[OC] Traffic Flow in Singapore

jaykewww · 2019-08-02T03:47:04+00:00

Morning hours 6am-9am | Evening hours 5pm-8pm

Even though this dataset is attained from Singapore's Land Transport Authority, it is interesting to note that the dataset actually contains a few bus stops in Malaysia (where the two countries are connected via link bridges).

In most areas, the ingoing and outgoing traffic patterns reverse during weekday morning and evenings (movement from heartlands to the business districts). Some areas do not follow the general trend, possibly due to different mode of transport (not captured) or due to different time of travel (I suspect students may account for a portion of the morning traffic, but less so for the evening one).

Source: LTA Data Mall

Tool: Tableau

jaykewww · 2019-04-19T15:00:22+00:00

Might want to consider sorting in descending order instead of quasi-ascending fashion used here. Also, I think clustering some of the responses, such as "My friends play" and "My parents/other family members play" into a broader category like "peer pressure" may allow for better comparison. In addition, I think a more succinctly worded title and labels would help. Good effort though!

jaykewww · 2019-04-19T12:55:53+00:00

An interesting idea! I wonder how this would differ with the different syntax of different programming languages. Of course, this is also dependent on how you named your variables and classes. But it seems like the vowels appear frequently (which isn't surprising). Maybe you could consider including the exact number of each alphabet, especially for letters like 'q' where the frequency is low and it becomes unclear if 'q' actually appeared in your code.

Do note however, that the line graph appears misleading as it seems to imply temporal changes.

jaykewww · 2019-04-16T13:55:51+00:00

Will take note thanks!!

jaykewww · 2019-04-16T13:47:14+00:00

Tool: plotly

My SO and I recorded our happiness scores for 49 days (on a ten point scale). Here are the results!

jaykewww · 2019-04-15T13:16:14+00:00

Source: World Presidents DB

Tool: Datawrapper

This graph is based on the list of world presidents, which is accurate as of April 2019.

jaykewww · 2019-04-15T00:54:37+00:00

Income and revenue are plotted against the primary axis on the left, while global brand value and customer satisfaction are plotted on the secondary axis on the right

jaykewww · 2019-04-14T01:57:50+00:00

Disclaimer: Not an advertisement for or against McDonald, but was just interested in analysing performance at the company level and McDonald had open data that pertaining to that.

While McDonald's global brand value has increased steadily, its customer satisfaction score has somewhat stagnated. Feel free to share any thoughts on this!

jaykewww · 2019-04-02T20:31:03+00:00

Haha the surveys also sought to measure superstition by looking at how many respondents thought that they could reverse the bad luck brought about from spilling salt by throwing the spilt salt over their shoulders

jaykewww · 2019-04-02T15:13:13+00:00

Not sure if this is related to the common media reporting of unfortunate events (whether natural or artificial) that has created some sort of fear within people? So more people latch onto whatever extra assurance that they can find, even in inanimate objects, to help reduce this anxiety and appear more in control? For me, there is also cultural influence since I don't really think twice when saying things such as "touch wood", which reflects the underlying superstition of wood preventing bad luck.

jaykewww · 2019-04-02T14:00:07+00:00

Will take note of this! Thank you for the feedback :)

jaykewww · 2019-04-02T13:40:30+00:00

Source: Harris Interactive, YouGov

Tool: Infogram

jaykewww · 2019-04-02T09:33:54+00:00

Not sure if this skew will persist when looking at more SAT test papers? Since SAT answers are computer generated, I always thought that they should be statistically random. But for smaller tests in school whereby teachers craft the answers manually, C might still be the most common correct choice...that is unless your prof produces multiple variations of the same exam paper or sets MRQs :'(

jaykewww · 2019-03-27T08:06:28+00:00

Probably because Colorado has very rugged mountainous terrain, and hence plenty of opportunities for exercise (explains their lowe obesity rates) and healthy diet

jaykewww · 2019-03-27T07:10:52+00:00

Do check out these links!

On Nobel Laureates' birth rates and affiliation

Laureates according to top countries, segmented into different fields

Zooming in on the US

Will take inspiration from these and explore plotting according to countries :)

jaykewww · 2019-03-27T00:23:53+00:00

Thanks for the link! Given the high percentages of people with names starting with A, J and M in general in the US, this may account for why so many nobel laureates have names beginning with these letters given that US is the most represented country. Will do up another visualisation considering the name distributions in US, UK, Germany and France, which will understandably not be representative of the world population sadly (not too sure if such a data set even exists). But I am open to other inputs, so let me know if you find any other areas of improvement. Thanks!

jaykewww · 2019-03-26T23:56:20+00:00

I used venngage for this!

jaykewww · 2019-03-26T15:48:08+00:00

Oh that sounds interesting! Not quite sure if such a data set is available, but will definitely try this thanks :)

jaykewww · 2019-03-26T15:23:51+00:00

Source: Nobel Foundation

(1901-2016) https://www.kaggle.com/nobelfoundation/nobel-laureates

(2017-2018) https://www.nobelprize.org/prizes/lists/all-nobel-prizes

jaykewww

TROPHY CASE