Neighborhood Walkability Intelligence for Targeted Cardiometabolic Prevention

Ester Cerin

Neighborhood Walkability Intelligence for Targeted Cardiometabolic Prevention

Author(s)	Ester Cerin
Country	United States
Abstract	Cardiometabolic prevention is commonly organized around individual clinical risk factors, yet the environments in which people live can repeatedly shape opportunities for routine physical activity, active transportation, access to daily destinations, and sedentary travel. Neighborhood walkability therefore represents a potentially important upstream component of population-level prevention. The present study introduces Neighborhood Walkability Intelligence as a decision-support approach that moves beyond descriptive walkability scoring by integrating geographic information system–derived built-environment characteristics with neighborhood cardiometabolic vulnerability for targeted preventive action. The proposed Neighborhood Walkability Intelligence Framework combines street connectivity, residential density, destination accessibility, public-transport proximity, and pedestrian-supportive infrastructure with contextual information on socioeconomic deprivation and population vulnerability. Because no real neighborhood GIS-health dataset was supplied, the study adopts a transparent simulation-based methodology rather than presenting synthetic observations as completed empirical evidence. A reproducible dataset of 420 hypothetical neighborhoods was generated using a 0–100 Walkability Intelligence Score together with neighborhood deprivation, older-adult population share, and a five-point simulated Cardiometabolic Risk Burden measure. Mean risk burden was 4.42 in low-walkability neighborhoods, 4.02 in moderately walkable neighborhoods, and 3.55 in highly walkable neighborhoods. Multiple regression produced a negative coefficient for Walkability Intelligence (B = −0.019) and positive coefficients for Neighborhood Deprivation (B = 0.227) and Older-Adult Share (B = 0.020), with the simulated model explaining 56.5% of outcome variation. These estimates are illustrative and are not population parameters. The framework argues that walkability data become most useful for prevention when they are connected with health burden and social vulnerability rather than used only to rank urban form. Low-walkability neighborhoods with elevated cardiometabolic vulnerability can consequently be prioritized for coordinated environmental, behavioral, and primary-care interventions. The approach provides a methodological basis for integrating urban planning, GIS, epidemiology, and precision public-health principles while retaining explicit safeguards against ecological overinterpretation.
Keywords	neighborhood walkability, cardiometabolic prevention, GIS, built environment, physical activity, diabetes prevention, obesity, urban health, precision public health
Field	Engineering
Published In	Volume 4, Issue 4, July-August 2023
Published On	2023-07-04

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American Journal of Advanced Multidisciplinary Innovation and Research

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Neighborhood Walkability Intelligence for Targeted Cardiometabolic Prevention

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