Abstract Summary
The well-being of the ageing population in urban areas has become a rising concern. The walkability of cities is a crucial factor in maintaining the mobility and health of older individuals. Therefore, it is of utmost importance for cities to prioritise the ageing population's needs, particularly regarding walkability. As we strive to create cities that cater to people of all ages, it is essential to grasp the nuances of walkability for older adults. This concept encompasses more than just pedestrian accessibility; it involves a comprehensive evaluation of urban environments, considering factors such as feasibility, accessibility, safety, comfort, and enjoyment. These factors form the "Hierarchy of Walking Needs," developed by Alfonzo in 2005 and widely utilised in research. A city's success in becoming age-friendly depends, in part, on its ability to address the needs and preferences of older pedestrians. Through extensive research, we have discovered that safety and comfort are the top priorities for seniors, indicating that these aspects should be given special consideration. Numerous assessments have been conducted to evaluate walkability, but few have concentrated on creating neighbourhoods specifically welcoming older adults. This paper aims to fill this crucial gap by developing a comprehensive spatial assessment model tailored to the elderly demographic. The study addresses three main questions: Does the Walkability index vary across different age groups? Can we build a walkability model for the elderly? Can we create an intuitive dashboard to help them choose the best walking routes? We select the pedestrian-friendly pathways in Zone One of Central London. These streets were determined by various built environmental characteristics at the neighbourhood and pedestrian eye levels. Along with the logic of quality-level walkable environments (i.e., Feasibility, Accessibility, Safety, Comfort, and Pleasurbility), we further identified the factors contributed to each level and quantified them by ADE02K deep learning model based on Google Street View(GSV) and ArcGIS tools. Overlayed index models were visualised on the PowerBi platform. Additionally, this study precisely describes the Multicriteria Spatial features of streets to identify how walkability varies between quality levels and age groups. These findings can benefit planners seeking to create age-friendly walking environments and older individuals looking for better walking routes.
