Population / Wellbeing
- John Bryant Bayesian Demography
- Harini Dissanayake Family Violence in the News: An analysis of media reporting of extreme family violence in New Zealand
- Raj Kulkarni and Tze Ming Mok Understanding gaps in income for fathers with new babies
- Taylor Winter Who is a visitor and who you think is a visitor, are they the same thing?
- Christine Bycroft and Vinayak Anand Kumar Seeking feedback on the Administrative Population Census
- Daniel Barnett and Andrew Sporle Population outcome visualisation
- Peter Edwards Urban Trees and Data Science: Examining Wellbeing
- Suzanne Woodward, Anna Brown, and Mike O’Sullivan Pathway modelling to optimise long-term policy impact in New Zealand
- Michael Nuth Building for wellbeing
Bayesian Demography Limited
If your data consists of tables rather than individual records, and if one of your variables is age, then you are doing applied demography. Bayesian statistical methods help you do applied demography better. Bayesian methods are particularly helpful if your data is disaggregated, or if you are forecasting. The technical barriers to doing Bayesian demography can, however, be daunting. The talk describes a long-term project to develop methods and tools that reduce these barriers.
Victoria University of Wellington & Data Scientist at DOT Loves Data
Family Violence in the News: An analysis of media reporting of extreme family violence in New Zealand
This study investigated whether coverage of extreme family violence in New Zealand media is biased across a range of key factors gender, ethnicity, and age of victims, as well as the victim’s relationship to the killer. Our results were derived from a cohort of 946 articles published online by New Zealand media outlets. Analyzing the number of media articles relating to victims from each group (exposure) and their online presence (prominence), we found that although media coverage is generally quite equitable, certain groups of victims are severely under-represented - particularly true for victims from Pacifica communities and elderly victims.
Raj Kulkarni and Tze Ming Mok
Social Wellbeing Agency
<Raj.Kulkarni at swa.govt.nz>
Understanding gaps in income for fathers with new babies
This piece of work followed the Having a Baby in South Auckland project, which applied the Social Wellbeing Agency’s representative timeline modelling method to produce insights into South Auckland families’ experiences around the birth of a child. As a result of one of these insights, we investigated fluctuations in new fathers’ income around time of the birth and found that a substantial proportion of fathers who usually earn around the minimum wage have income dips that suggest taking unpaid time off work. Yet, only half of them were eligible for two weeks of parental leave. This presentation discusses certain aspects of fathers’ life around the birth.
Who is a visitor and who you think is a visitor, are they the same thing?
Population definitions are well established into our mahi at Stats NZ. However, when Data Ventures reached out to our tourism customers, we identified a key disconnect between expected definitions of visitors and actual definitions. Specifically, our customers thought of visitors as tourists and needed these numbers to inform their COVID-19 response. We developed a new measure of visitors, named ‘short-term visitors’, that predominantly represents tourists and was validated against visa and migration data. We present a comparison between visitor definitions and discuss how these definitions may differ in value based on the context and customer expectation.
Christine Bycroft and Vinayak Anand Kumar
Seeking feedback on the Administrative Population Census
Stats NZ’s Census Transformation programme will release the first iteration of the experimental Administrative Population Census (APC) in August 2021. The APC uses administrative data to construct an annual census file, and will be an opportunity for groups to provide feedback on an early iteration of admin first Census outputs. At this presentation, we will outline:
- the features of the 2021 APC to demonstrate its potential value to the research community;
- how future iterations of the APC will build on the 2021 release .
Daniel Barnett and Andrew Sporle
The University of Auckland
<daniel.barnett at auckland.ac.nz>
<a.sporle at auckland.ac.nz>
Population outcome visualisation
Communicating analysis of social data to non-statistical decision makers is made even trickier when the results are subject to high levels of variability and uncertainty. Two such situations are the estimation of regional impact of the Covid-19 epidemic and the impact of Census counts on the number of Maori electorates. We have created two public domain tools with simple interfaces that allow non-statisticians to explore how the outcome measures vary with changes to multiple determining factors.
Manaaki Whenua Landcare Research
Urban Trees and Data Science: Examining Wellbeing
Trees and forests have major impacts on planetary and human wellbeing. In an increasingly urbanised world, urban trees and forests become important for human wellbeing. Many studies show the benefits of urban trees and forests – health, climate change, urban planning and ecological perspectives. Using a wellbeing framework, quantitative data from remote sensing, modelling and social and cultural administrative data, we aim to understand the impact of urban trees across a range of wellbeing domains. Using epidemiological methods, we aim to discover correlations and patterns between urban trees and human wellbeing in Singapore and Wellington. More information? Dr Peter Edwards — EdwardsP@landcareresearch.co.nz.
Suzanne Woodward, Anna Brown, and Mike O’Sullivan
University of Auckland, Massey
<s.woodward at auckland.ac.nz>
Pathway modelling to optimise long-term policy impact in New Zealand
Evaluating policy initiatives is inherently difficult. We can address this by considering how policy decisions inform social pathways across multiple sectors, and how pathway trajectories affect individual wellbeing. Trajectory data and narratives of lived experience that complement large, linked datasets, provide the opportunity to construct models that can accurately predict wellbeing outcomes at scale. Policymakers can use these models to assess the efficacy of policy initiatives, quantifying their contribution to wellbeing. This approach combines ideas and methods from the policy and social sciences, co-design, data science and mathematical modelling to address the wicked problem of policy design. Visit https://orua.blogs.auckland.ac.nz/project/policy-planning/.
<michael.nuth at branz.co.nz>
Building for wellbeing
The aim of this project is to develop a digital post occupancy evaluation to efficiently capture the self-reported qualitative perspectives of building occupants about the wellbeing performance of residential buildings. By doing so, my intention is to develop an app-based wellbeing assessment and data collection tool that is informed by the Government’s Living Standards Framework. Data collected via the app will ultimately help inform the planning, design and construction of residential buildings that meet the wellbeing needs of New Zealanders.
The app will be developed and trialled in collaboration with Auckland University of Technology, Tether Ltd and Kāinga Ora.
Contact Michael Nuth @ firstname.lastname@example.org for more information.