The varying effects of accessing high-speed rail system on China’s county development: a geographically weighted panel regression analysis

The construction of high-speed rail in China was initiated to answer increasing demand for fast and convenient transportation systems connecting large economic centers. It is commonly understood that access to HSR will have significant impact on economic development. It is, however, also quite possible that the benefits to economic development brought by HSR will have a diminishing marginal effect. With data of HSR stations distribution and a set of panel data of socioeconomic information at county-level from 2008 – 2015 in China, this study applies advanced spatiotemporal data analysis techniques to investigate the impact of HSR. Our results suggest that on average the presence of an HSR station suggests about 2.7% increase of that county’s per capita GDP. The geographically weighted panel regression suggests that in places where HSR is sparsely distributed, the relationship between HSR accessibility and GDP per capita is significant and positive. In places where HSR is densely distributed, the relationship is less apparent. We hope the results will offer significant insights of the relationships between infrastructure construction and county economic development in both China and beyond

Geochemical Investigation of an Offshore Sewage Sludge Deposit, Barcelona, Catalonia, Spain

For 20 years ending in the 1990’s the city of Barcelona discharged the products from a large primary sewage treatment plant directly into the Mediterranean Sea via underwater conduits. About ca. 3 million m3 of relict sewage sludge, rich in organic matter and heavy metals, has spread over an elongated area offshore, due to successive ruptures of the conduits. The use of the discharge pipes ceased, but he sludge deposit remains in place for the time being. To understand the history and present state of the sludge deposit in advance of future remediation, a program of geophysical mapping, sampling, and analytical work was undertaken. Rock Eval pyrolysis, although created for use in petroleum prospecting, can also be applied to environmental contamination studies. It offers a simple means to effectively delineate the sludge deposit, with the S2 parameter and the hydrogen and oxygen indices particularly useful. On the molecular level, the sludge flash pyrolysis products notably include relatively abundant C27 and C29 sterenes and steranes, likely produced from the pyrolysis of fecal and other steroids, including coprostanol, in the sewage sludge. Linear alkylbenzenes and trialkylamines, derived from surfactant residues in the sludge, are also detected. The indoles detected are likely the pyrolysis products of proteins, while the alkylnitriles and alkylamides in the pyrolyzate likely derive from bacterial biomass. Principal components analysis aided the interpretation of the large geochemical dataset and a geographic information system enabled the three-dimensional visualization of the results in their geospatial context. The distinctive pyrolysis products and the trace elements would be geochemical markers useful in planning and assessing a future remediation program. The recognition of a distinctive sewage pyrolysis-GC/MS signature in this deposit would facilitate the use of this method in the detection of sewage-contaminated sediments in urban waterways worldwide

Governance Policy Evaluation in the United States during the Pandemic: Nonpharmaceutical Interventions or Else?

Scientific evidence suggests that nonpharmaceutical interventions (NPIs) effectively curb the spread of COVID-19 before a pharmaceutical solution. Implementing these interventions also significantly affects regular socioeconomic activities and practices of social, racial, and political justice. Local governments often face conflicting goals during policymaking. Striking a balance among competing goals during a global pandemic is a fine science of governance. How well state governments consume the scientific evidence and maintain such a balance remains less understood. This study employs a set of Bayesian hierarchical models to evaluate how state governments in the United States use scientific evidence to balance the fighting against the spread of COVID-19 disease and socioeconomic, racial, social justice, and other demands. We modeled the relationships between five NPI strategies and COVID-19 caseload information and used the modeled result to perform a balanced governance evaluation. The results suggest that governmental attitude and guidance effectively guide the public to fight back against a global pandemic. The more detailed spatiotemporally varying coefficient process model produces 612,000 spatiotemporally varying coefficients, suggesting all measures sometimes work somewhere. Summarized results indicate that states emphasizing NPIs fared well in curbing the spread of COVID-19. With over 1 million deaths due to COVID-19 in the United States, we feel the balance scale likely needs to tip toward preserving human lives. Our evaluation of governance policies is hence based on such an argument. This study aims to provide decision support for policymaking during a national emergency

Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy

Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350–2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745–910 nm and 1,911–2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of Rt (correlation coefficient of testing set), RMSEt and RPD of 0.91, 0.27% and 2.41, respectively. The simulated EO-1 Hyperion data combined with Support Vector Machine (SVM) based recursive feature elimination algorithm produced the most accurate estimate of SOC content. For the testing set, Rt was 0.79, RMSEt was 0.19%, and RPD was 1.61. This practice provides an efficient, low-cost approach with potentially high accuracy to estimate SOC contents and hence supports better management and protection strategies for desert wetland ecosystems

Genome-wide identification and analysis of heterotic loci in three maize hybrids

Heterosis, or hybrid vigour, is a predominant phenomenon in plant genetics, serving as the basis of crop hybrid breeding, but the causative loci and genes underlying heterosis remain unclear in many crops. Here, we present a large-scale genetic analysis using 5360 offsprings from three elite maize hybrids, which identifies 628 loci underlying 19 yield-related traits with relatively high mapping resolutions. Heterotic pattern investigations of the 628 loci show that numerous loci, mostly with complete–incomplete dominance (the major one) or overdominance effects (the secondary one) for heterozygous genotypes and nearly equal proportion of advantageous alleles from both parental lines, are the major causes of strong heterosis in these hybrids. Follow-up studies for 17 heterotic loci in an independent experiment using 2225 F2 individuals suggest most heterotic effects are roughly stable between environments with a small variation. Candidate gene analysis for one major heterotic locus (ub3) in maize implies that there may exist some common genes contributing to crop heterosis. These results provide a community resource for genetics studies in maize and new implications for heterosis in plants

Simulating the Changes of Invasive Phragmites australis in a Pristine Wetland Complex with a Grey System Coupled System Dynamic Model: A Remote Sensing Practice

Biological invasion has been one of the reasons that coastal wetlands gradually lose their ecological services. The current study investigates the spread of a commonly found invasive species in coastal wetlands in Northeastern US, the Phragmites australis. Within a relatively pristine wetland complex in coastal New Jersey, we collected high-resolution multispectral remote sensing images for eight years (2011–2018), in both winter and summer seasons. The land cover/land use status in this wetland complex is relatively simple, contains only five identifiable vegetation covers and water. Applying high accuracy machine learning algorithms, we are able to classify the land use/land cover in the complex and use the classified images as the basis for the grey system coupled system dynamics simulative model. The simulative model produces land use land cover change in the wetland complex for the next 25 years. Results suggest that Phragmites australis will increase in coverage in the future, despite the stable intensity of anthropogenic activities. The wetland complex could lose its essential ecological services to serve as an exchange spot for nekton species from the sea

Modeling Owner-Occupied Single-Family House Values in the City of Milwaukee: a Geographically Weighted Regression Approach

This study investigates the spatial non-stationarity of the relationship between house values and various attributes in the City of Milwaukee. From the 2003 Master Property (MPROP) data file of the City of Milwaukee, a set of owner-occupied single family houses were randomly selected (representing 99% of confidence within a ±2% range of accuracy of the total population) to model how house values are related to various house attributes. Remote sensing information (the fraction of soil and impervious surface that represent degraded neighborhood environmental conditions) is added to fine-tune the relationship. A geographically weighted regression (GWR) approach is used to investigate spatial non-stationarity. The modeling revealed that significant spatial non-stationarity existed between house values and the predictors. Specifically, the study found that those house attributes - including floor size, number of bathrooms, air conditioners, and fire-places - add more value to houses in the more affluent areas (especially on the east side near Lake Michigan and in suburban areas) than in the relatively poor areas. In addition, older houses in the historical area are more expensive, which differs from other areas. Environmental conditions, though expected to have a negative impact on house values in most areas, did not affect house values in the historical area

Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades

During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing “big data” and modern urban study communities