New York City Heat Map based on FourSquare

Project Scope:

This project is data visualization of Four Square Check-ins in New York City. The used dataset is from Kaggle.. This dataset contains check-ins in NYC and Tokyo collected for about 10 month (from 12 April 2012 to 16 February 2013). It contains 227,428 check-ins in New York city and 573,703 check-ins in Tokyo. Each check-in is associated with its time stamp, its GPS coordinates and its semantic meaning (represented by fine-grained venue-categories). This dataset is originally used for studying the spatial-temporal regularity of user activity in LBSNs. This project uses Pandas to extract the information from .csv file and Flask in order to render the template. For more info check the repo on github.

Resources:

• FourSquare - NYC and Tokyo Check-ins Dataset from Kaggle
• Google Maps JS API

This is what the dataset looks like:

userId	venueId	venueCategoryId	venueCategory	latitude	longitude	timezoneOffset	utcTimestamp
470	49bbd6c0f964a520f4531fe3	4bf58dd8d48988d127951735	Arts & Crafts Store	40.71981038	-74.00258103	-240	Tue Apr 03 18:00:09 +0000 2012
979	4a43c0aef964a520c6a61fe3	4bf58dd8d48988d1df941735	Bridge	40.60679958	-74.04416981	-240	Tue Apr 03 18:00:25 +0000 2012
69	4c5cc7b485a1e21e00d35711	4bf58dd8d48988d103941735	Home (private)	40.71616168	-73.88307006	-240	Tue Apr 03 18:02:24 +0000 2012
395	4bc7086715a7ef3bef9878da	4bf58dd8d48988d104941735	Medical Center	40.7451638	-73.98251878	-240	Tue Apr 03 18:02:41 +0000 2012
87	4cf2c5321d18a143951b5cec	4bf58dd8d48988d1cb941735	Food Truck	40.74010383	-73.98965836	-240	Tue Apr 03 18:03:00 +0000 2012
484	4b5b981bf964a520900929e3	4bf58dd8d48988d118951735	Food & Drink Shop	40.69042712	-73.95468678	-240	Tue Apr 03 18:04:00 +0000 2012
...	...	...	...	...	...	...	...

Installation:

Downloading the project:

git clone https://github.com/Denisolt/FourSquare_HeatMap_NYC.git
cd FourSquare_HeatMap_NYC-master

Activation of virtual environment:

source local/bin/activate
pip install -r /path/to/requirements.txt

Execution:

python main.py

Output will be:

 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
 * Restarting with stat
 * Debugger is active!
 * Debugger PIN: 150-657-841