data Overview
Reveal an area's activity through people’s geotagged posts.
The Proximity dataset scores block groups across 72 behavioral segments. Sources include public geotagged posts from Twitter, Instagram, Facebook, Flickr, Foursquare, Meetup, and more.
52M
Social users
14.8M
Data points
92%
Block groups
72
Unique segments
CBG:
27053106000
Hip Hop Culture
79th Percentile
All About Hair
71st Percentile
Body Art
66th percentile
CBG:
27123042001
Hipster
93rd Percentile
Girl Squad
88th Percentile
Late-Night Leisure
74th Percentile
CBG:
27053024006
Dog Lovers
98th percentile
Bookish
92nd Percentile
Trendy Eats
85th percentile
What is Geosocial proximity?
Location-based social media data
The Geosocial Proximity dataset is a classification of areas based on what people are doing and saying there. It originates from where the post was tagged. By combining traditional demographics in trade areas with social behaviors in close proximity, retailers and restaurants can reveal hidden location characteristics that drive business success.
Partner Integration
Activate the Geosocial Proximity dataset in a mapping and analytics platform from one of our many partners.
Feature Service
Esri users can leverage a pre-built feature service and suite of plug-n-play reports and infographics.
Raw Data
Use the raw data in CSV or GeoJSON format within your statistical analysis software such as Python, R, Tableau, PowerBI, and Alteryx.
Key Features
Capture the heartbeat of an area
Demographics fall short especially when the visitors to a trade area don’t live in that location. Geosocial picks up the volume and activity of a location to capture the full picture of area characteristics.
Sharper predictive models
Enhance predictive models by differentiating demographically similar locations by social behavior. The different behaviors in an area can often explain high and low performing stores that are otherwise demographically the same.
Up-to-date
Unlike Census data which can lag up to 10 years, Geosocial Proximity is updated quarterly showing the current and historical trajectory of an area. Keep models up to date with current behavior and know the trajectory of an area to make smart real estate acquisitions.
How it works
An overview of how this dataset was built.
1
Creation
People geotag posts about what they are doing or experiencing in a location.
2
Collection
We clean posts and aggregate at the block group to analyze text and hashtags.
3
Categorization
Using machine learning we cluster mathematically related words into segments.
4
Scoring
We score block groups as percentiles 0 - 100 vs. the nation. Above 50 is above average.
Use Cases
Explore the most common use cases for Proximity.
Ready to get started?
Reach out to our sales team to see a personalized demo for your use case. Or explore the Proximity taxonomy to see the data for yourself.
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