With the rise in the use of physical activity tracking apps we explore the new insights these data can provide, alongside the sociodemographic profile of those using physical activity apps
Physical activity tracking apps enable low burden tracking of physical activity behaviours over long periods. We aim to characterise these behaviours in data from a large set of app user using machine learning methods
Geographically Weighted Regression (GWR) & Multi-scale GWR (MGWR) are under utilised methods in the investigation of built environment effects on physical activity. This work aims to investigate the utility of these methods with open source environmental data
Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand’s Ministry of Health (MoH) and the University of Canterbury’s GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline.
Cite: Oldroyd, R.A., Hobbs, M., Campbell, M. et al. Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom. Appl. Spatial Analysis (2021). https://doi.org/10.1007/s12061-021-09381-8
This unique book demonstrates the utility of big data approaches in human geography and planning. Offering a carefully curated selection of case studies, it reveals how researchers are accessing big data, what this data looks like and how such data can offer new and important insights and knowledge. Contributions from key scholars working in the field bring together an international series of case studies on demography and migration, retail and consumer analytics, health care planning, urban planning and transport studies. Chapters also discuss how data sets leveraged from commercial and public agency sources can greatly improve the data traditionally worked with in academic geography, regional science and planning. While addressing the challenges and limitations of big data, the book also demonstrates the usefulness of data sets held by commercial agencies and explores data linkage between big data and traditional public domain data sources. Focusing on the applications of big data to investigate issues in a spatial context, this book will be an essential guide for scholars and students of planning, mobility and human geography, particularly those who specialise in economic and transport geography. Its use of key case studies to demonstrate the applications of big data analytics in planning will also be useful for planners in these fields.
Cite book: CLARKE, G., BIRKIN, M., CORCORAN, J. & STIMSON, R. 2021. Big Data Applications in Geography and Planning: An Essential Companion, Edward Elgar Publishing, Incorporated.
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