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London Pedal Me Spatiotemporal Bicycle Delivery Graph
Retail & Consumer Behavior
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About
Weekly bicycle package deliveries across London throughout 2020 and 2021 are captured in this spatiotemporal collection. The information is structured as a graph where nodes represent specific geographical units and edges define proximity-based mutual adjacency relationships. Developed to support research into neural machine learning models, the records provide a unique view of urban logistics and signal processing within a major metropolitan environment.
Columns
- from: The identifier for the starting geographical node in the proximity relationship.
- to: The identifier for the destination or adjacent geographical node.
- weight: A numerical value representing the strength or proximity of the adjacency relationship between nodes, ranging from 0.01 to 1.00.
Distribution
The data is provided in a CSV file titled
pedalme_edges.csv, which is approximately 5.24 kB in size. It contains 225 valid records structured across 3 columns. The records maintain a 100% validity rate with no missing or mismatched entries reported.Usage
This resource is ideal for training spatiotemporal signal processing models and exploring neural machine learning architectures. It is well-suited for graph-based analysis of urban delivery networks and proximity modelling in logistics. Researchers can also use the adjacency weights to study the connectivity of different London regions within a cycling delivery context.
Coverage
The geographic scope is focused on London, specifically targeting regions serviced by bicycle delivery. Temporally, the instances cover the years 2020 and 2021. The data focuses on weekly delivery patterns rather than individual customer demographics.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
Data scientists can leverage these records to develop and benchmark temporal graph neural networks. Logistics analysts may utilise the proximity data to optimise route planning and understand regional adjacency in urban centres. Additionally, academic researchers in the field of spatiotemporal signal processing can find this a valuable resource for validating new machine learning algorithms.
Dataset Name Suggestions
- London Pedal Me Spatiotemporal Bicycle Delivery Graph
- Pedal Me London Delivery Adjacency Network 2020-2021
- Weekly Bicycle Package Delivery Metrics: London
- Graph-Based Urban Logistics: Pedal Me London Dataset
- Spatiotemporal Signal Processing Delivery Records
Attributes
Original Data Source:London Pedal Me Spatiotemporal Bicycle Delivery Graph
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