Week 5

Statistical Analysis of Urban Data

This week, we will discuss the unique features of geospatial data and the importance of considering the spatial structure of most urban data when conducting statistical analysis. We will then have our first introduction to individual-level GPS data, and will discuss different techniques to extract semantic meaning and quantitative behavioral measures from GPS observations.

In our first practical, we will build on our results from week 2 to create geodemographic classifications of urban areas using census data.

Prepare

📖 Take a look at two contrasting approaches to characterizing urban behavior: (1) with spatial clustering of demographic variables from the census [1], and (2) using individual-level behavioral data [2]. Finally, re-visit a classic review of mobile phone data mining [3].

• Creating the 2011 area classification for output areas (2011 OAC) [1]

  • This article is a dense methodological piece! Please just look at sections 1, 2, 7, and 8.
  • You can also explore the 2011 OAC: CDRC Mapmaker - 2011 Output Area Classification

• Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics [2]

  • This article gives a good introduction to the behavioral insights that can come from GPS mobility data. Contrast these results with the approach taken in [1].

• A review of Urban Computing for Mobile Phone Traces [3]

  • This is a classic review of trajectory creation and mining which we saw in week 3.

Participate

🖥️ Lecture 5-1 – Statistical Analysis of Urban Data

Perform

⌨️ Lab 5-1 – Statistical Analysis of Urban Data

⌨️ Lab 5-2 – Detecting Hotspots in Urban Areas

Back to course schedule ⏎

References

[1]

C. G. Gale, A. D. Singleton, A. G. Bates, and P. A. Longley, “Creating the 2011 area classification for output areas (2011 OAC),” Journal of Spatial Information Science, vol. 2016, no. 12, pp. 1–27, 2016, doi: 10.5311/JOSIS.2016.12.232.

[2]

Y. Yang, A. Pentland, and E. Moro, “Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics,” EPJ Data Science, vol. 12, no. 1, pp. 1–15, Dec. 2023, doi: 10.1140/epjds/s13688-023-00390-w.

[3]

S. Jiang, G. A. Fiore, Y. Yang, J. Ferreira Jr, E. Frazzoli, and M. C. González, “A review of urban computing for mobile phone traces: Current methods, challenges and opportunities,” in Proceedings of the 2nd ACM SIGKDD international workshop on urban computing, 2013, pp. 1–9.