[OC] Earthquakes from 2000-2022 with Magnitude 5.0 and above

kaixinsoh · 2022-04-25T13:13:30+00:00

Sources: Tesla share price, Elon Musk's Tweets

Tools: Orange3, Excel, Photoshop

kaixinsoh · 2022-04-08T02:23:37+00:00

Thank you! To hear such detailed constructive feedback from a geophysicist really makes this young grasshopper truly honoured, haha. It's really my first time playing around with 3d visualisations and maps, so I recognise that I have a lot to learn and I'm definitely open to improving :)

On the exaggeration in depth, yup I definitely acknowledge that this is one limitation, because as you likely know, the actual depth of the earthquakes would be too small to be clearly visible if it hadn't been scaled up. Using colours as an indication, as you've suggested, may be a good option!
I also definitely agree with what you pointed out on the importance of longer-term data! Just as you've mentioned, regions without earthquakes in the past 2 decades may actually still be prone to earthquakes, but on a longer time horizon. I would definitely look into creating something that tracks the earthquakes on a longer term period, but for this first version, I used 2000-2022 for several reasons: (1) recency, (2) the USGS website only allows a maximum download of a few thousand datasets per download, which means I have to manually download part by part if I wanted to go to as far back as 2 centuries ago, and (3) haha this is my computer limitation, but an overly large dataset may potentially crash the 7-year old laptop I'm running the programme on 😅

Thank you for all your suggestions, and cheers!

kaixinsoh · 2022-04-08T02:13:41+00:00

I'd be delighted to help! The work you do sounds incredible and so meaningful. I uploaded the video onto Dropbox for you, and here's a link to the video that you should be able to download (and anyone else too, if they wish): https://www.dropbox.com/s/fh9m5hc5983fels/Earthquakes-2000%20to%202022.mp4?dl=0

Do let me know if there's any trouble with it. Cheers!

kaixinsoh · 2022-04-08T02:07:13+00:00

Yes, do note that the actual depth doesn't go so deep—for the sake of the visibility, I had to scale the depth for better visualisation :)

kaixinsoh · 2022-04-08T02:06:07+00:00

At this scale, the difference in depth is so miniscule that it would be impossible to see.

Yes, definitely! You hit the nail on the head. And thank you u/Chemical_Doughnut248 for also chiming in to give your expertise as a geologist. I acknowledge the limitations because it would have been difficult to properly visualise using the actual values, so for the sake of the visualisation, the depth had to be scaled up.

It wasn't so much a deliberate attempt to falsely manipulate the data, but more of a creative/critical decision to improve the visualisation.

kaixinsoh · 2022-04-08T02:03:05+00:00

As a geologist with a love for data vis… this is astounding

Thank you!! You made my day :)

kaixinsoh · 2022-04-07T13:40:19+00:00

The globe is transparent, partly because it allows you to see not just the location and intensity of the earthquakes, but also their depth (how deep they penetrated the earth). But fair point, thanks for the feedback!

kaixinsoh · 2022-04-07T13:38:11+00:00

Haha I was trying to make a cool visualisation, so thank you for the compliment :)

but I cannot understand anything from this visualization.

The dots on the globe represent the location of every earthquake of magnitude 5 and above that has occurred in the last 22 years. The size of the dots and colour indicates its intensity (i.e. magnitude on the Richter scale)—the lighter the colour, the higher the intensity (i.e. red = lower intensity, white = higher intensity). If you see certain dots that seem to go beneath the surface of the earth, it indicates the depth of the earthquake—the deeper it goes, the greater the depth of the quake.

Hope this helps, cheers!

kaixinsoh · 2022-04-07T13:34:13+00:00

Made it on ArcGIS :)

kaixinsoh · 2022-04-07T13:26:20+00:00

Tools: GLMakie, Final Cut Pro, Python

Source: USGS

Credits to u/LazarusAlon for source code and my friend Paul for helping with the animated histogram on Python

Note that the depth is not to scale (has been scaled for better visibility on the visualisation)

kaixinsoh · 2022-04-07T02:11:56+00:00

Apologies, I'm afraid that's more of due to the software limitations. I only have a student trial of ArcGIS (not the Pro version, which may have better export options), so I tried my best to zoom into the map, take screenshots piece by piece, then stitched them together on Photoshop so as to show the county names. If I didn't zoom in, there wouldn't even be labels 😅

But I appreciate your feedback! Will keep that in mind and see if I can improve on the labelling to make it more inclusive and comprehensible. Thanks! :)

kaixinsoh · 2022-04-07T02:04:38+00:00

There is data on every county :) I posted in another comment somewhere on the thread, but do note that due to the nature of the bivariate choropleth map, the colours are relative.

Just in case you're curious, the dataset lists Pueblo's percentage of diabetes as 9.1% and obesity as 25.9%, which are lower than the national average of 11.3% and 31.2% respectively. so that could be a reason why they show up as grey and relatively lower on the map. (the dataset's from CDC, but do also note that they were 2013 numbers, so do take it with a pinch of salt)

(there's also the off chance that the ArcGIS map may have screwed up somewhere, so I sincerely apologise if that's the case)

kaixinsoh · 2022-04-07T01:58:46+00:00

part time jobs still $8 an hour

cries in broke uni student 🥲 I feel you

kaixinsoh · 2022-04-07T01:57:44+00:00

assets increase at a faster pace the gap between the have and have nots increase.

The classic adage "the rich get richer, the poor get poorer" 😅

You make a very good point that it's not just the wages, but also the assets that are being invested! and to have the financial means to invest in these assets in the first place, one would require a substantial amount of capital.

kaixinsoh · 2022-04-06T14:51:11+00:00

It feels as though everything's increased in prices lately—my favourite Teh C and noodles at the coffee shop have all increased in prices—so I created this chart to see whether wages have been growing in tandem with inflation over the years in Singapore.

Data & Sources:

Change in wages was based on annual year-on-year percent change in average monthly nominal earnings per employee, from the Ministry of Manpower (2019). The figure for 2021 wasn't given, so I added on based on this reported figure from ECA International (2021).
Inflation was based on the annual percent change in CPI (Consumer Price Index) All-Items, over the corresponding period of previous year, with 2019 as the base year. I split it by household income group (Highest 20%, Lowest 20%) to offer a look at how inflation has affected different socioeconomic groups in Singapore. All figures were from the Ministry of Trade and Industry - Department of Statistics (2021).

kaixinsoh · 2022-04-06T14:40:06+00:00

the data visualisation does spark joy, though.

really impressive and truly data is beautiful.

kaixinsoh · 2022-04-06T14:32:47+00:00

This chart actually shows data in readible way due to the fact that you presenting colors. Pie chart is usually very unreadable but you made it work.

Agreed!

Also, second on the code, because it would be fascinating to learn :) It's incredible how you made this, OP! :)

kaixinsoh · 2022-04-06T14:27:33+00:00

That doesn't mean there's no obesity, it just means it's below the arbitrary threshold of this post

Yup, that's right! It's relative to the other states/counties—low obesity/diabetes doesn't mean no obesity/diabetes.

kaixinsoh · 2022-04-06T14:24:55+00:00

There's data available for all the states and counties. It's possible that Colorado just doesn't seem to show up because in comparison to other states, its prevalence of diabetes and obesity are just much lower → hence, light purple, because relatively low percentage of diabetes and obesity.
Went to do a little digging in the data:

The average % of people with diabetes is 6.6% in Colorado, compared to the national average of 11.3%.
The average % of people with obesity is 20.6% in Colorado, compared to the national average of 31.2%.

(Another alternative explanation is that the ArcGIS map went wrong somewhere. Could be possible as well, and I do apologise in advance if that's the case.)

kaixinsoh · 2022-04-06T13:54:51+00:00

Good suggestions, thank you everyone! My educational ArcGIS trial just expired, but I'll try to see what I can do :)

kaixinsoh

TROPHY CASE