Our first in-person event in THREE YEARS is imminent! The session is happening in Appleton Tower, room AT_2.05 at 17:30 on 16th March.
Register here: https://www.meetup.com/edinbr/events/292017485
- Alessia Calafiore is a Lecturer in Sustainability and Urban Data Science at the Edinburgh Future Institute and the Edinburgh School of Architecture and Landscape Architecture of the University of Edinburgh. Prior to this, she was PDRA at the Geographic Data Science Lab of the University of Liverpool.
Her research sits at the intersection of Urban Planning, Geography and Computer Science and she is interested in developing new spatially informed computational methods to better understand the mutual relationship between human behaviours and their urban contexts. Alessia’s current substantive focus concerns how we can equitability manage Net Zero transitions within cities.
- Vanda Inacio is a Lecturer in Statistics at the School of Mathematics of the University of Edinburgh since 2016. Previously, she was an Assistant Professor at PUC Chile (2012–2016). Vanda received a PhD in Statistics from Universidade de Lisboa and a BSc in Applied Mathematics from Universidade Nova de Lisboa. Her main research interests are Bayesian (nonparametric) statistics, computational statistics, and biostatistics, with an emphasis on the statistical evaluation of medical tests. Vanda’s work has been published in some of the top-tier journals in the field, like Annals of Applied Statistics, Bayesian Analysis, Biostatistics, Biometrics, Statistics in Medicine, and Statistical Science. Vanda is also a co-author of the R package ROCnReg, the only package integrating both frequentist and Bayesian methods for estimation of ROC curves (with and without covariates).
R and the 15- 20- minute city
In this talk, I will show how we used R to measure the 15- 20- minute city in Liverpool City Region. We explored walk access to a variety of services in order to identify 20-minute neighbourhoods and perform an equity analysis on the distribution of such services. The study is published in Transportation Research Part D (https://doi.org/10.1016/j.trd.2021.103111) and the code for the analysis is available on GitHub (https://github.com/aelissa/LCR_20MN).
A tutorial on ROCnReg: an R package for receiver operating characteristic curve inference with and without covariates
The receiver operating characteristic (ROC) curve is the most popular tool for evaluating the ability of a classifier to distinguish between two states. Because the classifier’s performance may differ for different populations (as defined by, for instance, age and gender), the covariate-specific ROC curve arises as a natural tool in such a context. In this talk, I will introduce the R package ROCnReg that allows estimating the pooled ROC curve and the covariate-specific ROC curve by different methods, both from (semi) parametric and nonparametric perspectives and within Bayesian and frequentist paradigms. An example with real medical diagnosis data will be used thought the talk to illustrate the capabilities of the package. Read more in this journal article about the package.
March 2023: 15 minute cities and ROC curves</p></blockquote>