When to use commuting zones? An empirical description of spatial autocorrelation in U.S. counties versus commuting zones.
Ontology highlight
ABSTRACT:
SUBMITTER: Carpenter CW
PROVIDER: S-EPMC9278745 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected.
| S-EPMC8064870 | biostudies-literature
Project description:Introduction To examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate. Methods This study proposes three multiscale dimensions of spatial process, including “level of influence” (the percentage of population affected by a certain determinant across the entire area), “scalability” (the spatial process of a determinant into global, regional, and local process), and “specificity” (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021 and the analyses were conducted in May 2022. Results The results suggest that: (1) percent of Republican votes in 2020 presidential election is a primary influencer as 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) the spatial process underlying fully vaccinated rates is largely local. Conclusions The findings challenge the one-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.
| S-EPMC9259504 | biostudies-literature
Project description:In animals, competition for space and resources often results in territorial behaviour. The size of a territory is an important correlate of fitness and is primarily determined by the spatial distribution of resources and by interactions between competing individuals. Both of these determinants, alone or in interaction, could lead to spatial non-independence of territory size (i.e. spatial autocorrelation). We investigated the presence and magnitude of spatial autocorrelation (SAC) in territory size using Monte Carlo simulations of the most widely used territory measures. We found significant positive SAC in a wide array of competition-simulated conditions. A meta-analysis of territory size data showed that SAC is also a feature of territories mapped based on behavioural observations. Our results strongly suggest that SAC is an intrinsic trait of any territory measure. Hence, we recommend that appropriate statistical methods should be employed for the analysis of data sets where territory size is either a dependent or an explanatory variable.
| S-EPMC2821514 | biostudies-literature
Project description:Spatial autocorrelation plays an important role in geographical analysis; however, there is still room for improvement of this method. The formula for Moran's index is complicated, and several basic problems remain to be solved. Therefore, I will reconstruct its mathematical framework using mathematical derivation based on linear algebra and present four simple approaches to calculating Moran's index. Moran's scatterplot will be ameliorated, and new test methods will be proposed. The relationship between the global Moran's index and Geary's coefficient will be discussed from two different vantage points: spatial population and spatial sample. The sphere of applications for both Moran's index and Geary's coefficient will be clarified and defined. One of theoretical findings is that Moran's index is a characteristic parameter of spatial weight matrices, so the selection of weight functions is very significant for autocorrelation analysis of geographical systems. A case study of 29 Chinese cities in 2000 will be employed to validate the innovatory models and methods. This work is a methodological study, which will simplify the process of autocorrelation analysis. The results of this study will lay the foundation for the scaling analysis of spatial autocorrelation.
| S-EPMC3709922 | biostudies-literature
Project description:OBJECTIVE:To understand the role of county characteristics in the growing divide between rural and urban mortality from 1980 to 2010. DATA SOURCE:Age-adjusted mortality rates for all U.S. counties from 1980 to 2010 were obtained from the CDC Compressed Mortality File and combined with county characteristics from the U.S. Census Bureau, the Area Health Resources File, and the Inter-University Consortium for Political and Social research. STUDY DESIGN:We used Oaxaca-Blinder decomposition to assess the extent to which rural-urban mortality disparities are explained by observed county characteristics at each decade. PRINCIPAL FINDINGS:Decomposition shows that, at each decade, differences in rural/urban characteristics are sufficient to explain differences in mortality. Furthermore, starting in 1990, rural counties have significantly lower predicted mortality than urban counties when given identical county characteristics. We find changes in the effect of characteristics on mortality, not the characteristics themselves, drive the growing mortality divide. CONCLUSIONS:Differences in economic and demographic characteristics between rural and urban counties largely explain the differences in age-adjusted mortality in any given year. Over time, the role these characteristics play in improving mortality has increased differentially for urban counties. As characteristics continue changing in importance as determinants of health, this divide may continue to widen.
| S-EPMC6232410 | biostudies-literature
Project description:Previous research suggests that binge drinking among young men serves as a "costly signal" to potential mates, such that the binge drinker is capable of bearing the harmful consequences of alcohol consumption. Here, we propose that binge drinking among young adults is conditionally dependent upon the signaler's willingness to take risks, which is influenced by the local operational sex ratio (OSR). Using archived binge drinking estimates from 2009 to 2012 and Census Bureau records of OSRs, we tested the relationship between OSR and binge drinking rates at the county level across 3,143 U.S. counties against hypotheses drawn from evolutionary theory. Results from our mixed-effects models revealed that a higher overall OSR (i.e., more eligible men compared to women) was associated with higher male binge drinking rates but lower female binge drinking rates. A higher OSR particularly in the 20-29 and 50+ age groups predicted higher male binge drinking rates but lower female binge drinking rates. Our findings generally support predictions derived from evolutionary theory and suggest that binge drinking may function as a costly sexual signal, conditionally regulated by age and the local sex ratio.
| S-EPMC8211380 | biostudies-literature
Project description:Understanding spatiotemporal variation in environmental conditions is important to determine how climate change will impact ecological communities. The spatial and temporal autocorrelation of temperature can have strong impacts on community structure and persistence by increasing the duration and the magnitude of unfavorable conditions in sink populations and disrupting spatial rescue effects by synchronizing spatially segregated populations. Although increases in spatial and temporal autocorrelation of temperature have been documented in historical data, little is known about how climate change will impact these trends. We examined daily air temperature data from 21 General Circulation Models under the business-as-usual carbon emission scenario to quantify patterns of spatial and temporal autocorrelation between 1871 and 2099. Although both spatial and temporal autocorrelation increased over time, there was significant regional variation in the temporal autocorrelation trends. Additionally, we found a consistent breakpoint in the relationship between spatial autocorrelation and time around the year 2030, indicating an acceleration in the rate of increase of the spatial autocorrelation over the second half of the 21st century. Overall, our results suggest that ecological populations might experience elevated extinction risk under climate change because increased spatial and temporal autocorrelation of temperature is expected to erode both spatial and temporal refugia.
| S-EPMC6172201 | biostudies-literature
Project description:In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.
| S-EPMC4723244 | biostudies-literature
Project description:Rural U.S. adults' prevalence of meeting physical activity (PA) guidelines is lower than urban adults, yet rural-urban differences in environmental influences of adults' PA are largely unknown. The study's objective was to identify rural-urban variations in environmental factors associated with the prevalence of adults meeting PA guidelines. County-level data for non-frontier counties (n = 2697) were used. A five-category rurality variable was created using the percentage of a county's population living in a rural area. Factor scores from Factor Analyses (FA) were used in subsequent Multiple Linear Regression (MLR) analyses stratified by rurality to identify associations between environmental factor scores and the prevalence of males and females meeting PA guidelines. FA revealed a 13-variable, four-factor structure of natural, social, recreation, and transportation environments. MLR revealed that natural, social, and recreation environments were associated with PA for males and females, with variation by sex for social environment. The natural environment was associated with PA in all but urban counties; the recreation environment was associated with PA in the urban counties and the two most rural counties. Variations across the rural-urban continuum in environmental factors associated with adults' PA, highlight the uniqueness of rural PA and the need to further study what succeeds in creating active rural places.
| S-EPMC8307667 | biostudies-literature
Project description:Spatial autocorrelation and spatial interaction are two important analytical processes for geographical analyses. However, the internal relations between the two types of models have not been brought to light. This paper is devoted to integrating spatial autocorrelation analysis and spatial interaction analysis into a logic framework by means of Getis-Ord's indexes. Based on mathematical derivation and transform, the spatial autocorrelation measurements of Getis-Ord's indexes are reconstructed in a new and simple form. A finding is that the local Getis-Ord's indexes of spatial autocorrelation are equivalent to the rescaled potential energy indexes of spatial interaction theory based on power-law distance decay. The normalized scatterplot is introduced into the spatial analysis based on Getis-Ord's indexes, and the potential energy indexes are proposed as a complementary measurement. The global Getis-Ord's index proved to be the weighted sum of the potential energy indexes and the direct sum of total potential energy. The empirical analysis of the system of Chinese cities are taken as an example to illustrate the effect of the improved methods and measurements. The mathematical framework newly derived from Getis-Ord's work is helpful for further developing the methodology of geographical spatial modeling and quantitative analysis.
| S-EPMC7392341 | biostudies-literature