VoluntaryTracking.Enabled = true;

If you’ve been using Google Maps on your phone, you probably know there is a setting within that allows Google apps to use your device’s location any time it is on, or maybe not.



Or if you prefer the conspiracy theory version, you can read this.

The way I think of this is simply in a geeky way which is the title of this post. Since I had enabled voluntary tracking of my whereabouts, why not make good use to mash it up all in my Project GetFitY’all.

To get your location history in a timeseries of lat/long data points, go to the Google Maps Location history page. You have to sign in using your credentials first of course, so you are only seeing your own location history.


  1. Select the date/time range from the drop down list.
  2. To have a comprehensive list of location data points, click the link that says “Show All Points”.
  3. Click “Export to KML”. You would have downloaded an XML file in the Keyhole Markup Language (KML) format.

To make sense of these location history data points, the best means is to import this in PowerQuery. The steps were described in my previous posts about mashing the data in PowerQuery. The only exception is that I would open an XML file and use the KML file I downloaded. It’s supposed to be a simple step of expanding the 2 columns in PowerQuery which represent the <when> and <coord> elements but for some reason it expands the tables within those 2 columns in a weird way. Weird in the sense that for each <when> element it expands all of the <coord> elements for it. So if I have 1000 data points, I ended up having a total of 1000 X 1000 = 1 million rows. I referred to this blog post to try to expand the tables within the columns, but to no avail in my case.

Hence I did a workaround, a manual way of retrieving all the <when> rows and <coord> rows in two separate queries, then copying and pasting the combined data in a separate spreadsheet, finally saving it as a CSV file. Then I go back to PowerQuery to import the CSV file.

Next I need to retrieve my Fitbit data points. Good thing I already have a REST endpoint which does that for me. The REST API was implemented as a node.js app published at http://getfityall-api.azurewebsites.net/fitbit. I pass in the query strings which consists of the date and time range and VOILA!

Create a PowerMap, and I get this slightly different visualization below.

googlochistmashup So there you go, the results of VoluntaryTracking.Enabled = true; and then mashing your Google location history with Fitbit data.

PowerQuery invokes a GetFitYall API endpoint, and fun with PowerMap

Is that even possible? Yes and I’m talking about invoking that from within the PowerQuery add-in in Excel 2013, and then mucking around with the data which is represented in JSON. Pretty awesome I would think. To the layman, don’t worry about what’s this JSON thing, it’s all transparent to you, just consume the date.

In my previous post, I wrote about the REST API which I had exposed and it allows mashup on-demand which is perfect in the case of self-analytics using PowerBI. Here’s how you could consume this API from within PowerQuery specifically. It’s just another source of data like how I had retrieved the data from Azure Table Storage. Here are the steps:

1. From the PowerQuery ribbon, click “From Web”. Then enter the URL. The URL I’m entering is the REST endpoint I have. It should be HTTPS but then this is just a PoC so I’m keeping things simple here. I pass some query strings in the URL too.


2. Click List which contains an array of mashed up activity data points. Do NOT click “Into Table”, at least not yet.



3.  Now that you have expanded the List into a row of records, click “To Table”.powerquery-step2

4. Then you see the following dialog box, just click OK. No worries, it’ll be fine.


5. Select All Columns.


6. Fix the data type for the fields you care about, especially those you want to be used to visualize in PowerMap. Start with datetime. Click the column header, then at the ribbon, select Date/Time as the Data Type.


7. Fix Steps column as well. Choose Whole Number. This is because you don’t have a fraction of a step, just steps. :)


8. Fix Calories column, and set Data Type as Decimal Number.



9. At the ribbon, click Close and Load To. Then this dialog box pops up. Be sure to tick “Add this data to the Data Model”. The data needs to be in the Data Model in order for PowerMap to work on it after this.

powerquery-step810. The results are a number of rows retrieved from the REST endpoint. Look man, no JSON :)


11. If you want to look under the hood, I happen to be “tailing” the log of my node.js Azure website. Here’s proof that it’s the same 3,014 rows being returned. It took some 7 seconds to execute, this is what I mentioned in my previous post that I might not have optimized the mashup logic.


That’s it on the part of PowerQuery. Let’s do the fun stuff of visualizing this on PowerMap.

1. Map the geography and map level by selecting the lat and long fields.



2. Select the columns which we want to visualize in the PowerMap.powermap-step2


3. Change the width of the “skyscrappers” and the colors of course, and VOILA, you get this birds eye view of where the “action” happens. In this case, I walked the most around the Sydney CBD area. I attended the Mobile Monday Sydney meeting a couple weeks ago. powermap-step3


When I showed this to my wife the other day, she asked why were calories burned even when I was sitting idle in the bus. But then she answered her own question when she said “oh yeah we burn calories so as long as we are breathing!” LOL 😀





This API exposes a singular function at the moment which is to do the following:

  • Mashup on demand – Let client apps consume a mash up of fitness activity data points from different target APIs based upon user ID and time period for a specific date. Note: User ID is not implemented right now because my authorization website is not implemented fully yet.

Common usage of GetFitYall API
1. Self-service analytics tool such as PowerQuery and PowerMap pulls activity data points from HTTP/S endpoint(s) based upon query parameters such as user ID, date, and time period.

1. Get activities mash up

GET http://getfityall-api.azurewebsites.net/mashup?<userID>&ondate=YYYY-MM-DD&aftertime=HHmm&beforetime=HH:mm




Gets a mashup of activity data points from different fitness APIs based upon user ID and matching timestamps. Currently supports Fitbit intraday API and Strava API. In order to get activity data points down to 1-minute detail level, this API function only works for a specific date as required by the Fitbit intraday API.

Query parameters
userID The Fitbit user ID which has been authenticated and authorized by Fitbit OAuth API
date The specific date from which to pull the Fitbit activity data points. This works with 1 specific day because the Fitbit Get Intraday Time Series function only allows fetching a time series for a specific day but the data points would be down to 1 minute detail for the day. See https://wiki.fitbit.com/display/API/API-Get-Intraday-Time-Series
afterTime The start of the period, in the format HH:mm
beforeTime The end of the period, in the format HH:mm

Returns: Content-type = application/json
HTTP status codes
200 – OK
400 – Error in request
500 – Error in processing

Example response:

…. omitted for brevity….

This API is implemented as node.js app and deployed into a free/basic Azure website from WebMatrix. The reason why I had chosen node.js is mostly to learn a new server-side technology. The other reasons are:

  1. More programmable for mashup logic.
  2. Leverage many third-party Node.js modules such as node-strava, node-fitbit to rapidly develop prototypes of the GetFitYall API.
  3. Scalable as I configure this as an always on  basic Azure website and scale out accordingly.
  4. Due to the lightweight nature of node.js, the node.js app can handle a large amount of traffic with low overhead

Obvious Bottleneck

One potential problem is the sheer number of mashup requests. When I invoked this endpoint from PowerQuery,  I saw that there were 3 requests for each query, kind of weird but I’m not keen to find out why. Multiplied by thousands if not tens of thousands of users, it is pretty obvious that this would become a serious problem. Recommendation to address this bottleneck as follows:

  1. Cache the HTTP Response

A simple solution is to cache the response for similar requests (based on the same query parameters). The main benefits of caching response are reduced latency and network traffic.

2.   Mashup On-demand Optimization

The node.js app makes asynchronous calls to the Fitbit API to retrieve steps, calories out, floors, and elevation because this is how the Fitbit API works. A Javascript promise is used to determine when all calls are returned before processing the next step. This is another benefit of using node.js and by further using a 3rd party module such as Q, the callbacks hell can be avoided.

IoT Descriptive Analysis using PowerBI

Now comes the interesting part which is self-analytics of all the data that I have collected from “the Internet of My Things” (IoMT). As a recap I am currently ingesting activity data points from 2 devices, a Fitbit One and a Samsung S4 running 2 “sensor apps”; Strava and MapMyWalk. But it shouldn’t be limited to this as I also have a Garmin Edge 705 with heart-rate monitor (HRM) to track my MTB rides and a Polar FT40 wrist watch also with HRM to track other activities such as badminton and swimming (yeah my one and only wearable device which works under water). I have a small disclaimer: I’m not a regimented fitness geek. I just want to make sense of my activities. It all started with mountain biking and I just want to know how often I ride and for how long to try to justify to my wife why I bought 2 mountain bikes! :) During my rides, I wear a HRM because I just don’t want to over-exert myself during those steep climbs. When I looked at my dashboards I realized there is so much information which helped me to gain insights as to what I’m doing well and what not. It helps me to be better when I ride or play sports all without “killing” myself.

I chose PowerQuery and PowerMap, 2 very nifty PowerBI add-ins. I just want things to be simple and nothing beats self-analytics using a friendly tool like Excel (my wife is quite an Excel junkie from her previous life). These add-ins are available as free downloads from Microsoft to enhance the data access and data visualization capabilities of Microsoft Excel 2013. You should search for the latest download links. Using these tools I could retrieve data from a variety of sources and integrate that data as part of my Excel data model.

I’m particularly impressed that in the “Internet of my Own Things” the data generated were pretty sizeable. There were over 8000 Fitbit data points and 6000 Strava data points over a few days. And this is just for myself, imagine opening this up to more devices and more users? Obviously we needed a solution that is of cloud-scale to make this work. If you are doing self-analytics using Excel 2013, you may want to install a 64-bit version of Excel. Your Excel may crash working on all that data, I’d crashed the PowerQuery and PowerMap add-ins a few times, sent in feedback to Microsoft, they asked me if I could reproduce it, I say yeah when I work on huge datasets, they recommended I use the 64-bit version. Remember to download and install the corresponding 64-bit versions of the add-ins too.powerqueries


These data were retrieved from my Azure Table Storage which my Worker Roles diligently inserted (see my previous post). You could also import data from other sources which include Facebook, that’s pretty fun. Imagine being to compare my activities with my other buddies. I am a member of a couple of mountain biking groups in Strava. This could be a side-project later on.



You need your storage account details such as the name and the storage primary key which you can get from your Azure management portal.

After I had retrieved data from 2 Azure storage tables which stored my Fitbit and Strava data points, I could “mash” them y’all. The function for this is merge within PowerQuery ribbon.  First I select the Strava table, and then the Fitbit table. This is because there are more data points in Strava that maps out my lat/long coordinates versus 1 Fitbit data point recorded at every minute interval. Then I select the datetimestamp column to match and I only want to include matched rows. I name my merged query as Getfityall. Voila, I had just mashed up both data sources without writing any code! I had tried to write the code to do the matching but I don’t think my code was all that good, I had nested for loops. I then tried to use JSON path but a JSONpath library I used was painfully slow. So guess what, I just let Excel do what it does best! However in a later post, I will talk about how to import the mashed up data in Excel by calling a REST endpoint that returns the mashed up data in JSON.  And in this implementation  I do have nested for loops written in node.js! (yeah please do LOL :))

Things to note when you merge the 2 tables in Excel. you have to adjust the column formats especially for date/time and steps (by making it a whole number). Otherwise PowerMap doesn’t understand the format of your data and would be unable to render it. Then remember to load the data into the data model. This is required by PowerMap.

Next you insert a PowerMap. It is not available as a ribbon on its own. Rather go to the Insert ribbon, and under Map, click on it. You will notice a Launch PowerMap option. Wonder why is such a powerful feature tucked away here.


Create a new PowerMap tour and the fun begins. Select the latitude and longitude columns. It should automatically map correctly. Next I select other columns such as DateTime, Steps and CaloriesOut.


Be sure not to aggregate your columns under height. Otherwise you get weird-looking “skyscrappers”.  Next I configure the layer options by making the thickness smaller to about 25%, otherwise I get fat buildings and I can’t even see the route on the roads when I click play. I also changed the layer colors accordingly. I chose the national colors of Australia, green and gold to represent my steps and calories.


Turn on the map labels. Change the playback speed. Pan, zoom in and zoom out and just play around with the map. Then create a video and it’s cool! You could even add a soundtrack!

There you go, self-service analytics of data captured from the Internet of my things. This is just descriptive analytics, I’m just visualizing data that I already have. Next you could advance to predictive and prescriptive analytics which opens up many more possibilities. In another post I will talk about my thoughts about how far we have progressed being able to derive value of out of your own IoT solutions and projects in such an accelerated manner. And the best part is that you only focus on what you do best without worrying about the underlying plumbing and infrastructure. It just works!