The team of the Politecnico di Milano will define the profile and needs of the elderly by direct survey spur from the interaction with some elderly associations. The results will allow to identify the main types of elderly according to socio-economic and demographic characteristics, their health status, physical functioning, mobility patterns and propensity, social networks, and social activities, etc.; investigate barriers, and obstacles old people face in their daily life; identify the demand for transport and welfare services, distinguishing bottom-up practices of elderly residents; define which main patterns (i.e. elderly types, and neighborhoods’ characteristics) play a role in shaping the perception of the elders’ quality of life.
The team of the University of Naples will define a methodology for classifying the different types of the urban fabric, given the different levels of accessibility for elderly people. The overlapping of the areas of influence of the many activities of interest and the “density” and distribution of these types of services, on the one hand, and the presence of protected pedestrian paths and local public transport stops, on the other, will allow to identify which portions of the area investigated are more adequately meeting the demand of the elderly segment of the population and which ones, instead, lack in physical and/or functional supply, also taking into account the morphology of the area.
The team of the University of Groningen will coordinate the literature review, combing strand of literature on mobility issues and the use of public transports, the effect of aging on mobility and the well-being effects related to aging and mobility. The literature review will serve as a theoretical basis for the different empirical studies to be executed in Milan and Naples. The University of Groningen will also assist in the empirical research done with GIS Trackers, in which the university holds substantial experience. The GIS data can be combined with personal characteristics, neighborhood characteristics GIS data and for example data on weather conditions and the like. This will allow us to gain in-depth insight in mobility pattern of elderly people.