This project explores walkability and street network connectivity in Liverpool using Python and geospatial analysis tools. The aim is to understand how the walking network is structured and how spatial data can be used to represent urban accessibility.
The project uses OpenStreetMap data to create and analyse a walking network, then visualises the results through static and interactive maps.
- Python
- OSMnx
- GeoPandas
- pandas
- Matplotlib
- Folium
- NetworkX
- OpenStreetMap data
The project uses street network data retrieved from OpenStreetMap. The focus is on the walking network in Liverpool, United Kingdom.
The main spatial data includes:
- walking network nodes
- walking network edges
- street connectivity
- node degree values
- Liverpool boundary/location context
- Retrieved Liverpool walking network data using OSMnx
- Created a graph-based street network representation
- Converted network data into geospatial data structures
- Analysed node degree as a measure of connectivity
- Created static maps to visualise the walking network
- Created interactive maps to support exploration of the results
- Interpreted how street connectivity relates to walkability
This map shows the structure of Liverpool's walking network using OpenStreetMap data. It helps show the density and pattern of walkable streets across the city.
This map visualises street connectivity using node degree. Areas with higher node degree have more connected walking routes, while areas with lower node degree have fewer route options.
The interactive map allows users to explore Liverpool's walking network and connectivity patterns in more detail.
- Liverpool's walking network shows clear variation in street density and connectivity across different areas.
- Areas with higher node degree suggest stronger street connectivity and more route choices for pedestrians.
- Areas with lower node degree may offer fewer walking options and less direct movement through the network.
- Static maps are useful for showing the overall structure of the walking network.
- The interactive map makes it easier to explore local street patterns and compare different parts of the city.
These findings show how OpenStreetMap data and Python-based geospatial analysis can be used to explore walkability in an urban setting.
- Geospatial data analysis
- Street network analysis
- OpenStreetMap data retrieval
- Python-based spatial visualisation
- Graph/network thinking
- Static and interactive mapping
- Communicating urban data insights
This repository currently includes the main portfolio notebook, static map outputs, and an interactive map preview for the Liverpool walkability analysis. The project is structured as part of my data science and geospatial visualisation portfolio.
Further improvements will focus on adding more walkability indicators, improving the map design, and making the analysis more detailed.
- Add cleaned portfolio notebook
- Add static map outputs
- Add interactive map preview
- Improve explanation of walkability and connectivity
- Add clearer interpretation of high-connectivity and low-connectivity areas
- Explore additional accessibility indicators in future versions
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