# Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics

arXiv: https://arxiv.org/abs/2209.12095 · HTML: https://mlsanity.com/paper/2209.12095/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 62 references that are in the map.
- First posted on arXiv 24 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach](https://mlsanity.com/paper/1308.0723/) (2013)
- [Evidence for a Conserved Quantity in Human Mobility](https://mlsanity.com/paper/1609.03526/) (2016)
- [Sequences of purchases in credit card data reveal life styles in urban populations](https://mlsanity.com/paper/1703.00409/) (2017)
- [Limited communication capacity unveils strategies for human interaction](https://mlsanity.com/paper/1304.1979/) (2013)
- [Social media fingerprints of unemployment](https://mlsanity.com/paper/1411.3140/) (2014)
- [Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data](https://mlsanity.com/paper/1801.07826/) (2018)
- [Tales of Two Cities: Using Social Media to Understand Idiosyncratic Lifestyles in Distinctive Metropolitan Areas](https://mlsanity.com/paper/1701.06236/) (2017)
- [Mining urban lifestyles: urban computing, human behavior and recommender systems](https://mlsanity.com/paper/1911.05464/) (2019)
- [Characterizing Urban Lifestyle Signatures Using Motif Properties in Network of Places](https://mlsanity.com/paper/2204.01103/) (2022)

## Built on by (most cited first, 5 of 5)

- [From Narrative to Action: A Hierarchical LLM-Agent Framework for Human Mobility Generation](https://mlsanity.com/paper/2510.24802/) (2025)
- [Network-Level Measures of Mobility from Aggregated Origin-Destination Data](https://mlsanity.com/paper/2502.04162/) (2025)
- [Learning Individual Movement Shifts After Urban Disruptions with Social Infrastructure Reliance](https://mlsanity.com/paper/2510.23989/) (2025)
- [Discovering Sparse Counterfactual Factors via Latent Adjustment for Survey-based Community Intervention](https://mlsanity.com/paper/2605.04460/) (2026)
- [Do LLMs Know Your Neighborhood? Auditing LLM Priors for Neighborhood-Level Mobility Prediction and Structural Alignment](https://mlsanity.com/paper/2609.00345/) (2026)

## Abstract (arXiv)

Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitations, we found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time. Rather than describing individuals with a single lifestyle, we find that city dwellers' behavior is a mixture of those behaviors. Those detected latent activity behaviors are equally present across cities and cannot be fully explained by main demographic features. Finally, we find those latent behaviors are associated with dynamics like experienced income segregation, transportation, or healthy behaviors in cities, even after controlling for demographic features. Our results signal the importance of complementing traditional census data with activity behaviors to understand urban dynamics.