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.

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.

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.

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.

Error Measures for Trajectories Estimations with Geo-tagged Mobility Sample Data.

Although geo-tagged mobility data (e.g., cell phone data and social media data) can be potentially used to estimate individual space-time travel trajectories, they often have low sample rates that only tell travelers' whereabouts at the sparse sample times while leaving the remaining activities to be estimated with interpolation. This study proposes a set of time geography-based measures to quantify the accuracy of the trajectory estimation in a robust manner. A series of measures including activity bandwidth and normalized activity bandwidth are proposed to quantify the possible absolute and relative error ranges between the estimated and the ground truth trajectories that cannot be observed. These measures can be used to evaluate the suitability of the estimated individual trajectories from sparsely sampled geo-tagged mobility data for travel mobility analysis. We suggest cutoff values of these measures to separate useful data with low estimation errors and noisy data with high estimation errors. We conduct theoretical analysis to show that these error measures decrease with sample rates and people's activity ranges. We also propose a lookup table-based interpolation method to expedite the computational time. The proposed measures have been applied to 2013 geo-tagged tweet data in New York City and 2014 cell-phone data in Shenzhen, China. The results illustrate that the proposed measures can provide estimation error ranges for exceptionally large datasets in much shorter times than the benchmark method without using lookup tables. These results also reveal managerial results into the quality of these data for human mobility studies, including their distribution patterns.

Lost in the Black Belt South: health outcomes and transportation infrastructure.

The importance of transportation infrastructure to health outcomes has been increasingly recognized. However, the relationship between transportation and health is underexplored in rural areas. This study fills the gap by investigating rural health outcomes in association with two transportation infrastructures-highways and airports-in the Black Belt counties of the USA, a region characterized as predominantly rural and black and as having high poverty and unemployment. Spatial regression models are applied to analyze the 2010 data. The results suggest Black Belt counties have poorer health outcomes than their non-Black Belt counterparts, and the difference increases as the percentage of blacks increases. The results also show that the higher accessibility to an airport a county has, the better its health outcomes. Highways, however, do not have a statistically significant association with health outcomes. The poor health outcomes in the Black Belt counties are also influenced by poverty, rurality, unemployment, and low educational attainment. This research was the first to study transportation, especially airports, in the rural US South with relation to health outcomes. Our findings shed new light on removing the health disadvantages accumulated in the Black Belt.

Population stress: A spatiotemporal analysis of population change and land development at the county level in the contiguous United States, 2001-2011.

The past century has witnessed rapidly increasing population-land conflicts due to exponential population growth and its many consequences. Although the measures of population-land conflicts are many, there lacks a model that appropriately considers both the social and physical contexts of population-land conflicts. In this study we introduce the concept of population stress, which identifies areas with populations growing faster than the lands available for sustainable development. Specifically, population stress areas are identified by comparing population growth and land development as measured by land developability in the contiguous United States from 2001 to 2011. Our approach is based on a combination of spatial multicriteria analysis, zonal statistics, and spatiotemporal modeling. We found that the population growth of a county is associated with the decrease of land developability, along with the spatial influences of surrounding counties. The Midwest and the traditional "Deep South" counties would have less population stress with future land development, whereas the Southeast Coast, Washington State, Northern Texas, and the Southwest would face more stress due to population growth that is faster than the loss of suitable lands for development. The factors contributing to population stress may differ from place to place. Our population stress concept is useful and innovative for understanding population stress due to land development and can be applied to other regions as well as global research. It can act as a basis towards developing coherent sustainable land use policies. Coordination among local governments and across different levels of governments in the twenty-first century is a must for effective land use planning.

Neighbourhood built environment and walking behaviours: evidence from the rural American South.

This study examines the perceived neighbourhood characteristics and environmental barriers in association with two different types of walking - recreational and destination - in the context of a rural town in Mississippi. A cross-sectional survey was used to assess residents' walking behaviours, perceived neighbourhood characteristics, and perceived environmental barriers to walking in three types of neighbourhoods: traditional, early conventional suburban and late conventional suburban. Descriptive statistics, one-way analysis of variance (ANOVA) and regression analyses identified environmental factors correlated with walking. A total of 362 surveys were completed and returned by random adult members of the households contacted, for a 38.5% response rate. Perceived aesthetics are significantly associated with more frequent recreational and destination walking in this rural town. Higher perceived accessibility are associated with more frequent destination walking, and greater perceived social environment barriers to walking are associated with sedentary behaviour in the rural population studied. Of all factors related to a neighbourhood's built environment, the most important factor in promoting walking in rural towns is aesthetics. The relationships among accessibility, social environment and walking underscore the importance of community planning in incorporating mixed land uses, providing a connected pedestrian infrastructure and facilitating targeted social interventions to encourage more walking.

Spatiotemporal analysis of regional socio-economic vulnerability change associated with heat risks in Canada.

Excess mortality can be caused by extreme hot weather events, which are increasing in severity and frequency in Canada due to climate change. Individual and social vulnerability factors influence the mortality risk associated with a given heat exposure. We constructed heat vulnerability indices using census data from 2006 and 2011 in Canada, developed a novel design to compare spatiotemporal changes of heat vulnerability, and identified locations that may be increasingly vulnerable to heat. The results suggest that 1) urban areas in Canada are particularly vulnerable to heat, 2) suburban areas and satellite cities around major metropolitan areas show the greatest increases in vulnerability, and 3) heat vulnerability changes are driven primarily by changes in the density of older ages and infants. Our approach is applicable to heat vulnerability analyses in other countries.

Sociodemographics and access to organic and local food: A case study of New Orleans, Louisiana.

This study examined the association between physical accessibility to organic and local food, and sociodemographic factors in New Orleans, Louisiana. Spatial regression models were used to investigate how sociodemographic variables such as income, race/ethnicity, education, and age correlate with driving, bicycling, and walking distances to stores that sell organic or local food. The distances were calculated from GIS and real-time speed information from Google Maps. The results indicated that physical access to such stores is positively associated with population density, median housing value, education, non-Hispanic Blacks, and Hispanics, and is negatively associated with median housing age. We found no disparities in access to organic and local food on the basis of income and race.

Population projection accuracy: The impacts of sociodemographics, accessibility, land use, and neighbour characteristics.

Population projection is essential to governments, businesses, and research communities for many purposes. Although projection performance is often evaluated, we know very little about what factors affect projection accuracy. It is important to understand these factors in order to utilize the projections knowledgeably. This study fills this gap in the literature by comprehensively investigating the possible factors associated with population projection accuracy in 2010 for the continental US counties. The results indicate that the counties whose populations are more predictable tend to be desirable places-places with abundant employment opportunities, reliable public transportation infrastructure, easy access to work, and/or high land development potential; their neighboring counties tend to have a well-educated population and a higher income level. Also, projection accuracy is highly spatially associated. The findings provide important insights for population projection users to understand the characteristics of counties and their neighboring counties associated with their projection accuracy.

Different Places, Different Stories: A Study of Spatial Heterogeneity of County-Level Fertility in China.

BACKGROUND: China has been characterized by persistently low fertility rates since the 1990s. Existing literature has examined the relationships of fertility levels with social, economic, and policy-related determinants. However, the possible spatial variations in these relationships have not been investigated. OBJECTIVE: The purpose of this study is to examine the potential spatially varying relationships between county-level fertility rates and policy and socioeconomic factors in China. METHODS: Using geocoded 2010 county-level census data, this study adopts the geographically weighted regression (GWR) method to identify place-specific relationships between county-level total fertility rate (TFR) and socioeconomics and policy-related factors. CONCLUSIONS: We find relationships between TFR and widely used social, economic, and policy-related factors (rural Hukou, ethnic minority, female education, net migration rate, poor living standard, sex ratio at birth, and fertility policy compliance ratio) vary spatially in terms of the direction, strength, and magnitude. The spatial variation is largely due to the difference in local characteristics. The differences and the complexities of localities cannot be told by a single story of either government intervention or socioeconomic development. CONTRIBUTION: This study extends the existing fertility research in China by explicitly recognizing the spatial heterogeneity in the impacts of policy and socioeconomic factors on the local fertility rate. This study sets the stage for future research that will contextually analyze varying fertility rates at the sub-national level in China and other countries.

Safer Roads Owing to Higher Gasoline Prices: How Long It Takes.

OBJECTIVES: We investigated how much time passes before gasoline price changes affect traffic crashes. METHODS: We systematically examined 2004 to 2012 Mississippi traffic crash data by age, gender, and race. Control variables were unemployment rate, seat belt use, alcohol consumption, climate, and temporal and seasonal variations. RESULTS: We found a positive association between higher gasoline prices and safer roads. Overall, gasoline prices affected crashes 9 to 10 months after a price change. This finding was generally consistent across age, gender, and race, with some exceptions. For those aged 16 to 19 years, gasoline price increases had an immediate (although statistically weak) effect and a lagged effect, but crashes involving those aged 25 to 34 years was seemingly unaffected by price changes. For older individuals (≥ 75 years), the lagged effect was stronger and lasted longer than did that of other age groups. CONCLUSIONS: The results have important health policy implications for using gasoline prices and taxes to improve traffic safety.

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.

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.

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.

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.

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.