Neighbourhood correlates of average population walking: using aggregated, anonymised mobile phone data to identify where people walk.

Understanding and monitoring socio-spatial patterns of population walking mobility can inform urban planning and geographically targeted health promotion strategies aimed at increasing population levels of physical activity. In this study we use aggregated, anonymous mobile phone mobility data to examine the association between neighbourhood physical and social characteristics and residents' weekly walking behaviour across 313 neighbourhoods in a large metropolitan region of Queensland, Australia. We find that residents in neighbourhoods that are highly fragmented by streets with speed limits above 50 kmph, residents in neighbourhoods with high retail density and those living is economically disadvantaged neighbourhoods walk fewer kilometres and minutes on average per week than their counterparts. These findings can inform urban planning policy on the minimum specifications required in newly developing neighbourhoods and provide targets for retro-fitting features into existing neighbourhoods.

Community resilience to crime: A study of the 2011 Brisbane flood.

Understanding and enhancing community resilience is a global priority as societies encounter a rising number of extreme weather events. Given that these events are typically both sudden and unexpected, community resilience is typically examined after the disaster so there can be no before and after comparisons. As such, the extent to which existing community capacities buffer the effects of a traumatic event remains largely unexamined and untested in the literature. Drawing on a longitudinal study of 148 Brisbane suburbs, we examine the key community processes associated with community resilience to the crime before and after the 2011 Brisbane floods. We introduce a novel disaster severity index to simultaneously capture the direct and indirect impacts of the flood and embed this measure within our modeling framework. Results from the models provide important insights for predisaster preparedness and postdisaster rebuilding and recovery.

The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population.

BACKGROUND: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. RESULTS: The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion. CONCLUSIONS: Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries' settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense.