A prototype data grid & graph over data about waste

The working prototype can be accessed via this link.

The prototype pulls together 4 datasets:

1. “Generation and Management of Household Waste” (SEPA).
2. “Carbon footprint [CO2e]” (SEPA)..
3. “Population Estimates (Current Geographic Boundaries)” (NRS).
4. “Mid-Year Household Estimates” (NRS).

The datasets are fetched from statistics.gov.scot and Wikidata, using SPARQL; then matched; and finally, the per-citizen and per-household values are calculated.

The result is 17,490 data records.

The data was assembled using this executable Jupyter notebook. For a production-class implementation, that could easily be coded as automated, periodic process.

The web app containing the interactive data grid with a linked graph, was built using the DevExtreme web component library. Alternative libraries are viable, but the DevExtreme one is modern and free for non-commercial use.

The resulting data assembly and web app are stored as static files in the project’s GitHub repositories.

The prototype’s web page contains a graph and a configurable data grid. The graph automatically reflects the data selected in the data grid.

Detailed information about a graph’s data point is shown when the user hovers over it with the cursor.

The graph can be zoomed/unzoomed, and its current contents can be printed or saved as PNG, PDF, etc.

The data grid’s expand/collapse arrow-head icons allow the user to drilldown into slices of data. Below, we’ve expanded the Stirling → Recycled slice to reveal the data values per-material.

The data grid’s “Show Filed Chooser” icon pops up a control panel to allow the user to select data dimensions, axis assignments, value ranges, value filters, display order, etc., etc.

The data grid’s “Export to Excel file” icon will export the grid’s currently selected data to an Excel spreadsheet.

The resulting Excel files are nice because the export functionality preserves user-friendly fixed headers and some other formatting.

Finally, the prototype operates well on phones and tablets (although there is a sizing issue with pop-up panels that I haven’t investigated).

So, might (a production-class version of) such a tool, help our users to explore and understand open data about waste? Well, we won’t know until we have user tested it, but my guess is that:

1. users with no data analysis experience will find its configurability difficult to navigate.
2. users with low-to-medium data analysis experience may find it a useful as a single tool containing multiple datasets.
3. users with medium-to-high data analysis experience will prefer to use their own tools.

A presets feature has been added to the tool so that users can go to a particular configuration & data selection by simply clicking on a hyperlink. This supports an easy-access route to the tool for users with no data analysis experience, by answering their potential questions through presets such as:

1. How does Aberdeen City compare with Dundee (and Scotland as a whole) for the amounts of household waste per citizen that it landfills?
2. How many tonnes of each household waste material ended up recycled, landfilled, etc. in Stirling in 2018?
3. What proportion of a tonne of household waste has ended up recycled, landfilled, etc. in Edinburgh through the years?
4. What does the correlation look like between the amounts of household waste solids and their calculated carbon impacts?