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Rich Harris

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Not just nuisance. Spatializing social statistics
R code and tutorial to reproduce the analyses and graphics found in the chapter 'Not just nuisance. Spatializing social statistics' from the forthcoming book 'Bridging The Gap Between Geography and Social Policy' (edited by Adam Whitworth)
Hexograms: better maps of area based data
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.
What is chi-square? An example looking at Brexit
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
Fitting a multilevel index of segregation in R: using the MLID package
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.
Fitting a multilevel index of dissimilarity: a tutorial in R
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.
Using Maps and Data to look at the Geography of World Development
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.
Fitting a multilevel index of dissimilarity: a tutorial in R
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.
Publish Presentation
Presented at the Royal Statistical Society conference, 2016, in an invited session entitled Social Statistics: Advances in segregation analysis.