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Year : 2020  |  Volume : 64  |  Issue : 2  |  Page : 191-197

Using open-source data to explore distribution of built environment characteristics across Kerala, India

1 PhD Scholar, Achutha Menon Centre for Health Science Studies, SCTIMST, Thiruvananthapuram, Kerala, India
2 Professor, Achutha Menon Centre for Health Science Studies, SCTIMST, Thiruvananthapuram, Kerala, India
3 Scientist B, Achutha Menon Centre for Health Science Studies, SCTIMST, Thiruvananthapuram, Kerala, India

Correspondence Address:
Joanna Sara Valson
Achutha Menon Centre for Health Science Studies, SCTIMST, Thiruvananthapuram - 695 011, Kerala
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijph.IJPH_472_19

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Background: Built environment characteristics in the neighborhood are of utmost priority for a healthy lifestyle in the fast-urbanizing countries. These characteristics are closely linked to the disease burden and challenges in low- and middle-income countries (LMICs), which have been unexplored using open-source data. The present technology offers online resources and open source software that enable researchers to explore built environment characteristics with health and allied phenomena. Objectives: This article intends to delineate methods to capture available and accessible objective built environment variables for a state in India and determine their distribution across the state. Methods: Built environment variables such as population density and residential density were collated from the Census of India. Safety from crime and traffic were captured as crime rates and pedestrian accident rates, respectively, acquired from State Crime Records Bureau. Greenness, built-up density, and land slope were gathered from open-source satellite imagery repository. Road intersection density was derived from OpenStreetMap. Processing and analysis differed for each dataset depending on its source and nature. Results: Each variable showed a distinct pattern across the state. Population and residential density were found to be closely related to each other across both districts and subdistricts. They were both positively related to crime rates, pedestrian accident rates, built-up density, and intersection density, whereas negatively related to land slope and greenness across the subdistricts. Conclusion: Delineating the distribution of built environment variables using available and open-source data in resource-poor settings is a first in public health research among LMICs. Cost-effectiveness and reproducible nature of open-source solutions could equip researchers in resource-poor settings to identify built environment characteristics and patterns across regions.

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