Overview

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 pattens:

general busyness

commuting behaviour

lunchtime activities

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 examples

Principal Component Analysis (PCA)

Technique for reducing dimensionality in datasets

Isolate significant features; the 'principal components'

Features 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 presense of commuters

Component 3 - lunchtime surpression

Surpresses activity in the middle of the day

Component loadings < 0 will increase lunchtime footfall

Results (ii): Temporal Trends

Results (iii): Component Interactions