What makes one neighborhood similar to another?
Is it how they look? The people that live there? How easy they are to navigate? Or is it the percentage of global big box stores vs local small businesses? Vegan restaurants vs fast food chains?
At Topos we believe that understanding neighborhoods (and how they relate) encompasses all of these aspects, and more. We formed Topos earlier this year to develop a holistic understanding of cities through the interconnected lenses of data and artificial intelligence. From day one, we have been fascinated by a simple question: what does distance mean in the 21st century?
While we began by developing new concepts of distance and similarity in New York City, we have since scaled our platform to 15 additional metro regions. As part of this expansion, we have been studying and computing similarities between neighborhoods across the United States. Today, we are excited to share two ways for you to play with and explore this set of relationships.
Topos Similarity Index
Encompassing everything from topological analysis of urban form to the ratio of third-to-first wave coffee shops, the Topos Similarity Index (TSI) takes 50+ heterogeneous data sources, transforms them using a wide range of technological approaches (Natural Language Processing, Computer Vision, Topological Data Analysis, …) and integrates these inputs into a single normalized similarity metric. The TSI is then able to answer questions like ‘what is the Greenpoint of Boston’ (spoiler alert: it’s in Somerville :) and, more generally: ‘What does the Boston area look like if every Boston area neighborhood is scored based on how similar it is to Greenpoint.’
We’ve decided to release a view of the TSI in the hopes that people will find new ways to visualize and explore this set of relationships. What will you build with TSI? We’d love to know! Please share your thoughts/work with us in the comment section below or on twitter.
You can start working with TSI via our API. For additional views of TSI, including the full numerical distance matrix, please contact info@topos.ai
[x] Everywhere
Inspired by a diverse set of projects, ranging from The Naked City (Guy Debord) to Where is Williamsburg (Kate Ray), we built [x] Everywhere to provide a visual (and arguably insane) way to explore the TSI. Covering 1718 neighborhoods across the US, [x] Everywhere transcends traditional cartographic notions of distance to map a latent invisible city that mutates over time.
Cities change, neighborhoods change, relationships change; we will be regularly updating [x] Everywhere to account for these changes. Additionally, as we continue to scale our platform to the rest of the US (and the world), we’ll be adding new regions: check back regularly!
https://everywhere.topos.ai
This post is part of an ongoing series capturing different insights we generate while developing our platform. We would love to hear your feedback. If you enjoyed this article please share and 👏 a few times so other people can see it too.
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Topos: Transforming the way we understand cities with Artificial Intelligence.
