Exploring some of the area level correlates of Covid-19 deaths in London: Using a spatial difference-in-difference approach
This short and exploratory paper sets out a method of analysis known as spatial difference-in-difference to look at some of the area level correlates of higher Covid-19 death rates in London. It shows that, in particular, having higher percentages of those in their eighties or above raises the death rate compared to neighbouring locations but other factors include the percentage of the population that is Black Caribbean and the percentage that have never worked or are long-term unemployed. There is, however, a lot of unexplained variation (spatial heterogeneity). Local ‘hot spots’ (and ‘cold spots’) of the death rates are mapped - defined as places with a conditionally higher (or lower) death rate when compared to their neighbours.
Session 5: Spatial and Geographically Weighted Regression Analysis
Session 4: Spatial weights and neighbours
Session 3: Using R as a GIS
Session 2: Visualisation in R
In class exercise thinking about University league tables. This is a modified version with the output removed. A version that includes the output is at http://www.rpubs.com/profrichharris/uni-rankings
An introduction to the problems of University Rankings and other 'league tables' with examples using R. Taught to students taking the 'Convincing Stories? Numbers as evidence in the social sciences' unit in the University of Bristol.
This is the technical appendix for the book 'Ethnic Segregation Between Schools: Is It Increasing or Decreasing in England?' co-authored with Ron Johnston. It sets out the formal definitions of the various segregation measures used in the book.
Mapping London exercise for use at outreach events
This is a class exercise taught as part of the Convincing Stories? Numbers as evidence in the social sciences unit as the University of Bristol
This tutorial explores the use of hexograms to produce better maps of area based data and population distributions. Hexograms are a cross between hexagonal binning and cartograms that aim to redress the problem of 'invisibility' prevalent in conventional maps and also the problem of distortion caused by cartograms.
Chi-square has been described by the statistician Michael Crawley as something taught to geographers at school and misunderstood thereafter! It's a mischievous comment and a shame if true. Despite its off-putting calculations there is nothing particularly complicated about chi-square. It's just a way of asking if two 'things' are related to one another or not, and assessing the statistical evidence for it. This tutorial: - Shows how to fit a chi-square test to some data looking at the results of the referendum on leaving the EU - Discusses how the chi-square test - what it is actually doing and why - Notes that the chi-square test is often not that useful and that there are often better and simpler approaches that can be used instead
This tutorial introduces the tools and functions available in the MLID package to fit a multilevel index of dissimilarity, a measure of ethnic or social segregation that captures both of the two principal dimensions of segregation - unevenness and spatial clustering - and looks for scale effects as well as the contributions of particular places to the index value.
Produced as part of the Data Skills in Geography Project to support the use of quantitative data and methods in teaching the geography curricula in UK schools and colleges. The focus of the worksheet is on introducing data and data analysis in the context of global economic development issues.
This tutorial shows how to fit a multilevel Index of Dissimilarity (ID). The ID is one of the most widely used measures of segregation. It compares the geographical distribution of one group of people with the geographical distribution of another. Recently there has been interest in multilevel and multiscale methods of measuring segregation that allow the scales of segregation to be examined simultaneously, thereby considering the micro-, meso- and macro-level effects separately. The multilevel index of dissimilarity (MLID) takes forward this approach. As well as outlining how to fit the multilevel index, the tutorial explores various ways of examining spatial and scale effects and their impacts upon a traditional ID score.
Presented at the Royal Statistical Society conference, 2016, in an invited session entitled Social Statistics: Advances in segregation analysis.