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This paper explores neighborhood-built environment features related to 'better than expected' and 'as expected' early childhood development outcomes (ECD) in 14 Australian disadvantaged communities. This paper draws from mixed methods data collected in the Kids in Communities Study – an Australian investigation of community effects on ECD – in communities across five states and territories. In total, 93 interviews and 30 focus groups were conducted with service providers and parents, and geographic information systems were used to create built environment measures for each local community. Housing factors (e.g. better affordability, tenure, less high-density public housing) were consistently related to disadvantaged local communities with 'better than expected' ECD outcomes. Physical access to services and public transport, living in a walkable area, having high-quality public open space, and a mix of local destinations was perceived to be consistently important by community members in disadvantaged communities regardless of ECD outcomes. Findings may help policymakers to consider neighborhood features that contribute to better ECD outcomes. [ABSTRACT FROM AUTHOR]

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1745-5871.2009.00615.x Byline: ROBERT TANTON (1), ANN HARDING (1), JUSTINE MCNAMARA (1) Keywords: poverty; spatial microsimulation; urban poverty; rural poverty Abstract: Abstract This paper reports on the calculation of poverty rates for small areas in Australia using a spatial microsimulation model. The spatial microsimulation methodology used involves reweighting data from confidentialised unit record files (CURFs) from surveys conducted by the Australian Bureau of Statistics (ABS) to small area census data, also from the ABS. The method is described in this paper, and then maps of poverty using poverty rates derived from this small area estimation method are shown for the eastern coast of Australia and its capital cities. Further analysis of poverty rates in capital cities is then conducted. We find that areas of higher poverty risk can be clearly identified within Sydney, Melbourne, Canberra and Brisbane. We also find that areas of high poverty are frequently 'buffered' by areas of moderate poverty. This is not always the case since, in some areas, we find a high poverty area neighbouring a low poverty area but, generally, there appears to be a moderate poverty 'buffer' in most capital cities. Author Affiliation: (1)National Centre for Social and Economic Modelling (NATSEM), University of Canberra, 170 Haydon Drive, Bruce ACT 2617, Australia. Email:robert.tanton@natsem.canberra.edu.au Article History: Received 10 December 2008; Revised 25 February 2009; Accepted 7 July 2009