Data visualisation of Step count 2022


Step count information collected over a period of time can provide many insights about us through data visualisation by identifying patterns with colors.

I have collected my stepcount data from 2015 to 2022 and categorized it by year, month, day of week, date, hour and try finding patterns in the data and in turn they describe the insights about me such as sleep, how active i am through the day, When i am most active etc.,

If you see the heatmap above you can spot certain colors are together. Bluish black shows very low step count. Between 1:00 am and 6:00 am, and also after 10:00 pm , The colors indicate less activity because i am sleeping. The squares clearly show which year my sleep was more and when it was less. And for 2022 my sleep was very poor and this is captured well. I did plot this heatmap in various color scales and shown in the gallery below and confirming the repeatability of the pattern.

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With the color distinction it can be easily identified that i am active in the mornings between 4:00 am to 7:00 am and then in the evening 4:00 pm to 7:00 pm. The rest of the day i am being a couch potato. Whether this pattern repeats on all the months of 2022 can be visualised from the next heatmap.

There is a distinct color pattern before and after the month of august. I had my surgery in sepetember this year and therefore was not active thereafter which is clearly understandable. And because of this, color patterns don’t clearly explain what happened before august about my stepcount.

There is a distinct color pattern before and after the month of august. I had my surgery in sepetember this year and therefore was not active thereafter which is clearly understandable. And because of this, color patterns don’t clearly explain what happened before august about my stepcount. For this the information can be compared with previous years data.

The year-wise heatmap clearly shows i was active till August-2022.

There is no identifiable pattern for day of week over the years. So i am fairly distributing work, rest etc evenly in all days which is a good thing.

From the above bar chart it is clear that despite surgery i have been fairly active in 2022.

We can see more of data visualisation this year.

Thanks for visiting my blog.

My year in health


Gained a lot of weight this year during the lockdown period. 8 kg gain in 2021.

My body water content dropped from 56% in the beginning of the year to 51% at the end.

My body protein content dropped from 21.5% in the beginning of the year to 19.5% towards the end.

My fat content increased from 18% to 26% in a single year

Visceral fat also increased several points in 2021 after good drop initially.

Not much variation in muscle mass except in the beginning.

BMI is high.

Basal metabolism rate also increased significantly.

The graphs clearly show i am not in good shape. Though i was able to make lot of significant achievements in sports this year, my health has degraded a lot. Tracked my weight, protein, fat, water content, muscle mass using MI body composition scale. These are significant insights of degrading health but pretty useful data to make a comeback in 2022.

Though i have pedalled close to 20000 km in my bicycle in 2021, due to eating habits and sedentary working habits, i am facing the consequences. Though medical check up also indicated i am fine as of now, it is a warning for myself to change my habits. Thanks to quantified self habits. This will push me more into tracking and learn more about me and help me make a better version of myself.

Quantified self – My travels


For the first twenty-five years of my life, i wanted to travel but couldn’t. Later in the last eight years, i did went to places wherever and whenever possible. I just thought of compiling the places where i have lived and travelled over the years. Here is the list.

Places where i lived

  • Chennai,India – More than 25 years
  • Raichur, India – 2 years
  • Mangalore, India – 2 1/2 years
  • Sangatta, Indonesia – 8 months
  • Damanjodi, India – 3 months

Data collection is the first step in any process. Even places where you travel can be compiled into something as i have grouped below which helps to quantify.

Places where i travelled


  • Borra caves, Vishakapatinam, India
  • Belum caves, Andhra, India
  • Nellitheerta cave temple, Mangalore, India
  • Elephant cave, Bali, Indonesia
  • Vaishno devi cave shrine, India
  • Edakkal caves, India
  • Varaha caves, India

Hill Stations

  • Ooty, India
  • Darjiling & Gangtok, India
  • Mahabaleshwar, India
  • Mussoorie, India
  • Patnitop, India
  • Coorg, India


  • Arignar anna zoological park, Chennai, India
  • Jawaharlal nehru zoo, Hyderabad, India
  • Gembira loka zoo, Jogjakarta, Indonesia
  • Shivaram karanth zoo, Mangalore, India
  • Taveragoppa zoo, Shimoga, India
  • Van vihar, Bhopal, India
  • Guindy national park, Chennai, India
  • Ramnagar national park, Jammu, India

Wild life sanctuaries

  • Mudhumalai wildlife sanctuary
  • Bandipur wildlife sanctuary
  • Dandeli wildlife sanctuary
  • Vedanthangal bird sanctuary
  • Karakul bird sanctuary
  • Ranganthitoo bird sanctuary


  • St marys Island, India
  • Borneo,Indonesia,
  • Bali, Indonesia
  • Jawa, Indonesia
  • Beras basah, Indonesia


  • Mysore, India
  • Kolkatta, India
  • Hyderabad, India
  • Delhi, India
  • Haridwar, India
  • Bhopal, India
  • Jakarta, Indonesia,
  • Balikpapan, Indonesia
  • Samarinda, Indonesia
  • Jogjakarta, Indonesia,
  • Bontang, Indonesia

As i start typing i got more ideas for classifications of places even depending on activities i have carried out. I would have been to a place where i would have done adventure sports, have seen a water fall, relaxed at a beach, trekked a hill, safaried in a forest. So the method of presentation of lists only helps to boast that I have travelled quite a few places over the years. It is not appealing for the readers, not inspiring for anybody who is travelling. After all experiences are real treasures. Then why count places. The reasons are many. See you in next post with better presenting methods.

Quantified self – Time management 


This is how I spent my time during first three months in 2015.

It helped me to identify how much time I spend for relaxing, socializing etc., I.e in misc- 153 hours a month which according to me is high. So I started playing billiards(learning something new).

I found it difficult to accommodate playing billiards in my schedule even though I have 153 hours/ month in my account.

To analyse further I need breakup of my miscellaneous activities with time utilized. Right now my schedule is totally different till I join my office. Due to delay in getting my visa, I feel I am wasting more time. So I must have a backup plan of activities in case of availability of unplanned-abundant-free time.

Will publish more in future about self quantification. Have lots of data. Too busy……