Unraveling node, place, and resilience during disasters: Evident from a typhoon in Hong Kong
Mingzhi Zhou et al.
Abstract
Public transport-land use integration rarely considers performance during natural disasters, leading to mismatches in demand and functionality. This study introduces “resilience” into the node-place model to examine transport-land use dynamics when disasters occur in cities. Apart from operationalizing “node” (transport provision) and “place” (land use functionalities) at the station level, the “resilience” aspect is measured by node resilience (NR) and place-resilience (PR). NR measures the ability of stations to maintain transport functions and services, while PR assesses their ability to sustain land use functions and provide support. Using Hong Kong as a case, we measure node, place, NR, and PR of stations, and identify station clusters to explore their performance under this disaster. Examining a typhoon as a specific disaster, we apply a set of models to examine whether and how the proposed aspects and clusters explain trip dynamics during this disaster. Results show that stations with similarly balanced node-place states can exhibit different NR and PR values. Some stations may maintain their node and place functions, while others are vulnerable due to losses in transport accessibility, socioeconomic services, or community support during disasters. Integrating the node-place aspect as well as NR and PR plays an important and positive role in maintaining stations’ trip dynamics during the typhoon. The findings offer insights into policy and planning decision-makers to address the dynamics and adaptability of transport-land use integration amid disasters.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.