Residential dissonance and walking for transport

Abstract

The concept of residential dissonance contextualizes the combined impact of built environment and individual travel and land-use preferences on travel behavior. A limited number of studies have explored the effect of residential dissonance specifically on walking. However, evidence from the active travel literature suggests that the environmental characteristics associated with diverse active travel modes differ to some extent. This study addresses residential dissonance in a framework specific for walking outcomes, as the applied neighborhood boundaries, residential preferences and the observed built environment were operationalized with measures related to walking for transport. SoftGIS, a public participatory GIS method allowing the mapping of frequently visited destinations was used to survey the daily walking behavior of 772 respondents aged 25-40 years living in the Helsinki metropolitan area, Finland. Ordinal logistic regression analyses were used to assess the adjusted odds of walking a high share of estimated monthly trips and travel distance. The identified residential dissonance groups were found to have significant associations with the walking outcomes. Associations between the observed neighborhood walkability and the walking outcomes varied by trip purpose, being more consistent with walking to utilitarian than to recreational destinations. Overall, the results support views on the interconnectedness of individual attitudes and the built environment in facilitating walking for transport.

Shopping trip mode choice of older adults: an application of activity space and hybrid choice models in understanding the effects of built environment and personal goals

“…The individual home range model (IHR) is an individual-specific boundary method, which was first introduced by Hasanzadeh et al (2017) and later implemented in a number of other studies (e.g. Kajosaari et al 2019). Following the criteria suggested in the study by Hasanzadeh et al (2017), all daily destinations were listed based on their distance from the participant's home location.…”

Mapping with Stakeholders: An Overview of Public Participatory GIS and VGI in Transport Decision-Making

“…Keyword analysis can support the identification of the main areas of application since they are used by the authors to help indexers, search sources and consequently readers to find relevant papers; they generally represent the content of the manuscript and are quite specific to subject's field or subfield. From the analysis of the keywords of the papers of the review (Figure 2), conducted through pivot tables, beyond the terms included in the search strategy (GIS, VGI, PPGIS, Crowdsourcing and Transport), the words "Accessibility" [28][29][30][31][32][33], Smartphone [34][35][36][37][38] and "Activity Space" [39][40][41] emerge. In particular, GIS are fundamental to visualize the impact of transport solutions in terms of accessibility, and there is a growing literature on transport studies related to this topic [42][43][44][45].…”

“…More than the half of the studies (55%) deal with the identification of project types and the present situation analysis, due to the spatial GIS component providing an essential aid in definition of physical constraints. Research focused on: the building of comprehensive urban networks through VGI data [32,51]; road and traffic condition analysis through crowdsourcing [70][71][72][73]; analysis of vehicle behavior [74] and investigation of travel patterns [52,[75][76][77], with an extensive use of SoftGIS, an internet-based approach which relies on collecting, analyzing and delivering soft knowledge produced by the residents and other local actors in a certain area through interactive maps [29,41,[78][79][80][81]; urban connectivity assessment [82] and general analysis of urban public transport and sustainable mobility aspects through smartphones [37,[83][84][85][86][87][88]. Crowdsourcing techniques have been extensively used in studies related to non-motorized mobility, such as cyclist spatial patterns identification [39,53,54,56,58,71], their socio-economic analysis [89] and environmental related problems [55,90] or the identification of peculiar elements of the pedestrian network to improve the walking experience of vulnerable people [29,55,57].…”

A methodological framework for analysis of participatory mapping data in research, planning, and management

“…(1) Spatial values, perceptions, or attitudes, e.g., landscape values (Brown and Raymond 2007), perceived environmental quality factors (Kyttä et al 2013), and ecosystem service benefits (Ridding et al 2018, Fagerholm et al 2019a), in addition to perceived problems or unpleasant experiences (Raymond et al 2016); (2) Spatial behavior patterns, everyday practices, and activities, e.g., daily mobility patterns, and routes travelled (Laatikainen et al 2017, Kajosaari et al 2019, places visited (Sarjala et al 2015), and their temporal characters, e.g., seasonality, length, or frequency of visitation (Bijker and Sijtsma 2017); (3) Spatially defined future preferences or visions, e.g. development preferences (Brown 2006, Jankowski et al 2016, Kahila-Tani et al 2016, Engen et al 2018; and (4) Preferred place features referred to as 'geographic citizen science' (Haklay 2013), e.g., mapping road/trail networks (e.g., OpenStreetMap) and wildlife observations (Brown et al 2018a).…”