The Leeds Institute for Data Analytics is pleased to present the next seminar in our series showcasing data analytics.
The seminar will be held in Seminar Room 9.60, Level 9, Worsley building.
This talk will argue that population data should be thought about as forming complex three dimensional structures, and visualised much like spatial maps. Whereas spatial maps show how elevation varies with latitude and longitude, demographic ‘maps of time’, known as ‘Lexis surfaces’, show how quantity or risk (like risk of dying) varies with both age and year. Standard ways of visualising population data either tend to involve collapsing many numbers into a single number (e.g. standardised mortality rates and life expectancies), or cutting the data into thin slices along the age or year ‘plane’. In either case, most of the available data is missing to the researcher, and the often complex ways that age and time interact over the data structure are hidden from view. Standard (‘variable-based’) ways of modelling data, in which the effects of predictor variables are assumed to be independent in how they influence a response variable, further distort and hide the complex structures and relationships that population data tends to form and contain.
By contrast, using the cartographer’s toolkit to produce maps of risk over age and time, allows the full structure of the data to be revealed, and for tens of thousands of values relating to a population to be thought of as constituting a single ‘case’ (rather than many different variables to be ‘controlled for’), allowing more effective understanding of the population processes to be developed, and more appropriate statistical models to be specified when required.
Using examples of all-cause and cause-specific mortality in Scotland, this talk will illustrate how Lexis surfaces can throughout the population data research process, from initial exploration of the data, to the development, testing, and presentation of statistical models outputs. In particular, the talk will illustrate why, in Scotland, suicide and drug-related deaths both appear more similar to each other, and so more likely to share a common set of determinants, than alcohol-related deaths.
Dr Jon Minton is a Research Associate at the University of Glasgow, with a varied background covering the physical, social and health sciences. His main substantive areas of interest are in demography and public health, and methodological interests are in promoting good data science practices in quantitative research, and in complex exploratory data visualisation. He has been published in the BMJ and the International Journal of Epidemiology, but would prefer it if you didn’t care about such things, and instead aspired to make your papers and research open, timely, accessible and reproducible through services like socarxiv and Github. He can be followed on Twitter @JonMinton
15:30- 16:00: Maps as statistics? A call to adventure for perception research in geo(visualization)– Roger Beecham, Lecturer in Geographic Data Science, University of Leeds
16:00-17:00: Maps of time: Seeing population data as structures, not slices-Dr Jon Minton, Quantitative Research Associate (Urban Studies), University of Glasgow
17:00-18:00: Networking reception with drinks and nibbles hosted in the LIDA staff room
To book please email Hayley Irving with your name, occupation and faculty/organisation.