We have written a research report which walks through how we might build linked open data (LoD) about carbon savings from dissimilar data sources.
It outlines (using small samples from the datasets) how the data pipeline that feeds our prototype-6 webapp, works.
What do households put into their bins and and how appropriate are their disposal decisions?
To help provide an answer to that question, Zero Waste Scotland (ZWS) occasionally asks each of the 32 Scottish councils to sample their bin collections and to analyse their content. This compositional analysis uncovers the types and weights of the disposed of materials, and assesses the appropriateness of the disposal decisions (i.e. was it put into the right bin?).
Laudably, ZWS is considering publishing this data as open data. Click on the image below to see a web page that is based on an anonymised subset of this data.
We have bought the domain name
wastemattersscotland.org for the waste data website that we are developing.
At the time of writing, https://wastemattersscotland.org is being redirected to our latest prototype
prototype-6 – as can be seen in the screen shot below.
With Glasgow City hosting the UN Climate Change conference (COP26) later this year, it was appropriate that this year’s The Data Lab data analysis hackathon (held last week) had the theme “pollution reduction”.
Three organisations provided challenge projects for the hackathon teams: we provided a “waste management” project based on our easier-to-use datasets; Code the City provided an “air quality” project; and Scottish Power an “electric vehicle charging” project.
The hackathon was lead by a young Scottish tech start-up company called Filament. They have an interesting product that is basically a sharable, cloud-hosted Jupyter Notebook.
Each day a new cohort of teams would tackle the project challenges. We helped by answering their questions about our datasets, and by suggesting ideas for investigation.
At the end of each day the teams presented their findings.
It was informative to see how the teams (each with a mix of skills that included programming, data analysis and business acumen) organised themselves for group working, handled the data, and applied learned analysis techniques.
The teams had a relatively short amount of time to work on their projects so having easy to use datasets was a deciding factor in how much they could achieve. Therefore one take-away is clear, and helps substantiate an aim of our DCS project… open data needs to be easy to use, not just be accessible. Making data easier to use for non-experts, opens it to a much wider audience and to much more creativity.
“Trialling Wikibase for our data layer” described how we evaluated the use of Wikibase as a key implementation component in our bi-layer architecture. The conclusion was that Wikibase, although a brilliant product, does not fit our immediate purpose.
In our revised architecture…
Wikibase is replaced with (dcs-easier-open-data) a simple set of data files (CSV and JSON) hosted in a public repository (GitHub). These data files are generated by the Waste Data Tool (dcs-wdt). Together,
dcs-wdt implement the architecture’s data layer.
In the architecture’s revised presentation layer, the webapp reads (CSV/JSON formatted) data from the dcs-easier-open-data respository, instead of reading (via SPARQL) data from the Wikibase.
Stirling Council set a precedent by being the first (and still only) Scottish local authority to have published open data about their bin collection of household waste.
The council are currently working on increasing the fidelity of this dataset, e.g. by adding spatial data to describe collection routes. However, we can still squeeze from its current version, several interesting pieces of information. For details, visit the Stirling bin collection page on our website mockup.