Null effects of neighbourhood increases in visible minorities on radical right wing party mobilisation

In many European countries, a growing share of population with immigrant background coincides with the surge in support for radical right parties. In this paper we show how such increases affect radical right candidacy. We use Swedish register data which identifies political candidates. With geocoded data, we match individuals running for the Sweden Democrats to their local neighbourhood contexts, and measure changes in the share of visible minority residents at scales ranging from 100 meters to 2 kilometres. For those who stayed in the same neighbourhood between 2006 and 2010, the change in the share of visible minorities generally does not affect the decision to join the pool of party candidates. This result is robust when we introduce additional tests and select on the scale of the neighbourhood, unemployment terciles, change in share of visible minority groups terciles, and entry threshold into the pool of candidates. For those who stayed in the same neighbourhood, the only significant finding is a small mobilisation effect for a subsample of individuals who live in densely populated metropolitan neighbourhoods – here we also observe a halo effect, with negative association for small-scale changes and positive association for changes in the larger halo zone.

R|S Atlas: Identifying existing cohort study data resources to accelerate epidemiological research on the influence of religion and spirituality on human health

ObjectiveMany studies have documented significant associations between religion and spirituality (R/S) and health, but relatively few prospective analyses exist that can support causal inferences. To date, there has been no systematic analysis of R/S survey items collected in US cohort studies. We conducted a systematic content analysis of all surveys ever fielded in 20 diverse US cohort studies funded by the National Institutes of Health (NIH) to identify all R/S-related items collected from each cohort’s baseline survey through 2014.DesignAn R|S Ontology was developed from our systematic content analysis to categorise all R/S survey items identified into key conceptual categories. A systematic literature review was completed for each R/S item to identify any cohort publications involving these items through 2018.ResultsOur content analysis identified 319 R/S survey items, reflecting 213 unique R/S constructs and 50 R|S Ontology categories. 193 of the 319 extant R/S survey items had been analysed in at least one published paper. Using these data, we created the R|S Atlas (https://atlas.mgh.harvard.edu/), a publicly available, online relational database that allows investigators to identify R/S survey items that have been collected by US cohorts, and to further refine searches by other key data available in cohorts that may be necessary for a given study (eg, race/ethnicity, availability of DNA or geocoded data).ConclusionsR|S Atlas not only allows researchers to identify available sources of R/S data in cohort studies but will also assist in identifying novel research questions that have yet to be explored within the context of US cohort studies.

Limpieza, corrección y geocodificación de grandes bases de direcciones utilizando minería de texto

Para la georreferenciación de un gran número de direcciones, es necesaria la previa geocodificación mediante sistemas de carácter público o privado. La geocodificación no es una ciencia exacta porque las direcciones generalmente son escritas y almacenadas por personas, lo que provoca diferentes problemas de precisión en el registro, como errores ortográficos, datos innecesarios o falta de datos mínimos. Para enfrentar este problema, en este artículo se describe una metodología que limpia y corrige las direcciones optimizando el proceso de geocodificación utilizando los sistemas existentes. Para su desarrollo se utiliza el proceso Knowledge Discovery in Text (KDT). La metodología se aplica a una base de datos de direcciones de hechos delictivos proporcionada por la unidad de análisis penal de la Fiscalía Regional del Biobío, Chile. Los resultados muestran un aumento en el número de geocodificaciones de los sistemas implementados, que varía según el sistema utilizado. Palabras Clave: Georreferenciación, Geocodificación, Minería de Texto. Referencias [1]C. Davis y F. Fonseca, «Assessing the Certainty of Locations Produced by an Address Geocoding System,» Geoinformatica, vol. 11, pp. 103-129, 2007. [2]L. Hill, «Georeferencing in Digital Libraries,» D-Lib Magazine, vol. 10, nº 5, 2004. [3]J. Pontón y A. Santillán, «Seguridad Ciudadana: escenarios y efectos,» 2008. [4]D. W. Goldberg, «Spatial approaches to reducing error in geocoded data,» 2010. [5]D.-H. Yang, L. M. Bilaver, O. Hayes y R. Goerge, «Improving Geocoding Practices: Evaluation of Geocoding Tools,» Journal of Medical Systems, vol. 28, pp. 361-370, 2004. [6]T. Ah-Hwee, «Text mining: The state of the art and the challenges,» de PAKDD’99 workshop on Knowledge Discovery from Advanced Databases, Beijing, 1999. [7]R. Feldman y I. Dagan, «Knowledge discovery in textual databases,» de First International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995. [8]M. d. C Justicia de la Torre , «Nuevas Tecnicas de Mineria de Textos: Aplicaciones,» Granada, 2017. [9]M. Lutz, Programming Python, vol. 2, O'reilly & Associates, 2001, pp. 1-10. [10]W. McKinney, Python For Dara Analysis, O'Reilly, 2012, pp. 111-152. [11]E. Ukkonen, «Algorithms for Approximate String Matching,» de International Conference on Foundations of Computation Theory, 1985. [12]M. A. Alvarez Carmona, «Deteccion de similitud en textos cortos considerando traslape, ordeny relacion semantica de palabras,» Tonantzintla, Puebla, 2014. [13]V. I. Levenshtein, «Binary Codes Capble Of Correcting Deletions, Insertions, and Reversals,» Soviet Physics Doklady, vol. 10, p. 707, 2 February 1966. [14]Google, «Google Maps Plataform,» 2020. [En línea]. Disponible: https://developers.google.com/maps/documentation/javascript/geocoding?hl=es-419. [Último acceso: 29 Julio 2020]. [15]Mapquest, «Mapquest Developer,» 2020. [En línea]. Disponible: https://developer.mapquest.com/. [Último acceso: 25 Julio 2020]. [16]Microsoft Corporation, «Bing Maps Dev Center,» 2020. [En línea]. Disponible: https://www.bingmapsportal.com/. [Último acceso: 29 Julio 2020]. [17]Open Street Map Wiki, 2020. [En línea]. Disponible: https://wiki.openstreetmap.org/wiki/Main_Page. [Último acceso:29 Julio 2020]. [18]OpenAdrdresses, «OpenAdrdresses,» 2020. [En línea]. Disponible: https://openaddresses.io/. [Último acceso: 25 Julio 2020]. [19]OpenCage Geocoder, 2020. [En línea]. Disponible: https://opencagedata.com/. [Último acceso: 29 Julio 2020]. [20]Yahoo, «Yahoo Developer,» 2016. [En línea]. Disponible:https://developer.yahoo.com/. [Último acceso: 14 Agosto 2020]. [21]K. Jordahl, J. Van Den Bossche y J. Wasserman, «Geopandas/Geopandas: V0. 4.1. Zenodo,» 2020.

Surrounded and threatened: how neighborhood composition reduces ethnic voting through intimidation

Ethnic voting is an important phenomenon in the political lives of numerous countries. In the present paper, we propose a theory explaining why ethnic voting is more prevalent in certain localities than in others and provide evidence for it. We argue that local ethnic geography affects ethnic voting by making voters of ethnicity that finds itself in the minority fear intimidation by their ethnic majority neighbors. We provide empirical evidence for our claim using the data from round 4 of the Afrobarometer survey in Ghana to measure the voters’ beliefs that they are likely to face intimidation during electoral campaigns. Using geocoded data from rounds three and four of the Afrobarometer, as well as data from the Ghana Demographic and Health Survey, we find no evidence for local public goods provision as an alternative mechanism.

GDIS, a global dataset of geocoded disaster locations

This article presents a new open source extension to the Emergency Events Database (EM-DAT) that allows researchers, for the first time, to explore and make use of subnational, geocoded data on major disasters triggered by natural hazards. The Geocoded Disasters (GDIS) dataset provides spatial geometry in the form of GIS polygons and centroid latitude and longitude coordinates for each administrative entity listed as a disaster location in the EM-DAT database. In total, GDIS contains spatial information on 39,953 locations for 9,924 unique disasters occurring worldwide between 1960 and 2018. The dataset facilitates connecting the EM-DAT database to other geographic data sources on the subnational level to enable rigorous empirical analyses of disaster determinants and impacts.

Improving geocoding matching rates of structured addresses in Rio de Janeiro, Brazil

Strategies for improving geocoded data often rely on interactive manual processes that can be time-consuming and impractical for large-scale projects. In this study, we evaluated different automated strategies for improving address quality and geocoding matching rates using a large dataset of addresses from death records in Rio de Janeiro, Brazil. Mortality data included 132,863 records with address information in a structured format. We performed regular expressions and dictionary-based methods for address standardization and enrichment. All records were linked by their postal code or street name to the Brazilian National Address Directory (DNE) obtained from Brazil’s Postal Service. Residential addresses were geocoded using Google Maps. Records with address data validated down to the street level and location type returned as rooftop, range interpolated, or geometric center were considered a geocoding match. The overall performance was assessed by manually reviewing a sample of addresses. Out of the original 132,863 records, 85.7% (n = 113,876) were geocoded and validated, out of which 83.8% were matched as rooftop (high accuracy). Overall sensitivity and specificity were 87% (95%CI: 86-88) and 98% (95%CI: 96-99), respectively. Our results indicate that address quality and geocoding completeness can be reliably improved with an automated geocoding process. R scripts and instructions to reproduce all the analyses are available at https://github.com/reprotc/geocoding.

Community factors and excess mortality in first wave of the COVID-19 pandemic

Risk factors for increased risk of death from Coronavirus Disease 19 (COVID-19) have been identified1,2 but less is known on characteristics that make communities resilient or vulnerable to the mortality impacts of the pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality at the community level during the first wave of the pandemic in England. We used geocoded data on all deaths in people aged 40 years and older during March-May 2020 compared with 2015-2019 in 6,791 local communities. Here we show that communities with an increased risk of excess mortality had a high density of care homes, and/or high proportion of residents on income support, living in overcrowded homes and/or high percent of people with a non-White ethnicity (including Black, Asian and other minority ethnic groups). Conversely, after accounting for other community characteristics, we found no association between population density or air pollution and excess mortality. Overall, the social and environmental variables accounted for around 15% of the variation in mortality at community level. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed if England and other industrialised countries are to avoid further widening of inequalities in mortality patterns during the second wave.

The Geography of Ethnoracial Birth Weight Inequalities in the United States

Spatial factors feature prominently in theories of ethnoracial health disparities, yet we lack a foundational empirical understanding of the geography of health inequality. We provide this for infant health by conceptualizing ethnoracial disparities as spatial and relational phenomena and by estimating county-level low birth weight (LBW) rates and inequalities among eight ethnoracial groups: native-born Black, foreign-born Black, native-born Latinx, Mexican-born Latinx, native-born Asian, foreign-born Asian, Native American, and native-born white. Using geocoded data on every U.S. singleton birth from 2000-2010 (n=38 million) and robust descriptive empirical strategies, we document substantial county-level variation in LBW disparities. County-level LBW rates for native-born whites are only weakly related to LBW rates for groups of color. Further, county-level disparities between native-born whites and groups of color are explained more by variation in the LBW rate of groups of color than by variation in white LBW rates. Geographically, we document patchwork patterning alongside regional clustering.

The Impact of Parental Wealth on College Degree Attainment: Evidence from the Housing Boom and Bust

This study provides new evidence on the impact of parental wealth on college degree attainment. Using geocoded data from the Panel Study of Income Dynamics (1968-2017) linked to local housing price data from the Federal Housing Finance Agency, the empirical strategy analyzes parental housing wealth changes induced by local housing booms of the late 1990s-early 2000s and the subsequent housing bust of the 2007-2009 period. 2SLS/IV estimates show parental wealth significantly increases the likelihood of earning a four-year college degree. Moreover, the combined effects of parental income and wealth are significantly greater than the effects of income alone.

Proximity of Gun Stores to High Schools and Student Gun Carrying

Although research has examined various correlates of weapon and gun carrying at school and among adolescents, it has yet to consider the relationship between gun stores around schools and the carrying of guns at school. This study uses data from the 2015–2016 California Healthy Kids Survey, the California Department of Education, and geocoded data on public high schools and gun stores in Orange County, California, to examine the association between proximity of gun stores to schools and the carrying of guns by high school students. Using geographic information system analysis and hierarchical logistic regression, results indicate that the proximity of gun stores to schools is significantly associated with self-reported gun carrying at school. Implications for policy and practice are discussed.