Big Data

Improved Rating System

This is a concept for a rating system centered around a collapsible hierarchical list, so users can rate sub-items within categories. This enables users to add nuanced opinions without writing long reviews, which saves time for everyone. Less time stressing about writing a beautifully written review without typos, and fewer internal man-hours parsing poorly written reviews.

Restaurant Rating Example

Comparison between a written review and a multi-level rating system.

Not only is the image on the right easier for humans to read (given a few UI tweaks and different visualizations) but it's also easier for computers to understand. This makes data analysis much easier, since there's no need to extract nuance from written reviews. 

This concept could be deployed effectively for nearly all rating systems. App Store app ratings, Google Maps location ratings, Amazon product ratings, LinkedIn job ratings, etc. 

Suggested applications from left to right: Google Play Store, Apple App Store, Google Maps, Apple Maps, Amazon, Netflix.


I spent some time looking for the best user interface for nested lists and hierarchies. I used the macOS style for my mockup because it's simple and clean, but if you know of any prettier interfaces, please drop me a line in the comments below! 

View this article on Medium.

Detecting Poor Road Quality Using Our Phones

Roads are constantly falling apart. That’s just what happens. Sometimes the roads in greatest need of repair are neglected for long periods of time, but we can change that. We can use our collective computing power to help us prioritize which roads need to be fixed first.

The idea is to build a mobile application that records vibrations while you drive, and anonymously tracks your location to help build a crowdsourced map of how bumpy the roads are.

Vibrations due to cracks, uneven pavement, potholes — all recorded by the device’s accelerometer.

Vibrations due to cracks, uneven pavement, potholes — all recorded by the device’s accelerometer.

Given three example vibration datasets, the algorithm (statistical regression, probably, idk I’m not a data scientist) is able to determine which vibrations are road-related and which are anomalies, such as someone fumbling their phone around.

Comparing datasets against one another increases the quality of the roadmap, automatically identifying user errors.

Comparing datasets against one another increases the quality of the roadmap, automatically identifying user errors.

Read about the Law of Large Numbers for a mathematical explanation of the relationship between number of data inputs & accuracy.

Check out the video explanation of this idea, the outline in Workflowy, and feel free to join the discussion on Reddit (link 1link 2) and Twitter