城市化对传染病传播影响的动态模拟——以英国南安普顿市为例

在定量化和空间化模拟分析的基础上,探讨了城市化对传染病传播影响的模型框架。该模型框架是通过人口空间分布表面,集成了元胞自动机土地开发模型、人口预测矩阵模型和元胞自动机传染病模型,在动态的,随机的模拟环境中将城市化与传染病传播结合起来。以英国南安普顿市为研究区,对南安普顿市的“郊区化”对传染病传播的影响进行了动态模拟,场景模拟和定量分析。结果表明:4个模拟预测年期(2001年、2011年、2021年和2031年)的平均传染病例数没有太大的变化,“郊区化”对传染病的传播没有太大影响。但是,“郊区化”中的城市局部的家庭迁移和全市的年轻年龄组的迁移可能会通过在同年份减少平均传染病例数而影响传染病的传播。

A Scale Sequence Object-based Convolutional Neural Network(SS-OCNN)for crop classification from fine spatial resolution remotely sensed imagery

The highly dynamic nature of agro-ecosystems in space and time usually leads to high intra-class variance and low inter-class separability in the fine spatial resolution(FSR)remotely sensed imagery.This makes traditional classifiers essentially relying on spectral information for crop mapping from FSR imagery an extremely challenging task.To mine effectively the rich spectral and spatial information in FSR imagery,this paper proposed a Scale Sequence Object-based Convolutional Neural Network(SS-OCNN)that classifies images at the object level by taking segmented objects(crop parcels)as basic units of analysis,thus,ensuring that the boundaries between crop parcels are delineated precisely.These segmented objects were subsequently classified using a CNN model integrated with an automatically generated scale sequence of input patch sizes.This scale sequence can fuse effectively the features learned at different scales by transforming progressively the information extracted at small scales to larger scales.The effectiveness of the SS-OCNN was investigated using two heterogeneous agricultural areas with FSR SAR and optical imagery,respectively.Experimental results revealed that the SS-OCNN consistently achieved the most accurate classification results.The SS-OCNN,thus,provides a new paradigm for crop classification over heterogeneous areas using FSR imagery,and has a wide application prospect.

Moving interdisciplinary science forward:integrating participatory modelling with mathematical modelling of zoonotic disease in Africa

This review outlines the benefits of using multiple approaches to improve model design and facilitate multidisciplinary research into infectious diseases,as well as showing and proposing practical examples of effective integration.It looks particularly at the benefits of using participatory research in conjunction with traditional modelling methods to potentially improve disease research,control and management.Integrated approaches can lead to more realistic mathematical models which in turn can assist with making policy decisions that reduce disease and benefit local people.The emergence,risk,spread and control of diseases are affected by many complex bio-physical,environmental and socio-economic factors.These include climate and environmental change,land-use variation,changes in population and people’s behaviour.The evidence base for this scoping review comes from the work of a consortium,with the aim of integrating modelling approaches traditionally used in epidemiological,ecological and development research.A total of five examples of the impacts of participatory research on the choice of model structure are presented.Example 1 focused on using participatory research as a tool to structure a model.Example 2 looks at identifying the most relevant parameters of the system.Example 3 concentrates on identifying the most relevant regime of the system(e.g.,temporal stability or otherwise),Example 4 examines the feedbacks from mathematical models to guide participatory research and Example 5 goes beyond the so-far described two-way interplay between participatory and mathematical approaches to look at the integration of multiple methods and frameworks.This scoping review describes examples of best practice in the use of participatory methods,illustrating their potential to overcome disciplinary hurdles and promote multidisciplinary collaboration,with the aim of making models and their predictions more useful for decision-making and policy formulation.

Reconstruction of a Global 9km,8-Day SMAP Surface Soil Moisture Dataset during 2015-2020 by Spatiotemporal Fusion

Soil moisture,a crucial property for Earth surface research,has been focused widely in various studies.The Soil Moisture Active Passive(SMAP)global products at 36 km and 9 km(called P36 and AP9 in this research)have been published from April 2015.However,the 9 km AP9 product was retrieved from the active radar and L-band passive radiometer and the active radar failed in July 2015.In this research,the virtual image pair-based spatiotemporal fusion model was coupled with a spatial weighting scheme(VIPSTF-SW)to simulate the 9 km AP9 data after failure of the active radar.The method makes full use of all the historical AP9 and P36 data available between April and July 2015.As a result,8-day composited 9 km SMAP data at the global scale were produced from 2015 to 2020,by downscaling the corresponding 8-day composited P36 data.The available AP9 data and in situ reference data were used to validate the predicted 9 km data.Generally,the predicted 9 km SMAP data can provide more spatial details than P36 and are more accurate than the existing EP9 product.The VIPSTF-SW-predicted 9 km SMAP data are an accurate substitute for AP9 and will be made freely available to support research and applications in hydrology,climatology,ecology,and many other fields at the global scale.