Paper recently published

Liddle, J., W. Jiang, and N. Malleson (2025). Leveraging Principal Component Analysis to Uncover Urban Pedestrian Dynamics. Journal of Geographical Systems DOI: 10.1007/s10109-025-00469-0

Overview

Footfall data: counts of pedestrians

Context: A better understanding of urban dynamics through the analysis of footfall data

Principal Component Analysis to isolate key temporal patterns at various urban locations

Identify surprisingly clear patterns

Better understand the evolution of urban footfall dynamics

Context: Footfall Dynamics

Data

Melbourne Footfall Counters

Fantastic data available through the Melbourne Open Data Portal

Footfall counters

94 sensors counting hourly footfall, some date back to 2009

We use 24 months of data (all of 2018 and 2019, avoiding COVID)

Footfall Data

Mean daily and weekly footfall counts

Some regularity but also huge variation

Principal Component Analysis (PCA)

Technique for reducing dimensionality in datasets

Isolate significant features; the 'principal components'

Raw data can be approximated by a linear combination of component loadings

Here: components are vectors of length 24 (or 168 if we look at weeks)

How many principal components?

Proportion of explained variance

Results (i): Most Important Components

Most Important Components

Component 1 - busyness

Almost identical to mean daily activity

Busy places: component loadings > 1

Quiet places: component loadings < 1

Component 2 - commuting

Peaks in the morning and afternoon - typical rush hour

Component loadings represent presence of commuters

Component 3 - lunchtime suppression

Suppresses activity in the middle of the day and increase it over lunch

Results (ii) Component Loadings

Examples: Southern Cross Station and Southbank

Results (iii): Component Interactions

Results (iv): Evolution of Usage Patterns

Southern Cross

Commuting is largest driver of footfall; still lower than pre-pandemic levels

Additional footfall might be encouraged by making it attractive to commuters

Results (iv): Evolution of Usage Patterns

Lygon St

Steady decline in busyness, but a gradual increase in commuting?

Possible transition towards a more commuting-oriented area