Open adrianblanco opened 6 years ago
Cool, make sure to get those zipcodes to make sure the data is nice and granular. Maybe also consider taking means of wait times to make the data more manageable?
Nice, I think the most important thing right now is to keep your data as compressed as possible to proceed with your analysis. Basically, what Max just wrote. Excited to see all of our animated maps \o/
Status update:
New charts plotted without styling
avg response time per zipcode, respiratory problems
number of respiratory incidents per zip code, respiratory problems
TODOs:
Clearly a project that demands a map as zipcodes by themselves are difficult to interpret. Let's reconvene when the map is presentable?
Showing your findings in a map would be great, if you manage to pull this off - go for it! Regarding the style of your bar charts I'd play a little with matplotilb, customize it aso.
Howdy! I'm a little robot, here for a surprise inspection.
You need some feedback, let me summon @kevinlitman-navarro, @Katerinavts, @SimoneLuc for you
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Visuals improved:
Moving to a map soon!
i think, yes, move to a map! i want to know where these place are and not ever be there. can you do the opposite and give us the places with the shortest response times? or include other types of emergencies? i am interested!
also, change the seconds into minutes and seconds. no one will ever say, 'it took the ambulance more than 2500 seconds to arrive.' this measure should be reader-friendly.
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Pitch
Summary
The black spots to have an emergency in NYC.
My data come from Emergency Medical Services (EMS) hosted on NYC Open Data.
I was inspired by this project:
Details
Possible headline(s):
Data set(s): https://data.cityofnewyork.us/Public-Safety/EMS-Incident-Dispatch-Data/76xm-jjuj
Code repository: https://github.com/adrianblanco/data-studio/tree/master/code/01-ems
Possible problems/fears/questions:
Work so far
Checklist
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[Project]
in the title