Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas.

Characterizing the spatiotemporal heterogeneity of the COVID-19 vaccination landscape

It is critical that we maximize vaccination coverage across the United States so that SARS-CoV-2 transmission can be suppressed, and we can sustain the recent reopening of the nation. Maximizing vaccination requires that we track vaccination patterns to measure the progress of the vaccination campaign and target locations that may be undervaccinated. To improve efforts to track and characterize COVID-19 vaccination progress in the United States, we integrate CDC and state-provided vaccination data, identifying and rectifying discrepancies between these data sources. We find that COVID-19 vaccination coverage in the US exhibits significant spatial heterogeneity at the county level and statistically identify spatial clusters of undervaccination, all with foci in the southern US. Vaccination progress at the county level is also variable; many counties stalled in vaccination into June 2021 and few recovered by July, with transmission of the Delta variant rapidly rising. Using a comparison with a mechanistic growth model fitted to our integrated data, we classify vaccination dynamics across time at the county scale. Our findings underline the importance of curating accurate, fine-scale vaccination data and the continued need for widespread vaccination in the US, especially in the wake of the highly transmissible Delta variant.

Multiple Dimensions of Gender (Dis)Parity: A County-Scale Analysis of Occupational Attainment in the USA, 2019

Gender economic parity comprises an integral part of the United Nation’s 17 goals toward attaining sustainable development. Women have historically been confined to feminine occupations associated with lower pay, which have negatively impacted their economic wellbeing. This paper examines gendered dimensions of occupational (dis)parity across US counties and their association with educational attainment. Drawing on five years’ American Community Survey estimates (2015–2019) data from the National Historical Geographic Information System, I conduct descriptive statistical analysis of occupation-based location quotients and education, followed by an in-depth share analysis of 26 gender-based sub-categories of occupations. The correlation analysis provides insights into the multiple dimensions of gendered inequalities. Women’s largest engagements still include sales/office (28.66%), service (21.15%), and education/legal/community-service/arts/media (15.03%)—accounting toward 65% of all employed women in the US. Women majoring in science/engineering and related disciplines are still the lowest, which manifests into their alarmingly lower representations in science/engineering and related occupations. This suggests strategic policy interventions to advance women in STEM education. This analysis, however, also suggests occupational parity for women with a master’s education and above who share almost similar types of relationships with major categories of occupations, even though the coefficients are more favorable for males in managerial jobs.

The County-Scale Economic Spatial Pattern and Influencing Factors of Seven Urban Agglomerations in the Yellow River Basin—A Study Based on the Integrated Nighttime Light Data

The integrated night light (NTL) datasets were used to represent the economic development level, and visual analysis was carried out on the evolution characteristics of the economic spatial pattern of various urban agglomerations in the Yellow River Basin (YRB), at a county-scale, in 1992, 2005, and 2018. The Global Moran’s I and the local Getis-Ord G methods were used to explore the overall spatial correlation and local cold–hot spot of economic development levels, respectively. The spatial heterogeneity of the influence of relevant factors on the economic development level at the municipal scale was analyzed by using the multi-scale geographically weighted regression (MGWR) model. The results show that the county-level economic spatial pattern of urban agglomeration in the YRB has an obvious “pyramid” characteristic. The hot spots are concentrated in the hinterland of the Guanzhong Plain, the Central Plains, and the Shandong Peninsula urban agglomeration. The cold spots are concentrated in the junction of urban agglomerations, and the characteristics of “cold in the west and hot in the east” are obvious. Labor input and import and exporthave a positive impact on the economic development level for each urban agglomeration, government force has a negative impact, and education shows both positive and negative polarization on economic development.

Spatiotemporal Hotspots of Study Areas in Research of Gastric Cancer in China Based on Web-Crawled Literature

Gastric cancer is a common malignancy worldwide and poses a serious threat to human public health. The difficulty in obtaining epidemiological data limits the development of cross-disciplinary related research. In this study, 99,364 publications on gastric cancer from 1991 to 2019 were obtained using web-crawler technology, and a technical framework for extracting toponyms from these publications was constructed to analyze spatiotemporal hotspots of study areas in gastric cancer research in China. The results showed the following: (1) The accuracy of toponym extraction was greatly improved after eliminating the systematic exclusion words and adding historical toponyms, with a precision of 95.31% and a recall of 94.86%. (2) Gastric cancer research (GCR) and gastric cancer research with toponyms (GCRWT) are attracting increasing amounts of attention. The amount of GCR results published in Chinese and English is gradually leveling off, and the imbalance between those of GCRWT is gradually widening. (3) The spatial distribution of gastric cancer research in China is uneven, and the hotspots are mainly located in the eastern coastal areas. There were huge advances in gastric cancer research at the province/city/county scale in Eastern China, while the central region has only increased research at the county scale. We suggest that gastric cancer research should pay more attention to the central region, which has the highest gastric cancer incidence/mortality. This study provides important clues for research on and investigations of gastric cancer.

Drought and wildfires in Portugal at the county scale

<p>Weather and climate extreme events contribute to the increase of wildfire risk. A recent study carried out in Mainland Portugal for the period 1981 – 2017, using Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to assess drought conditions, revealed that drought affects 70% of the months and a very strong relationship between the occurrence of drought and the spatio-temporal distribution of extreme wildfires (> 5,000 ha). These results raised additional scientific questions that need to be answered, such as: Is the relationship between droughts and fires equally strong for wildfires of smaller size? The study was carried out at the country level, but what are the regions where the relationship is more and less strong? Therefore the objective of this study is to assess the influence of drought on fire incidence, considering all wildfires or classes of wildfire sizes and in each of the 278 counties of Continental Portugal characterized by different features (landscape, weather/climate, drought and fire incidence). This study benefits from the existence of long and reliable meteorological and wildfire datasets. The methodology comprises cluster analysis, contingency tables, accuracy metrics, statistical measures of association to test the independence and help find interactions between these two natural hazards. Main results include: (i) the characterization of spatio-temporal distribution of drought number, duration, severity, intensity, extension; (ii) wildfire space-time distribution within drought periods and affected area; and, (iii) the assessment of the relationship between droughts and wildfires at county scale. The authors believe that the findings of this study are very useful for the definition of adaptation and mitigation strategies for the impacts of droughts in wildfire occurrence and to assess the climatic wildfire hazard/risk.</p><p><strong>Acknowledgements</strong></p><p>This work was supported and conducted in the framework of the FEMME project (PCIF/MPG/0019/2017) funded by FCT - Portuguese Foundation for Science and Technology. The study was also supported by: i) National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020; and, ii) National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UID/AMB/50017/2019. Data were provided by the European Forest Fire Information System – EFFIS (http://effis.jrc.ec.europa.eu) of the European Commission Joint Research Centre.</p>

Discussion on the Unified Survey and Evaluation of Cultivated Land Quality at County Scale for China’s 3rd National Land Survey: A Case Study of Wen County, Henan Province

Under the task requirements of China’s 3rd national land survey on the grade determination of cultivated land, research on a reasonable and unified survey and evaluation system of cultivated land quality (CLQ) is of great importance. From the three dimensions of agricultural climate, production potential, and health status components of cultivated land, six factors are selected in this study to construct a set of simple, practicable, and operable county-level CLQ survey and evaluation system. These factors are climate conditions of cultivated land, soil characteristics, tillage conditions, the attributes of cultivated land type, environmental conditions, and biological activity. The developed survey and evaluation system meets the demands of national engineering for the inclusion and coordination of multiple departments based on the current evaluation system and evaluation methodology of all relevant land administrative departments. Wen County, Henan Province is used for field verification and evaluation. Results demonstrate that the average quality index of cultivated land in Wen County is 2196.33, ranging from 660.70 to 2802.96 with the largest area of the first-class and second-class land accounting for 20.98% and 52.61% of the county’s cultivated land, respectively; the third-class and fourth-class land, 12.63% and 13.78%. The obvious regional differentiation characteristics are presented along the boundary of Qingfengling with the quality of northern cultivated land higher than that in the south. The comparison with the results of the national utilization gradation in 2018 infers that they are in accordance with the distribution of CLQ, which bears a significant positive correlation trend with the measured grain output of the field. The constructed evaluation system serves as a rewarding attempt of a unified survey and evaluation of CLQ at the county scale to reflect the production capacity of local crops, realize the sharing of data platforms, save manpower and capital investment, improve the practical connection of supervision and management of cultivated land protection in different departments, and meet the requirements of current cultivated land protection and management.