Many areas of the world are without basic information on the socioeconomic well-being of the residing population due to limitations in existing data collection methods. Overhead images obtained remotely, such as from satellite or aircraft, can help serve as windows into the state of life on the ground and help "fill in the gaps" where community information is sparse, with estimates at smaller geographic scales requiring higher resolution sensors. Concurrent with improved sensor resolutions, recent advancements in machine learning and computer vision have made it possible to quickly extract features from and detect patterns in image data, in the process correlating these features with other information. In this work, we explore how well two approaches, a supervised convolutional neural network and semi-supervised clustering based on bag-of-visual-words, estimate population density, median household income, and educational attainment of individual neighborhoods from publicly available high-resolution imagery of cities throughout the United States. Results and analyses indicate that features extracted from the imagery can accurately estimate the density (R$^2$ up to 0.81) of neighborhoods, with the supervised approach able to explain about half the variation in a population's income and education. In addition to the presented approaches serving as a basis for further geographic generalization, the novel semi-supervised approach provides a foundation for future work seeking to estimate fine-scale information from aerial imagery without the need for label data.

ABSTRACT As governments race to implement new climate adaptation policies that prepare for more frequent flooding, they must seek policies that are effective for all communities and uphold climate justice. This requires evaluating policies not only on their overall effectiveness but also on whether their benefits are felt across all communities. We illustrate the importance of considering such disparities for flood adaptation using the FEMA National Flood Insurance Program Community Rating System and its dataset of 2.5 million flood insurance claims. We use CausalFlow, a causal inference method based on deep generative models, to estimate the treatment effect of flood adaptation interventions based on a community's income, diversity, population, flood risk, educational attainment, and precipitation. We find that the program saves communities $5,000-15,000 per household. However, these savings are not evenly spread across communities. For example, for low-income communities savings sharply decline as flood-risk increases in contrast to their high-income counterparts with all else equal. Even among low-income communities, there is a gap in savings between predominantly white and non-white communities: savings of predominantly white communities can be higher by more than $6000 per household. As communities worldwide ramp up efforts to reduce losses inflicted by floods, simply prescribing a series flood adaptation measures is not enough. Programs must provide communities with the necessary technical and economic support to compensate for historical patterns of disenfranchisement, racism, and inequality. Future flood adaptation efforts should go beyond reducing losses overall and aim to close existing gaps to equitably support communities in the race for climate adaptation. INTRODUCTION Flooding constitutes nearly a third of all losses from natural disasters worldwide (Reuters 2022). By the end of the century, rising sea levels and coastal flooding are estimated to cost the global economy $14.2 trillion (a fifth of the global GDP) in damaged assets (Kirezci et al. 2020).

Currently, the United States healthcare system has a "cruel tendency to delay or deny high-quality care to those who are most in need of it but can least afford its high cost," (Shmerling) resulting in rampant disparities in health outcomes throughout the nation. The news of today is riddled with stories of people receiving poor care due to systematic biases present in the modern healthcare system and the effect of the increasingly unaffordable cost of life-saving medication. In order to better understand the degree to which this inequality exists, we investigated which socioeconomic indicators model health outcomes best.

Summary: Men who spend several years living alone or experience serial relationship breakups are at increased risk of inflammation, a new study reports. Living alone for several years and/or experiencing serial relationship break-ups are strongly linked to raised levels of inflammatory markers in the blood–but only in men–finds a large population study published online in the Journal of Epidemiology & Community Health. Although the inflammation was classified as low grade, it was persistent, and most likely indicates a heightened risk of age-related ill health and death, suggest the researchers. Divorce and committed relationship break-ups, which are often followed by a potentially lengthy period of living alone, have been associated with a heightened risk of poor physical and mental health, lowered immunity, and death. But most previously published studies have focused on the impact of one partnership dissolution, and then usually only on marital break-ups.

In 2016, the majority of full-time employed women in the U.S. earned significantly less than comparable men. The extent to which women were affected by gender inequality in earnings, however, depended greatly on socio-economic characteristics, such as marital status or educational attainment. In this paper, we analyzed data from the 2016 American Community Survey using a high-dimensional wage regression and applying double lasso to quantify heterogeneity in the gender wage gap. We found that the gap varied substantially across women and was driven primarily by marital status, having children at home, race, occupation, industry, and educational attainment. We recommend that policy makers use these insights to design policies that will reduce discrimination and unequal pay more effectively.

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, $\sim$40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate $\sim$0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the "missing heritability" problem -- i.e., the gap between prediction R-squared and SNP heritability. The $\sim$20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.

A child's performance at school is widely considered to be a complex combination of inherited ability, the way they were brought up, the quality of teaching they received and a bit of luck. But a new study has suggested it may be possible to predict a person's academic achievement by looking at their DNA alone. Researchers have developed a new genetic scoring technique that explains almost 10 per cent of the differences between children's educational attainment by the age of 16-years-old. A DNA test could soon be used to predict how a child will do when they are at school after researchers found they can explain 10 per cent of a person's academic achievement by the age of 16-years-old by creating what is known as a polygenic score based on 74 genetic variants thought to play a role in educational performance The IQ test has long been dismissed as an inaccurate way to discern how intelligent a person really is - but now scientists may have found a better way. Researchers at the University of Warwick say MRI scans can measure human intelligence, and define exactly what it is.