Spatial Accessibility
2023-11-19
GF2SFCA: Towards a smart healthy city: A generalised flow-based 2SFCA method for incorporating actual mobility data in healthcare accessibility evaluation
This paper introduces a Generalised Flow-Based Two-Step Floating Catchment Area method that utilizes mobility data patterns to adaptively determine parameters and attractiveness measures in order to improve accessibility modeling using the classic 2SFCA framework.
Abstract:
Worldwide efforts have been made to prioritise equal access to hospitals, given its importance to health equality and the necessity of building healthy cities in the post-pandemic society. As the most used spatial accessibility technique to identify the under-served areas, however, the two-step floating catchment area (2SFCA) method was criticised for adopting presumed parameters that might lead to biased and misleading results. With the increasing availability of urban mobility data, it is now possible to incorporate people’s actual service-seeking behaviour into the methodological framework of the classic 2SFCA. In this regard, this study proposes a Generalised Flow-based 2SFCA (GF2SFCA) method, which builds upon the demand-driven characteristic of healthcare facilities and utilises underlying patterns in geographical flow data to adaptively measure hospitals’ influential range, distance-decay effect, and attractiveness. Specifically, two new indicators, namely, the global popularity index and local preference index, are proposed to synthetically evaluate hospital attraction from the perspective of actual healthcare demand, taking into account visitation counts, travel distance, and the collective preference of communities associated with a hospital. While alleviating certain subjectivity caused by parameters pre-selection, the GF2SFCA framework helps evaluate healthcare accessibility precisely. The experimental results, using Wuhan, China as a case study, confirmed the effectiveness of the proposed framework in producing realistic estimations and demonstrated its robustness to the potential uncertainty in mobility data. The data-driven property of GF2SFCA also allows for practical implementation in various contexts, which would benefit the survey of regional healthcare equality and its dynamics.
Summary:
- Introduces a Generalised Flow-Based Two-Step Floating Catchment Area (GF2SFCA) method to improve 2SFCA by using mobility data.
- Uses a power distribution based on associated visitation flows for each hospital to determine catchment size and decay coefficient.
- Develops a Global Popularity (GP) index based on visit counts and travel distance to measure hospital popularity.
- Develops a Local Preference (LP) index using community-level flows to capture preferences of similar populations.
- Integrates GP and LP into a Huff model to evaluate attraction and selection probability.
- Applies GF2SFCA in a case study of Wuhan, China using taxi trace data.
- Results show GF2SFCA produces realistic estimates and is robust to uncertainty in mobility data.
- Provides a generalised framework to incorporate mobility data into accessibility modelling.
Study questions and answers:
| Study Question | Finding |
|---|---|
| What is the key contribution of this paper? | Introducing a GF2SFCA method that uses mobility data to improve 2SFCA accessibility modelling. |
| How does it determine decay coefficient and catchment size? | Uses power distribution of flows for each hospital based on visit counts and distance. |
| What indices are proposed to measure hospital attractiveness? | Global Popularity (GP) index and Local Preference (LP) index. |
| How are GP and LP integrated into the 2SFCA framework? | Incorporated into a Huff model to evaluate attraction and selection probability. |
| What data was used in the case study demonstration? | Taxi trace data in Wuhan, China. |