Using hand-draw maps of residential neighbourhood to compute level of circularity and investigate its predictors

Abstract

Investigating individuals' Hand-Drawn Residential Neighbourhood (HDRN) maps may help determine some of the basic geometrical properties found in cognitive maps of meaningful geographical spaces. The main objective was to empirically determine an optimal radius size for measuring environmental attributes of the RN when using circular spatial buffers. HDRN maps from the Making Connections study were explored using minimum bounded circles (MBC). Baseline data on 4,742 community-dwelling adults showed that 30% of people drew HDRN that included > 70% of the MBC. We recommend 0.25 miles as an optimum radius size when attempting to measure aspects of the RN. Multivariate linear regressions indicated lower levels of circularity for females, non-Latinoothers, for people living longer in their residential neighbourhood, and for large drawn polygons. In contrast, regression results indicated higher levels of circularity for nonLatino-blacks (compared to non-Latino-whites) and those with some college education (compared to those with a bachelors' degree and beyond). We conclude with advice on best practices for measuring environmental attributes of residential neighbourhoods when using circular spatial buffers.

Geographic regions for assessing built environmental correlates with walking trips: A comparison using different metrics and model designs

“…Researcher-defined neighborhoods commonly use spatial buffers around participants’ homes (Saelens et al, 2012) or census geography (Witten et al, 2012). Self-defined neighborhoods are captured by participant self-report (Bailey et al, 2014; Campbell et al, 2009; Gebel et al, 2011; Ivory et al, 2015) or by participant-drawn maps (Colabianchi et al, 2014; Siordia and Coulton, 2015; Suminski et al, 2015). Activity spaces are based on origin and destination travel diaries (Schönfelder and Axhausen, 2003) or Global Positioning System (GPS) data loggers (Chaix et al, 2013; Hirsch et al, 2014; Tribby et al, 2016; Zenk et al, 2011) to delineate the portion of an environment experienced by participants over a given time period.…”

“…For example, research on self-defined neighborhoods compares these regions to census tracts (Coulton et al, 2013, 2001; Spilsbury et al, 2012), assesses the accessibility of recreational or exercise facilities (Hoehner et al, 2005; Ivory et al, 2015), or uses self-defined neighborhoods to estimate an optimum home buffer size (Siordia and Coulton, 2015). Prior research with activity spaces explores how different Geographic Information Systems (GIS)-based analyses and representations produce different built environment summaries compared to researcher-defined neighborhoods (Boruff et al, 2012; James et al, 2014; Rundle et al, 2016; Tribby et al, 2016).…”