Understanding urban human activity and mobility patterns using large-scale location-based data from online social media

By Samiul Hasan, Xianyuan Zhan, Satish V. Ukkusuri. UrbComp '13, 2013

339 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/6e35e143fed4879d2df9614edaac3be43e215d9f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Billy Pik Lik Lau
2. Chau Yuen
3. Yuren Zhou

In Statistical and Nonlinear Physics

1. Chau Yuen
2. Yuren Zhou
3. Billy Pik Lik Lau

In Transportation

1. Chau Yuen
2. Billy Pik Lik Lau
3. Sebastien Blandin

Citations in the corpus

• Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics (2016, background): “A considerable amount of literature has been focused on extracting spatio-temporal activity patterns from large cellular network datasets [1], in combination with social media [11], and in some cases leveraging models from statistical physics [10].”
• Understanding Urban Human Mobility through Crowdsensed Data (2018, methodology): “…in [11], 2) fitting statistical models to describe mobility attributes, such as fitting truncated power laws to social media check-in data in [7], 3) conducting statistical test to evaluate research findings, such as using Monte Carlo simulations to test significance of wavelet analysis in [2].”