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Category Archives: Recreational

Personal Analytics with the Suunto Ambit


Suunto Ambit

Half a year ago my wife bought me the Suunto Ambit multi-function sport watch and heart rate monitor. It is a fantastic device, with very precise GPS, lots of add-on functionality and an interesting online portal and community.

There is some configuration and setup involved, such as pairing the Ambit with your heart rate belt and in the case of cycling with a cadence pod. You charge the batteries by plugging it into a USB port, which is also the way how you upload the data form the device to your computer or a website.

While the device itself and its programmability is quite advanced, I want to focus here on the associated online portal called Movescount.com where you can upload and visualize all your data for free – and share it with friends or the community if you’re so inclined. This amount of personal data collecting and analyzing is a fairly recent phenomenon, often referred to as Personal Analytics.

Each recorded session with the Suunto can be uploaded and classified into one of many sports, such as hiking, cycling, basketball, or indoor exercise. Each session is called a move, and with the portal you can collectively visualize all your moves. The current theme at movescount.com has a black background with mostly orange bars and charts. One of the first controls to organize your moves is either a list or a calendar control.

Calendar Control for Moves

Calendar Control for Moves

This already gives you a good overview over the type of sports activities and the distribution over weekday and weekends. A summary display is available in various forms, such as the following simple bar charts.

Summary information about heart rate zones

Summary information about heart rate zones

Another display format summarizes your selected moves, such as all moves in a particular month together with commensurate calorie consumption and breakdown of hours by type of move.

Moves Summary Display

Moves Summary Display

You can now select either a single or multiple moves (or group by the type of move) and display more information about this particular move. Note the x-axis can be set to display either distance or time and one can zoom in on any part of the entire recorded move. One can alos overlay multiple measures in the same chart by selecting more than one factor, although I find this to lead to very busy and confusing charts.

Graph and BarChart details per move

Graph and BarChart details per move

There are many individual measurements available for display, some based on individual sensors (like heart rate or GPS location or temperature or altitude / air pressure), others based on calculations and estimates (such as speed, recovery parameter “R&R”, EPOC or VO2).

MapGraph

Of particular interest to me as a cyclist is the ability to overlay the GPS-track on a Google map. Not only is it a very detailed recording of the route, but it is color-coded based on the currently selected measure. For example, the color-range shows the heart rate in the same colors as the above bar charts. One can clearly see where one is just warming up at the beginning (low heart rate, green color) or where one is riding up “into the red”, i.e. towards the limits of one’s own heart rate. Selecting points along the route displays some information about that particular point of the ride.

One interesting feature would be a time-geo correlation of any portable photo camera when taking pictures along the ride. Based on synchronized time one could then easily geo-code the photos even without any GPS capability within the camera itself.

The Suunto Ambit can do a lot more, including customizing the display mode and storing your configurations in so-called apps. One idea I have for this is to display an estimate of the total calorie consumption for a known route when continuing at the current pace (but I haven’t played with the programming yet). The Ambit seems to be particularly well suited to hiking, mountain biking or skiing due to its altimeter; however I don’t get to leverage that in flat Florida. Only the few bridges over the Intracoastal waterway show up as bumps in the vertical – with the corresponding acceleration of the heart rate on the uphill side.

One of the downsides is the fact that the heart rate sensor worn around the chest does not work in the water. Hence any swimming in the Ocean or the pool can not be measured precisely. (I replace the measurements with estimates.) And sure enough, just recently Suunto announced the new Ambit 2, which overcomes this limitation. Such is the world of new electronic toys, that the half-life of their innovation is getting shorter and shorter.

Bubble Chart of set of rides

Bubble Chart of set of rides

Measures in Bubble Chart

Measures in Bubble Chart

One last chart I wanted to point out is the flexible bubble chart. Shown above is a selection of all my rides in the first half of 2013 (47 rides minus two outliers, very long rides which would have changed the scale and compressed the rest of the chart). This gives a good feel for the distribution and variance of personal rides over a longer period of time – from the quick half hour duration to the more typical rides of a good 2 hours. Note that one can select any of about 30 measures in any of the three drop-down boxes (X-Axis, Y-Axis, Bubble-Size).

One side-effect of measuring and visualizing so many moves is that we find some interesting differences in our respective exercise habits and corresponding energy consumption. While I burn most of my calories on the bicycle, my wife gets more exercise out of indoor circuit exercises and Yoga than I do. For me, after literally decades of recreational cycling, I can raise my heart rate to much higher levels for extended periods of time on the bike compared to indoor circuit exercises. In a way that is not surprising, given the strength and oxygen consumption of the large leg muscles compared to smaller shoulder and arm muscles. But I would not have expected the difference to be so pronounced and could not have quantified it nearly as precisely as without such personal analytics.

It can be expected that the field of healthcare and personal analytics will converge and provide much more personalized data and insight into the specific life of any patient. Medical indicators like heart rate, blood sugar, blood pressure or factors like exercise and diet will become much more quantifiable and individually tracked over time. The hope is that this will also lead to better, more personal and generally more preventive care and medical treatments to any personal condition.

 
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Posted by on June 30, 2013 in Recreational

 

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2012 in review

The WordPress.com stats helper monkeys prepared a 2012 annual report for this blog.

Here’s an excerpt:

4,329 films were submitted to the 2012 Cannes Film Festival. This blog had 36,000 views in 2012. If each view were a film, this blog would power 8 Film Festivals

Click here to see the complete report.

 
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Posted by on December 31, 2012 in Recreational

 

Superstorm Sandy – Visualizing Hurricanes

Superstorm Sandy – Visualizing Hurricanes

Time-lapse animation of Sandy Oct-28 from geostationary orbit, 1 frame per minute, 11 hours of daylight. Although “only” a category 1 hurricane, this superstorm has enormous size. Tropical storm force winds extend out over an area 900 miles in diameter.

Living in South Florida makes you alert to tropical storms during hurricane season from May to November. Exactly 7 years ago, at the end of October 2005, the eye of category 3 hurricane Wilma swept over our home in West Palm Beach in South Florida – the most powerful natural weather event I have ever witnessed. After avoiding a direct hit since then, we got a massive rain event from Isaac earlier this year, but again avoided a direct hit. To be sure, often the flooding associated with hurricanes is worse than the wind damage. For example, when hurricane Katrina hit New Orleans in August 2005, most of the devastation came from flooding after the levees were breached. But the first question is always where the storms will make landfall and how strong they are when they hit your area.

Tropical storms are being tracked and forecast in great detail, in particular by the National Hurricane Center of the National Weather Service. There are many great visualizations illustrating the path, windspeed, rainfall, extent of tropical storm force winds, etc. Due to the convenience for browsing, I have almost completely switched to following hurricane or weather updates from the iPad. (In this case I’m using the Hurr Tracker app from EZ Apps.)

Last week a new tropical storm emerged in the Carribean and was named ‘Sandy’. A few days ago with Sandy’s center over the Bahamas, the path looked like this:

Path of hurricane Sandy as of Oct-25 (Hurr Tracker iPad app)

Note the use of color for wind speed and the cone of uncertainty in the lower segment, as well as the rings around the center indicating the size of the area with storm-force winds.

Naturally curious whether South Florida was likely to get hit, another image gave us some relief:

5 Day tracking map for hurricane Sandy

Now a few days later, while we did get some strong northerly winds and pounding surf leading to beach erosion, Sandy was not a particularly disturbing event for South Florida. At the same time, however, Sandy is forecast to make landfall on the Jersey shore within about 24 hours during the night from Monday to Tuesday.

One interesting set of maps with a color code displaying the probability of an area experiencing winds of a certain speed, say at least tropical storm force winds (>= 39 mph). The following map was issued this afternoon and indicates the very large area (mostly offshore) with near 100% probability of exceeding tropical storm force winds in purple.

Tropical storm force wind speed probabilities for hurricane Sandy as of Oct-28

This indicates how large Sandy is – an area the size of Texas with tropical storm force winds! Meteorologists are concerned for the Northeast due to Sandy converging with two other weather events, a storm from the West and cold air coming down from the North. This is expected to intensify the weather system, similar to the Perfect Storm of 1991. Due to the timing around Halloween this is why Sandy was also called a ‘Frankenstorm’.

One of the most chilling pictures is this animated GIF from WeatherBELL. A story in the Atlantic earlier today writes this:

Dr. Ryan Maue, a meteorologist at WeatherBELL, put out this animated GIF of the storm’s approach yesterday. “This is unprecedented –absolutely stunning upper-level configuration pinwheeling #Sandy on-shore like ping-pong ball,” he tweeted. It shows how cold air to the north and west of the storm spin Sandy into the mid-atlantic coastline.

(Click the image if the animation doesn’t play in your browser.)

Animation of hurricane Sandy moving into the NorthEast (Source: WeatherBELL)

Understandably this forecast of superstorm Sandy has the authorities worried. The full moon tomorrow exacerbates the tides and New York City is expecting up to 11 ft storm surge. Cities across the Northeast are taking precautions as of this writing. For example, the New York City subway metro transit system is shutting down tonight and several hundred thousand people in low-lying coastal areas are under mandatory evacuation order. More than 5000 flights to the area on Monday have been cancelled. Take a look at the expected 5 day precipitation forecast in the Northeast. Some areas may get up to 10 inches of rain and/or snow!

5 day precipitation forecast with Sandy’s impact for the Northeast

The first priority is to use such visualizations to communicate the weather impact and allow people to take necessary precautions. One can use similar hurricane charts to visualize other uncertain events, such as the future outcomes of development projects. We will look at this in an upcoming post on this Blog.

 

Addendum 11/4/12: The NYTimes has provided some interactive graphics detailing the location and size of power outages caused by superstorm Sandy in the New York and New Jersey area. The New York City outages have been summarized in this chart, normalized to the percentage of all customers. As can be seen, the efforts to restore power over the first 6 days have been fairly successful, especially in Manhattan and Staten Island, less so in Westchester.

6 day tracking map of power outages caused by Sandy in New York City

 
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Posted by on October 28, 2012 in Recreational, Scientific

 

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Olympic Medal Charts

Olympic Medal Charts

The 2012 London Olympic Games ended this weekend with a colorful closing ceremony. Media coverage was unprecedented, with other forms of competition around who had the most social media presence or which website had the best online coverage of the games.

In this post I’m looking at the medal counts over the history of the Olympic Games (summer games only, 27 events over the last 116 years, no games in 1916, 1940, and 1944). Nearly 11.000 athletes from 205 countries competed for more than 900 medals in 302 events. The New York Times has an interactive chart of the medal counts on their London 2012 Results page:

Bubble size represents the number of medals won by the country, bubble position is roughly based on a world map and bubble color indicates the continent. Moving the slider to a different year changes the bubbles, which gives a dynamic grow or shrink effect.

Below this chart is a table listing all gold, silver, bronze winners for each sport in that year, grouped by type of sport such as Gymnastics, Rowing or Swimming. Selecting a bubble will filter this to entries where the respective country won a medal. This shows the domination of some sports by certain countries, such as Diving (8 events, China won 6 gold and 10 total medals) or Cycling – Track (10 events, Great Britain won 7 gold and 9 total medals). In two sports, domination by one country was 100%: Badminton (5 events, China won 5 gold and 8 total medals), Table Tennis (4 events, China won 4 gold and 6 total medals).

There is also a summary table ranking the countries by total medals. For 2012, the United States clearly won that competition, winning more gold medals (46) than all but 3 other countries (China, Russia, Britain) won total medals.

Top 10 countries for medal count in 2012

Of course countries vary greatly by population size. It is remarkable that a relatively small nations such as Jamaica (~2.7 million) won 12 medals (4, 4, 4), while India (~1.25 billion) won only 6 medals (0, 2, 4). In that sense, Jamaica is about 1000x more medal-decorated per population size than India! In another New York Times graphic there is an option to compare medal count adjusted for population size, i.e. with the medal count normalized to a standard population size of say 100 million.

Directed graph comparing medal performance adjusted for country size

Selecting any node in this graph will highlight countries with better, worse or comparable relative medal performance. (There are different ways to rank based on how different medals are weighted.)

The Guardian Data Blog has taken this a step further and written a piece called “alternative medals table“. This post not only discusses multiple factors like population, GDP, or number of athletes and how to deal with them statistically; it also provides all the data and many charts in a Google Docs spreadsheet. One article combines GDP adjustment with cartographical mapping across Europe:

Medals GDP Adjusted and mapped for Europe

If you want to do your own analysis, you can get the data in shared spreadsheets. To do a somewhat more historic analysis, I used a different source, namely Wolfram’s curated data source accessible from within Mathematica. Of course, once you have all that data, you can examine it in many different directions. Did you know that 14853 Olympic medals were awarded so far in 27 summer Olympiads? The average was 550 medals, growing about 29 medals per event with nearly 1000 awarded in 2008 and 2012.

A lot of attention was paid to who would win the most medals in London. China seemed in contention for the top spot, but in the end the United States won the most medals, as it did in the last 5 Olympiads. Only 7 countries won the most medals at any Olympiad. Greece (1896), France (1900), the United Kingdom (1908), Sweden (1912), and Germany (1936) did so just once. The Soviet Union (which no longer exists) did it 8 times. And the United States did it 14 times. China, which is only participating since 1984, has yet to win the most medals of any Olympiad.

Aside from the top rank, I was curious about the distribution of medals over all countries. Both nations and events have increased, as is shown in the following paired bar chart:

Number of participating nations and total medals per Summer Games

The number of nations grew steadily with only two exceptions during the thirties and the seventies; presumably due to economic hardship many nations didn’t want to afford participation. 1980 also saw the Boycott of the Moscow Games by the United States and several other delegations over geopolitical disagreements. At just over 200 the number of nations seems to have stabilized.

The number of medals depends primarily on the number of events at each Olympiad. This year there were 302 events in 26 types of Sports. Total medal count isn’t necessarily exactly triple that since in some events there could be more than 1 Bronze (such as in Judo, Taekwondo, and Wrestling). Case in point, in 2012 there were 968 medals awarded, 62 more than 3 * 302 events.

What is the distribution of those medals over the participating nations? One measure would be the percentage of nations winning at least some medals. Another measure showing the degree of inequality in a distribution is the Gini index. Here I plotted the percentage of nations medaling and the Gini index of the medal distribution over all participating nations for every Olympiad:

Percentage and Gini-Index of medal distribution by nations

Up until 1932 3 out of 4 nations won at least some medals. Then the percentage dropped down to levels around 40% and lower since the sixties. That means 6 of 10 nations go home without any medals. During the same time period the inequality grew from Gini of about .65 to near .90 One exception were the Third Games in 1904 in St. Louis. With only 13 nations competing the United States dominated so many sports to yield an extreme Gini of .92 All of the last five Games resulted in a Gini of about .86, so this still very large amount of medal winning inequality seems to have stabilized.

It would be interesting to extend this to the level of participating athletes. Of course we know which athlete ranks at the top as the most decorated Olympic athlete of all time: Michael Phelps with 22 medals.

 
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Posted by on August 15, 2012 in Recreational

 

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London Tube Map and Graph Visualizations

London Tube Map and Graph Visualizations

The previous post on Tube Maps has quickly risen in the view stats into the Top 3 posts. Perhaps it’s due to many people searching Google for images of the original London tube map in the context of the upcoming Olympic Games.

I recently reviewed some of the classes in the free Wolfram’s Data Science course. If you are interested in Data Science, this is excellent material. And if you are using Mathematica, you can download the underlying code and play with the materials.

It just so happens that in the notebook for the Graphs and Networks: Concepts and Applications class there is a graph object for the London subway.

Mathematica Graph object for the London subway

As previously demonstrated in our post on world country neighborhood relationships, Mathematica’s graph objects are fully integrated into the language and there are powerful visualization and analysis functions.

For example, this graph has 353 vertices (stations) and 409 edges (subway connections). This one line of code  highlights all stations no more than 5 stations away from the Waterloo station:

HighlightGraph[london, 
  NeighborhoodGraph[london, "Waterloo", 5]]

Neighborhood Graph 5 around Waterloo

Since HighlightGraph and NeighborhoodGraph are built-in functions, this can be done in one line of code.

Export["london.gif",
  Table[HighlightGraph[london, 
    NeighborhoodGraph[london, "King's Cross St. Pancras", k]],
   {k, 0, 20, 1}]]

creates this animated GIF file:

Paths spreading out from the center

Shortest paths can easily be determined and visualized:

HighlightGraph[london, 
  FindShortestPath[london, "Amersham", "Woolwich Arsenal"]]

A shortest path example

There are many other graph functions such as:

GraphDiameter[london]   39
GraphRadius[london]     20
GraphCenter[london]     "King's Cross St. Pancras"
GraphPeriphery[london]  {"Watford Junction", "Woodford"}

In other words, the King’s Cross St. Pancras station is at the center, with radius up to 20 out into the periphery, and 39 the shortest path between Watford Junction and Woodford, the longest shortest path in the network.

Let’s look at distances within the graph. The built-in function GraphDistanceMatrix calculates all pairwise distances between any two stations:

mat = GraphDistanceMatrix[london]; MatrixPlot[mat]

Graph Distance Matrix Plot

For the 353*353 = 124,609 pairs of stations, let’s plot a histogram of the pairwise distances:

Histogram[Flatten[mat]]

Graph Distance Histogram

The average distance between two stations in the London subway system is about 14.

So far, very little coding has been required as we have used built-in functions. Of course, the set of functions can be easily extended. One interesting aspect is the notion of centrality or distance of a node from the center of the graph. This is expressed in the built-in function ClosenessCentrality

cc = ClosenessCentrality[london];
HighlightCentrality[g_, cc_] := 
   HighlightGraph[g, 
    Table[Style[VertexList[g][[i]], 
      ColorData["TemperatureMap"][cc[[i]]/Max[cc]]], 
        {i, VertexCount[g]}]];
HighlightCentrality[london, cc]

Color coded Centrality Map

Another interesting notion is that of BetweennessCentrality, which is a measure indicating how often a particular node lies on the shortest paths between all node-pairs. The following nifty little snippet of code identifies the 10 most traversed stations – along the shortest paths – of the London underground:

HighlightGraph[london,
 First /@ SortBy[
 Thread[VertexList[london] -> BetweennessCentrality[london]],
 Last][[-10 ;;]]]

10 most traversed stations

I have often felt that progress in computer science and in languages comes from raising the level of abstraction. It’s amazing how much analysis and visualization one can do in Mathematica with very little coding due to the large number of powerful, built-in functions. The reference documentation of these functions often has many useful examples (and is also available for free on the web).
When I graduated from college 20 years ago we didn’t have such powerful language platforms. Implementing a good algorithm for finding shortest paths is a good exercise for a college-level computer science course. And even when such pre-built functions exist, it may still be instructive to figure out how to implement such algorithms.
As manager I have always encouraged my software engineers to spend a certain fraction of their time searching for built-in functions or otherwise pre-existing code to speed up project implementation. Bill Gates has been quoted to have said:

“There is only one trick in software: Use a piece of code that has already been written.”

With software engineers, it is well known that productivity often varies not just by small factors, but by orders of magnitude. A handful of talented and motivated engineers with the right tools can outperform staffs of hundreds at large companies. I believe the increasing levels of abstraction and computational power of platforms such as Mathematica further exacerbates this trend and the resulting inequality in productivity.

 
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Posted by on July 11, 2012 in Education, Recreational

 

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Interactive Tournament Map

Interactive Tournament Map

I hadn’t followed the UEFA 2012 European football championship (called soccer in the US) and wanted to catch up on where things stand. Enter the interactive tournament map on the official UEFA website:

Row selection highlights games at that stadium

When you first enter the map it animates the timeline from left to right by drawing the colored lines for each team. The tabular layout shows time in daily columns from left to right and teams in rows by 4 tournament groups. Today’s day column is always highlighted. Here are some of the interactive elements:

  • Mouse over any of the colored lines highlights the corresponding team’s games along it’s timeline.
  • Clicking on a particular day column header highlights the games played on that date.
  • Clicking on the stadium symbol at the right end highlights the games played at that stadium.
  • Clicking on any circle brings up a dialog with details for that game.
  • Clicking on a row header on the left brings up a dialog with details for that team.
  • Selecting the tournament stage at the bottom (quarter-, semi-, final) moves to the date interval.

Detail for team Spain

Spain is the reigning football world champion, so they are clearly one of the favorites of this tournament and will actually play their semi-final against Portugal later this evening.

The final will be played in the Olympic Stadium in Kyiv, capital of participating host country Ukraine.

Detail with game schedule for stadium

From these details you can click on the games and get to yet more detail (videos, comments, etc.) for that particular game.

When I first looked at the map, the amount of information displayed had me a bit confused. The color scheme is often difficult to separate, for example the three orange-red tones in Group B. The black background feels attractive, although I could do without the pattern overlay, which doesn’t add information and only distracts. Lastly, I could do without the colorful advertisements around the map. On first glance I thought the stadium symbols on the right were also just colored ads.

The interactive nature made the map grow on me. It’s intuitive and the tabular layout easy to navigate. You may not have a screen wide enough to see the map in its entirety, but I suppose you wouldn’t want to see time down the vertical axis, would you?

Postscript 7/1/12: Sure enough, Spain beat Italy 4:0 in today’s final and went on to become the European football champion 2012.

 
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Posted by on June 27, 2012 in Recreational

 

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Self-publishing to Apple bookstore

Self-publishing to Apple bookstore

Over the last couple of weeks I finished writing the book about my adventure of a lifetime: Panamerican Peaks, cycling from Alaska to Patagonia and climbing the highest mountain of every country along the way. By now I have successfully self-published the book to the Apple bookstore. This post gives a recap of the steps involved in that process, with a focus on the tools, logistics and finally some numbers and sales stats.

Disclaimer: In my personal life I am an avid Apple fan, and this post is heavily biased towards Apple products. In particular, the eBook is only available for the iPad. So the tools and publishing route described below may not be for everybody, but the process and lessons learnt may still be of interest.

Path to self-publishing on Apple bookstore

Creating Content

The first step is obviously to create, select and edit the content of the book. During the actual trip I tried documenting my experiences via the following:

  • Taking about 10,000 photos with digital camera (Olympus and Panasonic)
  • Taking daily notes with riding or climbing stats (on iPhone or NetBook)
  • Shooting about 200 video clips (Flip Mino)
  • Uploading photos (to Picasa) and videos (to YouTube)
  • Writing posts on my personal Blog

In the months after coming home I refined some of the above material. Using iMovie I created ~ 5 min long movies based on video clips, photos and map animations, typically with some iTunes song in the background and a bit of explanatory text or commentary. I shared those videos on my personal Blog and on my Panamerican Peaks YouTube channel.

I loaded all photos into Aperture on our iMac and tagged and rated them. That allowed me to organize them by topic or as required. The ‘Smart Folders’ feature of Aperture comes in handy here, as it allows to set up filters and select a subset of photos without having to copy them. For example, if I wanted photos rated 4 stars or higher related to camping, or photos of mountains in Central America, I just needed to create another Smart Folder. This was very useful for example for the Panamerican Peaks Synopsis video which features quick photo sets by topics (cycling, climbing, camping, etc.).

Google Earth proved to be a very useful tool as I could easily create maps of the trip based on the recorded GPS coordinates from my SPOT tracker. One can even retrace the trip in often astonishing detail thanks to Google Street View. For example, in many places along the Pacific Coast I can look at campgrounds or road-side restaurants where I stopped during my journey. I even created a video illustrating the climbing route on Mount Logan from within Google Earth.

The heart and soul of any book is of course the story and the text used to tell it. I created multiple chapters using MS Word because I am so used to it, but one can of course use any modern text writing tool. In addition, I created some slides for presentations I gave last summer using Keynote.

Book Layout

Once all the ingredients were available, it was time to compose the actual book. As I had decided to build an eBook for the iPad I used Apple’s new iBooks Author tool on my MacBook Pro. This meant choosing the layout and including the text and media. iBooks provides a few interactive widgets and accepts all widgets that can be installed into the OS X dashboard. This in particular allowed me to link to the various YouTube videos. I could always get a preview of the book copied out to my attached iPad 2.

After many weeks of busy work putting the finishing touches on the book and adding various edits from a few trusted friends I got to the point where I needed to figure out how to get the book published in Apple’s bookstore. There are two steps required here:

  1. Creating a developer account with Apple via iTunes Connect
  2. Managing one’s content via iTunes Producer

The creation of the account is fairly straightforward through the web browser. To get started, I visited Apple’s Content Provider FAQ page and filled out an application. One submits basic information such as name, address, tax ID, credit card information, and ties it all to an existing Apple account. It can take a while. I never received the account validation email I was promised. So after a few days I started inquiring in Apple’s support forum. This had happened to others. Finally I just tried connecting via web browser to itunesconnect.apple.com and it worked – I had an account to publish from.

The packaging of all material and uploading is done via the free iTunes Producer app on the Mac. iBooks Author exports the book in .ibooks format, which becomes part of the iTunes Producer package. One can also provide a free sample for the book. This can be any subset or variation of the full book, unlike with Amazon’s bookstore, where the free sample is always the first N pages.

Next, one needs to provide additional metadata such as book category, description, author name, optional sample screen shots etc. One also has to provide an ISBN (International Standard Book Number) for the book. These can be obtained from publishers or directly purchased from Bowker. This stems from the need to catalogue and identify physical books in inventory or libraries, but seems a bit anachronistic for electronic books. The prices for ISBNs are very high, especially for small volumes (1 for $125, 10 for $250, 100 for $500, 1000 for $1000). But since Bowker has a monopoly in the US you don’t have a choice in that matter. This expense seemed to be the only marginal upfront cost to publishing the book (aside from the tools to create the content).

Finally one can determine the pricing and the markets where the content is to be sold. Apple follows the agency model of book publishing: As author you get to set the price. As distributors they take a share of your proceeds, here 30%. (By contrast, in the wholesale model you sell to the distributor at a discount, say 50% of the suggested retail price; the seller has sole discretion to set the price.)

Book Review

Much has been written about the very restrictive terms and conditions Apple puts on authors using their iBooks Author tool. Essentially it locks you in as an author to sell only through Apple. For many authors that is not a viable option. It also allows Apple to reject your work at their sole discretion. So as an author you are completely at the mercy of Apple’s review process.

Apple is also strict with enforcing certain rules regarding the content it allows you to sell. For example, your book cannot contain any links to YouTube videos or Amazon books. They rejected my first revision with YouTube links and suggested to embed all videos. This would have bloated the download size of the book by more than 1 GB. As a compromise, I created short 1 min teaser versions of all videos and included those. At the end they display a screen to go to the companion website (my personal Blog) for the full versions.

After 3 revision cycles and about a week later I finally had my book on sale in 24 countries around the world, for $9.99 or the equivalent in Euro or other countries’ currencies.

Book Marketing

Publishing is not selling. Here are some of the things I did to promote my own book:

  • Email – Customized note to Hotmail contacts (~ 300 contacts)
  • Twitter – Tweets and direct messages to influencers for retweets (~ 2000 followers)
  • FaceBook – My daughter posted on her wall (~ 1000+ friends)

Sending the emails was not without hiccups. I used MS Word and Outlook to do a mail merge with text blocks and individual text from an Excel spreadsheet. First, the Mail Merge Filter condition dialog has a bug which replicates the last AND condition and adds it as an OR condition. This screws up your filter and ends up selecting lots of folks you didn’t mean to. I found this bug during a test with the first 5 addresses. (I sent them each an apologetic email explaining this.) Then after I did the filtering all in the spreadsheet it worked and Outlook cranked out the emails. After a short while, Hotmail decided that my account had apparently been hacked and used for spam, so they locked my account down! In a way this is good, but I didn’t consider my carefully crafted and personalized emails spam. So I had to change my password and unlock my account again.
The email was very effective. I got lots of positive responses and a few folks decided to buy right away. I had sold my first copy. Every journey of 1000 miles starts with a single step.

As a result of my daughter posting the news on Facebook I noticed a spike (4x average) in the views of my Blog and Book page. I also offered promo codes for free book download to influential twitter users if they would retweet the book announcement to their followers. Within a couple of days a handful of them accepted the offer and retweeted, which exposed the tweet to a total of 2,000+ followers.

I had emailed the Apple bookstore, and to my delight they actually featured my book in their Travel & Adventure category.

My book featured in Apple’s bookstore, Travel & Adventure section

Book Sales

With all these promotion efforts I couldn’t wait until the next morning to see the sales numbers. (iTunes Connect updates their sales numbers only once a day.) I had the first ratings and reviews come in, all at 5-stars. Naturally, I hoped to see the sales numbers go up. After all, I had reached hundreds, if not thousands of people, most of which either know me or are somewhat interested in adventure. The result? Tiny sales numbers. To date after one week I have sold 14 copies, with a maximum of four (4) copies per day. At my $10 price and 70% share this amounts to just under $100 for the first week. Not exactly enough to retire on.

I’ll revisit this topic at some point in the future when I have more data. Obviously, the iPad is just a fraction of the entire book market with Kindle, Nook and other devices. (Although, the iBook looks much better on the iPad than on many other readers, in particular the smaller black & white e-Ink display Kindle readers.) While the selection of titles seems comparable on Apple’s and Amazon’s bookstores, about 1.35 million each (see a spreadsheet of my recent sample here), there don’t appear to be many shoppers in Apple’s bookstore. Of course, Travel & Adventure is only a small fraction of the book market. But even there, on a day where I sold two copies my book briefly ranked 30.th in the Top Charts. 30.th out of 11,800 titles (in Travel & Adventure)! That means the other 11,770 titles sold even less than mine (i.e. one or none) during the sampling time interval. Book sales appear very unevenly distributed, another case of huge online inequality.

But more importantly, most of the people reached by my promotional efforts don’t engage to the level of actually following the links, downloading the sample and finally buying the book. From my experience, one needs to reach more than 100 people for every one book sold. Fellow adventure traveller and author Andrew Hyde – whose book coincidentally is featured just above mine in the screen-shot above – has recently written about his book sales here. His stats show a similar small fraction of sales to views. I just don’t have the millions of Twitter followers to generate meaningful sales this way!

 
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Posted by on June 21, 2012 in Recreational

 

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