as you need by clicking the corner of the arrow
box and dragging it in or out. There are a number of different arrow styles you
can choose from in the Item Properties panel (in the “arrows” folder) or keep it
the standard style. Place your north arrow somewhere where it’s not blocking
your data.
27. The final element your map needs is a title. Click Add Item → Add Label and
draw a box on your map. A blank text box with a small lorem ipsum in the
corner will appear. In the Item Properties panel, give it a title. I wont tell you
exactly what to title it, but think about what the map is showing. You are
looking at the relationship between two variables in the Sydney region so your
title should be about this.
28. Once you have settled on a title, you will need to increase the text size. To fix
this, click on the Font button in the Item Properties panel and adjust the size
so that it stands out. Depending on the colour scheme, you may also find that
the title text is difficult to read over the background. To fix this, go to the Item
Properties panel of the title, and scroll down to the Frame and Background
sections. Try ticking one or both of these and see if it improves your title
visibility.
29. Once you’re satisfied with the size and positioning of your map and its
elements, first click the Layout menu and save your project again, and then
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again click Layout → Export as Image and save it as an image file to your
computer.
Congratulations! You have now made your first map.
The following instructions for making the other four maps for your assignment
will be more brief than those for the above map as most of the steps are
similar or the same as what you did in your first map, and I will reference the
steps you need to follow if you have already done it in map 1 and it’s not
obvious. I won’t be giving you any more instructions on how to make your
maps as they are the same steps as you follow for map 1. It’s also now your
responsibility to save your work as you go.
Second map: IRSAD vs percentage born overseas
30. This map is quite similar in design to the first map you created, but there are
some other steps you will need to follow to format your data. As you did
before, create a new map file, add the Open Street Maps basemap and focus
on Sydney again.
31. Next add the Greater Sydney and IRSAD shapefiles again. You will notice
that these files have reverted back to being simple fill shapefiles that don’t
display any specific information so you will need to go back through steps 6 –
10 to make your maps displaying the Greater Sydney boundary and the levels
of advantage and disadvantage for each LGA. Remember to have the Greater
Sydney boundary on top of the other maps when it’s time to create your map.
NOTE: it’s best to use the same colour scheme for IRSAD in all of your
maps.
32. Next we are going to use some raw Australian Bureau of Statistics (ABS) data
to calculate the percentage of people in each SA2 zone who were born
overseas. This can be a bit tricky, but as geographers, it’s a useful skill to be
able to sort through census data. Open the “Born_OS” folder and you will see
2 spreadsheets: “2016 SA2 total population” and “2016 SA2 born overseas”.
First open the born overseas sheet. Initially the columns are all bunched up
and you can’t read much of the data. Widen the columns so that you can read
the headings. Most columns will have the same data in every cell, but you will
also see sections for SA2 region number, SA2 region name, and
OBS_VALUE. OBS_VALUE is the most important column as it is the number
of people in each SA2 region that were born overseas. Minimise this
spreadsheet and open the total population sheet. You will see that it’s
basically the same layout as the born overseas spreadsheet, but in this case
the numbers in the OBS_VALUE column represents the total population for
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each region. To avoid confusion between the two, you may want to rename
the OBS_VALUE heading in the born OS sheet to “Number born OS” and the
OBS_VALUE heading in the total population to “Total population”.
Maybe you can see what we are going to do here. What we need to do is
copy the data from the “Number born OS” column of the “born OS”
spreadsheet into the “total population” spreadsheet. The first thing we will do
is highlight the J column in the born overseas spreadsheet, right click the
data and select Copy.
Next go to the total population spreadsheet, go to the first empty column (it
should be column M), highlight this column, right click and select Paste.
33. Now we need to calculate the percentage of the population of each SA2
region was born overseas. Go to column N and in the first cell type “Percent”
(don’t use a % symbol as this causes problems in QGIS). Now in cell N2, add
the following formula:
=M2/I2*100
(Note that I2 is the letter i followed by the number 2.)
To apply this formula to the rest of the column, click on cell N2, then click the
little square in the bottom right corner of the cell and drag it to the bottom of
the data.
34. There’s one final thing we need to do to make this data work in QGIS.
Unfortunately because column N contains a formula rather than a simple
number, it will not display in QGIS without doing this step. Highlight column N,
right click and select copy. Then with the column still highlighted, right click on
it again and select Paste values (the default paste or control/command + v
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will not work). When you right click, you are looking for the button that looks
like this: . When you do this, it won’t look like anything has changed, but
you will know if it has worked if you click on one of the cells in column N and
in the formula bar it shows a number rather than a formula.
Do not skip this step, otherwise your data wont work in QGIS. Ask your
tutor if you are not sure how to do this step, or view instructional video
3 at 11 minutes.
Once you have done this, save your spreadsheet and close it.
35. Now that we have the data for the percentage of the population of each region
that was born overseas, we are ready to add it to QGIS. Go back into your
QGIS project and click Layer → Add Layer → Add Vector Layer and add
the updated “2016 SA2 total population” spreadsheet you edited. Once you’ve
added it, nothing will change on your map, but you will see the file in your
Layers panel.
36. To display the data, we need to join it to a shapefile. As this is SA2 data, we
need to join it to a SA2 shapefile. Click Layer → Add Layer → Add Vector
Layer again and this time navigate to the NSW_SA2 folder and add the
“SA2_2016_NSW.shp” shapefile to your project.
37. Double-click on the SA2_2016_NSW file in your Layers panel and this time go
to the Joins section. Click the button near the bottom of the window and
when the window pops up, fill out this window according to the image below.
Basically what this is doing is joining the population data to the SA2 shapefile.
The numerical codes in the “ASGS_2016” column of the population sheet are
the same as those in the “SA2_MAIN” column of the SA2 shapefile, so we can
use this to join the spreadsheet to the shapefile. After this, press Ok and close
the window.
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38. We want to display the data of people born overseas as symbols over the
IRSAD shapefile, the same style as the last map. Open up the symbology of
the SA2_2016_NSW shapefile and repeat steps 15 and 16 to display the data
as Graduated data with Centroid Fill. The value we are displaying should be
called “2016 SA2 total population_Percent”. Select a different colour ramp for
this data than what you chose for unemployment in the previous map.
Note: if this field is not available in the symbology, it is related to a
mistake made in step 34. Remove the current “2016 SA2 total
population” sheet from your QGIS project, go back to that step and
make sure you use the correct copy and paste function on column N,
save it and add it to your project again.
39. The final thing we want to change for the symbology of this map is the
classification mode. At the moment, you will notice that the points around
Sydney all look quite similar. This is because the data is unevenly distributed
and there are many areas outside of Sydney without many people born
overseas.
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To fix this, go to the symbology of this shapefile again. Near the bottom of the
window there is a section called Mode with a drop-down menu automatically
set to “Equal count (Quantile)”. Click on this and change it to Equal Interval.
This will make the display of data around Sydney more varied.
40. Now you should have 5 files in your map: the Greater Sydney boundary, the
2016 SA2 total population spreadsheet, the SA2_2016_NSW shapefile
displaying the percentage or people born overseas as symbols, the
IRSAD_LGA shapefile, and the OpenStreetMap. Now you need to arrange
them in the Layers panel so that they are all visible and then go back to steps
18 - 29 to create a map using your new data.
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Third map: IRSAD vs Education Level and Personal Income
41. Our next map will compare levels of advantage and disadvantage with the
percentage of residents who have a university degree, as well as their weekly
income.
42. Create a new map and add the OpenStreetMap, Greater Sydney boundary
and IRSAD shapefile, edit them the same way you did for the first two maps.
43. Next we want to add the SA2 data on the weekly personal income. Add
another vector layer and this time navigate to the Income folder and add the
SA2_2016_income.shp shapefile. Right click on the file in the Layers panel
and look at the attribute table. This shapefile has data on the median age of
residents (Med_age), the median mortgage repayments (Med_motrtga),
median weekly rent paid (Med_rent) and the median weekly income
(Med_inc_wk). We’re mostly interested in the median weekly income data for
this map. Close the attribute table when you’re done.
44. Open up the symbology for this shapefile. As before, we want to display this
data as Centroid Fill with Graduated Data. We are displaying the
Med_inc_wk data this time. Go back over steps 15 and 16 if you need to
refresh your memory on how to do this. Like in map 2, change the display
mode to equal interval again.
45. Next we will add the data of the percentage of the population with a university
degree. This data is only available in SA4 format, so the data points will be
more sparse than those in a SA2 map. This data also comes in spreadsheet
form, but no calculations will need to be made this time. First, navigate to the
Education folder and open up the “207107 – educational qualifications sa4”
spreadsheet. This spreadsheet is more simple than the last ones and the only
categories it has are SA4, B_degree_above (number of people with a
bachelor degree or higher), No_degree (number of people without a degree),
Total (total population) and Percent_degree (percentage of the population
with a degree). Close this spreadsheet when you’re done.
46. Next add this spreadsheet to your current QGIS project as you did in step 36.
47. As we are working with SA4 data, you need to add a SA4 shapefile to join it
to. Using the Add Vector Layer function, navigate to the NSW_SA4 folder and
add the SA4_2016_NSW shapefile to your project.
48. Join the SA4_2016_NSW shapefile to the educational qualifications
spreadsheet using the same way you did in step 38. Use the below
screenshot as a guide for which fields to join.
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49. Edit the symbology of the SA4 shapefile so that the “207107 - educational
qualifications sa4 — Sheet1_Percent_degree” value is displayed as
Graduated with Centroid Fill. Make sure you use a different colour scheme to
the income symbols. This time we want to change the shape and size of the
symbols so that they stand out from the others. In the Symbol Settings,
change the size to 4 and the shape to a diamond. Then press Ok. Have a look
at your map. It’s probably looking quite spotty right now, but that’s completely
normal. Can you see any relationship between income and education? How
do levels of advantage and disadvantage relate to these variables?
50. Now that you have data showing IRSAD, income and education. You will want
to make a map of this. Go back to steps 18 – 29 to make it look good. Think
about what relationships your map is showing when you are coming up with a
suitable title.
Fourth and fifth maps: IRSAD vs number of vehicles, commuting
distance to work and access to railway stations
51. These last two maps are related to one another and share the same dataset,
so these instructions will be combined. This time we will be looking at the way
access to public transport influences car ownership and commuting distance
to work, and how IRSAD deciles might relate to this.
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52. Open up a new map and bring up OpenStreetMaps and IRSAD (Greater
Sydney boundary is not necessary in this one). Edit the IRSAD symbology as
you have for the previous maps.
53. First we will add the data for number of vehicles owned by each household.
Add a vector layer, navigate to the Transport folder and add the shapefile
“SA2_VEHICLES”. Open up the attribute table for this shapefile and you will
see all the census data used in the calculation. The data we will be displaying
from this shapefile is the “MODE_ANS” column as this is the most common
response for number of vehicles per household in each region.
54. Open up the symbology of this shapefile. This time we are displaying the data
as Categorised rather than graduated as there are only a few different
responses to this question. By default the colour choice is random. This is
fine. You can change the colours of each point manually by double clicking
them and changing it, or you can choose a colour gradient. Choose a colour
scheme that is sufficiently different to your IRSAD colours without it looking
too messy.
55. Then we will change the symbol type to Centroid Fill again so that this data
can be displayed as circles on top of the map.
56. Next we want to add the data for the median distance travelled to work by
residents. Add a new vector layer and navigate to the Transport folder again
and add the “DTW_SA4” shapefile this time.
57. Edit the symbology for this shapefile so that it is Graduated and Centroid Fill.
The value we are displaying will be “MED_DTW_KM” as this is showing the
median distance travelled to work by residents in that region in kilometres.
This time, make the symbol size 4 and square-shaped symbols. This will help
it stand out from the other symbols on your map.
58. The final element to add to this map is the location of train stations. This will
help tie together the variations in number of vehicles and commuting distance
to work. Add a new vector layer, stay in the Transport folder, and add the
“i5605_railwaystoppoints.shp” file. This is showing the location of all train
stations in Sydney.
59. Currently it might be difficult to differentiate the stations from the other points,
so we are going to edit the symbology so that they stand out more. Open up
the symbology and don’t change it from Single Symbol. Instead, click on
“Simple Marker” and change the symbol to a different shape and a unique
colour so that it stands out on your map but doesn’t look too messy. You may
or may not want to also change the symbol size. Test out different sizes,
shapes and colours and decide what works best for you. Can you see any
relationships between access to stations, number of cars owned and median
distance travelled to work? Do these trends show any relationship with the
levels of advantage and disadvantage in Sydney?
60. As a checklist, your maps should have shapefiles of OpenStreetMaps, IRSAD,
number of vehicles per household, median commuting distance, and train
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stations. Make sure they are ordered in your layers window so that all the data
is visible.
61. We are going to make 2 different maps using this data. To make the first one
(which should be map 4), you will need to focus on the area around the least
advantaged LGA in Sydney (use the IRSAD shapefile to work this out).
62. Your final map (map 5) will be similar to the last one, but this time your map
should be zoomed in on the advantaged suburbs around Sydney Harbour.
63. When making these maps, you can create multiple print layouts from a single
QGIS project. So my advice is to create and export the first map, close this
map, then create another new print layout and make your second map there.
64. And that’s it for your maps! Well done on getting through this part. Now you
just need to add them to your assignment and write up a discussion. You will
find the instructions for this on the assignment page for this task on Canvas.
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
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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
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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
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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).
OK, let’s get started.
Making your first map: IRSAD vs unemployment
For this unit you will be using QGIS, an open source GIS software. It’s quite similar
in functionality to ArcGIS that you would have used if you took GEOS1001 last
semester, though it still will probably take some time to become familiar with the
layout of this software. The advantage of using QGIS over ArcGIS is that it’s free for
anyone to download and use, and it works for both PC and Mac computers. If you
haven’t yet installed the software on your computer, go back to the Canvas page for
this assignment and follow the linked instructions to download and install it. You will
also need to download the “GIS data payload” zip file from the Canvas page and
extract the files to your computer.
We suggest creating a new map file for each map as otherwise your map window will
get very crowded and it becomes easier to lose information. If you have the software
installed and running on your computer, you can begin making your first map using
the instructions below.
1. Run QGIS and click on the “New Project” button in the top left corner
2. Next we will add a basemap, this will provide a background to the shapefiles
that you will be displaying and manipulating. In the browser panel on the left
5
side of the screen, expand the “XYZ Tiles” tab and double click
“OpenStreetMap”. This will open up a global street map.
3. Zoom in on the Sydney region on the east coast of Australia. This is the
region we are working with for all of our maps. You don’t have to zoom in too
far, just enough so you can see the greater Sydney area and some of the
regions and water around it.
6
4. Now is a good time to save your work. I will add some reminders to save
throughout these instructions, but it’s a good idea to regularly save your work
as you go as the software can occasionally crash. Call your file “Map 1” and
save it to the hard drive if you are using your own computer, or to OneDrive or
a USB stick if you are using a university computer.
5. Next we will add the first shapefile to your map.
NOTE: If you haven’t yet unzipped your “GIS data payload.zip” file, you will
not be able to add it to QGIS. To extract the file in Windows, right click on it
and click “Extract All”. On a Mac, double-click the zip file and it should
automatically extract the files. If you look through the files you will be using,
you will notice many files with the same name and a different filename
extension to it – this is how GIS shapefiles are stored. They typically have 5 or
6 files associated with each GIS file.
To add your first shapefile, click the “Layer” menu at the top of the screen and
then navigate down to “Add Layer” and click “Add Vector Layer”
Browser panel
Layers panel
7
When the “Data Source Manager” window pops up click on the button
(next to “Source”) and navigate to the “GIS data payload” folder and open it
up. Go to the “Sydney_boundary” folder. In there, you will find 6 files, all
named “2016_GreaterSydney”. The file you need to add is called
“2016_GreaterSydney.shp”. When adding any shapefiles, the “.shp” file will
always be the one you add – it can also be easily identified if your computer
shows the size of each file, as it will always be the largest file in the folder by
a big margin. Once you have selected the .shp file, click “Open”, and then in
the Data Source Manager window, click “Add”. A window may pop up asking
you to select the transformation for this file, just click “Ok”, close the Data
Source Manager and the file will display in your QGIS map window. Note: the
colour of the shapefile is randomised when added, so yours may be a
different colour to the one in my screenshot.
8
6. This shapefile is displaying most of Greater Sydney, and this is the area we
will be working with. How much you see will depend on your zoom level and
your screen size, but as long as most of this shapefile is visible, this is okay.
Obviously as a solid shape it doesn’t allow us to see much, so we are going to
edit it so that it is just an outline. Double-click the “2016_GreaterSydney” file
in the layers panel on the left side of the window and go to the “Symbology”
section. You will see that it is currently displayed as a single symbol with a
simple fill of one colour. If you click on “Simple Fill” near the top of this
window, more details on the type of fill will appear. We want to change this so
this file is just displayed by its outline. Next to “Symbol Layer Type”, click
“Simple Fill”. In the drop down menu, change it to “Outline: Simple Line”.
Next change the Stroke width to 0.5 millimetres and press Ok.
9
7. The shapefile should now display as an outline with the map of Sydney visible
beneath it.
10
8. Next we will add the Index of Relative Advantage and Disadvantage shapefile
to our map. Using the instructions from step 5, click the Layer → Add Layer
→ Add Vector Layer menu item, click the 3 dots, navigate to the IRSAD
folder and add the “IRSAD_LGA.shp” file. You’ll notice that this map is much
larger than the Greater Sydney one, it covers all of New South Wales. You’ll
also notice that this shapefile is broken up into smaller pieces (called
polygons), this is because it has been broken up into LGAs. Stay focused on
Sydney though.
9. This map looks quite simple, but contains a lot more information than it initially
seems. We can view this information using the attribute table. Before you
can view the attribute table, you first need to activate the plugin. Open
the Plugins menu at the top of the screen and then click on Manage and
Install Plugins. When the window pops up, start typing in
RasterAttributeTable and it should appear, then you need to click “Install
Plugin”. NOTE: if you are doing this task on your own computer, once you
have installed this plugin, it will work every time you run the software. If you
are doing this task on a university computer, you will need to install this plugin
every time you run the QGIS software.
11
Once you have installed the Raster Attribute Table plugin, close this window
and right click on the IRSAD_LGA file in the layers panel and click “Open
Attribute Table”. You’ll see that for each LGA, this file has the data on the
name, area, population (in 2016) and some other categories – Score, Rank,
Decile and Percentile. “Decile” is the category we are interested in, as this is
the IRSAD ranking. Once you are finished looking at the table, you can close
it.
10. What we want to do is display the map according to the IRSAD decile of each
LGA. To do this, double click on the IRSAD_LGA file and open up the
Symbology section, same as what you did for the Greater Sydney shapefile
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in step 6. This time, click on Single Symbol at the top of the screen and
change it to Categorised. Under “Value”, we want to select Decile, then near
the bottom of the window click “Classify”. The values of 1 - 10 and “all other
values” will pop up.
The first thing to do is remove the “all other values” category. We don’t need
it. Click the “all other values” text and click the button near the bottom of
the screen. The rest of the categories we will change from random colours to
a meaningful colour scheme. Under “Colour Ramp”, click on the down arrow
to the right of where it says “Random colours” and select a colour scheme that
is a spectrum (that is, gradually transforming from one colour to another). In
the image below, I chose the “Cividis” colour scheme. Feel free to use this or
choose a colour scheme that you like.
13
11. If you followed the steps above, your map should look something like the
above image (with whichever colour scheme you chose). To see what these
colours mean, click the little arrow next to “IRSAD_LGA” in the layers panel
on the left and a legend will pop up (you can see this in the above image).
What this is showing is that each colour is associated with an IRSAD value,
where an LGA with a value of 1 is ranked as the most disadvantaged and an
LGA with a value of 10 is the most advantaged.
Take a moment to look at your map. What trends can you see about the
spatial distribution of the more advantaged and disadvantaged regions of
Sydney?
12. Now is probably a good time to save your work again!
13. You will notice that with the IRSAD_LGA shapefile on display, you can no
longer see the 2016_GreaterSydney boundary. This is because QGIS uses a
top-down display where the files that are highest on the list in the Layers
panel are on top of the files below them. If you untick the IRSAD_LGA file in
this panel, it will disappear and you can see the greater Sydney boundary
again. Tick the IRSAD shapefile so it is on display again and make the
2016_GreaterSydney shapefile visible by right clicking it and clicking “Move
to Top”. This will move the file to the top of the list and make it visible again.
14. Next we will add another shapefile to compare the rate of unemployment with
the levels of advantage and disadvantage in Sydney. Click Layer → Add
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Layer → Add Vector Layer again, navigate to the “Unemployment” folder
and add the “SA2_unemployment.shp” file. This will look quite similar to the
IRSAD shapefile when you first added it, though divided into smaller areas –
this is because this file is categorised according to Statistical Area Level 2
(SA2) rather than LGA. If you view the attribute table for this file (step 9) you
will see another column – “PER_UNEMPL”, this column is giving us the
percentage of residents that were unemployed in each SA2 region in 2016.
15. Close the attribute table and double click on the SA2_unemployment file and
open up Symbology. We are now going to change the symbology of this
shapefile in a similar way to what we did with the IRSAD file in step 10 but
with a few changes. In the Symbology window, click “Single Symbol” and this
time change it to “Graduated”. Under “Value” choose “Per_UNEMPL” and
click classify at the bottom. The unemployment rate data should pop up, but
this time in ranges (0 - 4.1%, 4.1 - 5.3% etc.). The colour scheme should
automatically be a spectrum for this shapefile, so click on the down arrow next
to “Colour ramp” and choose a colour scheme you would like to use to display
levels of unemployment. Click Ok and have a look at the map you’ve created.
Can you see any trends of which areas of Sydney have higher levels of
unemployment than others?
16. What would be useful would be to see if there is any relationship between
levels of unemployment and levels of advantage and disadvantage in Sydney,
but unfortunately your shiny new unemployment map is now in the way of
your IRSAD map. To fix this, we are going to change the way unemployment
is displayed so that the IRSAD levels are also visible.
Go back to the symbology settings for the unemployment shapefile. Near the
top of this window is a category called “Symbol” with a solid block of a
random colour next to it. Either click on this block of colour or click the arrow
next to it and then select “Configure Symbol”. The Symbol Settings window
will pop up you need to click on the text that says “Simple Fill”. In the section
that appears below, under “Symbol layer type” you need to click on “Simple
Fill” and change it to “Centroid Fill”. This means that instead of displaying the
unemployment rate as shaded polygons, it will instead be displayed as a
smaller symbol, allowing you to see both layers. Genius!
15
In the new section that appears, untick “Draw markers on every part of multi-
part features” and then click “Simple Marker” to edit the marker you will use.
16
At this point you probably don’t need to change much, but you can come back
to this window if you want to use a different shape or change the shape size.
For now, press Ok and close the Symbology window. Your map should now
look something like this, according to whichever colour scheme you chose:
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Make sure that the two colour schemes you have chosen are different enough
from each other to see the trends! You’ll find that a red circle on a red
background is rather difficult to follow.
17. Save your work again
18. Now that we have the shapefiles we need for our first map, we are going to try
and put them all together to make a polished map worthy of your GIS
assignment. But before this, we want to move the Greater Sydney shapefile to
the top of the list so that it is on display again (right click → move to top).
Once you’ve done this, click the Project menu and click New Print Layout. A
window will pop up asking you to give this map a name, call it something
meaningful such as “IRSAD vs Unemployment” and press Ok. A New blank
window will appear.
19. The first thing we need to do is add the map to this page. Click the Add item
menu and choose Add map. Initially it will look like nothing has happened but
you will notice that the cursor has changed. Try drawing a box on the blank
sheet and see what happens. Your map is back! Click and drag from the
corners of the map so that it fills up the whole sheet.
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Your map should cover an area similar to this. If you need to move it or zoom
in/out, click on the map and in the Item Properties panel that appears on the
right side and click the button. This will allow you to click and drag your
map so that it is focused on the right area. You can also use your mouse
wheel to zoom in and out. If you find that it zooms in and out too much, you
can manually change the zoom by altering the Scale value in the Item
Properties panel (hint, I find that a scale of somewhere between 300,000 and
500,000 is the best for most of my maps of Sydney, but play around with it to
see what works for you). Once you are satisfied with the display area, click
the button on the left side (or possibly top) of the screen.
20. Now we need to add some display elements to your map. Try to remember
that every GIS map you make needs to have a title, scale bar, north
arrow and legend. This is important and is an easy way to lose marks if you
forget.
21. The first thing we will add is a legend. This is probably the trickiest thing to
add, but I know you can do this. Click Add Item → Add Legend and draw a
box somewhere on your map. Try to not block too much of your data, so the
best place to have your legend is probably going to be over the Pacific Ocean.
22. Your legend will probably look something like this:
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While this legend does convey some meaning, it’s not very descriptive. It
would be useful to change the title of your shapefiles so that they are easier to
understand and there is no reason for your legend to include OpenStreetMap.
Go to the Item Properties panel at the side of the screen. The first thing to do
is to title your legend. So in the title section, simply write “Legend”. Next we
will remove OpenStreetMap. In the Item Properties panel, scroll down to the
Legend Items section (if this isn’t visible, click on your legend and it should
appear). First, untick Auto update, then find the OpenStreetMap text, click on
it and press the button to remove it.
23. Great! Now we will change the shapefile names so that the person viewing
your map understands what the colours mean. The first thing to do is either
minimise the map window or close it. Don’t worry, if you close the map
window, you can get it back by clicking Project → Layouts → IRSAD vs
Unemployment in the main QGIS window. Now that we are back on the main
QGIS page, what we need to do is rename the shapefiles so that even a
person who knows nothing about geography or this assignment can
understand it. First right-click on the IRSAD_LGA file and click Rename
Layer. Then give it a meaningful name, such as “Levels of advantage and
disadvantage”. Next, rename the SA2_unemployment layer to
“Unemployment rate %” and rename 2016_GreaterSydney to “Greater
Sydney boundary”. Remember to include the units whenever you rename
these files.
24. Now that this is done, go back to your map layout window and just like that,
your legend should be looking much nicer!
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25. The next thing we want to add a scale bar. This part is easy. Go to Add Item
→ Add Scale Bar and draw a box on the screen. A basic scale bar should
appear. It can be edited in the Item Properties panel on the side if you want a
different style or scale. Feel free to change it as you like.
26. Then we will add a north arrow. Go to Add Item → Add North Arrow and
draw a box. Adjust the size as you need by clicking the corner of the arrow
box and dragging it in or out. There are a number of different arrow styles you
can choose from in the Item Properties panel (in the “arrows” folder) or keep it
the standard style. Place your north arrow somewhere where it’s not blocking
your data.
27. The final element your map needs is a title. Click Add Item → Add Label and
draw a box on your map. A blank text box with a small lorem ipsum in the
corner will appear. In the Item Properties panel, give it a title. I wont tell you
exactly what to title it, but think about what the map is showing. You are
looking at the relationship between two variables in the Sydney region so your
title should be about this.
28. Once you have settled on a title, you will need to increase the text size. To fix
this, click on the Font button in the Item Properties panel and adjust the size
so that it stands out. Depending on the colour scheme, you may also find that
the title text is difficult to read over the background. To fix this, go to the Item
Properties panel of the title, and scroll down to the Frame and Background
sections. Try ticking one or both of these and see if it improves your title
visibility.
29. Once you’re satisfied with the size and positioning of your map and its
elements, first click the Layout menu and save your project again, and then
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again click Layout → Export as Image and save it as an image file to your
computer.
Congratulations! You have now made your first map.
The following instructions for making the other four maps for your assignment
will be more brief than those for the above map as most of the steps are
similar or the same as what you did in your first map, and I will reference the
steps you need to follow if you have already done it in map 1 and it’s not
obvious. I won’t be giving you any more instructions on how to make your
maps as they are the same steps as you follow for map 1. It’s also now your
responsibility to save your work as you go.
Second map: IRSAD vs percentage born overseas
30. This map is quite similar in design to the first map you created, but there are
some other steps you will need to follow to format your data. As you did
before, create a new map file, add the Open Street Maps basemap and focus
on Sydney again.
31. Next add the Greater Sydney and IRSAD shapefiles again. You will notice
that these files have reverted back to being simple fill shapefiles that don’t
display any specific information so you will need to go back through steps 6 –
10 to make your maps displaying the Greater Sydney boundary and the levels
of advantage and disadvantage for each LGA. Remember to have the Greater
Sydney boundary on top of the other maps when it’s time to create your map.
NOTE: it’s best to use the same colour scheme for IRSAD in all of your
maps.
32. Next we are going to use some raw Australian Bureau of Statistics (ABS) data
to calculate the percentage of people in each SA2 zone who were born
overseas. This can be a bit tricky, but as geographers, it’s a useful skill to be
able to sort through census data. Open the “Born_OS” folder and you will see
2 spreadsheets: “2016 SA2 total population” and “2016 SA2 born overseas”.
First open the born overseas sheet. Initially the columns are all bunched up
and you can’t read much of the data. Widen the columns so that you can read
the headings. Most columns will have the same data in every cell, but you will
also see sections for SA2 region number, SA2 region name, and
OBS_VALUE. OBS_VALUE is the most important column as it is the number
of people in each SA2 region that were born overseas. Minimise this
spreadsheet and open the total population sheet. You will see that it’s
basically the same layout as the born overseas spreadsheet, but in this case
the numbers in the OBS_VALUE column represents the total population for
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each region. To avoid confusion between the two, you may want to rename
the OBS_VALUE heading in the born OS sheet to “Number born OS” and the
OBS_VALUE heading in the total population to “Total population”.
Maybe you can see what we are going to do here. What we need to do is
copy the data from the “Number born OS” column of the “born OS”
spreadsheet into the “total population” spreadsheet. The first thing we will do
is highlight the J column in the born overseas spreadsheet, right click the
data and select Copy.
Next go to the total population spreadsheet, go to the first empty column (it
should be column M), highlight this column, right click and select Paste.
33. Now we need to calculate the percentage of the population of each SA2
region was born overseas. Go to column N and in the first cell type “Percent”
(don’t use a % symbol as this causes problems in QGIS). Now in cell N2, add
the following formula:
=M2/I2*100
(Note that I2 is the letter i followed by the number 2.)
To apply this formula to the rest of the column, click on cell N2, then click the
little square in the bottom right corner of the cell and drag it to the bottom of
the data.
34. There’s one final thing we need to do to make this data work in QGIS.
Unfortunately because column N contains a formula rather than a simple
number, it will not display in QGIS without doing this step. Highlight column N,
right click and select copy. Then with the column still highlighted, right click on
it again and select Paste values (the default paste or control/command + v
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will not work). When you right click, you are looking for the button that looks
like this: . When you do this, it won’t look like anything has changed, but
you will know if it has worked if you click on one of the cells in column N and
in the formula bar it shows a number rather than a formula.
Do not skip this step, otherwise your data wont work in QGIS. Ask your
tutor if you are not sure how to do this step, or view instructional video
3 at 11 minutes.
Once you have done this, save your spreadsheet and close it.
35. Now that we have the data for the percentage of the population of each region
that was born overseas, we are ready to add it to QGIS. Go back into your
QGIS project and click Layer → Add Layer → Add Vector Layer and add
the updated “2016 SA2 total population” spreadsheet you edited. Once you’ve
added it, nothing will change on your map, but you will see the file in your
Layers panel.
36. To display the data, we need to join it to a shapefile. As this is SA2 data, we
need to join it to a SA2 shapefile. Click Layer → Add Layer → Add Vector
Layer again and this time navigate to the NSW_SA2 folder and add the
“SA2_2016_NSW.shp” shapefile to your project.
37. Double-click on the SA2_2016_NSW file in your Layers panel and this time go
to the Joins section. Click the button near the bottom of the window and
when the window pops up, fill out this window according to the image below.
Basically what this is doing is joining the population data to the SA2 shapefile.
The numerical codes in the “ASGS_2016” column of the population sheet are
the same as those in the “SA2_MAIN” column of the SA2 shapefile, so we can
use this to join the spreadsheet to the shapefile. After this, press Ok and close
the window.
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38. We want to display the data of people born overseas as symbols over the
IRSAD shapefile, the same style as the last map. Open up the symbology of
the SA2_2016_NSW shapefile and repeat steps 15 and 16 to display the data
as Graduated data with Centroid Fill. The value we are displaying should be
called “2016 SA2 total population_Percent”. Select a different colour ramp for
this data than what you chose for unemployment in the previous map.
Note: if this field is not available in the symbology, it is related to a
mistake made in step 34. Remove the current “2016 SA2 total
population” sheet from your QGIS project, go back to that step and
make sure you use the correct copy and paste function on column N,
save it and add it to your project again.
39. The final thing we want to change for the symbology of this map is the
classification mode. At the moment, you will notice that the points around
Sydney all look quite similar. This is because the data is unevenly distributed
and there are many areas outside of Sydney without many people born
overseas.
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To fix this, go to the symbology of this shapefile again. Near the bottom of the
window there is a section called Mode with a drop-down menu automatically
set to “Equal count (Quantile)”. Click on this and change it to Equal Interval.
This will make the display of data around Sydney more varied.
40. Now you should have 5 files in your map: the Greater Sydney boundary, the
2016 SA2 total population spreadsheet, the SA2_2016_NSW shapefile
displaying the percentage or people born overseas as symbols, the
IRSAD_LGA shapefile, and the OpenStreetMap. Now you need to arrange
them in the Layers panel so that they are all visible and then go back to steps
18 - 29 to create a map using your new data.
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Third map: IRSAD vs Education Level and Personal Income
41. Our next map will compare levels of advantage and disadvantage with the
percentage of residents who have a university degree, as well as their weekly
income.
42. Create a new map and add the OpenStreetMap, Greater Sydney boundary
and IRSAD shapefile, edit them the same way you did for the first two maps.
43. Next we want to add the SA2 data on the weekly personal income. Add
another vector layer and this time navigate to the Income folder and add the
SA2_2016_income.shp shapefile. Right click on the file in the Layers panel
and look at the attribute table. This shapefile has data on the median age of
residents (Med_age), the median mortgage repayments (Med_motrtga),
median weekly rent paid (Med_rent) and the median weekly income
(Med_inc_wk). We’re mostly interested in the median weekly income data for
this map. Close the attribute table when you’re done.
44. Open up the symbology for this shapefile. As before, we want to display this
data as Centroid Fill with Graduated Data. We are displaying the
Med_inc_wk data this time. Go back over steps 15 and 16 if you need to
refresh your memory on how to do this. Like in map 2, change the display
mode to equal interval again.
45. Next we will add the data of the percentage of the population with a university
degree. This data is only available in SA4 format, so the data points will be
more sparse than those in a SA2 map. This data also comes in spreadsheet
form, but no calculations will need to be made this time. First, navigate to the
Education folder and open up the “207107 – educational qualifications sa4”
spreadsheet. This spreadsheet is more simple than the last ones and the only
categories it has are SA4, B_degree_above (number of people with a
bachelor degree or higher), No_degree (number of people without a degree),
Total (total population) and Percent_degree (percentage of the population
with a degree). Close this spreadsheet when you’re done.
46. Next add this spreadsheet to your current QGIS project as you did in step 36.
47. As we are working with SA4 data, you need to add a SA4 shapefile to join it
to. Using the Add Vector Layer function, navigate to the NSW_SA4 folder and
add the SA4_2016_NSW shapefile to your project.
48. Join the SA4_2016_NSW shapefile to the educational qualifications
spreadsheet using the same way you did in step 38. Use the below
screenshot as a guide for which fields to join.
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49. Edit the symbology of the SA4 shapefile so that the “207107 - educational
qualifications sa4 — Sheet1_Percent_degree” value is displayed as
Graduated with Centroid Fill. Make sure you use a different colour scheme to
the income symbols. This time we want to change the shape and size of the
symbols so that they stand out from the others. In the Symbol Settings,
change the size to 4 and the shape to a diamond. Then press Ok. Have a look
at your map. It’s probably looking quite spotty right now, but that’s completely
normal. Can you see any relationship between income and education? How
do levels of advantage and disadvantage relate to these variables?
50. Now that you have data showing IRSAD, income and education. You will want
to make a map of this. Go back to steps 18 – 29 to make it look good. Think
about what relationships your map is showing when you are coming up with a
suitable title.
Fourth and fifth maps: IRSAD vs number of vehicles, commuting
distance to work and access to railway stations
51. These last two maps are related to one another and share the same dataset,
so these instructions will be combined. This time we will be looking at the way
access to public transport influences car ownership and commuting distance
to work, and how IRSAD deciles might relate to this.
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52. Open up a new map and bring up OpenStreetMaps and IRSAD (Greater
Sydney boundary is not necessary in this one). Edit the IRSAD symbology as
you have for the previous maps.
53. First we will add the data for number of vehicles owned by each household.
Add a vector layer, navigate to the Transport folder and add the shapefile
“SA2_VEHICLES”. Open up the attribute table for this shapefile and you will
see all the census data used in the calculation. The data we will be displaying
from this shapefile is the “MODE_ANS” column as this is the most common
response for number of vehicles per household in each region.
54. Open up the symbology of this shapefile. This time we are displaying the data
as Categorised rather than graduated as there are only a few different
responses to this question. By default the colour choice is random. This is
fine. You can change the colours of each point manually by double clicking
them and changing it, or you can choose a colour gradient. Choose a colour
scheme that is sufficiently different to your IRSAD colours without it looking
too messy.
55. Then we will change the symbol type to Centroid Fill again so that this data
can be displayed as circles on top of the map.
56. Next we want to add the data for the median distance travelled to work by
residents. Add a new vector layer and navigate to the Transport folder again
and add the “DTW_SA4” shapefile this time.
57. Edit the symbology for this shapefile so that it is Graduated and Centroid Fill.
The value we are displaying will be “MED_DTW_KM” as this is showing the
median distance travelled to work by residents in that region in kilometres.
This time, make the symbol size 4 and square-shaped symbols. This will help
it stand out from the other symbols on your map.
58. The final element to add to this map is the location of train stations. This will
help tie together the variations in number of vehicles and commuting distance
to work. Add a new vector layer, stay in the Transport folder, and add the
“i5605_railwaystoppoints.shp” file. This is showing the location of all train
stations in Sydney.
59. Currently it might be difficult to differentiate the stations from the other points,
so we are going to edit the symbology so that they stand out more. Open up
the symbology and don’t change it from Single Symbol. Instead, click on
“Simple Marker” and change the symbol to a different shape and a unique
colour so that it stands out on your map but doesn’t look too messy. You may
or may not want to also change the symbol size. Test out different sizes,
shapes and colours and decide what works best for you. Can you see any
relationships between access to stations, number of cars owned and median
distance travelled to work? Do these trends show any relationship with the
levels of advantage and disadvantage in Sydney?
60. As a checklist, your maps should have shapefiles of OpenStreetMaps, IRSAD,
number of vehicles per household, median commuting distance, and train
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stations. Make sure they are ordered in your layers window so that all the data
is visible.
61. We are going to make 2 different maps using this data. To make the first one
(which should be map 4), you will need to focus on the area around the least
advantaged LGA in Sydney (use the IRSAD shapefile to work this out).
62. Your final map (map 5) will be similar to the last one, but this time your map
should be zoomed in on the advantaged suburbs around Sydney Harbour.
63. When making these maps, you can create multiple print layouts from a single
QGIS project. So my advice is to create and export the first map, close this
map, then create another new print layout and make your second map there.
64. And that’s it for your maps! Well done on getting through this part. Now you
just need to add them to your assignment and write up a discussion. You will
find the instructions for this on the assignment page for this task on Canvas.
