Foursquare Check-Ins: Insights about NYC using data

Posted on Feb 5, 2018

The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


"Are you listening?", I asked my best friend as she tapped the screen of her space grey iPhone with her right index finger as she smiled at the screen. She wasn't. I looked around the restaurant, and I noticed that she was not the only one engrossed in her phone.

This exchange inspired the idea for my Shiny app. The goal of my Shiny app was to visualize consumer behavior and interaction with social media. Specifically, what can we tell about New Yorkers by the way they use a specific app such as Foursquare?

Foursquare is a search-and-discovery app that gives users recommendations for things to do, and places to eat and visit based on the user's current location and previous purchases, browsing, and check-in history.

The Data Set

Back in 2012, the Foursquare app was different. Users had to actively "check-in" on their app to receive recommendations. The dataset that I used contains 227,428 check-ins in New York, which were collected over ten months (from 12 April 2012 to 16 February 2013). Each check-in has location data associated with it as well as the type of venue and the time of the check-in. This dataset was used initially for studying the spatial-temporal regularity of user activity in LBSNs (research paper).

The venues of the check-ins are categorized into nine broad categories:
- Bar & Nightlife
- Business, Government & Other
- Education
- Food & Beverage
- Home & Personal
- Leisure & Outdoors
- Religious
- Shopping
- Travel & Transporation

Exploratory Data Analysis & Visualization

  • Β Part A: NYC MapΒ 

Under the tab, "NYC Map" on my app, I visualized each check-in on the map of New York. The control widget panel on the left side of the screen allows you to select between the layers you would like to see.

The yellow-orange animation shows all of the check-ins in the dataset.

The control widget panel on the right side allows you to toggle with other features of this map: Venue Category, Day of the Week, and New York Boroughs.

The inspiration for this interactive map came from a video (you can find the video under the tab, "Video" on my app) created by the data team at Foursquare, which visualized check-ins over the course of one year.

  • Part B: Analytics & Insights

Under the tab, "Analytics & Insights" on my app, I further explored and analyzed the data set through a bar chart and time series charts.

The bar chart shows you the variation in the number of check-ins over the weekdays versus weekend. An example of an insight that we can draw from the bar chart is that more people checked in from the Bar & Nightlife category over the weekend than the weekday.

The time series charts show the variation in the number of check-ins throughout the day per venue category. An example of an insight that we can draw from the time series chart of the Food & Beverage venue category is that the average New Yorker checked in around 5:00 PM, which is presumably when they were having coffee or dinner.


In this digital age, there is an enormous amount of data collected about each one of us. The real challenge is to harness insights to allow us to make data-driven decisions. The data scientists at Foursquare figured out how and perhaps, the insights they capitalized upon contributed to the app's strong user base of 45 million (and growing).

Thank you for taking the time to check out my blog post. Feel free to check out my GitHub, leave a comment or send me a message on LinkedIn.

About Author

Neha Chanu

Ms. Chanu, 2017 Hesselbein Student Leader Fellow, was one of 50 selected from more than 800 student leader nominees from around the world. She is an honors graduate of the University of Pittsburgh and the Cornell Pre-Law Summer...
View all posts by Neha Chanu >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI