Locally-varying explanations behind the United Kingdom\u27s vote to leave the European Union

Explanations behind area-based (Local Authority-level) voting preference in the 2016 referendum on membership of the European Union are explored using aggregate-level data. Developing local models, special attention is paid to whether variables explain the vote equally well across the country. Variables describing the post-industrial and economic successfulness of Local Authorities most strongly discriminate variation in the vote. To a lesser extent this is the case for variables linked to metropolitan and big city contexts, which assist the Remain vote, those that distinguish more traditional and nativist values, assisting Leave, and those loosely describing material outcomes, again reinforcing Leave. Whilst variables describing economic competitiveness co-vary with voting preference equally well across the country, the importance of secondary variables - those distinguishing metropolitan settings, values and outcomes - does vary by region. For certain variables and in certain areas, the direction of effect on voting preference reverses. For example, whilst levels of European Union migration mostly assist the Remain vote, in parts of the country the opposite effect is observed

Locally-varying explanations behind the United Kingdom\u27s vote to leave the European Union

Explanations behind area-based (Local Authority-level) voting preference in the 2016 referendum on membership of the European Union are explored using aggregate-level data. Developing local models, special attention is paid to whether variables explain the vote equally well across the country. Variables describing the post-industrial and economic successfulness of Local Authorities most strongly discriminate variation in the vote. To a lesser extent this is the case for variables linked to metropolitan and big city contexts, which assist the Remain vote, those that distinguish more traditional and nativist values, assisting Leave, and those loosely describing material outcomes, again reinforcing Leave. Whilst variables describing economic competitiveness co-vary with voting preference equally well across the country, the importance of secondary variables - those distinguishing metropolitan settings, values and outcomes - does vary by region. For certain variables and in certain areas, the direction of effect on voting preference reverses. For example, whilst levels of European Union migration mostly assist the Remain vote, in parts of the country the opposite effect is observed

Treemap Cartography for showing Spatial and Temporal Traffic Patterns

Depicting spatial and temporal aspects of traffic flows of different types is challenging. We use a treemap based technique that is able to show multiple aspects of large quantities of spatial and temporal traffic data simultaneously. Treemaps present multivariate data as a hierarchy of rectangles that are nested within each other. Each level of the hierarchy is used to carry information about one variable, with rectangle size, arrangement and colour being potential information-carrying ‘channels’ for reflecting properties of the data. We show information about the vehicles operated by eCourier (UK) Ltd. by location, vehicle type, day of the week and hour of the day. Our two maps use colour to show the volumes and speeds of traffic at each of 82,320 combinations of location, vehicle type, day of week and hour of day, concurrently. Crucially, we use a regular grid to represent location, give all the treemap nodes a constant size and order them spatially. This cartographic representation allows multiple aspects of large traffic datasets to be viewed concurrently, such that spatial and temporal patterns can be identified

Visualizing the Effects of Scale and Geography in Multivariate Comparison

Abstract-Our research investigates the sensitivities and complexities of visualizing multivariate data over multiple scales with the consideration of local geography. We investigate this in the context of creating geodemographic classifications, where multivariate comparison for the variable selection process is an important, yet time-consuming and intensive process. We propose a visual interactive approach which allows skewed variables and those with strong correlations to be quickly identified and investigated and the geography of multi-scale correlation to be explored. Our objective with this paper is to present comprehensive documentation of the parameter space prior to the development of the visualization tools to help explore it

Estimating the Impact of a Flood Event on Property Value and Its Diminished Effect over Time

With the increase in natural disasters, flood events have become more frequent and severe calling for mortgage industries to take immediate steps to mitigate the financial risk posed by floods. This study looked more closely at the underlying effects of flood disasters on historical house prices as part of a climatic stress test. The discount applied on house prices due to a flood event was achieved by leveraging a causal inference approach supported by machine learning algorithms on repeat sales property and historic flood data. While the Average Treatment Effect (ATE) was employed to estimate the effect of a flood event on house prices in an area, the Conditional Average Treatment Effect (CATE) aided in overcoming the heterogeneous nature of the data by calculating the flood effect on property prices of each postcode. LightGBM as a base estimator of the causal model worked as an advantage to capture the nonlinear relationship between the features and the outcome variable and further allowed us to interpret the contribution of each feature towards the decay of these discounts using SHAP values

Rectangular Hierarchical Cartograms for Socio-Economic Data

We present rectangular hierarchical cartograms for mapping socio-economic data. These density-normalising cartograms size spatial units by population, increasing the ease with which data for densely populated areas can be visually resolved compared to more conventional cartographic projections. Their hierarchical nature enables the study of spatial granularity in spatial hierarchies, hierarchical categorical data and multivariate data through false hierarchies. They are space-filling representations that make efficient use of space and their rectangular nature (which aims to be as square as possible) improves the ability to compare the sizes (therefore population) of geographical units. We demonstrate these cartograms by mapping the Office for National Statistics Output Area Classification (OAC) by unit postcode (1.52 million in Great Britain) through the postcode hierarchy, using these to explore spatial variation. We provide rich and detailed spatial summaries of socio-economic characteristics of population as types of treemap, exploring the effects of reconfiguring them to study spatial and non-spatial aspects of the OAC classification

Visual analysis of social networks in space and time using smartphone logs

We designed and applied novel interactive visualisation to investigate how social networks - derived from smartphone logs - are embedded in time and space. Social networks were identified through direct calls between participants and calls to mutual contacts of participants. Direct contact between participants was sparse and deriving networks through mutual contacts helped enrich the social networks. Our resulting interactive visualisation tool offers four linked and co-ordinated views of spatial, temporal, individual and social network aspects of the data. Brushing and altering techniques help us investigate how these aspects relate. We also simultaneously display some demographic and attitudinal variables to help add context to the behaviours we observe. Using these techniques, we were able to characterise spatial and temporal aspects of participants' social networks and suggest explanations for some of them. We reflect on the extent to which such analysis helps us understand social communication behaviour