Towards completely caring 15-minute neighbourhoods

The “15-Minute City” concept has been embraced by global leaders to promote human-scale neighbourhoods with transport and land-use designs that support short trips to daily necessities. This paper bridges the 15-Minute City to “Mobility of Care”, a framework that foregrounds travel to care destinations, travel done predominately by women. This focus contrasts the more commonly studied travel to employment and leisure destinations. While the 15-Minute City concept is flexible enough to consider all destination types, gendered examinations are relatively lacking in the literature, and little research to date has focused explicitly on care destinations. This gap is addressed in this paper by identifying which areas in the city of Hamilton, Canada are ‘caring 15-minute neighbourhoods’. To do so, a database of care destinations was compiled to estimate the number (using the cumulative opportunity accessibility measure) and diversity of mix (using an entropy measure) of care destinations within a 15-minute walk from residential parcels. Using data-driven machine learning techniques (Self-Organizing Maps and Decision Trees), neighbourhoods were classified into ‘caring 15-minute neighbourhood’ typologies that are examined across residential socio-economic profiles. Our results suggest that the majority of caring 15-minute neighbourhoods are in the urban core, where more lower-income households currently reside. In contrast, areas that lack caring 15-minute neighbourhoods are in higher-income peripheral areas. Areas that make good candidates for urban policy intervention are identified and the implications of enhancing 15-minute walkable caring access are discussed in relation to equtiy and gender mainstreaming in transportation planning and limitations of this work. • What destinations matter? “Mobility of Care” is joined to the 15-Minute City concept. • A case study of access to care destinations in Hamilton, Canada is explored. • 15-minute walking access and diversity of care category access is measured. • “Caring 15-minute neighborhoods” are identified through machine learning approaches. • Residential profiles of these “complete” and “caring” typologies are examined.