Exploring the effect of spatial scales in studying urban mobility pattern

TL;DR

This study investigates how different spatial scales affect the accuracy of the gravity model in explaining urban mobility patterns in Singapore, revealing an optimal intermediate scale and the superiority of distance-based aggregation.

Contribution

It systematically evaluates the impact of spatial scale choices on gravity model performance, providing guidance for urban mobility analysis and planning.

Findings

Optimal intermediate spatial scale improves model performance.

Fine-scale data introduces noise, reducing accuracy.

Distance-based aggregation outperforms administrative boundaries.

Abstract

Urban mobility plays a crucial role in the functioning of cities, influencing economic activity, accessibility, and quality of life. However, the effectiveness of analytical models in understanding urban mobility patterns can be significantly affected by the spatial scales employed in the analysis. This paper explores the impact of spatial scales on the performance of the gravity model in explaining urban mobility patterns using public transport flow data in Singapore. The model is evaluated across multiple spatial scales of origin and destination locations, ranging from individual bus stops and train stations to broader regional aggregations. Results indicate the existence of an optimal intermediate spatial scale at which the gravity model performs best. At the finest scale, where individual transport nodes are considered, the model exhibits poor performance due to noisy and highly variable travel patterns. Conversely, at larger scales, model performance also suffers as over-aggregation of transport nodes results in excessive generalisation which obscures the underlying mobility dynamics. Furthermore, distance-based spatial aggregation of transport nodes proves to outperform administrative boundary-based aggregation, suggesting that actual urban organisation and movement patterns may not necessarily align with imposed administrative divisions. These insights highlight the importance of selecting appropriate spatial scales in mobility analysis and urban modelling in general, offering valuable guidance for urban and transport planning efforts aimed at enhancing mobility in complex urban environments.