Traveling Wave Solutions of a SIR Epidemic Model with Spatio-Temporal Delay

In this paper, we studied the traveling wave solutions of a SIR epidemic model with spatial-temporal delay. We proved that this result is determined by the basic reproduction number R0and the minimum wave speed c*of the corresponding ordinary differential equations. The methods used in this paper are primarily the Schauder fixed point theorem and comparison principle. We have proved that when R0>1and c>c*, the model has a non-negative and non-trivial traveling wave solution. However, for R01and c≥0or R0>1and 0cc*, the model does not have a traveling wave solution.

Constructing a raster-based spatio-temporal hierarchical data model for marine fisheries application

Marine information has been increasing quickly. The traditional database technologies have disadvantages in manipulating large amounts of marine information which relates to the position in 3-D with the time. Recently, greater emphasis has been placed on GIS （geographical information system）to deal with the marine information. The GIS has shown great success for terrestrial applications in the last decades, but its use in marine fields has been far more restricted. One of the main reasons is that most of the GIS systems or their data models are designed for land applications. They cannot do well with the nature of the marine environment and for the marine information. And this becomes a fundamental challenge to the traditional GIS and its data structure. This work designed a data model, the raster-based spatio-temporal hierarchical data model （RSHDM）, for the marine information system, or for the knowledge discovery fi＇om spatio-temporal data, which bases itself on the nature of the marine data and overcomes the shortages of the current spatio-temporal models when they are used in the field. As an experiment, the marine fishery data warehouse （FDW） for marine fishery management was set up, which was based on the RSHDM. The experiment proved that the RSHDM can do well with the data and can extract easily the aggregations that the management needs at different levels.

Spatio-temporal epidemic type aftershock sequence model for Tangshan aftershock sequence

Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.

Spatio-temporal model for soil characteristic of reclamation land

The development of spatio-temporal data model is introduced. According to the soil characteristic of reclamation land, we adopt the base state with amendments model of multi-layer raster to organize the spatio-temporal data, using the combined data structure on linear quadtree and linear octree to code. The advantage of this model is that it can easily obtain the information of certain layer and integratedly analyze the data with other methods. Then, the methods of obtain and analyses are introduced. The method can provide a tool for the research of the soil characteristic change and spatial distribution in reclamation land.

Grain Yield Prediction of Henan Province Based on Spatio-temporal Regression Model

By using correlation analysis method,regression analysis method and time sequence method,we combine time and space,to establish grain yield spatio-temporal regression prediction model of Henan Province and all prefecture-level cities.At first,we use the grain yield in prefecture-level cities of Henan in the year 2000 and 2005,to establish regression model,and then taking the grain yield in one year as independent variable,we predict the grain yield in the fifth year afterwards.Taking the dependent variable value as independent variable again,we predict the grain yield at an interval of the same years,and based on this,predict year by year forward until the year we need.The research shows that the grain yield of Henan Province in the year 2015 and 2020 is 59.849 6 and 67.929 3 million t respectively,consistent with the research results of other scholars to some extent.

Dynamic Spatio-Temporal Modeling in Disease Mapping

Spatio-temporal models are valuable tools for disease mapping and understanding the geographical distribution of diseases and temporal dynamics. Spatio-temporal models have been proven empirically to be very complex and this complexity has led many to oversimply and model the spatial and temporal dependencies independently. Unlike common practice, this study formulated a new spatio-temporal model in a Bayesian hierarchical framework that accounts for spatial and temporal dependencies jointly. The spatial and temporal dependencies were dynamically modelled via the matern exponential covariance function. The temporal aspect was captured by the parameters of the exponential with a first-order autoregressive structure. Inferences about the parameters were obtained via Markov Chain Monte Carlo (MCMC) techniques and the spatio-temporal maps were obtained by mapping stable posterior means from the specific location and time from the best model that includes the significant risk factors. The model formulated was fitted to both simulation data and Kenya meningitis incidence data from 2013 to 2019 along with two covariates;Gross County Product (GCP) and average rainfall. The study found that both average rainfall and GCP had a significant positive association with meningitis occurrence. Also, regarding geographical distribution, the spatio-temporal maps showed that meningitis is not evenly distributed across the country as some counties reported a high number of cases compared with other counties.

EEG Source Localization Using Spatio-Temporal Neural Network

Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.

Modelling of the spread of a potential invasive pest,the Siberian moth (Dendrolimus sibiricus) in Europe

Background： The Siberian moth （Dendrolimus sibiricus） （SM） defoliates several tree species from the genera Larix, Piceo and Abies in northern Asia, east of the Urals. The SM is a potential invasive forest pest in Europe because Europe has several suitable host species and climatic conditions of central and northern Europe are favourable for the SM. Methods： This study developed a grid-based spatio-temporal model for simulating the spread of the SM in case it enters Europe from Russia via border stations. The spread rate was modeled as a function of the spatial distribution of host species, climatic suitability of different locations for the SM, human population density, transportation of moth-carrying material, and flying of moths from tree to tree. Results and conclusions： The simulations showed that the SM is most likely to spread in the forests of northeast Belarus, the Baltic countries, and southern and central Finland. Climatic conditions affected the occurrence of the SM more than human population density and the coverage of suitable host species.

Spatio-Temporal Variation of HIV Infection in Kenya

Disease mapping is the study of the distribution of disease relative risks or rates in space and time, and normally uses generalized linear mixed models (GLMMs) which includes fixed effects and spatial, temporal, and spatio-temporal random effects. Model fitting and statistical inference are commonly accomplished through the empirical Bayes (EB) and fully Bayes (FB) approaches. The EB approach usually relies on the penalized quasi-likelihood (PQL), while the FB approach, which has increasingly become more popular in the recent past, usually uses Markov chain Monte Carlo (McMC) techniques. However, there are many challenges in conventional use of posterior sampling via McMC for inference. This includes the need to evaluate convergence of posterior samples, which often requires extensive simulation and can be very time consuming. Spatio-temporal models used in disease mapping are often very complex and McMC methods may lead to large Monte Carlo errors if the dimension of the data at hand is large. To address these challenges, a new strategy based on integrated nested Laplace approximations (INLA) has recently been recently developed as a promising alternative to the McMC. This technique is now becoming more popular in disease mapping because of its ability to fit fairly complex space-time models much more quickly than the McMC. In this paper, we show how to fit different spatio-temporal models for disease mapping with INLA using the Leroux CAR prior for the spatial component, and we compare it with McMC using Kenya HIV incidence data during the period 2013-2016.

Probabilistic Model for Wind Speed Variability Encountered by a Vessel

As a result of social awareness of air emission due to the use of fossil fuels, the utilization of the natural wind power resources becomes an important option to avoid the dependence on fossil resources in industrial activities. For example, the maritime industry, which is responsible for more than 90% of the world trade transport, has already started to look for solutions to use wind power as auxiliary propulsion for ships. The practical installation of the wind facilities often requires large amount of investment, while uncertainties for the corresponding energy gains are large. Therefore a reliable model to describe the variability of wind speeds is needed to estimate the expected available wind power, coefficient of the variation of the power and other statistics of interest, e.g. expected length of the wind conditions favorable for the wind-energy harvesting. In this paper, wind speeds are modeled by means of a spatio-temporal transformed Gaussian field. Its dependence structure is localized by introduction of time and space dependent parameters in the field. The model has the advantage of having a relatively small number of parameters. These parameters have natural physical interpretation and are statistically fitted to represent variability of observed wind speeds in ERA Interim reanalysis data set.

亚马孙河流域地表覆盖时空变化知识建模

亚马孙河流域地表覆盖时空变化影响着全球气候和生态系统的稳定,目前亚马孙河流域地表覆盖的研究广泛,具有海量数据和信息,但缺少系统性的时空知识体系。为了更好地了解亚马孙河流域地表覆盖时空变化知识,本文首先引入本体建模理论,提出了亚马孙河流域地表覆盖时空变化知识建模方法,利用Protégé工具完成了亚马孙河流域地表覆盖模式层的设计和构建;然后利用提取的亚马孙河流域地表覆盖时空变化数据、时空变化信息和景观格局指数等,完成了亚马孙河流域的建模,从而为亚马孙河流域的分析决策提供支撑。

SPATIO-TEMPORAL VARIATION OF ACTUAL EVAPOTRANSPIRATION AND ITS RELATION WITH CLIMATE PARAMETERS IN THE PEARL RIVER BASIN,CHINA

Spatio-temporal variation of actual evapotranspiration(ETa) in the Pearl River basin from 1961 to 2010 are analyzed based on daily data from 60 national observed stations. ETa is calculated by the Advection-Aridity model(AA model) in the current study, and Mann-Kendall test(MK) and Inverse Distance Weighted interpolation method(IDW)were applied to detect the trends and spatial variation pattern. The relations of ETa with climate parameters and radiation/dynamic terms are analyzed by Person correlation method. Our findings are shown as follows: 1) Mean annual ETa in the Pearl River basin is about 665.6 mm/a. It has significantly decreased in 1961-2010 at a rate of-24.3mm/10 a. Seasonally, negative trends of summer and autumn ETa are higher than that of spring and winter. 2) The value of ETa is higher in the southeast coastal area than in the northwest region of the Pearl River basin, while the latter has shown the strongest negative trend. 3) Negative trends of ETa in the Pearl River basin are most probably due to decreasing radiation term and increasing dynamic term. The decrease of the radiation term is related with declining diurnal temperature range and sunshine duration, and rising atmospheric pressure as well. The contribution of dynamic term comes from increasing average temperature, maximum and minimum temperatures in the basin. Meanwhile, the decreasing average wind speed weakens dynamic term and finally, to a certain extent, it slows down the negative trend of the ETa.

基于土地利用变化的湖南长江岸线生境质量评价

岸线是长江湿地生态系统的重要组成部分,研究其土地利用变化及其生境质量时空响应对长江岸线生态保护和土地资源可持续利用具有重要意义。本文以1990、2000、2010、2020年4期土地利用数据为基础,采用生态系统服务和权衡的综合评估(integrated valuation of ecosystem services and tradeoffs,InVEST)模型,研究了湖南长江岸线30年来土地利用变化及生境质量时空演变特征。结果表明:(1)1990—2020年湖南长江岸线土地利用格局发生了明显变化,草地、建设用地、未利用土地面积持续增加,占比分别增加了3.69%、0.81%和0.56%,耕地和水域面积持续减小,占比分别降低了2.46%和1.88%。(2)1990—2020年湖南长江岸线生境质量均值为0.8075,其中1990—2010年生境质量呈下降趋势,2010年后生境质量明显提高。生境质量空间分布表现为长江干线到防洪大堤逐渐降低。(3)生境质量等级以“优”和“良好”为主。相较于1990年,2020年生境质量“优”等级和“良好”等级面积占比共减少了0.25%。(4)土地利用转化产生的负面影响大于正面影响,土地利用转化导致生态环境退化和改善的土地利用变化贡献指数(land use change contribution index,CI)分别为-1.8151和0.9569,其中草地、水域转为耕地和建设用地是造成研究区生境质量下降的主要原因。该研究有助于进一步了解长江岸线土地利用变化与生境质量之间的关系,可为长江经济带生态保护和可持续发展提供科学支撑。

应用时空复合趋势面Kriging方法插值地表沉降

通过布设监测点可反映地表沉降变化趋势,确保施工和运营期间安全,但沉降监测点数量有限,且易受施工作业、天气等因素干扰导致数据缺失或污染,因此需对监测区域数据进行插值处理。针对采用传统地统计插值方法估计地铁地表沉降趋势精度较低的问题,提出了一种基于时空复合趋势面的Kriging插值方法。以乌鲁木齐地铁3号线2017年6月24日—9月3日共15期地铁地表沉降观测值为实验数据,分别采用两种方法对沉降监测点缺失数据进行插补。结果表明,相较于传统方法,基于时空复合趋势面的Kriging插值方法的拟合精度更高,RMSE可降低约35%。

基于遥感生态指数改进模型的沱江流域生态环境质量时空变化及驱动力研究

[目的]沱江流域面临严重的水土流失、水环境污染和土壤侵蚀等生态环境问题,开展流域生态环境质量评价对推动长江上游生态环境保护和流域社会经济高质量可持续发展具有重要意义。[方法]基于谷歌地球引擎平台,选取2000-2020年的Landsat遥感影像,以绿度、湿度、热度、干度、生物丰度指数构建生态环境质量评价模型,采用变异系数、一元线性回归和空间自相关分析方法对流域内生态环境质量的时空演变格局进行分析,并运用最优参数地理探测器模型(optimal parameter geographic detector model,OPGD)对其驱动力进行探测。[结果](1)2000-2020年,沱江流域生态环境质量多年平均值为0.66,等级以良为主,占比为63.01%。成都周边区县、内江、自贡、泸州等城镇地区生态环境质量较差,呈现南北低中间高的空间分布格局。(2)沱江流域生态环境质量总体呈下降趋势,生态环境质量变差的趋势显著,轻微退化和显著退化面积占比分别为19.75%和5.88%。人口密度大,社会经济发展水平高的城镇化地区,生态环境质量以较高波动为主,占比为18.15%。(3)2000-2020年沱江流域全局自相关莫兰指数范围为0.769~0.870,主要分布在第1象限和第3象限,局部自相关的高-高集聚区和低-低集聚区范围随时间呈显著变化,景观斑块破碎化明显。(4)OPGD结果表明,土地利用类型、坡度、高程、夜间灯光为流域生态环境质量变化的主要驱动因子,人口密度、气温和GDP为次要驱动因子,降水量和坡向因子影响较小。土地利用类型与其他因子交互作用增强对流域内生态环境质量变化的影响。[结论]改进的遥感生态指数模型对开展区域生态环境质量评价提供新方法,研究结果为生态环境保护提供参考。

“双碳”目标下黄河流域旅游绿色生产率时空分异特征及影响因素——以陕西段为例

陕西作为黄河流域旅游资源赋存高密度区,其绿色发展对黄河流域高标准高质量地实现“双碳”目标起着重要作用。收集2010—2021年相关数据,采用非期望产出的SBM模型、ML指数及Tobit回归模型等方法对陕西旅游绿色生产率(TGTFP)的时空分异特征及影响因素进行了分析。研究表明:(1)在时序发展上,2010—2021年内陕西旅游能源消耗与碳排放总量呈上升态势,其静态TGTFP唯有2017年实现有效,动态ML指数在2010—2013年和2014—2019年内实现有效,但其他年份均表现为无效,在发展过程中均呈左高右低的山峰型。(2)在空间分异上,TGTFP呈现“关中最强,陕北高于陕南”的分异特征,三大区域内部市域发展渐趋均衡,而三大区域间分化显著导致了陕西总差异扩大。(3)影响因素上,政府干预、环境规制及城镇化水平等外部因素对陕西旅游绿色生产率具有显著的推动作用,但仍有强烈的内部阻力来自旅游总收入、旅游能源消费及碳排放。

2000—2020年青藏高原产水服务时空分布格局及影响因素

产水服务是生态系统服务的重要组成部分,对生态系统平衡和区域可持续发展具有重要意义.青藏高原是“亚洲水塔”,是我国和亚洲的“江河源”,它提供了大量的淡水资源和产水服务.基于InVEST模型分析2000—2020年青藏高原产水服务时空分布及时空演变特征,利用地理探测器方法探究气候、植被、土壤、地形、土地利用等不同因素对青藏高原产水服务空间分异的驱动机制.结果表明,近20年来青藏高原产水量和降水量年际变化呈波动增加的趋势,且具有明显的空间分异性,产水量高值集中分布在青藏高原东南部,低值区主要分布在柴达木盆地和西部的阿里地区;青藏高原产水服务呈增加趋势、减少趋势和无明显变化的面积分别占79.08%,16.45%和4.47%,年降水量与实际蒸散量的交互作用对产水保持服务空间分异的影响最强.研究结果可以为区域水资源综合利用及合理配置提供科学支撑.

山地旅游目的地“三生”系统耦合协调时空演化分析——以贵州省为例

以贵州省9个市(州)为例,构建“生产—生活—生态”系统耦合协调评价指标体系,运用加权TOPSIS法对该区三大系统的综合发展水平进行评价,再基于重心模型和耦合协调模型分析“三生”系统的时空耦合态势。结果表明:(1)研究期内,贵州省各市(州)“三生”系统的发展水平不一致,其综合评价指数差异显著。(2)“三生”系统的重心一直位于贵阳市境内,但偏移方向有所不同;“三生”系统两两之间空间耦合性从高到低依次为“生产—生活”系统、“生活—生态”系统、“生产—生态”系统。(3)从时间上看,“三生”系统耦合协调度以波动上升和保持稳定为主,空间上呈现出以贵阳、遵义为核心的“核心—边缘”式结构特征。

不同土地利用情景下河南省生态系统服务价值时空特征分析

河南省是黄河流域生态屏障的支撑带,探究其土地利用变化对生态系统服务价值(ESV)的影响对推进黄河流域生态保护和高质量发展具有深远意义。该文通过修订ESV当量表并纳入建设用地生态价值构建2000-2020年河南省ESV系数表,结合FLUS模型解析河南省不同土地利用情景下ESV历史演变特征及未来30年的变化趋势。结果表明:①2000-2020年河南省ESV经历了先升后降再回升的波动过程,整体呈上升趋势,空间上呈西高东低的分布特征;②河南省地级市中南阳、信阳、洛阳的ESV及其变化量较高,对河南省ESV的总贡献率接近50%;③耕地、林地、水域的ESV占比均较高,研究期内ESV的正向增长主要受益于水体与林地面积的扩大,而耕地缩减与建设用地无序扩张则是导致ESV下降的关键因素;④2025-2050年耕地保护情景下河南省ESV最高,自然情景下最低。未来30年保护耕地资源、扩大林草地和水域面积以及合理控制建设用地扩张对于维护和提升生态系统服务价值具有决定性作用。

京津风沙源区NPP时空变化及其对治理工程实施的响应

利用遥感大数据对生态治理工程区域的净初级生产力和固碳能力进行长期动态监测可以实现对治理工程的实施效果的评价,同时为区域“碳中和”目标的实现及可持续发展提供有力支撑。利用Google Earth Engine(GEE)云计算平台,基于改进的CASA模型对京津风沙源治理工程区域的NPP进行计算。运用Sen斜率分析和MK趋势分析方法对2001—2020年间的NPP进行时空变化分析,并分析NPP对京津风沙源治理工程实施的响应。结果表明:1)在京津风沙源工程治理期间,京津风沙源区NPP总体呈波动上升趋势,平均增速为2.21 gC m^(-2)a^(-1),其中极显著增加区域占38.03%,说明京津风沙源治理工程对我国“碳中和”任务起到了积极作用,增加了区域的固碳能力;2)空间尺度上,京津风沙源区NPP和固碳量空间异质性较大,空间分布上主要呈现东部高,西部低的特点,其中,暖温带落叶阔叶林区域最高,温带荒漠区域最低;3)治理工程的实施所带来的NPP增长的速度在不同的区域并不一致,2001—2020年的NPP增速京津冀地区(4.74 gC m^(-2)a^(-1))>山西地区(4.52 gC m^(-2)a^(-1))>陕西地区(3.53 gC m^(-2)a^(-1))>内蒙古地区(1.55 gC m^(-2)a^(-1));4)生态工程实施所带来的生态环境的改善总体呈现先慢后快的特点,绝大部分区域后十年间的变化速率都显著大于前十年,而在生态环境较为恶劣、荒漠区域广布的内蒙古地区,生态环境的改善则具有一定的滞后性,2001—2010年NPP增速仅为0.04 gC m^(-2)a^(-1),直到2011年,NPP才开始有较为明显的增长,2011—2020年NPP增速为1.67 gC m^(-2)a^(-1)。风沙源治理工程需要长期坚持,才能取得更明显的成效。