Tag: Waste

How I chanced on Longannet in the data

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I’ve added a “Household vs business waste” time-series to our map-oriented webapp from last week. The business data was parsed from SEPA’s Business Waste Data Tables.

When I watched the waste amounts change through time on this map, Fife’s amounts really stood out…​

Household vs business waste, thru time

Fife was generating so much more waste from business, than the other council areas. But why?

To look at the data in more detail, I loaded it into the data grid & graph tool that we built a couple of months ago.

First, I filtered the data grid to show me: Fife’s four largest, business wastes vs their averages link.

Fife’s four largest, business wastes vs their averages

Fife’s combustion waste stands out from the average.

Secondly, I filtered the data grid to show me: the business combustion waste quantities by sector link.

Business combustion wastes by sector

Unfortunately this data isn’t broken down by council area, but it clearly shows that most of the combustion wastes are generated by the power industry.

An internet search with this information – i.e. “Fife combustion power” – returns a page full of references to Longannet – the coal fuelled power station.

Longannet power station (courtesy of Scottish Power)

According to Wikipedia, Longannet power station was the 21st most polluting in Europe when it closed, so no wonder that its signature in the data is so obvious! It was closed on 24th March 2016, which correlates with the sharp return towards the average in 2016, of the combustion wastes graph line for Fife.

Of course this isn’t a real discovery – SEPA, Scottish Power and the people who lived around the power station will be very familiar with this data anomaly and its cause. But I think that its mildly interesting that a data lay person like me could discover this from looking at these simple data visualisations.

Waste quantities through time, on a map

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Preface

Shortly before the end of 2020, I attended the Code The City 21: Put Your City on the Map hack weekend which explored ideas for putting open data onto geographic maps.

It ran several interesting projects. There was one was especially inspiring to me: the Bioregion Dashboard. Its idea is to tell an evidence-backed story-through-the-years, involving interactive data displays against a map. James Littlejohn introduces it in this YouTube video.

This got me thinking about new ways to depict the information that is bound up in the data about waste…​

In particular, thinking about a means to convey at-a-glance, to the lay person, how councils areas compare through time in respect of the amounts of (household solid) waste that they process. Now, the grid & graph prototype that we built a couple of months back, conveys that same information very well (and with a greater fidelity than we will mange in this work) but, to the lay parson like me, it isn’t attention grabbing. I like seeing something with movement and with features that I can relate to, such as animated charts and a geographical map.

The prototype webapp

Leveraging what I learnt at the Code the City 21 hack weekend, I hacked together a prototype webapp that shows how waste quantities change through time, on a geographic map.

The below, animated image of the webapp, it conveys that landfilled-waste is reducing over time whilst total-waste is remaining fairly constant.

Managed solid waste, through time

UI controls

  • The dataset of interest is chosen through the dropdown control, either:
    1. Tonnes of managed solid household waste per person per year.
    2. Tonnes of C02 equivalent from household waste per person per year.
  • Use the slider control to travel through time.
  • Each pie chart depicts the waste-related quantities for a council area.
    • The sizes of its slices and its overall size, are related to the quantities that it depicts.
  • Hover over a council area to see detailed metrics in the detail panel.
  • The usual map zoom and pan controls are supported.

Software and datasets

CO2 equivalent

‘Live’ instance

A ‘live’ instance of this webapp can be accessed here .

Closing thoughts

I haven’t seen these datasets about waste shown in this way before, and I think that it usefully conveys aspects of the datasets in a catchy and easy to understand way. It is low fidelity when compared to a full data grid with graph solution, but the idea is to hold the attention of the average person in the street.

Future work could integrate additional waste-relevant datasets that have geography and time dimensions. Also we should consider alternative metrics (such as ratios), alternative charts (such as bar or polar) and alternative statistics (such as deviation or trend). I went with the ‘most straightforward’ but user-testing might indicate that an alternative is better.

Mocking-up features in a placeholder WCS web application

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The narratives in Anna and Hannah’s “Scenarios” document, tantalise with mentions of the features supported by their fictional Waste Commons Scotland (WCS) web application. This week, mocked versions of some of those features have been added to the placeholder WCS web application (source code) – with the idea that their animation will make the features easier to understand and assess. 

Stirling Council’s waste-management dataset as linked open data 

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Kudos to Stirling Council for being the only Scottish local authority to have published household waste collection data as open data. This data is contained in their waste-management dataset. It consists of: 

  • Core data, per year CSV files.  
  • Metadata that includes a basic schema for the CSV files, maintenance information and a descriptive narrative. 

For that, Stirling Council have attained 3 stars on this openness measure.  

To reach 5 stars, that data would have to be turned into linked open data, i.e. gain the following: 

  • URIs denoting things. E.g. have a URI for each waste type, each collection route and each measurement. 
  • Links to other data to provide context. E.g. reference commonly accepted identifiers/URIs for dates, waste types and route geographies. 

This week I investigated aspects of what would be involved in gaining those extra two stars. 

This executable notebook steps through the nitty-gritty of doing that. The steps include: 

  1. Mapping the data into the vocabulary for the statistical data cube structure – as defined by the W3C and used by the Scottish government’s statistic office. 
  2. Mapping the date values to the date-time related vocabulary – as defined by the UK government. 
  3. Defining placeholder vocabularies for waste type and collection routes. Future work would be to: map waste types to (possibly “rolled-up” values) in a SEPA defined vocabulary; and map collection routes to a suitable geographic vocabulary. 
  4. Converting the CSV source data into RDF data in accordance to the above mappings. This results in a set of .ttl – RDF Turtle syntax – files.  
  5. Loading the .ttl files into a triplestore database so that their linked data graph can be queried easily. 
  6. Running a few SPARQL queries against the triplestore to sanity-check the linked data graph. 
  7. Creating an example infographic (showing the downward trend in missing bins) from the linked data graph:  

 Conclusions 

  • It took a not insignificant amount of consideration to convert the 3-star non-linked data to (almost) 5-star linked data. But I expect that the effort involved will tail off if we similarly converted further datasets, because of the experience and knowledge gained along the way. 
  • Having a linked data version of the waste-management dataset promises to make its information more explicit and more compostable. But for the benefits to be fully realised, more cross-linking needs to be carried out. In particular, we need to map waste types to a common (say, SEPA controlled) vocabulary; and map collection routes to a common geographic vocabulary. 
  • We might imagine that if such a linked dataset were to be published & maintained – with other local authorities contributing data into it – then SEPA would be able to directly and constantly harvest its information so, making period report preparation unnecessary.  
  • JimT and I have discussed how the Open Data Phase2 project might push for the publication of linked open data about waste, using common vocabularies, and how our Data Commons Project could aim to fuel its user interface using that linked open data. In order words, the linked open data layer is where the two project meet.