# A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering

arXiv: https://arxiv.org/abs/2312.00411 · HTML: https://mlsanity.com/paper/2312.00411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [(k, l)-Medians Clustering of Trajectories Using Continuous Dynamic Time Warping](https://mlsanity.com/paper/2012.00464/) (2020)
- [Explore Spatiotemporal and Demographic Characteristics of Human Mobility via Twitter: A Case Study of Chicago](https://mlsanity.com/paper/1508.00188/) (2015)
- [Mobile Communication Signatures of Unemployment](https://mlsanity.com/paper/1609.01778/) (2016)

## Abstract (arXiv)

Human mobility demonstrates a high degree of regularity, which facilitates the discovery of lifestyle profiles. Existing research has yet to fully utilize the regularities embedded in high-order features extracted from human mobility records in such profiling. This study proposes a progressive feature extraction strategy that mines high-order mobility features from users' moving trajectory records from the spatial, temporal, and semantic dimensions. Specific features are extracted such as travel motifs, rhythms decomposed by discrete Fourier transform (DFT) of mobility time series, and vectorized place semantics by word2vec, respectively to the three dimensions, and they are further clustered to reveal the users' lifestyle characteristics. An experiment using a trajectory dataset of over 500k users in Shenzhen, China yields seven user clusters with different lifestyle profiles that can be well interpreted by common sense. The results suggest the possibility of fine-grained user profiling through cross-order trajectory feature engineering and clustering.