Regionally-structured explanations behind area-level populism: An update to recent ecological analyses

Heavy geographic patterning to the 2016 Brexit vote in UK and Trump vote in US has resulted in numerous ecological analyses of variations in area-level voting behaviours. We extend this work by employing modelling approaches that permit regionally-specific associations between outcome and explanatory variables. We do so by generating a large number of regional models using penalised regression for variable selection and coefficient evaluation. The results reinforce those already published in that we find associations in support of a ‘left-behind’ reading. Multivariate models are dominated by a single variable—levels of degree-education. Net of this effect, ‘secondary’ variables help explain the vote, but do so differently for different regions. For Brexit, variables relating to material disadvantage, and to a lesser extent structural-economic circumstances, are more important for regions with a strong industrial history than for regions that do not share such a history. For Trump, increased material disadvantage reduces the vote both in global models and models built mostly for Southern states, thereby undermining the ‘left-behind’ reading. The reverse is nevertheless true for many other states, particularly those in New England and the Mid-Atlantic, where comparatively high levels of disadvantage assist the Trump vote and where model outputs are more consistent with the UK, especially so for regions with closer economic histories. This pattern of associations is exposed via our regional modelling approach, application of penalised regression and use of carefully designed visualization to reason over 100+ model outputs located within their spatial context. Our analysis, documented in an accompanying github repository, is in response to recent calls in empirical Social and Political Science for fuller exploration of subnational contexts that are often controlled out of analyses, for use of modelling techniques more robust to replication and for greater transparency in research design and methodology

A Template for a New Generic Geographically Weighted R Package gwverse

GWR is a popular approach for investigating the spatial variation in relationships between response and predictor variables, and critically for investigating and understanding process spatial heterogeneity. The geographically weighted (GW) framework is increasingly used to accommodate different types of models and analyses, reflecting a wider desire to explore spatial variation in model parameters and outputs. However, the growth in the use of GWR and different GW models has only been partially supported by package development in both R and Python, the major coding environments for spatial analysis. The result is that refinements have been inconsistently included within GWR and GW functions in any given package. This paper outlines the structure of a new gwverse package, that may over time replace GW model, that takes advantage of recent developments in the composition of complex, integrated packages. It conceptualizes gwverse as having a modular structure, that separates core GW functionality and applications such as GWR. It adopts a function factory approach, in which bespoke functions are created and returned to the user based on user-defined parameters. The paper introduces two demonstrator modules that can be used to undertake GWR and identifies a number of key considerations and next steps. Volume54, Issue

A Template for a New Generic Geographically Weighted R Package gwverse

GWR is a popular approach for investigating the spatial variation in relationships between response and predictor variables, and critically for investigating and understanding process spatial heterogeneity. The geographically weighted (GW) framework is increasingly used to accommodate different types of models and analyses, reflecting a wider desire to explore spatial variation in model parameters and outputs. However, the growth in the use of GWR and different GW models has only been partially supported by package development in both R and Python, the major coding environments for spatial analysis. The result is that refinements have been inconsistently included within GWR and GW functions in any given package. This paper outlines the structure of a new gwverse package, that may over time replace GW model, that takes advantage of recent developments in the composition of complex, integrated packages. It conceptualizes gwverse as having a modular structure, that separates core GW functionality and applications such as GWR. It adopts a function factory approach, in which bespoke functions are created and returned to the user based on user-defined parameters. The paper introduces two demonstrator modules that can be used to undertake GWR and identifies a number of key considerations and next steps

The impact of the COVID-19 pandemic on the dynamics of topics in urban green space

Urban residents’ daily lives have been impacted by the COVID-19 pandemic in various aspects such as social, leisure, and physical activities. Fortunately, urban green spaces (UGSs) have become a main outdoor destination, due to the policies encouraging people to visit UGS and keeping them open. This study aimed to comprehensively investigate the impact of the COVID-19 pandemic on topics discussed on social media by UGS visitors over space and time. Data was collected from geo-referenced Tweets across London in spring 2019, 2020, and 2021. Structural Topic Modelling (STM) was used to identify UGS topics and describe the dynamics of topic proportions. The inverse distance weighted (IDW) interpolation method was used to explore spatial distributions of all topics. The study identified seven main types of UGS topics over all study periods, with topics such as Lockdown and exercise and Social and friends showing a decreasing trend in topic proportions, indicating that visitors' outdoor activities were restricted. The study not only identifies the main types of topics in UGS during the COVID-19 pandemic period but also reflects people’s attitudes and perceptions towards restriction measures, which can provide guidance for future urban policies, especially during crises

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis

Geographic profiling as a novel spatial tool for targeting infectious disease control

<p>Abstract</p> <p>Background</p> <p>Geographic profiling is a statistical tool originally developed in criminology to prioritise large lists of suspects in cases of serial crime. Here, we use two data sets - one historical and one modern - to show how it can be used to locate the sources of infectious disease.</p> <p>Results</p> <p>First, we re-analyse data from a classic epidemiological study, the 1854 London cholera outbreak. Using 321 disease sites as input, we evaluate the locations of 13 neighbourhood water pumps. The Broad Street pump - the outbreak's source- ranks first, situated in the top 0.2% of the geoprofile. We extend our study with an analysis of reported malaria cases in Cairo, Egypt, using 139 disease case locations to rank 59 mosquitogenic local water sources, seven of which tested positive for the vector <it>Anopheles sergentii</it>. Geographic profiling ranks six of these seven sites in positions 1-6, all in the top 2% of the geoprofile. In both analyses the method outperformed other measures of spatial central tendency.</p> <p>Conclusions</p> <p>We suggest that geographic profiling could form a useful component of integrated control strategies relating to a wide variety of infectious diseases, since evidence-based targeting of interventions is more efficient, environmentally friendly and cost-effective than untargeted intervention.</p

Association of depression and anxiety with clinical, sociodemographic, lifestyle and environmental factors in South Asian and white European people at high risk of diabetes

AIM: To investigate the prevalence and correlates of depressive and anxiety symptoms within South Asian and white European populations at high risk of developing Type 2 diabetes. METHODS: Data were collected at baseline, and at 12, 24 and 36 months from 1429 white European people (age 64±7 years, 35.8% women) and 160 South Asian people (age 59±9 years, 30.6% women) who were at high risk of Type 2 diabetes and who took part in two Type 2 diabetes prevention trials in Leicestershire, UK. The Hospital Anxiety and Depression Scale was administered during each study visit. Clinical, sociodemographic, lifestyle and environmental data were collected. RESULTS: At baseline, the burden of depressive symptoms varied by ethnic group and gender, with 9.9% of white European men, 14.9% of white European women, 23.6% of South Asian men and 29.2% of South Asian women exceeding the cut-off score for mild-to-severe depression. During the course of the study and after adjustment for clinical, sociodemographic, lifestyle and environmental factors, depressive symptoms remained higher in the South Asian compared to the white European participants [score higher by 1.5, 95% CI 0.9-2.1]. Levels of anxiety were also higher in the South Asian participants, although associations were attenuated after adjustment. Social deprivation, BMI, proximity to fast-food outlets and physical activity were correlates for depression in both the South Asian and white European participants. CONCLUSIONS: A higher burden of depressive symptoms was consistently evident among the South Asian participants, even after adjustment for multiple covariates. It is important to understand both the reasons why these differences are present, to help reduce health inequalities, and whether higher levels of depressive symptoms affect the uptake of and retention rates in diabetes prevention programmes in South Asian communities. This article is protected by copyright. All rights reserved

Participation in Transition(s):Reconceiving Public Engagements in Energy Transitions as Co-Produced, Emergent and Diverse

This paper brings the transitions literature into conversation with constructivist Science and Technology Studies (STS) perspectives on participation for the first time. In doing so we put forward a conception of public and civil society engagement in sustainability transitions as co-produced, relational, and emergent. Through paying close attention to the ways in which the subjects, objects, and procedural formats of public engagement are constructed through the performance of participatory collectives, our approach offers a framework to open up to and symmetrically compare diverse and interconnected forms of participation that make up wider socio-technical systems. We apply this framework in a comparative analysis of four diverse cases of civil society involvement in UK low carbon energy transitions. This highlights similarities and differences in how these distinct participatory collectives are orchestrated, mediated, and subject to exclusions, as well as their effects in producing particular visions of the issue at stake and implicit models of participation and ‘the public’. In conclusion we reflect on the value of this approach for opening up the politics of societal engagement in transitions, building systemic perspectives of interconnected ‘ecologies of participation’, and better accounting for the emergence, inherent uncertainties, and indeterminacies of all forms of participation in transitions

Induction of Endothelial Cell Apoptosis by Solid Tumor Cells

peer reviewedThe mechanisms by which tumor cells extravasate to form metastasis remain controversial. Previous studies performed in vivo and in vitro demonstrate that the contact between tumor cells and the vascular wall impairs endothelium integrity. Here, we investigated the effect of breast adenocarcinoma MCF-7 cells on the apoptosis of human umbilical vein endothelial cells (HUVEC). TUNEL labeling, nuclear morphology, and DNA electrophoresis indicated that MCF-7 cells induced a two- to fourfold increase in HUVEC apoptosis. Caspase-3 activity was significantly enhanced. Neither normal cells tested (mammary epithelial cells, fibroblasts, leukocytes) nor transformed hematopoietic cells tested (HL60, Jurkat) induced HUVEC apoptosis. On the contrary, cells derived from solid tumors (breast adenocarcinoma, MDA-MB-231 and T47D; fibrosarcoma, HT 1080) had an effect similar to that of MCF-7 cells. The induction of apoptosis requires cell-to-cell contact, since it could not be reproduced by media conditioned by MCF-7 cells cultured alone or cocultured with HUVEC. Our results suggest that cells derived from solid tumors may alter the endothelium integrity by inducing endothelial cell apoptosis. On the contrary, normal or malignant leukocytes appear to extravasate by distinct mechanisms and do not damage the endothelium. Our data may lead to a better understanding of the steps involved in tumor cell extravasation