Multilevel modelling

How consumer behaviours changed in response to COVID-19 lockdown stringency measures - A case study of Walmart

We investigate the impact of non-pharmaceutical interventions, in the form of lockdown stringency measures, on consumer purchasing behaviours for essential goods over the onset of the pandemic

Local urban attributes defining ethnically segregated areas across English cities. A multilevel approach

We employed a series of multilevel models to explore the within and between city variations in the relationship across ethnic segregation, and key socioeconomic and built environment features of neighbourhoods.

Sensing Global Changes in Local Patterns of Energy Consumption in Cities During the Early Stages of the COVID-19 Pandemic

We provide high resolution estimates (450m2) of spatio-temporal changes in urban energy consumption in response to COVID-19.

Modelling Count Data in R: A Multilevel Framework

A Quick Practical Guide One of my research areas of interest revolves around understanding human mobility and migration flows. Key attributes of flow data are that they are counts and a right-skewed, overdispesed and often zero-inflated distribution.

Measuring Urban Forest Using Street-Level Imagery and Deep Learning

We develop a method based on computer vision and a hierarchical multilevel model to derive an Urban Street Tree Vegetation Index which aims to quantify the amount of vegetation visible from the point of view of a pedestrian. Our approach unfolds in two steps. First, areas of vegetation are detected within street-level imagery using a state-of-the-art deep neural network model. Second, information is combined from several images to derive an aggregated indicator at the area level using a hierarchical multilevel model. The comparative performance of our proposed approach is demonstrated against a widely used image segmentation technique based on a pre-labelled dataset. The approach is deployed to a real-world scenario for the city of Cardiff, Wales, using Google Street View imagery. Based on more than 200,000 street-level images, an urban tree street-level indicator is derived to measure the spatial distribution of tree cover, accounting for the presence of obstructing objects present in images at the Lower Layer Super Output Area (LSOA) level, corresponding to the most commonly used administrative areas for policy-making in the United Kingdom. The results show a high degree of correspondence between our tree street-level score and aerial tree cover estimates. They also evidence more accurate estimates at a pedestrian perspective from our tree score by more appropriately capturing tree cover in areas with large burial, woodland, formal open and informal open spaces where shallow trees are abundant, in high density residential areas with backyard trees, and along street networks with high density of high trees. The proposed approach is scalable and automatable. It can be applied to cities across the world and provides robust estimates of urban trees to advance our understanding of the link between mental health, well-being, green space and air pollution.

Changing post-school pathways and outcomes. Melbourne and regional students

This report examines the changes in pathways, transitions and choices of three cohorts of young Victorians.

Determinants of post-school choices of young people. The workforce, university or vocational studies?

This report seeks to identify the factors that influence the postschool choices to (1) work; (2) enter university or, (3) undertake vocational studies.