Use Case: Building a dataset for external processing

Introduction

There are several ways to export data from AURIN for use with another software.

  • Export as .csv – Keep the spatial identifier’s code if you want to re-spatialise eg:SA2 code
  • Export as .shp – If you have aggregated data you can spatialise the dataset (described below)
  • Export as .png – Make a flat image as a screenshot

This use-case will cover the following skills:

  1. Choose region and aggregation level
  2. Find data of interest
  3. Check coverage and understand attributes (some charting)
  4. Spatialise this data if eventual geometry download is required
  5. Merge/join the data sets
  6. Upload your own data to be joined
  7. Join your own data
  8. Load Point data
  9. Point Join
  10. Check
  11. Download attributes (with or without geometry)

Selecting Area and Data, and Spatialising Data

To begin, navigate to Greater Melbourne GCCSA and load these datasets:

image0011

[Click to Enlarge]

 

To spatialise your dataset, choose:

[Tools > Spatial Data Manipulation > Spatialise Aggregated Data]

and run this on each of the three data sets – it is important to do this before joining the datasets together. Spatialising the datasets gives:

[Click to Enlarge]

[Click to Enlarge]

 

Note that when a data set is spatialised, geometry is added to it with other identification data. Note also that the important data element – the proportion of people who purchased alcohol – is now shown with its machine readable (internal) name (numeric) rather than the somewhat more informative title used previously (LGA Estimate). This applies to all the attribute names.

[Click to Enlarge]

[Click to Enlarge]

 

In the case of the EGM data the name changes are still fairly easy to interpret. These differences depend in part on the metadata provided by the data custodian. Any pop-up explanations of the data disappear as well.

[Click to Enlarge]

[Click to Enlarge]

 

Joining Data and Downloading

 

Now we need to join all three datasets together. This can be done by using the Tabular Inner Join tool

[Tools > Data Manipulation Tools > Tabular Inner Join]

When the three data sets have been joined, there are many columns. Because the area code and area name are added to the data set in each Spatialise operation, these appear many time. The important data is mixed in among these. Having joined these data sets together we can now download them for use in a spreadsheet (CSV) or a mapping product (SHP). This can be done either from the display window of the final joined data set:

[Click to Enlarge]

[Click to Enlarge]

 

Or from the control panel:

image0111

[Click to Enlarge]

In the case of a CSV download the product is a folder – similarly named to your data – containing the CSV files itself, which is easily opened in Excel, and a ‘.json’ file with some metadata. The latter is not important to most people.

[Click to Enlarge]

[Click to Enlarge]

 

In the case of an shapefile download you get a similarly named folder and the various files that make up an SHP coverage (all of these are vital for use in a GIS!).

[Click to Enlarge]

[Click to Enlarge]

Opening and using the external dataset

Once you have downloaded the shapefile (.shp), this can be used in an external GIS product, such as QGIS for processing, analysis and map-making (shown below)

[Click to Enlarge]

[Click to Enlarge]

Alternative you can do the mapping inside the AURIN Portal (as described in the Mapping tutorials).

Remember, you are not restricted to working with AURIN’s already aggregated data sets. You could upload you own attribute data, such as in a CSV (e.g. some LGA level crime statistics), or a shapefile (.shp), or work with non-aggregated data in the AURIN collection.

Point Join

We will now look at using a Point file of Liquor Outlets in Victoria. These data sets come with their own geometry, which need to be loaded with that attribute (the blue Geometry attribute) selected:

[Click to Enlarge]

[Click to Enlarge]

To view this on the map, you need to select ‘Visualise Geometry’ from the drop down box of the dataset:

[Click to Enlarge]

[Click to Enlarge]

This produces a new entry within Visualise your Data and you can then switch on the map:

[Click to Enlarge]

[Click to Enlarge]

In order to make some meaningful comparison of the availability of outlets and purchase or spending on alcohol, we need to determine how many of these 19,154 outlets are in each of our spatial units. In this case LGAs but we might be working with much smaller units within a municipality for example. To link the Point data to our spatial units (polygons) we use the Point Join tool.

[Click to Enlarge]

[Click to Enlarge]

 

 

We now have a table in which each Liquor Outlet has associated with it the purchasing and other data associated with the LGA in which the Outlet is situated.

[Click to Enlarge]

[Click to Enlarge]

This can be downloaded for further processing.

Importing to QGIS

Opening a shapefile in QGIS:

  1. Click on the Add Vector button
  2. Click File
  3. Browse to the dataset named yourfile.shp
  4. Click Open