Product name: Employment Clustering tool to evaluate the imactos of planned activity centres on Local Employment and Acessibility.
Primary users: Economists, Strategic Planners, Researchers
This project responds to a consensus among local policy makers, that Melbourne needs to adopt a multi-nodal metropolitan planning strategy in order to foster local economic development and reduce commuting. For decades, metropolitan planning strategies have sought to promote non-CBD centres in Melbourne. The tool further responds to a consensus among economic development planners that ABS data is insufficient to identify local urban clusters for analysis. We wish to understand whether spatial policies aimed at cluster development have actually resulted in employment clusters. This tool moves us toward examining those policies by providing a framework to identify whether and where local employment clusters have formed.
Our tool is comprised of a hierarchical clustering algorithm that makes use of three integrated sub-tools (A Spatial dissimilarity index tool, A Value chain tool, A Polygon splitting tool) , each of which provides various functions and analysis options. Further information relating to the technical specification is available from Sophie Sturup: email@example.com
Instructional Product Information:
To use the tool requires the user to logon to the AURIN portal via: https://apps.aurin.org.au/gate/index.html
Once in the portal follow the instructions:
– Find the dataset you wish to use. It will need to be a shapefile with all the files combined into a single .ZIP.
– Click (Data Cart)Upload> enter a title and abstract> browse to the zipped SHP> click Upload> click Add into Data Cart.
– Click Workflow> Employment Clustering> M Wards Clustering. Now fill out parameters and Click Add Workflow (An example interactive screen is shown below)
– In Data Cart, click the eye next to M Wards Clustering. Click Execute.
– The algorithm will start. It could take some time if you chose a large dataset…
– The message “Execution of workflow is successfully completed …” will eventually appear in the the Workflow Execution console.
– The data results will appear at the bottom of the Data Cart. Click the eye to view the results as a table.
– Create a choropleth clicking (Visualisations)Viz & Widget> Special Visualisations> Feature Colorist:
Dataset = Data result: Wards Clustering (the second one),
Attribute = wardclut,
Classifier = pre-classified,
Palette = qualitative,
Centroid = false,
– Click Add Visualisation, then click the eye next to Feature Colorist.
Product (or Product Components) Re-usability Information:
The code is licensed under creative commons.
The source code is available within the GitHub: https://github.com/AURIN/thirdparty-analytics
The authours of the tool hope that the tool will be useful to analysts interested in economic and spatial analysis in Melbourne, and also to urban planners revisioning melbourne Melbourne’s Metropolitan spatial plans. Our nect steo is to use the tool to analyse the effects of Melbourne’s sub centering strategies implemented since 1981. In particular the team are intereted in understandin whether industry clusters are occurring in the region, and if so, in which industries.