Local Spatial Analysis and Dynamic Simulation of Childhood Obesity and Neighbourhood Walkability in a Major Canadian City

Abstract

Body weight is an important indicator of current and future health and it is even more critical in children, who are tomorrow's adults. This paper analyzes the relationship between childhood obesity and neighbourhood walkability in Calgary, Canada. A multivariate analytical framework recognizes that childhood obesity is also associated with many factors, including socioeconomic status, foodscapes, and environmental factors, as well as less measurable factors, such as individual preferences, that could not be included in this analysis. In contrast with more conventional global analysis, this research employs localized analysis and assesses need-based interventions. The one-size-fit-all strategy may not effectively control obesity rates, since each neighbourhood has unique characteristics that need to be addressed individually. This paper presents an innovative framework combining local analysis with simulation modeling to analyze childhood obesity. Spatial models generally do not deal with simulation over time, making it cumbersome for health planners and policy makers to effectively design and implement interventions and to quantify their impact over time. This research fills this gap by integrating geographically weighted regression (GWR), which identifies vulnerable neighbourhoods and critical factors for childhood obesity, with simulation modeling, which evaluates the impact of the suggested interventions on the targeted neighbourhoods. Neighbourhood walkability was chosen as a potential target for localized interventions, owing to the crucial role of walking in developing a healthy lifestyle, as well as because increasing walkability is relatively more feasible and less expensive then modifying other factors, such as income. Simulation results suggest that local walkability interventions can achieve measurable declines in childhood obesity rates. The results are encouraging, as improvements are likely to compound over time. The results demonstrate that the integration of GWR and simulation modeling is effective, and the proposed framework can assist in designing local interventions to control and prevent childhood obesity.

Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

“…The third drawback with existing research employing GWR to study obesity that causes reservation is that several previous applications report a relatively low level of explanatory power (i.e, ! ) (Procter et al, 2008;Wen, Chen, & Tsai, 2010;Chen & Truong 2012;Chalkias et al, 2013;Guettabi & Munasib, 2014;Shahid & Bertazzon, 2015;Jun & Namgung, 2018). Studies that report low global explanatory power, sometimes accounting for less than 10% of the variation in the dependent variable, are not likely capturing robust relationships.…”

“…A more general lack of reporting is the fourth issue found in previous obesity applications of GWR. For example, some studies present GWR results without reporting any output for an analogous global model (Shahid & Bertazzon, 2015;Jun & Namgung, 2018). As mentioned above, it is paramount to first find a robust global model before moving onto a local model and in these cases, it is unclear whether or not this step was undertaken.…”

“…Furthermore, the results from a global model are useful for interpreting the results from a GWR in order to indicate which local relationships deviate from the global model and in which way. Another example is that several studies are either vague or do not report the choice of kernel function employed within their GWR analysis (Edwards et al, 2010;Xu & Wang, 2015;Shahid & Bertazzon, 2015;Jun & Namgung, 2018). This can have implications for how the bandwidth is interpreted as an indicator of process scale, as well as limit the ability to replicate or reproduce a methodology.…”

“…This can have implications for how the bandwidth is interpreted as an indicator of process scale, as well as limit the ability to replicate or reproduce a methodology. Moreover, the bandwidth is also frequently not reported (Procter et al, 2008;Edwards et al, 2010;Wen, Chen, & Tsai, 2010;Chen & Truong 2012;Fraser et al, 2012;Chalkias et al, 2013;Shahid & Bertazzon, 2015;Xu & Wang, 2015;Neelon et al, 2017;Faka et al, 2019), leaving valuable insights regarding process scale and spatial context untapped.…”

“…This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants (e.g., Boardman et al, 2005;Cutts et al, 2009;Greeves Grow et al, 2010;Slack et al, 2014;Bower et al, 2015;Gartner et al, 2016;Zhao, Kwan, & Zhou 2018). In particular, geographically weighted regression (GWR) is a method that is frequently employed to understand how spatial determinants of obesity vary across space (Procter et al, 2008;Edwards et al, 2010;Wen, Chen, & Tsai, 2010;Fraser et al, 2012;Chen & Truong 2012;Chi et al 2013;Shrestha, Mahabir, & Di, 2013;Chalkias et al, 2013;Black, 2014;Guettabi & Munasib, 2014;Shahid & Bertazzon, 2015;Xu & Wang, 2015;Benjamin Neelon et al, 2017;Jun & Namgung, 2018;Faka et al, 2019). A drawback of GWR is that it assumes that all of the relationships being modeled vary at a single spatial scale, limiting the potential to characterize spatial context.…”