Showing papers by "David Wong published in 2019"
TL;DR: This paper reviews the development of spatial segregation measures, particularly focusing on the mathematical formulation of spatial arrangement/relations, and presents an overview of existing software tools that are readily available for calculating some of the reviewed measures.
Abstract: Quantitative indices of segregation are powerful tools for summarising the spatial relationships between population groups and thereby providing the basis for analysis and public policy intervention. While the broad concept of segregation may be intuitive, measurement is challenging because of the complexity of varied dimensions and spatial arrangements. Many traditional measures can be criticised for over-simplification or over-reduction, not least in their treatment of geographical space. Over the last several decades, however, a series of measures has been developed to explicitly incorporate the spatial arrangement of population groups as well as their interactions. This paper reviews the development of spatial segregation measures, particularly focusing on the mathematical formulation of spatial arrangement/relations. In addition, several related issues are discussed, including representation of spatial interaction, spatial scale and statistical inferences. Also, this paper presents an overview of existing software tools that are readily available for calculating some of the reviewed measures. Finally, discussions on challenges and future research are provided.
54 citations
TL;DR: The promise of big data extends from etiology research to the evaluation of large-scale interventions and offers the opportunity to accelerate translation of health disparities studies, which will require continual assessment for efficacy, ethical rigor, and potential algorithmic or system bias.
Abstract: Background: Despite decades of research and interventions, significant health disparities persist. Seventeen years is the estimated time to translate scientific discoveries into public hea...
26 citations
TL;DR: It is demonstrated that current SA statistics tend to overestimate SA when errors of the estimates are not considered, and the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation.
Abstract: Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics Popular SA statistics implicitly assume that the reliability o
12 citations
28 Jun 2019
7 citations