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Data to Decisions: Methods to Create Neighbourhood Built Environment Indicators Relevant for Early Childhood Development

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  • Karen Villanueva

    (Centre for Urban Research, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia
    Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia)

  • Amanda Alderton

    (Centre for Urban Research, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia)

  • Carl Higgs

    (Centre for Urban Research, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia)

  • Hannah Badland

    (Centre for Urban Research, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia)

  • Sharon Goldfeld

    (Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
    Department of Paediatrics, The University of Melbourne, Parkville, VIC 3052, Australia)

Abstract

Healthy development in the early years lays the foundations for children’s ongoing physical, emotional, and social development. Children develop in multiple contexts, including their local neighbourhood. Neighbourhood-built environment characteristics, such as housing, walkability, traffic exposure, availability of services, facilities, and parks, are associated with a range of health and wellbeing outcomes across the life course, but evidence with early years’ outcomes is still emerging. Data linkage techniques were used to assemble a dataset of spatial (objectively-measured) neighbourhood-built environment (BE) measures linked to participant addresses in the 2015 Australian Early Development Census (AEDC) for children living in the 21 most populous urban and regional Australian cities (n = 235,655) to help address this gap. This paper describes the methods used to develop this dataset. This linked dataset (AEDC-BE) is the first of its kind worldwide, enabling opportunities for identifying which features of the built environment are associated with ECD across Australia at scale, allow comparisons between diverse contexts, and the identification of where best to intervene. National data coverage provides statistical power to model real-world complexities, such as differences by city, state/territory, and remoteness. The neighbourhood-built environment can be modified by policy and practice at scale, and has been identified as a way to help reduce inequitable early childhood development outcomes.

Suggested Citation

  • Karen Villanueva & Amanda Alderton & Carl Higgs & Hannah Badland & Sharon Goldfeld, 2022. "Data to Decisions: Methods to Create Neighbourhood Built Environment Indicators Relevant for Early Childhood Development," IJERPH, MDPI, vol. 19(9), pages 1-18, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5549-:d:807848
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    1. Fiona J. Andrews & Elyse Warner, 2020. "‘Living outside the house’: how families raising young children in new, private high-rise developments experience their local environment," Journal of Urbanism: International Research on Placemaking and Urban Sustainability, Taylor & Francis Journals, vol. 13(3), pages 263-285, July.
    2. Mitra, Raktim & Buliung, Ron N., 2012. "Built environment correlates of active school transportation: neighborhood and the modifiable areal unit problem," Journal of Transport Geography, Elsevier, vol. 20(1), pages 51-61.
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    4. Schochet, Owen N. & Johnson, Anna D. & Ryan, Rebecca M., 2020. "The relationship between increases in low-income mothers’ education and children’s early outcomes: Variation by developmental stage and domain," Children and Youth Services Review, Elsevier, vol. 109(C).
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