Visualising the value of the NBN - calculated with Observable

A University of Melbourne training on Interactive Data Visualisation was held on 17,19 April, 2018.

Observable is a remarkable tool that combines, html control elements, data visualisation and javascript calculations to produce interactive, attractive and reactive calculations and visualisations. See my Observable NBN model here.

See an interesting Observable model of a spinning globe and another(using D3).
See my simpler version here:

Documenting the Observable Model

A 5 page explanation of the model is available at Figshare:

Sample screenshots

Model with sliders

Figure 1. Financial Graph from Model (v1)

Model with sliders

Figure 2. Cashflow Graph, Summary values and Model Sliders (Final Model)

Calculating the value of the NBN

I used Observable to make a dynamic, interactive model to calculate the value of NBN. I used html sliders to control various assumptions, such as mobile vs fixed broadband use, % of gigabit users, % debt repaid per year.

See Observable and training material, from the Unimelb course, at:

See sample Heatmap from Observable output

Link to full page.

Visualising with Plotly - Where are Australian jobs growing?

A University of Melbourne training on Plotly.js was held on 22-23 March, 2017. See summary notes here - The focus was on using the Plotly javascript library to enhance research output. Focus on interactivity, through buttons, rollovers were useful. Loading csv files were especially important for me. I loaded two of my datasets below.

See DataVis at:

The 1st dataset compares 4K Hisense TV prices, by size, by retail vs RRP, by model. Data …is in Source.

The 2nd dataset looks at World GDP over 2,000 years as a bubble chart

This 3rd dataset looks at regional growth of employees, from 2002 to 2014. Regional growth is shown as a % change over four years.

See a major output below.
Data comes from: Figshare: see Full Screen

Making a shareable timeline

New #datavis at: My NBN Timeline: Feb-July 2017.

To make your own timeline, create a Google Sheet with details; Start here:

NBN Speedtest - 4-8 Aug 2017

My new NBN link: Telecube HFC 50/20 - tested 8.00pm Fri to 8.00am Mon
New #datavis at: (including data).

Valuing FTTN FTTP - Aster plot - multidimensional and weighted

Creating a visualisation of FTTN vs FTTP, using an Aster Plot.
Strengths: An Aster plot is multi-dimensional like a radar plot, but can assign a weighting to dimensions.
Weakness: An Aster plot can only show one Value target at a time. Perhaps I can overlay with photoshop…
Update: below I placed two Aster plots side by side, using iframes inside an html table…

Data: (Author’s rating of FTTN FTTP value dimensions)

Content at:

To amend the data, and play with the Aster Chart, you need the following files:
Download the files at:
style.css |
Data: aster_dataFTTN.csv | aster_dataFTTP.csv
Javascript: drawFTTN.js | drawFTTP.js
Html: indexFTTN.html | indexFTTP.html
Table to draw together two html files into a table with iframe: valueFlower.html (html in this page; View Source)

Discussed at:; Rating of value dimensions in Table 1.
Aster diagram; multidimensional, dimensions vary in weight; scores 0 (Max.) - 100 (Min.)
Following a reviewer’s comment^^, the scale is now revised to: scores 0 (Min) - 100 (Max). Items starred(^^) below now reversed.

  • Closer to centre indicates less^^ value.
  • Higher^^ score (in bullseye) indicates more value.
      Key: Dimensions (weight):

      CAPEX (25)[Red]
      Future CAPEX (10)
      Time to rollout (25)
      Life of Asset (25)
      OPEX / Revenue (20)
      Current need (10)
      Future need (25)
      Reliability (30)
      Equity (10)
      Simplicity (5)
      Beauty (5)
      Unknown Unknowns(25)[Blue]
      NB: closer to the bullseye is lower^^ value
      Data: weightings and ratings are author's assessment.
      Bullseye rating is summary overall; the higher^^ the better.

Valuing FTTN FTTP - radar map comparison

Creating a visualisation of FTTN vs FTTP, using a Radar Map.

Content at:

Discussed at:

NB: Yellow - FTTN on a number of dimensions;
NB: Red - FTTP on a number of dimensions;
Now with a reversed scale, per reviewer.
Original: greater value closer to centre.
Amended: greater value closer to edge, greater area.

Future: update to graph showing weightings on significance of each dimension. See for instance Aster Plot here.

Pulsar Voices

Background / Goal

Pulsar data is available from ATNF at Our team at #SciHackMelb has been working on a #datavis to give researchers and others a novel way to explore the Pulsar corpus, especially through the sound of the frequencies at which the Pulsars emit pulses.

Source data:

The Team:

  • Tony - web dev, php, JavaScript, SQL, phpmyadmin, CDs, html, visualising pulsar
  • Gary - python tech, visualising sound of a single pulsar
  • Chris - data manager, accessing CSIRO pulsar data and manipulating it to a useful format
  • Anderson - SQL
  • Michael - transforming pulsar coordinates, azimuthal to cartesian
  • Richard - data analyst, datavis advisor, data management
  • AAD - R analysis of pulsar distance, frequency distribution
  • Advisors: CSIRO Jess Robertson (focus on simple, doable outcome), AAD Dr Ben Raymond


  • RA - east/west coordinates (0 - 24 hrs, roughly equates to longitude)
  • Dec - north/south coordinates (-90, +90 roughly equates to latitude i.e. 90 is above north pole, and -90 south pole)
  • P0 - the time in seconds that a pulsar repeats its signal
  • f - 1/P0 which ranges from 700 cycles per sec, to some which pulses which occur every few seconds
  • kps - distance from Earth in kilo-parsecs. 1 kps = 3,000 light years. The furthest data is 30 kps. The galactic centre is about 25,000 light years away i.e. about 8kps.
  • See description at: figshare: DOI: 10.6084/m9.figshare.3084748.v1


We plotted RA, Dec on a rectangular screen roughly to see pulsar location. One of us, Gary, worked on one pulsar data, turning the frequency into sound. A graph shows the variation in pulse between pulses. Another of the team, piotted a histogram of the range of pulsar frequencies, which shows a nice bi-modal distribution. Why is it bi-modal, we will have to ask a pulsar scientist.

What next, still to do

  • load data, description, images fileset to figshare :: DOI 16.3.16 ; DONE
  • add overview images as option eg frequency bi-modal histogram
  • colour code pulsars by distance; DONE
  • add pulsar detail sound to Top three Observants; 16 pulsars processed but not loaded
  • add tones to pulsars to indicate f; DONE
  • add tooltips to show location, distance, frequency, name; DONE
  • add title and description; DONE
  • project data onto a planetarium dome with interaction to play pulsar frequencies; DONE kind of, see below
  • zoom into parts of sky to get separation between close data points; DONE kind of, see below.
  • set upper and lower tone boundaries, so tones aren’t annoying
  • colour code pulsars by frequency bins e.g. >100 Hz, 10 - 100, 1 - 10, <1 Hz

Projected Pulsar location to Google Maps / Google Earth

How to turn Pulsar data into Google map/Google Earth;

Google Drive -> Add new Google Map -> Import -> psrcatSparse.csv -> Add Title, description

Google Maps; -> Login -> Menu -> My Maps -> Select Pulsar Map -> See Map -> Click Earth (bottom left corner). Data projected onto Google Earth. -> Collapse Side Panel

Can rotate, zoom in/out. To see data on map marker; view -> expand side panel. Doesn’t seem any way to share Google Earth view, but can share Map view.

See Google Map Status of map: Public on the web - Anyone on the Internet can find and view

Pulsars projected onto Google Earth. NB: now noted that Google capped data upload at 2,000 rows. Missing points over Atlantic are 296 missing data points. See Google Map link above for reworked data.