Evaluation of COVID-19 Cases and Vaccinations in the State of Georgia, United States: A Spatial Perspective

This study evaluates the distribution of COVID-19 cases and mass vaccination campaigns from January 2020 to April 2023. There are over 235,000 COVID-19 cases and over 733,000 vaccinations across the 159 counties in the state of Georgia. Data on COVID-19 was acquired from usafact.org while the vaccination records were obtained from COVID-19 vaccination tracker. The spatial patterns across the counties were analyzed using spatial statistical techniques which include both global and local spatial autocorrelation. The study further evaluates the effect of vaccination and selected socio-economic predictors on COVID-19 cases across the study area. The result of hotspot analysis reveals that the epicenters of COVID-19 are distributed across Cobb, Fulton, Gwinnett, and DeKalb counties. It was also affirmed that the vaccination records followed the same pattern as COVID-19 cases’ epicenters. The result of the spatial error model performed well and accounted for a considerable percentage of the regression with an adjusted R squared of 0.68, Akaike Information Criterion (AIC) 387.682 and Breusch-Pagan of 9.8091. ESDA was employed to select the main explanatory variables. The selected variables include vaccination, population density, percentage of people that do not have health insurance, black race, Hispanic and these variables accounted for 68% of the number of COVID-19 cases in the state of Georgia during the study period. The study concludes that both COVID-19 cases and vaccinated individuals have spatial peculiarities across counties in Georgia state. Lastly, socio-economic variables and vaccination are very important to reduce the vulnerability of individuals to COVID-19 disease.

Assessing the Spatial Equality of COVID Testing Sites Maintaining Zero COVID Policy

Rapid and timely testing is essential to minimize the COVID-19 spread. Decision makers and policy planners need to determine the equal distribution and accessibility of testing sites. This study mainly examines the spatial equality of COVID-19 testing sites that maintain a zero COVID policy in Guangzhou City. The study has identified the spatial disparities of COVID testing sites, characteristics of testing locations, and accessibility. The study has obtained information on COVID testing sites in Guangzhou City and population data. Point pattern analyses, Euclidian distance and allocation, and network analyses are the main methods used to achieve the research objectives, and 1183 total COVID testing sites can be recognized in Guangzhou City. Results revealed that spatial disparities could be noticed over the study area. Testing locations of Guangzhou City are highly clustered. The most significant testing sites are located in Haizhu District, which has the third largest population. The highest population density can be identified in Yuexiu District. However, only 94 testing sites are located there. According to all the results, higher disparities can be identified, and a lack of testing sites is located in the north part of the study area. Some people in the northern part have to travel more than 10 km to reach a testing site. Finally, this paper suggests increasing the number of testing sites in the north and south parts of the study area and keeping the same distribution, considering the area, total population, and population density. This kind of research will be helpful to decision-makers in making proper decisions to maintain a zero COVID policy.

Temporal and Spatial Dynamics of Vaccine-Derived Poliovirus (VDPV) in Democratic Republic of Congo from 2018 to 2023

Background: The Democratic Republic of Congo (DRC) has been facing outbreaks of VDPV since 2017. These wild poliovirus variants are responsible for poliomyelitis, which is in the process of eradication.. In the following lines, we try to show the evolution of VDPV cases across the country in order to understand their chronological dynamics and seasonal influence. Methods: We conducted a cross-sectional study of of VDPV notified in the DRC from 2018 to 2023. Maps of the spatial dynamics of VDPV cases were produced from attack rates with QGIS® (3.22.8). As for temporal dynamics, time series were decomposed and presented in the form of graphs showing the chronological evolution of VDPV cases and their seasonal trend, using R.4.0 software package. Results: A total of 1196 Cases of VDPV types 1, 2 and 3 were recorded in the biological confirmation databases of the INRB and the Expanded Program of Immunization during the study period across25 provinces. The eastern part of the country reporting the most cases. The general trend is upwards, with a peak in 2022 of 527 cases, whereas in 2021 there was a notable drop of 31 cases. Analysis of the temporal breakdown suggests a seasonal pattern, with peaks between the months of September and December, considered being rainy periods in some provinces. Conclusion: During the 6 years of our study (2018 - 2023) almost all the Health Zones were hit by VDPV epidemics. The eastern part was the most impacted. The seasonal component is well marked suggesting a rise in detection in the rainy season and during pivotal periods of climate change.

A Cautionary Note on the Application of GIS in Spatial Optimization Modeling

Spatial optimization as part of spatial modeling has been facilitated significantly by integration with GIS techniques. However, for certain research topics, applying standard GIS techniques may create problems which require attention. This paper serves as a cautionary note to demonstrate two problems associated with applying GIS in spatial optimization, using a capacitated p-median facility location optimization problem as an example. The first problem involves errors in interpolating spatial variations of travel costs from using kriging, a common set of techniques for raster files. The second problem is inaccuracy in routing performed on a graph directly created from polyline shapefiles, a common vector file type. While revealing these problems, the paper also suggests remedies. Specifically, interpolation errors can be eliminated by using agent-based spatial modeling while the inaccuracy in routing can be improved through altering the graph topology by splitting the long edges of the shapefile. These issues suggest the need for caution in applying GIS in spatial optimization study.

Effect of Quadrat Shape on Spatial Point Pattern Performance of Haloxylon ammodendron

In this study, we investigated the natural growth of Haloxylon ammodendron forest in Moso Bay, southwest of Gurbantunggut Desert. Random sample analysis was used to analyze the spatial point pattern performance of Haloxylon ammodendron population. ArcGIS software was used to summarize and analyze the spatial point pattern response of Haloxylon ammodendron population. The results showed that: 1) There were significant differences in the performance of point pattern analysis among different random quadrants. The paired t-test for variance mean ratio showed that the P values were 0.048, 0.004 and 0.301 respectively, indicating that the influence of quadrat shape on the performance of point pattern analysis was significant under the condition of the same optimal quadrat area. 2) The comparative analysis of square shapes shows that circular square is the best, square and regular hexagonal square are the second, and there is no significant difference between square and regular hexagonal square. 3) The number of samples plays a decisive role in spatial point pattern analysis. Insufficient sample size will lead to unstable results. With the increase of the number of samples to more than 120, the V value and P value curves will eventually stabilize. That is, stable spatial point pattern analysis results are closely related to the increase of the number of samples in random sample square analysis.

Spatial Morphology Evolution Characteristics Analysis of the Resident Population Distribution in Henan, China

The population spatial distribution pattern and its evolving pattern play an important role in regional allocation of social resources and production factors, formulation of regional development plans, construction of a better life society, and promotion of regional economic development. Based on the resident population statistics data of Henan province from 2006 to 2021, with county as the basic study unit, the paper studies the spatial morphology characteristics and its evolution patterns of resident population distribution, by using spatial analysis methods such as population distribution center, standard deviation ellipse, and spatial auto correlation analysis. The results show that: the resident population spatial distribution shows unbalanced state, the population agglomeration areas mainly distribute in the northeast part and north part, where the resident population growth rate is significantly higher than other regions, over time, this trend is gradually becoming significant. The resident population distribution has a trend of centripetal concentration, with the degree and trend of centripetal gradually strengthening. The resident population distribution has obvious directional characteristics, but the significance is not high, the weighted resident population average center is approximately located at (4.13740˚N, 113.8935˚E), and the azimuth of the distribution axis is approximately 11.19˚. The population distribution has obvious agglomeration characteristics, with the built-up areas of Zhengzhou and Luoyang as their centers, where have a significant siphon effect on the surrounding population. The southern and southwestern regions in the province form a relatively stable belt area of Low-Low agglomeration areas.

The Effect of the Menstrual Cycle on Cognitive Performance: Spatial Reasoning, Visual & Numerical Memory

The menstrual cycle has been a topic of interest in relation to behavior and cognition for many years, with historical beliefs associating it with cognitive impairment. However, recent research has challenged these beliefs and suggested potential positive effects of the menstrual cycle on cognitive performance. Despite these emerging findings, there is still a lack of consensus regarding the impact of the menstrual cycle on cognition, particularly in domains such as spatial reasoning, visual memory, and numerical memory. Hence, this study aimed to explore the relationship between the menstrual cycle and cognitive performance in these specific domains. Previous studies have reported mixed findings, with some suggesting no significant association and others indicating potential differences across the menstrual cycle. To contribute to this body of knowledge, we explored the research question of whether the menstrual cycles have a significant effect on cognition, particularly in the domains of spatial reasoning, visual and numerical memory in a regionally diverse sample of menstruating females. A total of 30 menstruating females from mixed geographical backgrounds participated in the study, and a repeated measures design was used to assess their cognitive performance in two phases of the menstrual cycle: follicular and luteal. The results of the study revealed that while spatial reasoning was not significantly related to the menstrual cycle (p = 0.256), both visual and numerical memory had significant positive associations (p < 0.001) with the luteal phase. However, since the effect sizes were very small, the importance of this relationship might be commonly overestimated. Future studies could thus entail designs with larger sample sizes, including neuro-biological measures of menstrual stages, and consequently inform competent interventions and support systems.

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

The Spatial-Temporal Characteristics of Meteorological Disasters in the Southwest Region of Zhejiang Province during 1953-2022

Meteorological disasters are some of the most serious and costly natural disasters, which have larger effects on economic and social activity. Liuchun Lake is an ecotourism area in the southwest region of Zhejiang province, where also has experienced meteorological disasters including rainstorm and cold wave. Understanding the temporal-spatial characteristics of meteorological disasters is important for the local tourism and economic development. Based on the daily temperature and precipitation from 18 meteorological stations in the southwest of Zhejiang province during 1953-2022 and some statistical approaches, the temporal and spatial characteristics of meteorological disasters (Freezing, Rainstorm, Cold wave) are analyzed. The results indicate that 1) Rainstorm occurred frequently around the Liuchun lake, the frequency was about 8 times/a, it can also reach about 3 times/a in the other region. Freezing and cold wave (including strong cold wave and extremely cold wave) had the same spatial distribution as rainstorm, however, except for Liuchun lake, they occurred less than one time in the other regions;2) The trend of rainstorm had larger spatial difference, it increased in all the study area, but it increased more significantly around the study area than around Liuchun lake. Freezing was on the downtrend in the whole region, with 93.3% of the stations passed the 95% significant level. Cold wave also showed a declined trend, but it was insignificantly at most of the stations, only 33% of the stations passed the 90% significant level. Compared with cold wave, strong cold wave and extremely strong cold wave had weaker decline in all the regions. In general, from 1953 to 2022 rainstorm showed an increasing trend, it was the main meteorological disaster in the study area, cold wave displayed a decreasing trend, but it still occurred about 2 - 3 times/a in most regions.

New Fusion Approach of Spatial and Channel Attention for Semantic Segmentation of Very High Spatial Resolution Remote Sensing Images

The semantic segmentation of very high spatial resolution remote sensing images is difficult due to the complexity of interpreting the interactions between the objects in the scene. Indeed, effective segmentation requires considering spatial local context and long-term dependencies. To address this problem, the proposed approach is inspired by the MAC-UNet network which is an extension of U-Net, densely connected combined with channel attention. The advantages of this solution are as follows: 4) The new model introduces a new attention called propagate attention to build an attention-based encoder. 2) The fusion of multi-scale information is achieved by a weighted linear combination of the attentions whose coefficients are learned during the training phase. 3) Introducing in the decoder, the Spatial-Channel-Global-Local block which is an attention layer that uniquely combines channel attention and spatial attention locally and globally. The performances of the model are evaluated on 2 datasets WHDLD and DLRSD and show results of mean intersection over union (mIoU) index in progress between 1.54% and 10.47% for DLRSD and between 1.04% and 4.37% for WHDLD compared with the most efficient algorithms with attention mechanisms like MAU-Net and transformers like TMNet.

Numerical Simulation of Super-Resolution Structured Illumination Microscopy (SIM) Using Heintzmann-Cremer Algorithm with Non-Continuous Spatial Frequency Support

We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.

Geospatial Analysis of Urban Heat Island Effects and Tree Equity

In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest access. Urban Heat Island Effects are measurable phenomenon that are being experienced by the world’s most urbanized areas, including increased summer high temperatures and lower evapotranspiration from having impervious surfaces instead of vegetation and trees. Tree canopy cover is our natural mitigation tool that absorbs sunlight for photosynthesis, protects humans from incoming radiation, and releases cooling moisture into the air. Unfortunately, urban areas typically have low levels of vegetation. Vulnerable urban communities are lower-income areas of inner cities with less access to heat protection like air conditioners. This study uses mean evapotranspiration levels to assess the variability of urban heat island effects across the state of Tennessee. Results show that increased developed land surface cover in Tennessee creates measurable changes in atmospheric evapotranspiration. As a result, the mean evapotranspiration levels in areas with less tree vegetation are significantly lower than the surrounding forested areas. Central areas of urban cities in Tennessee had lower mean evapotranspiration recordings than surrounding areas with less development. This work demonstrates the need for increased tree canopy coverage.

Environmental Analysis Using Integrated GIS and Spatial Configurations in Israel

The scope for environmental analysis constitutes a critical factor in recent times, yet demanding importance due to the concerns of environmental sustainability. The study aims at analysing the prospects of implementing an integrated GIS and spatial configuration for environment analysis in Israel. The study adopts an empirical study design to consider the multi-dimensional utilisation of an integrated GIS and spatial configuration for environment analysis. The study considers the materials and methods of the GIS system modelling as well, consisting of satellite imagery, GPS-based location identification, Esri ArcGIS, CyberGIS, and BIM integration to present a comprehensive system for the environmental analysis of Israel. The results of the study indicate that the threats of natural disasters and climate change can be identified based on the synergy of spatial data within an integrated GIS modelling. In many cases, it is also used in collaboration with a BIM to ensure that planning and decision-making processes are sustainable, economically beneficial and environmentally considered. Thus, it is concluded that environmental analysis through the projection of visually represented satellite imagery within an integrated GIS with spatial configurations in Israel can minimise the conflicts between the infrastructural designs, human activities, and environmental sustainability.

基于Spatial OLAP的城市交通综合管理系统设计与实现

空间联机分析处理技术将GIS与联机分析处理技术OLAP相结合,提供在空间维度上进行不同尺度钻取的解决方案——SOLAP。该文基于SOLAP技术,构建一个城市交通的综合管理系统,并在电子地图上展示不同区域、路段、时段的交通流信息,为交通管理者进行决策提供技术支持。

“外察”与“内省”:环境威胁引发的绿色消费意愿研究——基于道德情绪的双路径模型

营销学者在探究绿色消费议题方面做了众多开创性工作,然而针对诱因不同的环境威胁是否及通过怎样的心理路径影响消费者绿色消费意愿的研究仍然匮乏。本文将环境威胁分为自然因素引发的环境威胁和人为因素引发的环境威胁两类,通过4组实验检验了两类环境威胁影响消费者绿色消费意愿的心理机制。研究结果证实了无论哪类环境威胁,都会对绿色产品购买行为产生积极影响,但不同类型的环境威胁会通过差异化认知过程和情绪反应影响绿色产品购买行为。当消费者面对自然因素引发的环境威胁时,其“外察”思维模式会起主导作用,此时敬畏感是促进绿色产品购买意愿的中介变量;当消费者面对人为因素引发的环境威胁时,其“内省”思维模式会起主导作用,此时内疚感是促进绿色产品购买意愿的中介变量。研究也证实了环境威胁对道德情绪的影响会受自然联结性和道德提升感的调节。

煤系中关键金属元素的成矿作用研究进展与展望

煤系中关键金属元素的赋存状态、分布规律、成矿机制以及有效提取和综合利用的研究是近10年来煤地质学等相关领域的研究前沿和热点问题,国内外学者在煤系中关键金属矿床的发现,关键金属元素赋存状态,成矿物质来源、迁移、富集和保存,开发利用潜力评价,精细勘查技术研发,关键元素的分离提取等方面取得了重要研究进展,为煤系关键金属矿产资源的开发利用奠定了坚实的理论和技术基础,同时在揭示与煤系关键金属相关重大地球科学问题方面取得了长足进展。本文结合国内外近些年来的进展,对煤系中关键金属元素(主要是锗、镓、锂、铌、锆等)的成矿作用进行了总结和凝练,指出了存在的问题及其解决方案和思路。

2000—2020年非洲耕地资源时空格局变化分析

非洲地区面临严重的粮食安全问题,研究其耕地资源时空格局变化对于保障非洲粮食安全具有重要意义.基于2000—2020年间联合国粮农组织的统计数据,通过重心迁移、标准差椭圆及局部空间自相关等方法对非洲耕地资源时空格局的变化特征进行了分析.结果表明:1)非洲耕地整体数量呈稳定上升趋势,20年间耕地面积增加了6321.92万hm^(2),增长率为53.44%,但不同国家间存在差异;2)非洲耕地在几何中心以北区域分布较多,耕地重心迁移方向先向东北后转向偏西方向,移动总距离为111.86 km,偏移速率先快后慢,从前5年的平均10.27 km/a降至后15年的平均4.00 km/a左右;3)非洲耕地分布呈西北—东南向,20年来标准差椭圆转角有向水平方向倒伏趋势,椭圆面积不断缩小,向心性不断增强;4)非洲耕地分布集聚主要出现在西非的尼日尔和尼日利亚,异常区为南非及贝宁.

基于数值试验的降雨径流过程对不透水面空间分布的响应分析

为解析降雨径流过程对不透水面空间分布的响应关系,构造了一个3 km×3 km的假想试验区域,面向试验区域构建了包含6个不透水率的36种不透水面空间分布,并建立相应的数值试验模型,模拟了重现期分别为2、5、10、20、50、100 a共6种不同重现期短历时设计降雨下的降雨径流过程,基于模拟结果对不同不透水面空间分布情景下的降雨径流过程进行了分析。结果表明:在同一不透水率下,不同不透水面空间分布的径流系数差异相对较小,不透水面空间分布对径流特征的影响主要体现在径流峰值和峰现时间上;在同一降雨条件下,不透水率变化是径流过程变化的主导因素,径流系数与不透水率呈线性相关关系,随着不透水率的增加,径流过程线逐渐由“矮胖”变得“尖瘦”。

新质生产力水平的统计测度与时空演变特征研究

文章基于新质生产力的内涵和主要特征,测算了2010—2021年中国30个省份的新质生产力水平,并分析了新质生产力水平的区域差异及动态演变特征,结果显示:(1)研究期内,中国新质生产力水平整体上呈现上升趋势,地区间差异较大,形成“东高西低”的区域发展格局。(2)新质生产力水平存在显著的区域异质性特征,东部地区内部的新质生产力水平差异大于中西部地区,东-西部及东-中部的地区间差异是导致中国新质生产力水平区域差异扩大的主要原因。(3)从新质生产力水平的分布及演变特征来看,全国及东西部地区的新质生产力水平均存在极化现象。从时空动态演进特征看,中国新质生产力水平不存在跨越式跃迁,整体上呈现梯度上升趋势,新质生产力水平较高的省份能够发挥辐射带动作用,促进邻近省份的发展,而当邻近省份的新质生产力水平较低时,该省份将趋于保持相对稳定的状态。

我国高等职业教育对产业结构优化升级的空间溢出效应

从招生规模看,高等职业教育已经占我国高等教育的半壁江山。新时代教育高质量发展要求高等职业教育主动适应产业发展,促进产业结构优化升级。采用熵值法测算我国各省份2014—2021年高等职业教育发展指数,通过Moran’s I检验高等职业教育、产业结构优化、产业结构升级的空间相关性,运用空间杜宾模型分析高等职业教育对产业结构优化和产业结构升级两个维度的空间作用机制、溢出效应和区域异质性。研究发现:高等职业教育、产业结构优化和产业结构升级在空间分布上都具有正向空间自相关关系,高等职业教育高-高集聚区主要分布在东部和中部地区,低-低集聚区主要分布在西部地区;高等职业教育对本地区产业结构优化具有显著正向影响,对邻近地区产业结构优化具有显著负向空间溢出效应,其中在东部地区呈现显著正向直接效应,西部地区呈现显著负向直接效应和间接效应;高等职业教育对本地区产业结构升级具有显著正向影响,对邻近地区的产业结构升级具有显著正向溢出效应,在东部、中部和西部地区均具有显著正向直接效应,在西部地区还具有显著正向间接效应。建议从宏观层面构建层次分明、有序衔接的职业教育发展格局,从中观层面加强职业教育区域协同发展,从微观层面落实产教融汇、丰富专业群建设内涵。