Welcome to Beacons in New York City

NYU CUSP IoT Team, 2021



Beacons are tiny devices that use the Bluetooth Low Energy (BLE) signals to periodically transmit signals to mobile devices within a short distance or proximity of the beacons. Compared to devices based on Global Positioning System (GPS), beacons provide more accurate location information and can be used for indoor location technology. Various types of beacons exist, which can be classified based on their type of Beacon protocol, power solution and location technology. Major protocols for beacons are iBeacon, Eddystone and AtlBeacon.

  • iBeacon: a beacon protocol released by Apple in 2013. It is the first beacon protocol in the market. iBeacon works with iOS and Android.

  • Eddystone: a beacon protocol released by Google.

  • AtlBeacon: a beacon protocol released by Radius Network.

UUID: The beacon using the iBeacon protocol transmits a so-called Universally Unique Identifier (UUID). The UUID is a string of 24 numbers, which communicate with an installed Mobile App. 

Screen Shot 2021-11-19 at 4.35_edited.png

Our Goal

Through this project we intent to inform how private corporations track consumers in physical spaces in public environments, and whether they do so responsibly or not. We investigated in New York City and in doing so, investigated the local policies and law regulations.

Screen Shot 2021-11-19 at 4.42_edited.png

Our Work




Technology and Manufacturing Companies

We began with conducting industry research to find beacon manufacturers and their product UUID formats. Our methods included internet research, media research, cold calls and product demos. On the technology side, we conducted research on SDKs and app-libraries to understand the information flow ecosystem between retailer, beacon device, app and the user. Our primary technology research method was through the internet.

Data Collection

Feasibility Checks and Testing

While the research was in progress, we parallelly tested out several methods for ensuring successful data capture, often undergoing several iterations to find solutions to problems encountered.

Large scale data collection

We have covered areas such as Lower Manhattan, Tribeca, Lower East Manhattan, Chelsea, Hudson Yards, parts of Midtown, SoHo, NoHo, East Village, DUMBO, Downtown Brooklyn, Cobble Hill, Brooklyn Heights, parts of Bedford Stuyvesant and Williamsburg.

Data processing

The data from the server is read and saved as ‘.json’ files, while GPS records are read from the ‘.csv’ files using Jupyter Notebook. The beacon dataset contains all beacon related information as nested list of dictionaries within 2 columns: ‘beacons’ and ‘ibeacon’. To get useful beacon information, we extracted and expanded those columns, and then merged the beacon dataset with GPS location datasets using timestamps. Consequently, we achieved a consolidated dataset with 27 columns containing all information about beacon signals detected and the GPS coordinates.

Analysis and Visualizations

To communicate the findings, we used several tools to build effective visualizations such as Google Maps JavaScript API, D3.js, Plotly JS, Mapbox and Tableau Dashboard.


Ethics and Privacy


The implications for many individuals regarding their personal data being collected without their consent is huge. Data subjects must be informed about the collection and use of their personal data when the data is obtained.

Data Insights and Results

In a span of 65 days, a total of 2,555 minutes and 102 kilometers of data collection across 129 census tracts in Manhattan and Brooklyn:  

No. of beacon signals detected: 346,887 
No. of beacon devices detected: 10,357



by Apple

7667 beacons follow iBeacon protocol
203 unique iBeacon UUIDs



by Google

2664 beacons follow Eddystone UID protocol
17 beacons follow Eddystone url protocol
11 unique Eddystone Namespace IDs (Eddystone version of UUID)



by Radius Network

9 beacons follow Altbeacon protocol
2 unique Altbeacon UUIDs


Unique Beacons Detected

Where We Found Them

Google Maps JavaScript API was used to map the beacon locations, using the package “gmplot”. The GPS location data is plotted as grey dots, whereas the Beacon approximate locations are plotted using red pinpoints. In order to avoid unnecessary overlapping of markers, we also combined adjacent markers into one based on 4-digit GPS. Through the overlap of both datasets, one can witness the scale of detected beacons compared to the scale of locations we covered. Upon hovering on the markers, information such as ibeacon uuid, street address and GPS locations can also be gathered.


Geographical Distribution of Beacons by UUIDs

Using Census Tract of NYC

To visualize the distinct UUID level statistics of detected beacon devices, a combination of D3.js, Plotly JS and Mapbox was used. This visualization combines a bar plot and a census tract level map of NYC, and interactively displays counts of every UUID found, mapped back to its manufacturing company (if identified), and displays on the map the locations it was found at.
Hover and Click on the bars to explore!


Tree Diagram of Census-tract-level Beacon Distribution

The visualization used census-tract-level beacon distribution data standardized by time spent on each census tract. The tree diagram is colored based on no. of beacon devices detected per minute.

仪表板 1 (1).png

Additional Recources

Available for Download

Github Repository


Beacons in New York City Team

Get to Know Us

Anbo Guo.JPG

Anbo Guo

NYU CUSP Student

Our Sponsor

Danny Y. Huang

Assistant Professor

NYU mLab

Chenghao Du

Ph.D. Student

NYU mlab


Contact Us

New York University
Center for Urban Science + Progress

370 Jay Street, 13th Floor
Brooklyn, NY 11201