Richard Burton Watson
ABSTRACT
This paper describes a set of waste management Application Programming Interfaces (APIs) written in the python language and using the Pandas, NumPy, Matplotlib, Basemap, Haversine and other big data analytics libraries. These access open datasets provided by the City of Wyndham, in Melbourne, Australia's western suburbs and stored on the Australian government's open data portal. These APIs read the data and process it to make it more useful to stakeholders including council administrators, waste management contractors and the general public. They provide visualization of the data in the form of plots of smart bin locations and fullnesses on maps accessed from Esri's ArcGIS API; bar charts of the frequency of fullness levels for both individual bins and all bins; and line charts of the fullness levels of a specified bin over time. The routes which can be followed by waste collection trucks are also given in terms of the legs from one bin to the next, specified in Javascript Object Notation (JSON) and also plotted on city street maps. These form heuristic solutions to the waste collection vehicle routing problem. The code used in the APIs is potentially transferable to analyses of data from other smart bin systems and other local government areas.
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Index Terms
- Visualization and Waste Collection Route Heuristics of Smart Bins Data using Python Big Data Analytics
Index terms have been assigned to the content through auto-classification.
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