Community post
HIVE user counts by countries based on profile location data
I prepared this analysis result a couple weeks ago but haven't published since we have been quite busy recently working on ecency. It's better to publish before i forget this small project ever existed :)
Analysis result is based on "location" field in account data.
Although not all users fill that field or some of them enter non-location text, the result provides a general overview.
Input
Data source comes from hivemind's db with query below:
SELECT location from hive_accounts where location != '';
Result dataset size was 249465 while i was working on the project.
Code
Project source code is here. It uses geopy and OpenStreetMap's Nominatim API for location detection from raw text.
You can see lots of caching mechanisms in code. Since it is a long running script, i didn't want to start over to data collection from zero every time it crashes or stalls.
It took almost 32 hours to complete it at background and extracted 234117 country out of raw dataset.
Result 🎉
| Country | Count | Percent |
|---|---|---|
| 🇺🇸 United States | 43051 | 18.39% |
| 🇻🇪 Venezuela, Bolivarian Republic of | 20380 | 8.71% |
| 🇮🇩 Indonesia | 18221 | 7.78% |
| 🇮🇳 India | 12047 | 5.15% |
| 🇳🇬 Nigeria | 9479 | 4.05% |
| 🇩🇪 Germany | 9051 | 3.87% |
| 🇬🇧 United Kingdom | 8816 | 3.77% |
| 🇰🇷 Korea, Republic of | 7443 | 3.18% |
| 🇨🇦 Canada | 7372 | 3.15% |
| 🇧🇩 Bangladesh | 6906 | 2.95% |
| 🇵🇭 Philippines | 6496 | 2.77% |
| 🇦🇺 Australia | 4584 | 1.96% |
| 🇵🇰 Pakistan | 4454 | 1.90% |
| 🇹🇷 Turkey | 3455 | 1.48% |
| 🇫🇷 France | 3415 | 1.46% |
| 🇵🇱 Poland | 3313 | 1.42% |
| 🇨🇳 China | 2959 | 1.26% |
| 🇪🇸 Spain | 2749 | 1.17% |
| 🇳🇱 Netherlands | 2602 | 1.11% |
| 🇮🇹 Italy | 2459 | 1.05% |
| 🇧🇷 Brazil | 2253 | 0.96% |
| 🇷🇺 Russian Federation | 2249 | 0.96% |
| 🇲🇾 Malaysia | 2156 | 0.92% |
| 🇿🇦 South Africa | 2155 | 0.92% |
| 🇲🇽 Mexico | 2005 | 0.86% |
| 🇳🇿 New Zealand | 1990 | 0.85% |
| 🇨🇴 Colombia | 1872 | 0.80% |
| 🇩🇴 Dominican Republic | 1765 | 0.75% |
| 🇷🇴 Romania | 1639 | 0.70% |
| 🇻🇳 Viet Nam | 1425 | 0.61% |
| 🇯🇵 Japan | 1402 | 0.60% |
| 🇦🇷 Argentina | 1314 | 0.56% |
| 🇦🇹 Austria | 1255 | 0.54% |
| 🇹🇭 Thailand | 1145 | 0.49% |
| 🇺🇦 Ukraine | 1135 | 0.48% |
| 🇨🇭 Switzerland | 1110 | 0.47% |
| 🇬🇷 Greece | 1073 | 0.46% |
| 🇧🇪 Belgium | 931 | 0.40% |
| 🇵🇹 Portugal | 919 | 0.39% |
| 🇸🇪 Sweden | 912 | 0.39% |
Of course US in the top of the list. Surprisingly Venezuela is second. Also Turkey surprised me. It is quite top of the list. I didn't know that amount of users from my country around here.
Hive on!
Replies (23)
Interesting stats. Wonder how participation will delope over time?
I am sure STEEM users are from Korea and China mainly now.
Good stats!
@tipu curate
Upvoted 👌 (Mana: 0/5)
Nice one.
We still have some Koreans around :)
Yeah, thats for sure. Thanks!
I am surprised that many accounts had a location and many will not have a real value there. I would expect Korea to be higher as they seem to have a lot of active users for Steem. Most of these will have joined before Hive started anyway.
These are web stats for hive for contrast.
and Steemit
Wohoooo south africa lol almost at 1%
I am not surprised by that at all. Neither by Indonesia and Nigeria.
Interesting - does the code recognise that 'Manchester' and 'Swansea' are both from Britain?
It's interesting how many people misfilled that section in!
It does take a long time to scour the entire chain I guess!
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Eh, so unpopular in Russia!
Thanks for very interesting statistics
Sad that Estonia didn't make the list
I'm actually surprised to see the United States as number 1. I live in the states, but have noticed that interactions on Hive and the discord see more conversations during the night instead of the day.
:)
No idea. We'll see...
I'm surprised UK has that many people!! As to Turkey, a while ago I spoke to someone from Turkey and he reckons there's only about 20 to 30 users in the country.
The number of people writing in Turkish is few. Since Turkish articles have very low upvote value, everyone writes non-Turkish articles.
That makes sense
Yes, it recognizes that 'Manchester' and 'Swansea' are both from UK. You can check on OpenStreetMap manually. The code uses Nominatim API. So same data as on openstreetmap.org.
OK cheers, I don't know too much about Python and what it connects too, thanks for clarification!
Lol, where are all those Indians?
Why can't I see them.
1990 Kiwis?
Have to admit I couldn't name 10 active ones...
With the help of the location, you can really collect a lot of information. Sometimes such accessibility and opportunities even scare. GPS phone tracker showed all available features in this question. I realized that even with minimal information, you can do an advanced search and find out a lot of details about a person of interest.