Differences in Perceptions of COVID-19 Risks in a Fishing Community in Alaska, 2020-2021.

Bristol Bay in Alaska is home to the world's largest commercial salmon fishery. During an average fishing season, the population of the Bristol Bay region more than doubles as thousands of workers from out of state converge on the fishery. In the months leading up to the 2020 commercial fishery opening, as the COVID-19 pandemic exploded worldwide, great uncertainty existed about the health risks of opening the fishery. Bristol Bay residents had not yet experienced any cases of COVID-19, yet the livelihoods of most were closely tied to the commercial fishery opening. To better understand how COVID-19 risk perceptions affected decisions to participate in the fishery, we administered an online survey to community members and fishery participants. We collected standard socioeconomic data and posed questions to gauge risk perceptions related to COVID-19. We find that COVID-19 risk perceptions vary across race/ethnic groups by residency and income. People with below median income who are members of minority groups-notably, non-resident Hispanic workers and resident Alaska Native respondents-reported the highest risk perceptions related to COVID-19. This study highlights the important linkages among risk perceptions, socioeconomic characteristics, and employment decisions during an infectious disease outbreak.

Ecosystem degradation or restoration? The evolving role of land use in China, 2000-2020.

Dramatic land use change in China affects ecosystem degradation and restoration. Identifying the evolving role of land use in ecosystem degradation and restoration in China is essential for sustainable land policy making. However, it is not clear how land use affects ecosystem degradation and restoration over time. Here, we used the revised benefit transfer approach and spatial statistics based on land use data to determine the evolving role that land use plays in ecosystem degradation and restoration in China during 2000-2020. The study results pointed out that the deterioration of the forestland ecosystem during the study period was the main reason for ecosystem degradation, while the conversion of arable land to forestland was the main cause for ecosystem restoration. Every 1% increase of land use intensity in the periods 2000-2005, 2005-2010, 2010-2015, and 2015-2020 resulted in -1.754%, 0.697%, 1.098%, and -0.058% of the changes in ecosystem services, respectively. This study provided important policy implications for future sustainable land use management in China.

Exposure to anti-Black Lives Matter movement and obesity of the Black population.

RATIONALE: Black Lives Matter (BLM) is a social movement against systematic injustice and police violence toward Black people whose goal is to ensure their safety and the expression of their culture. As BLM gained momentum, counter-movements emerged, such as All Lives Matter (ALM), White Lives Matter (WLM), and Blue Lives Matter (BlueLM). Because they undermine support for Black people's safety and culture, exposure to stances against BLM can be a race-related stressor. Although the perception of racial discrimination has been associated with negative health outcomes in Black people, it is not clear whether exposure to negative stances on a race-related social issue is associated with worse health outcomes. OBJECTIVE: We investigated whether living in areas of the United States with a high prevalence of negative stances on BLM is associated with worse health outcomes, such as higher body mass index (BMI) and prevalence of obesity. METHODS: We scraped geo-coded tweets (N = 51,020) that contained #BLM, #ALM, #WLM, and #BlueLM from 2014 to 2016. We determined the stances of the tweets on BLM using machine learning algorithms and aggregated stances at the metropolitan or micropolitan statistical area (MMSA) levels. Participants' BMI and obesity status were derived from the 2017 BRFSS SMART data in 76 MMSAs, as compiled by the Centers for Disease Control and Prevention (N = 20,530). RESULTS: After controlling for individual- and regional-level covariates, regional measures of racism and police brutality rate, and baseline BMI in 2014 aggregated on MMSA level, Black people had a higher BMI and prevalence of obesity in areas that showed higher negative stances on BLM. Stances against BLM were positively associated with implicit racism against Black people and can be an acute race-related stressor associated with negative downstream health outcomes. CONCLUSION: Negative societal sentiments around race-related issues may be detrimental to the health outcomes of minority populations.

Spatial proximity to wildfires as a proxy for measuring PM2.5: A novel method for estimating exposures in rural settings.

Background: Climate change impacts humans and society both directly and indirectly. Alaska, for example, is warming twice as fast as the global mean, and researchers are starting to grapple with the varied and inter-connected ways in which climate change affects the people there. With the number of wildfires increasing in Alaska as a result of climate change, the number of asthma cases has increased, driven by exposure to small particulate matter. However, it is not clear how far away smoke from wildfires can affect health. In this study, we hope to establish a relationship between proximity to wildfires and asthma in locations where direct PM2.5 measurement is not easily accomplished. Methods: In this study, we examined whether proximity to wildfire exposure is associated with regional counts of adults with asthma, calculated using Behavioral Risk Factor Surveillance System (BRFSS) survey data and US Census data. We assigned "hotspots" around population centers with a range of various distances to wildfires in Alaska. Results: We found that wildfires are associated with asthma prevalence, and the association is strongest within 25 miles of fires. Conclusions: This study highlights the fact that proximity to wildfires has potential as a simple proxy for actual measured wildfire smoke, which has important implications for wildfire management agencies and for policy makers who must address health issues associated with wildfires, especially in rural areas.

A tale of three cities: uncovering human-urban interactions with geographic-context aware social media data.

Seeking spatiotemporal patterns about how citizens interact with the urban space is critical for understanding how cities function. Such interactions were studied in various forms focusing on patterns of people's presence, action, and transition in the urban environment, which are defined as human-urban interactions in this paper. Using human activity datasets that utilize mobile positioning technology for tracking the locations and movements of individuals, researchers developed stochastic models to uncover preferential return behaviors and recurrent transitional activity structures in human-urban interactions. Ad-hoc heuristics and spatial clustering methods were applied to derive meaningful activity places in those studies. However, the lack of semantic meaning in the recorded locations makes it difficult to examine the details about how people interact with different activity places. In this study, we utilized geographic context-aware Twitter data to investigate the spatiotemporal patterns of people's interactions with their activity places in different urban settings. To test consistency of our findings, we used geo-located tweets to derive the activity places in Twitter users' location histories over three major U.S. metropolitan areas: Greater Boston Area, Chicago, and San Diego, where the geographic context of each location was inferred from its closest land use parcel. The results showed striking spatial and temporal similarities in Twitter users' interactions with their activity places among the three cities. By using entropy-based predictability measures, this study not only confirmed the preferential return behaviors as people tend to revisit a few highly frequented places but also revealed detailed characteristics of those activity places.

Bride Kidnapping and Gendered Labor Migration: Evidence from Kyrgyzstan.

Because the decision to migrate is a product of gendered negotiations within households, households formed through forced marriage may have different migration strategies than households formed through voluntary marriage. In Kyrgyzstan, we anticipate two possible effects of the traditional practice of bride kidnapping on migration. Households headed by a kidnap couple may be more cohesive and patriarchal, facilitating men's labor migration and remittance-sending. Alternately, women may use migration to escape such households. We test these two hypotheses using a sample of 1,171 households in rural Kyrgyzstan. Kidnap households are more likely to include women migrants, compared to other households. Kidnap households are also more likely to be receiving remittances, even when controlling for migrant household members. However, traditional beliefs about kidnapping are negatively associated with men's and women's migration. While higher levels of remittance receipt among kidnap households resembles the unified, patriarchal households envisioned in the New Economics of Labor Migration, it also appears that women use labor migration as a means to escape patriarchal constraints. We demonstrate that forced marriage in Kyrgyzstan plays a larger social role than is often believed, and highlight a new pathway through which gendered power dynamics can shape household migration strategies.

Assessing the validity of mobile device data for estimating visitor demographics and visitation patterns in Yellowstone National Park.

Monitoring visitor demographics and temporal visitation patterns can help national park managers understand their visitors and allocate resources more effectively. Traditional approaches, such as visitor surveys or vehicle counts, are limited by time, space, labor, and financial resources. More recently, mobile device data have been adopted for monitoring visitors in park-related or tourism research. However, few studies validated mobile device data with traditional visitor surveys or count data. Combining mobile device data with the American Community Survey (ACS), this study assessed mobile device data's validity in a national park context with three approaches: Points of Interest (POIs), visitor demographics, and temporal visitation patterns. The results revealed that only half of the POIs inside Yellowstone National Park are valid. Compared to traditional visitor surveys, mobile device data are limited due to platform bias and the exclusion of international visitors, resulting in discrepancies in visitor demographics, such as education and income levels. Conversely, mobile device data have strong correlations with count data regarding monthly and daily visitation patterns. The results suggest that with careful consideration, mobile device data can serve as an additional and complementary source of information to traditional survey data for understanding visitor demographics and temporal visitation patterns.

Spatial mismatch of ecosystem service demands and supplies in China, 2000-2020.

The supply capacity of ecosystem services (ES) in the past decades has shown a significant decrease globally, while ES demand capacity has increased. Identifying the spatial mismatch of ES supply and demand (ES S&D) can provide valuable knowledge about where the gaps are. Existing studies, however, lack specifics about the spatial mismatch of ES S&D-that is, few studies consider the coupling and decoupling relationship of ES S&D at the national scale. This study tries to fill the gap by examining the spatiotemporal distribution of ES S&D capacity in China from 2000 through 2020 using the land use/land cover matrix method. The spatial mismatch between ES S&D was ultimately identified by using coupling and decoupling analysis models. A continuous increase was found in the ES demand capacity in China during the period studied, while a continuous decline was found in the ES supply capacity. The coupling degree of the ES S&D was relatively higher in the plains areas. The strong negative decoupling was the dominant relationship between ES S&D, which was widely distributed in eastern and southeastern China. The spatial mismatch of ES S&D in China has increased substantially from 2000 through 2020. The findings in this study provide important implications for ES management and effective allocation of resources.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates.

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

An Evaluation of Geo-located Twitter Data for Measuring Human Migration.

This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary.

Area- and Individual-Level Correlates of Self-Rated Health: Implications for Geographic Health Disparities.

BACKGROUND: Self-rated health (SRH) is a common measure of overall health. However, little is known about multilevel correlates of physical and mental SRH. METHODS: Patients attending primary care clinics completed a survey before their appointment, which we linked to community data from American Community Survey and other sources (n = 455). We conducted multilevel logistic regression to assess correlates of excellent/very good versus good/fair/poor physical and mental SRH. RESULTS: 43.9% of participants had excellent/very good physical SRH, and 55.2% had excellent/very good mental SRH. Physical SRH was associated with age (odds ratio[OR] = 0.82 per 10 years; 95% confidence interval[CI] = 0.72-0.93) and community correlates, including retail establishment density (OR = 0.94, 95% CI = 0.90-0.99) and percent of students eligible for free/reduced lunch (OR = 1.60, 95% CI = 1.08-2.38) (all P < .05). Mental SRH was not associated with any characteristics. CONCLUSIONS: Practitioners in public health, social work, and medicine could use zip codes to intervene in patients and communities to improve physical SRH.

Remittances and Household Spending Strategies: Evidence from the Life in Kyrgyzstan Study, 2011-2013.

Globalized labor migration and remittances can help alleviate household poverty and provide supplemental income in many countries. Kyrgyzstan, like other Central Asian countries, has experienced dramatic geopolitical changes, economic reform, and rapid demographic shifts in the post-Soviet-Union era. Based on measurements of GDP, it is one of the most remittance-dependent countries in the world. This study uses data from the Life in Kyrgyzstan Study collected from 2011 to 2013 to break down household budgets into eight consumption categories as part of a detailed analysis of how varying remittance receipt is related to household spending. We address two methodological concerns: 1) the endogeneity of remittances and 2) population heterogeneity. In so doing, we find remittances have limited effects on household spending-while changes in remittances do yield small changes on the budget shares of food and medical expenses, no effects were found on other consumption shares. These results suggest that households in Kyrgyzstan may take remittances as permanent income and proportionally alter consumption shares along with changes in remittances. By focusing on a country whose GDP relies heavily on remittances, the findings increase our understanding of how remittances affect spending at the household level.

Impact of the COVID-19 Pandemic on Chlamydia and Gonorrhea Screening in the U.S.

INTRODUCTION: This study evaluates the impact of the COVID-19 pandemic on testing for common sexually transmitted infections. Specifically, changes are measured in chlamydia and gonorrhea testing and case detection among patients aged 14-49 years during the COVID-19 pandemic. METHODS: U.S. chlamydia and gonorrhea testing and positivity were analyzed on the basis of >18.6 million tests (13.6 million tests for female patients and 4.7 million tests for male patients) performed by a national reference clinical laboratory from January 2019 through June 2020. RESULTS: Chlamydia and gonorrhea testing reached a nadir in early April 2020, with decreases (relative to the baseline level) of 59% for female patients and 63% for male patients. Declines in testing were strongly associated with increases in weekly positivity rates for chlamydia (R2=0.96) and gonorrhea (R2=0.85). From March 2020 through June 2020, an expected 27,659 (26.4%) chlamydia and 5,577 (16.5%) gonorrhea cases were potentially missed. CONCLUSIONS: The COVID-19 pandemic impacted routine sexually transmitted infection services, suggesting an increase in syndromic sexually transmitted infection testing and missed asymptomatic cases. Follow-up analyses will be needed to assess the long-term implications of missed screening opportunities. These findings should serve as a warning for the potential sexual and reproductive health implications that can be expected from the overall decline in testing and potential missed cases.

A multi-modal approach towards mining social media data during natural disasters - a case study of Hurricane Irma.

Streaming social media provides a real-time glimpse of extreme weather impacts. However, the volume of streaming data makes mining information a challenge for emergency managers, policy makers, and disciplinary scientists. Here we explore the effectiveness of data learned approaches to mine and filter information from streaming social media data from Hurricane Irma's landfall in Florida, USA. We use 54,383 Twitter messages (out of 784K geolocated messages) from 16,598 users from Sept. 10 - 12, 2017 to develop 4 independent models to filter data for relevance: 1) a geospatial model based on forcing conditions at the place and time of each tweet, 2) an image classification model for tweets that include images, 3) a user model to predict the reliability of the tweeter, and 4) a text model to determine if the text is related to Hurricane Irma. All four models are independently tested, and can be combined to quickly filter and visualize tweets based on user-defined thresholds for each submodel. We envision that this type of filtering and visualization routine can be useful as a base model for data capture from noisy sources such as Twitter. The data can then be subsequently used by policy makers, environmental managers, emergency managers, and domain scientists interested in finding tweets with specific attributes to use during different stages of the disaster (e.g., preparedness, response, and recovery), or for detailed research.

GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data.

Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.

A general approach to detecting migration events in digital trace data.

Empirical research on migration has historically been fraught with measurement challenges. Recently, the increasing ubiquity of digital trace data-from mobile phones, social media, and related sources of 'big data'-has created new opportunities for the quantitative analysis of migration. However, most existing work relies on relatively ad hoc methods for inferring migration. Here, we develop and validate a novel and general approach to detecting migration events in trace data. We benchmark this method using two different trace datasets: four years of mobile phone metadata from a single country's monopoly operator, and three years of geo-tagged Twitter data. The novel measures more accurately reflect human understanding and evaluation of migration events, and further provide more granular insight into migration spells and types than what are captured in standard survey instruments.

A first look at COVID-19 information and misinformation sharing on Twitter.

Since December 2019, COVID-19 has been spreading rapidly across the world. Not surprisingly, conversation about COVID-19 is also increasing. This article is a first look at the amount of conversation taking place on social media, specifically Twitter, with respect to COVID-19, the themes of discussion, where the discussion is emerging from, myths shared about the virus, and how much of it is connected to other high and low quality information on the Internet through shared URL links. Our preliminary findings suggest that a meaningful spatio-temporal relationship exists between information flow and new cases of COVID-19, and while discussions about myths and links to poor quality information exist, their presence is less dominant than other crisis specific themes. This research is a first step toward understanding social media conversation about COVID-19.

Ecosystem Services and Their Driving Forces in the Middle Reaches of the Yangtze River Urban Agglomerations, China.

The impact of human activities on ecosystems can be measured by ecosystem services. The study of ecosystem services is an essential part of coupled human and natural systems. However, there is limited understanding about the driving forces of ecosystem services, especially from a spatial perspective. This study attempts to fill the gap by examining the driving forces of ecosystem services with an integrated spatial approach. The results indicate that more than US$430 billion of ecosystem services value (ESV) is produced annually in the Middle Reaches of the Yangtze River Urban Agglomerations (MRYRUA), with forestland providing the largest proportion of total ESV (≥75%) and hydrological regulation function accounting for the largest proportion of total ESV (≥15%). The average ESV in the surrounding areas is obviously higher than those in the metropolitan areas, in the plains areas, and along major traffic routes. Spatial dependence and spatial spillover effects were observed in the ecosystem services in the MRYRUA. Spatial regression results indicate that road density, proportion of developed land, and river density are negatively associated with ecosystem services, while distance to a socioeconomic center, proportion of forestland land, elevation, and precipitation are positively associated with ecosystem services. The findings in this study suggest that these driving factors and the spillover effect should be taken into consideration in ecosystem protection and land-use policymaking in urban agglomerations.

Energy Choices in Alaska: Mining People's Perception and Attitudes from Geotagged Tweets.

Alaska is at the forefront of climate change and subject to salient challenges including energy consumption. It is important to understand Alaskans' perceptions and opinions about energy consumption to solve Alaska's domestic energy problems and creating a sustainable future. However, it is challenging to collect public opinions about energy consumption using conventional survey methods, which are often expensive, labor-intensive, and slow. This study utilizes information-rich Twitter data to investigate Alaskans' perceptions and opinions on various energy sources and in particular clean energy sources. Using the geotagged Twitter data collected in Alaska from 2014 to 2016, a lexicon-based sentiment analysis approach was first applied to analyze the polarity in the expressed opinions. Further, a novel fuzzy-based theory is employed to derive the sentiment of the opinion in each tweet. The results indicate that there is a valuable growth rate for a set of energy-related keywords, such as "sun", "power", and "nuclear". The rank of top 20 renewable energy-related keywords shows the word "Tidal" has the highest ranking followed by "solar panel". Moreover, the attention to various types of energy is increasing dramatically among Alaskans. Importantly, Alaskans' attitudes toward energy and renewable energy changed positively from 2014 to 2016, indicating that Alaskans' energy choices are more acceptive towards or even favor renewable energy in the future.

Temporal and spatial trends and determinants of aggressive prostate cancer among Black and White men with prostate cancer.

PURPOSE: Few studies have reported temporal and spatial trends of aggressive prostate cancer (PC) among black men who are known to have more aggressive disease. We examined these trends for highly aggressive PC at diagnosis among black and white men in Pennsylvania (PA). METHODS: Men, aged ≥ 40 years, with a primary, clinical PC diagnosis were identified from the Pennsylvania Cancer Registry, 2004-2014. Joinpoint analysis was used to evaluate the temporal trend of highly aggressive PC (clinical/pathologic Gleason score ≥ 7 [4 + 3], clinical/pathologic tumor stage ≥ T3, or distant metastasis) and identify change points by race in which annual percent change (APC) was calculated. Logistic regression analyses were used to examine the association between race and highly aggressive PC, after adjusting for covariates with and without spatial dependence. RESULTS: There were 89,133 PC cases, which included 88.7% white and 11.3% black men. The APC of highly aggressive PC was 8.7% from 2011 to 2014 among white men and 3.6% from 2007 to 2014 among black men (p values ≤ 0.01). The greatest odds of having highly aggressive PC among black compared to white men were found in counties where the black male population was ≤ 5.3%. CONCLUSIONS: Highly aggressive PC increased for both black and white men in PA between 2004 and 2014. Black men had more aggressive disease, with the greatest odds in counties where the black male population was small. The increase in highly aggressive PC may be due to less screening for PC, resulting in more advanced disease at diagnosis.