Additional file 1 of How urban densification shapes walking behaviours in older community dwellers: a cross-sectional analysis of potential pathways of influence
Creators: name: Cerin, Ester
name: Barnett, Anthony
name: Zhang, Casper J. P.
name: Poh-Chin Lai
name: Sit, Cindy H. P.
name: Lee, Ruby S. Y.
Publisher: name: figshare
Year: 2020
Subjects: subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: Ecology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Sociology
subject: FOS: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified
subject: Science Policy

description: Additional file 1: Table S1. Definitions of environmental variables (exposures), data sources and spatial resolution. Table S1. Outline of regression analyses. Table S2. Models 1M: Direct effect of neighbourhood residential density [exposure] on street intersection density, public transport density and density of four destination types [mediators 1]. Table S3. Model 2aM: Direct effects of neighbourhood residential density and mediators 1 on park area [mediator 2]. Table S4. Model 2bM: Direct effects of neighbourhood residential density and mediators 1 on household car ownership [mediator 2]. Table S5. Model 3M: Direct effects of neighbourhood residential density, mediators 1, park area and household car ownership [mediators 2] on frequency of within-neighbourhood walking [outcome]. Table S6. Model 4M: Direct effects of neighbourhood residential density, mediators 1, mediators 2 and domain-matching frequency of within-neighbourhood walking [mediator 3] on amount of within-neighbourhood walking [outcome]. Table S7. Model 5M: Direct effects of neighbourhood residential density, mediators 1, mediators 2, and mediator 3 on frequency of outside-neighbourhood walking [outcome]. Table S8. Model 6M: Direct effects of neighbourhood residential density, mediators 1, mediators 2, mediator 3, domain-matching frequency of outside-neighbourhood walking and amount of within-neighbourhood walking [mediators 4] on amount of outside-neighbourhood walking [outcome]. Table S9. Housing type as a moderator of total effects of neighbourhood residential density on walking measures. Table S10. Housing type as a moderator of direct effects of neighbourhood residential density and mediators 1 on household car ownership [mediator 2]. Table S11. Housing type as a moderator of direct effects of neighbourhood residential density, mediators 1 and park area on frequency of within-neighbourhood walking [outcome]. Table S12. Housing type as a moderator of direct effects of neighbourhood residential density, mediators 1 and park area on amount of within-neighbourhood walking [outcome]. Table S13. Housing type as a moderator of direct effects of neighbourhood residential density, mediators 1 and park area on frequency of outside-neighbourhood walking [outcome]. Table S14. Housing type as a moderator of direct effects of neighbourhood residential density, mediators 1 and park area on amount of outside-neighbourhood walking [outcome]. Figure S1. Directed acyclic graph (DAG) depicting the hypothesised relations between neighbourhood residential density, other environmental attributes, household car ownership, covariates and measures of transportation walking. Through the DAG, we identified which covariates to include in the statistical analyses to sufficiently control for potential confounders. Figure S2. Directed acyclic graph (DAG) depicting the hypothesised relations between neighbourhood residential density, other environmental attributes, household car ownership, covariates and measures of recreation walking. Through the DAG, we identified which covariates to include in the statistical analyses to sufficiently control for potential confounders.