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Everyday IoT: Tank Water Levels

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A few years ago I moved house and became the owner of a nice backyard with a garden.  However with early arrival of summer, I quickly discovered the challenge of keeping everything alive!  As holidays and heat-waves approached it became pretty clear we needed an automated watering, and one that could keep track of tank levels to avoid running the tank dry and damaging the water pump. Add to that the fact I'm always out to save a buck, I was inspired to do a little IoT project... Final set-up, sorry about the spoilers I iterated on this idea for quite a while with ideas ranging from full computer controlled watering system to something much simpler.  In the end I decided my requirements were: Have a watering system with an automatic timer Be able to switch off the pump remotely  Be able to check tank levels remotely I eventually realised that two of these would be much easier (and probably no more expensive) to buy as off-the-shelf components but there was still and IoT proje
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On your bike... to where?

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The great bike rush of 2020 is in full swing! COVID-19 restrictions means bike shops are sold out and there's a huge increase in cycling everywhere... or so media (social and otherwise) would have you believe, but I haven't seen any details on what is actually happening. So as a cyclist and data scientist I thought I would try and find out. I wrote a deconstruction of the reported 700% increase in cycling in Cycling, the new COVID-19 side effect? where found some serious sampling bias issues and proposed a more realistic results of 10-150% increase on weekends and a reduction on weekday at most location. While far more robust (in my option anyway) it still did not provide extensive coverage and did not look at changes in where people were riding. So here we are for round 2 so lets try and make it slightly scientific. My hypothesis (theory of what's happening) is that over the lockdown period: weekday cycle counts will be lower and weekend counts remain unchanged or higher,
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Cycling, the new COVID-19 side effect?

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I've been reading a bit about the increased number of people cycling while the world is in lockdown but last week there were a few articles about the bicycle numbers in Melbourne that I was a little dubious about. So I did what no sane person does, I dived into the data... The article ( Car parks out, footpaths and cycling lanes in as city prepares for post-COVID commuters ) in The Age on 7th May 2020 cited cycling growth figures of 138% to 778% which seemed a little high to be believable. The figures apparently came from an article and report by the Bicycle Network detailing changes in cycling activity based on surveys they had conducted. Now firstly, I couldn't find matching figures in the report for all the sites listed in The Age article, but more importantly I had a few issues with the data being used for this kind of analysis. Bicycle network published the data from 4 surveys, one on Sunday 10th of November 2019 and three in April 2020 on a Wednesday, a
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Graph makeover: Pie crashes

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I noticed a post on linkedIn about a published report with a nice looking chart... that I couldn't really make sense of despite working in the industry for 13 years. So I opened the larger version and looked harder. While it didn't take too long to understand there was a lot of interpretation required and I think I decided it would be a good example for a makeover before I had even finished reading it. And yes, I totally stole the idea of doing a graph makeover . Chart makeover: left - original, right - makeover Ok so firstly, pie charts are generally avoided by the data visualisation community, perhaps most famously by Stephen Few , and for good reason, they are notoriously hard to read and interpret with only a few exceptions. The three rules of Pie I use and recommend are: Don't use a pie chart Don't use a pie chart Try using another chart first (FYI, the main exceptions to the rule are when you only 2-3 catagories and they are of very different sizes.
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Data Visualisation Challenge: Airline Holiday Travel

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I recently spend about 8 weeks as a public transport commuter rather than cycle commuter and had the opportunity to listen to a lot of podcasts (some of which I've already posted about ). One that I really enjoyed was Story Telling With Data which is where I heard about the monthly challenge that Cole runs. The December challenge was to make a holiday themed visualisation so I decided to investigate how travel patterns change in the holiday period, which for Australia is about a quarter to half of December and all of January. The data set I found was for international departures from Australia however the destinations appear to be only to the first stop of the flight. Unfortunately the data did not include spatial information for the airports or the standard airport codes so I had to do some wrangling to make everything work. This and the fact it took me a while to draw out the interesting (and communicable) insights meant that I well and truly missed the challenge deadlin
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Know your bias

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xkcd: Survivorship Bias Are you biased ? Would you know if you were? Statistics views biases as something to be avoided or corrected, and for good reason, because statistics typically seeks to use a sample to represent an entire 'population'. But I recently heard a new take on it which advocated that you should use your bias in doing data analysis, data visualisations and decision making. In order to use your biases the first thing to know is that everyone is biased due to their differing circumstances, and of course being human. The idea is that a person will have a unique point of view which is valuable, but that it won't be possible to fully understand others point of view or how that point of view will affect others. Therefore the key is collaborating with others that have different biases, with some level of understand of your biases and those of your collaborators. In many ways this is no different to the statistical approach but rather than trying to find
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Victorian Property Overlay Map

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My wife and I are currently house hunting and this will be the first house I've ever bought so I'm a little nervous about not messing it up (perfectionist and all that). So when we find a property we're interested in I dig into the details by checking for easements, looking at the drainage and checking planning overlays since I know a little about these things from my time in a Local Government engineering department many moons ago. Anyway it turns out that a lot of these things are available as open data, for the whole of Victoria! So to make my life easier I put together a little web app that also works on mobile devices for when you are actually at an inspection. Victorian Property Info Having recently built the Cycle Melbourne map , this one was super quick to put together. I used the same layout, almost the same background maps and just added 4 WMS map layers from land.vic.gov.au . The Leaflet Extras package also allowed me to add a search function and a
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