Background

My hiking companion and I recently completed the Overland Track in Tasmania, and they posted a picture of our packs on a related group on Facebook. There was an overwhelming response, ranging from good job! to you’re a liar or you surely didn’t have a tent or you must be on a tour and didn’t bring food.

I can understand the skepticism. Upon inspecting what people brought, and never used, there is definitely a preference for people to pack their fears. Considering this track seemed to be the first time many people have done a multi-day backpacking trip, there were a lot of things they likely would not pack after gaining a little more experience.

The consequences were very real. Most people had knee or foot problems by the time they reached Narcissus Hut, and I was one of only a few people able to hike out (~18km) when the ferry was cancelled (made it in about 3hr 45min and made my transport). One of my more lasting memories from the hike was stumbling on a couple hiking and one of the people being unable to cross a fallen tree because their bag was too heavy, and their partner had to help push them up.

The track was likely especially scary for newcomers given the weather we encountered was “the worst so far this season”, according to our track transport. A week before we went the weather was supposed to be six days of glorious 5 - 10 C temperatures with only a little drizzle. The night before we flew out of Sydney it was forecasted to snow 1 - 2 mm one of the days. The actual weather was non-stop rain or snow, temperatures ranging from -2 to 3 C, and we only saw blue sky once. Once. I don’t remember ever seeing the sun from start-to-finish. It felt like we spent more time walking in streams or mud than on actual dry soil.

The Overland Track. Plan ahead and say goodbye to mobile reception.

Given we encountered just about the worst the trail could throw at us outside of winter, how do we know we brought just the right amount of stuff?

1. We were one of only two parties from our van transport that even stayed in our tent
2. We never shivered a single time, nor did we think we were ever in any danger
3. The ferry was cancelled when we rolled in to Narcissus Hut, so 16 extra people had to stay overnight; about five of them didn’t have enough food, and we were the ones giving them food because we had plenty of food remaining (that said, we were also 1 1/2 days ahead of schedule)
4. People routinely borrowed our lighter, as the piezo on their stove was broken
5. We had hot meals every night, coffee twice a day, and still had fuel left over
6. We let three people charge their phones at NH so they could sort out travel arrangements, as they didn’t have a spare power bank

Suggestion From What We Saw

Here’s a list of things that we saw people bring, but with suggested substitutes that would reduce overall weight while not reducing safety or comfort. You might think “that’s only 50g savings”, but it all adds up. We had by far the lightest bags with a total pack weight of around 8 kg (with 1 litre of water), and the heaviest in our van was 23kg. Most were around 15kg.

Camp Shoes

Seen Brought: 2nd pair of sneakers for camp shoes

Better: Crocs/Flip-Flops or hotel slippers

Best: Plastic bags

A lot of people brought a 2nd pair of sneakers just for walking around the huts. Many people were a little more wise and brought a lighter pair of Crocs (good for socks, but mine weight 349g) or flip-flops (mine weigh 155g), but I’d argue that hotel slippers (mine weigh just 39g) serve the same purpose. Or bring two bread bags and when you get to the hut take off your wet socks, put them immediately on or near the heater, put on your sleep socks, and put on bread bags on top of them. You can then wear your wet shoes without getting your sleep socks wet.

Fresh Fruit / Veggies

Seen Brought: Fresh Fruit/Veggies

Better: Dried fruit / dehydrated veggies

Best: None

Feel free to bring a fresh apple for lunch the first day, but fresh fruit is extremely heavy for the calories they provide. We saw people four days in giving away cucumbers / zucchini. To put this in perspective, 100g of cucumbers have 72 calories vs. 100g of peanut m&m have 516 calories.

Tinned Food

Seen Brought: Cans of tuna

Better: Starkist tuna packets

Best: Jerkey or biltong or just nuts or peanut butter m&m

On my first multi-day hike several years ago with this hiking companion my shopping instructions where: if it has to cook, then it needs to be able to be done in less than 3 minutes, and no cans or jars of anything. Bringing in a can of tuna, which isn’t even that calorie dense to begin with, means you have to keep carrying that tin your entire hike. If you must bring in a tuna packet, but do a little research, as you can save significant weight by paying attention to the food you bring. Please see Skurka’s post for some overall tips, and then over at Greenbelly for some actual food/weight breakdown. Please see below for our food breakdown. We deviated a little bit in what we ended up buying in TAS (e.g. no banana chips), but it was plenty. If you have the time, and like planning, then consider doing the same. Alternatively, and this is the guideline I follow if I do not intend to do much planning, then try to buy food at Woolies or Coles that is as close to 2000kJ per 100g as possible.

Initial food planning for two

Tools / Kitchen Stuff

Seen Brought: Small cast iron skillet, hunting knives

Better: Not a cast iron skillet

A lot of people were cooking pretty elaborate meals, which is pretty impressive. They also brought four pans and three canisters of fuel. I hesitate making recommendations on food, but I’d probably suggest getting some dehydrated meals from Snowys a few weeks in advance, and you won’t need all those pots and pans. Another nice thing is you can use the package as a container, so one less bowl to bring and clean. Bring one spoon with a long handle (sporks might sound nice, but if you’re eating cous cous or something small, then you can’t easily scoop it up, and I would hesitate that you might pop a hole in a dehydrated bag).

Duplicate Clothing / Cotton

Seen Brought: “I’m wearing four fleeces” or duplicates of every item

Best: Skurka’s Core 13

TAS Parks provides a list of minimum gear that you need to bring, but I don’t think you need to bring more than what is on it. Even better would be to read the article by Andrew Skurka on the Core 13 items he suggests you bring. We surprisingly saw quite a bit of cotton shirts / pants being worn, which was a surprise.

Big Trowel

Seen Brought: Metal garden trowels

Better: Deuce of spaces

Best: Nada

I typically carry a ‘deuce of spades’ on any overnight, but in the case of the Overland, if doing it again, I’d probably skip bringing it. There are toilets at every site, and the ground was pretty moist, so digging a cat hold wouldn’t be a problem.

Water

Seen Brought: People carrying 5L of water

Better: 2L

Best: 1L + Sawyer Squeeze (filter)

When we went there was water everywhere, like, it felt like most of our time was walking in streams. If you aren’t in a stream, then you are no more than 1km away from crossing some stream. Our van driver / ex-guide said she didn’t filter often, but for some reason I have a fear of water, so I tend to always filter unless high up in the mountains. I brought a 1L balance water bottle and a filter, and never once needed more than that. Most of the time I just filtered at the huts and filled up there.

First Aid Kits

Seen Brought: 1kg kits from Big W

Better: make it yourself

It seems like quite a few people thought “huh, I need a FAK, I’ll get the next one I see” and end up with something that has a million bandaids and big gauze pads, but nothing you actually need. You can see what is in my FAK, which probably still has too many wipes, but I can deal with the most common issues: blisters, and soreness. It weights about 60g / 2oz.

Books

Seen Brought: several hardback books

Better: Kindles

Best: skip books and chat with people or Audible

One lady opened her bag and pulled out multiple books, read for 20 minutes, then chatted with people. Bring a kindle. Or realise it is only 4 - 6 days, and leave the books at home and chat with people. I tend to load up my phone with books on Audible or podcasts.

We Wish We Brought…

You can read above that we had planned more for the experience than most people, and we have done several other multi-day hikes previous. I am immensely glad I read one of Ray Jardin’s books back in ~2002 to learn how to prepare for backpacking and stop packing my fears.

There wasn’t much we wish we would have brought, except for perhaps some type of mittens that would have blocked the wind. When hiking on the ridges the temperature ended up dropping significantly, and when combined with the wind, it made my hands quite cold. We kept moving and that kept us generating heat, but stopping on a ridge would have been uncomfortable.

I recently purchased an Echo from Amazon, and we were contemplating how else to better integrate it with our somewhat minimalistic home. I thought it would be interesting to get it to link to a WiFi-enabled power outlet, but unfortunately they are pretty expensive in Australia.

Then I stumbled across the Sonoff devices by Itead, and learned that they were somewhat hackable via a custom firmware. Coincidentally I received the two devices on the same day my daughter was off sick, so when she had her nap, I got hacking.

The first bottleneck was discovering that the units I received did not have any headers. A little quick soldering later, and we had headers.

No headers mom :(

Now we have headers!

A few notes of warning: the $2 programmer I got from AliExpress has 3.3v and 5v, but the output is 5v. I’m glad I measured it with my multimeter, and used a random 3.3v breadboard supply instead.

In hindsight I wish I had just purchased the FTDI programmer from Itead. It looks pretty neat.

After following the rest of the Tasmoto hardware instructions, and then the PlatformIO instructions, I was able to successfully flash both my units with the custom firmware.

I then created a Lambda function that sends a signal to CloudMQTT, and connected the two devices.

Voila!

While in Myanmar on a recent trip I did a brief taste comparison of the three main beers available in most supermarkets.

Andaman - Not to my taste, perhaps like XXXX, VB, Natural Light, or a light Steel Reserve.
Myanmar - Quite refreshing, a bit like similar beers in the region, e.g. Chang, Tiger, or Laos Beer.

ABC - An extra stout (and 8%!) in such a hot country? That’s a surprise.

I first used Splunk about 10 years ago after an old colleague installed it on a computer in the corner, and ever since then I have preached about it. If you have log data, of any kind, I’d recommend you give it a go.

The Splunk people have a a few pretty good options for trying Splunk out, as you can either use Splunk Storm or Splunk Free. The first option is obviously hosted, and has a generous storage option, but also does not allow long term storage of data. I send system log data to Splunk Storm.

However, what if you don’t have a lot of data, but you want to keep that data forever? After reading Ed Hunsinger’s Go Splunk Yourself entry about using it for Quantified Self data, I knew I had to do the same.

From personal experience, Splunk requires at least 1GB to even start. You can probably get it to run on less, but I haven’t had much success. This leaves two options: look at Low End Box for a VPS with enough memory (as cheap as $5/month), of use OpenShift. Red Hat generously provides three “gears” to host applications, for free, and each with 1GB of memory. I have sort of a love-hate relationship with OpenShift, maybe a bit like using OAuth. Red Hat calls OpenShift the “Open Hybrid Cloud Application Platform”, and I can attest that it is really this. They have provided a method to bundle an application stack and push it into production without needing to fuss about infrastructure, or even provisioning and management of the application. It feels like what would happen if Google App Engine and Amazon’s EC2 had a child. Heroku or dotCloud might be its closest alternatives.

Anyways, this isn’t a review of OpenShift, although it would be a positive review, but instead on how to use OpenShift to host Splunk. I first installed Splunk in a gear using Nginx as a proxy, and it worked. However, this felt overly complex, and after one of my colleagues started working on installing Splunk in a cartridge, I eventually agreed this would be the way to go. The result was a Splunk cartridge that can be installed inside any existing gear. Here are the instructions; you need an OpenShift account, obviously. The install should take less than ten clicks of your mouse, and one copy/paste.

From the cartridge’s GitHub README:

1. Create an Application based on existing web framework. If in doubt, just pick “Do-It-Yourself 0.1” or “Python 2.7”
2. Click on “Continue to the application overview page.”
3. On the Application page, click on “Or, see the entire list of cartridges you can add”.
4. Under “Install your own cartridge” enter the following URL: https://raw.github.com/kelvinn/openshift-splunk-cartridge/master/metadata/manifest.yml
5. Next and Add Cartrdige. Wait a few minutes for Splunk to download and install.
6. Logon to Splunk at: https://your-app.rhcloud.com/ui

More details can be read on the cartridge’s GitHub page, and I would especially direct you to the limitations of this configuration. This will all stop working if Splunk makes the installer file unavailable, but I will deal with that when the time comes. Feel free to alert me if this happens.

I was talking to a friend about what education levels might look like across Sydney, and a friend challenged me to map it.

The map was derived by combining three datasets from the Australian Bureau of Statistics (ABS - a department releasing some great datasets). The first dataset was the spatial data for “SA2” level boundaries, the second the population data for various geographic areas, and the third from the 2011 Census on Non-School Qualification Level of Education (e.g. Certificates, Diplomas, Masters, Doctorates). I aggregated all people with bachelors or higher in an SA2 region, and then divided that number by the total number of people in that region. A different methodology could have been used.

EDIT: I should have paid more attention to mapping education levels. I mapped the percentage of overall population, but should have mapped the percentage of 25 to 34 year olds, as this would have aligned to various government metrics.

Reported education levels differ vastly by region, e.g. “North Sydney - Lavender Bay” (40%) vs. “Bidwell - Hebersham - Emerton” (3%). It is interesting to look at the different urban density levels of the areas, as well as the commute times to the nearest centre.

Without trying to sound too elitist, I was hoping to use this map to guide me where to consider moving (i.e. looking for a well educated, clean area with decent schools and frequent public transport). It was interesting to discover that the SA2 region I currently live in has the second highest percentage in NSW.

EDIT 12-03-2025: I accidentially broke the maps when deleting my AWS account, as the mbtiles were hosted there. Oops.

In Sydney Commute Times Mapped Part 1 I took a small step to a bigger goal of mashing together public transport in Sydney, and the Metropolitan Strategy for Sydney to 2031. The question I wanted to answer is this: how aligned is Sydney’s public transport infrastructure and the Metropolitan Strategy’s of a “city of cities”?

I decided to find out.

Thanks to the release of GTFS data by 131500 it is possible to visualise how long it takes via public transport to commute to the nearest “centre”.

Cities and Corridors - Metropolitan Strategy for Sydney to 2031

The Australian Bureau of Statistics collects data based on “mesh blocks”, or roughly an area containing roughly 50 dwellings. Last week I had some fun mapping the mesh blocks, as well as looking at Sydney’s urban densities. These mesh blocks are a good size to look at for calculating commute times.

The simplified process I used was this, for the technical minded:

1. Calculate the centre of each mesh block
2. Calculate the commute time via public transport from each block to every “centre” (using 131500’s GTFS and OpenTripPlanner’s Analyst tool)
3. Import times in a database, calculate lowest commute time to each centre
4. Visualise in TileMill
5. Serve tiles in TileStache and visualise with Leaflet

The first map I created was simply to indicate how long it would take to the nearest centre. There appears to be rapidly poorer accessibility on the fringe of Sydney. I was also surprised of what appears to be a belt of higher times between Wetherill Park and all the way to Marrickville. There also appears to be poorer accessibility in parts of Western Sydney. It is worth noting that I offer not guarantee of the integrity of the data in these maps, and I have seen a few spots where the commute times increase significantly in adjacent mesh blocks. This tells me the street data (from OpenStreetMap) might not be connected correctly.

My next map shows what areas are within 30 minutes.

These maps were both created using open data and open source tools, which I find quite neat.

I have been interested in mapping traffic for a number of years, maybe ever since arriving in Sydney. It is sort of a hobby; I find making maps relaxing. My first little map was way back in 2008, where I visualised speed from a GPS unit. A little later I added some colour to the visualisations, and then used this as an excuse to create a little GUI for driving speed. My interest in visualising individual vehicles has decreased recently, as it has now shifted to the mapping wider systems. Have an idea you would like to see mapped? Leave a note in the comments.

YS and I were recently interviewed about self-tracking and Quantified Self by one of the major news channels in Australia. I will reflect on the experience after the show has aired, but it was an overall great experience. We have a new respect for filming what may ultimately be just a two minute segment. Depending on how the editing is done it will either provoke the hosts to contemplate the value of a data-centric macroscopic view of the world, or give them lots of fodder.

That said, as you would expect, I had to track my heart rate during the interview - see below. My interpretation is that my heart rate jumped at the start of every questions, and went down as I answered the question. It also dropped when the interview finished. I wish I had a more expensive heart rate monitor (e.g. Zephyr BioHarness or Scanadu) that tracked skin temperature and breathing. My hands felt cold by the end.

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One of the things I like Tableau, a piece of software to visualise data, is that it aggregates on dates really well. Below is a spread of beer / wine / coffee over 18 months, but grouped by what hour is fell in. You can see some trends, like I usually consume coffee in the morning, and that I usually drink after 17:00. There are exceptions, of course, like that beer I had at 10AM, and that coffee I had at 1AM.

There is the possibility I will be giving an interview on the Quantified Self “movement”. What follows is a brief summary of QS, the things I track, and some pretty charts.

What is Quantified Self

I suppose it depends on who you talk to. Wikipedia states that it is “a movement to incorporate technology into data acquisition on aspects of a person’s daily life in terms of inputs”, but I side more on the idea that the movement is “a collaboration of users and tool makers who share an interest in self knowledge through self-tracking.” It is at this point that it is probably important to interject that most people are self-trackers: weight, height, reps at the gym, hours worked, and so forth. If you have ever made a goal, you probably tracked how you could reach it. What makes us QS folk a bit different is that we tend to track lots of things, correlate between them, and share our results. So, with this theme, let me share what I track.

What I Track, and How

This is a list of some of the things I track, and the tools I use to do so.

• Weight / Body Fat / Temperature / Measurements -> scales, callipers, ear thermometer
• Resting Heart Rate -> oximeter
• Drinks (wine, beer, coffee – and previously water) -> Android app (bespoke)
• Drugs and vitamins -> Android app (bespoke)
• Various conditions (headaches, “colds”, itchiness, nausea, sore throats, “the runs”) -> Android app (bespoke)
• Finances (family) -> Android app (TOSHL)
• Start/Stop times of work -> Excel…
• Mood (Terrible to Great) -> Android app (How Are You Feeling)
• Indoor air quality (not really QS) -> various sensors
• Computer activity (Keystrokes / mouse clicks / mouse movement) -> WorkRave
• Location -> Google Latitude
• Steps & sleep -> Fitbit
• Fitness –> Android app (Sports Tracker) and a Zephyr Bluetooth Heart Rate Monitor
• Health History -> Microsoft HealthVault
• Photo every day -> Android app (PhotoChron)

You can see that this list seems utterly normal, but still gives me enough to work with to start forming a macroscopic view of life.

A Few Charts

I created these using Tableau, a fabulous piece of software for putting meaning behind numbers. These are not good examples of what the software is capable of, but it is the quickest way for me to visualise them.

I like coffee. It is, in all honesty, a drug. There have been times (I could probably find the date!) when I went from two cups a day to none, and I had withdrawals (headaches and nausea). I track the amount of coffee I consume to remind myself to not get into the habit of having two cups/day for too long. It is also bad for my stomach.

If I chart the days of the week I like to drink coffee over the last 18 months, it turns out I drink the most amount of coffee on Saturday. 

 I also enjoy an alcoholic drink from time to time, but was told in January to cut back (for my stomach’s sake).

I track both beer and wine consumption. I have managed to cut back on wine, but not so much on beer.

This can be explained because I tend to have beer when I go out with work colleagues or friends, but wine at home. It appears to have been easier to stop drinking with dinner than when out.

For the last two years I have been wearing a FitBit, usually, and using it to “track” my sleep.

It looks like I averaged about 7500 steps/day, yet started walking more in January of this year. Walking more was not a New Year’s Resolution. In May I broke the clip to my FitBit, but a friend was kind enough to give me their’s as a replacement. I should walk more.

I should also sleep more. It appears as though maybe, just maybe, I am starting to sleep more. My average is about 7.5hr/night. This is one area I would like to experiment more with.

I have also started tracking happiness on a simple Terrible -> Great! scale.

This graph shows my average happiness on a weekly basis for the last ~8 months. We could conclude that I’m getting more happy, and was really unhappy around Christmas.

And here we have my happiness levels when grouped by day of the week. We could conclude that I am, on average, the most content on a Sunday. I would like to believe it is just a coincidence that I am most content on a Sunday, and drink the least amount of coffee.

This is the standard deviation of my happiness tracking on a monthly basis. It looks like I am also getting less moody.

And finally, weight. Nothing interesting here. I need to get back down to 77KG, which is a more natural weight for me. I use a normal scale so only record every few months - if I had a wi-fi scale, I would be able to record much more frequently. 

Final Thoughts

In the last ~18 months I have become more happy and less moody, with Sunday being my happiest day, and Monday and Wednesday being my least content. I have put on three KG. I drink the most amount of coffee on Saturday, the least amount on Sunday, and have been able to drink less wine, but keep drinking the same amount of beer.

By looking at this evaluation I know I should probably start to incorporate a lunchtime walk into my daily routine, and stop drinking coffee on one day of the weekend. I should also drink my beer at a slower pace when I’m out, as this will prevent me from buying more than one, or, even harder to resist, friends and colleagues buying it for me.

Finally: I know none of the charts have a title. Read the text.

EDIT 12-03-2025: I accidentially broke the maps when deleting my AWS account, as the mbtiles were hosted there. Oops.

I quite like open data. I like data based on open standards (or mostly open standards) even better. Many transport operators around the world have started releasing their timetable data using (mostly) open standards, e.g. GTFS. One of the nice things about using a standard is that clever people have created tools to work with the timetable data, and those tools can now be used to manipulate timetable data from hundreds of agencies. The magnificent OpenTripPlanner is one such tool, and it works well with 131500’s GTFS data.

New South Wales Planning & Infrastructure have released a draft plan for how they hope to shape Sydney’s growth, which is where they detail the idea of a “city of cities”. I thought it would be interesting to mash these smaller “cities” with 131500’s transport data, and then display a map with the shortest commute to the nearest city. Various cities, I believe including Melbourne, have goals of re-achieving a “20-minute” city, or something similar (i.e. X% of the population can reach X% of the city within X minutes).

This map is the first stage. It only displays the commute time to St Leonards from every Mesh Block in the greater Sydney area. I used the open source tool OpenTripPlanner to computer the commute times, with OpenStreetMaps to support walking distances. The next map I release will probably have all the regional cities, and a similar styled map depicting time to nearest “centre”.