Power BI Python Data Visualisation Geospatial

Melbourne Pedestrian Counting System

Interactive Power BI dashboard analysing pedestrian movement patterns across Melbourne using real-time sensor data from almost 100 locations

Project Overview

The Melbourne Pedestrian Counting System is a comprehensive Power BI dashboard analysing pedestrian movement patterns in Melbourne. It provides insights into pedestrian traffic trends using sensor data from the City of Melbourne's counting network.

Features

  • Daily pedestrian counts across all sensors
  • Average pedestrian counts per hour of day
  • Data filtered by day of week, weekday/weekend, and year
  • Slicer to isolate data from any of the ~100 sensors
  • Mapped sensor locations via longitude and latitude

Data Source

Data sourced from pedestrian.melbourne.vic.gov.au — hourly pedestrian counts from sensors across Melbourne CBD since January 2010.

Data Preparation

Python was used for data collection and transformation into a format suitable for Power BI analysis. New measures including 4-week and 52-week rolling averages were created within Power BI.

The Jupyter Notebook for producing the required CSV is on GitHub.

Dashboard Pages

  • Daily Pedestrian Count: Total pedestrian counts on a daily basis across selected sensors.
  • Pedestrian Count Summary: Average traffic per hour, broken down by day of week, weekday/weekend, and year.