Spatiotemporal evolution and influencing factors of urban resilience in the Yellow River Basin,China

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment.Industrialization and urbanization promote social-economic development,but they also have generated a series of environmental and ecological issues in this basin.Previous researches have evaluated urban resilience at the national,regional,urban agglomeration,city,and prefecture levels,but not at the watershed level.To address this research gap and elevate the Yellow River Basin’s urban resilience level,we constructed an urban resilience evaluation index system from five dimensions:industrial resilience,social resilience,environmental resilience,technological resilience,and organizational resilience.The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin.The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010,2015,and 2020.Furthermore,the grey correlation analysis method was utilized to explore the influencing factors of these differences.The results of this study are as follows:(1)the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010–2015,and significant spatial distribution differences were observed,with a higher resilience level in the eastern region and a low-medium resilience level in the western region;(2)the differences in urban resilience were noticeable,with industrial resilience and social resilience being relatively highly developed,whereas organizational resilience and environmental resilience were relatively weak;and(3)the correlation ranking of resilience influencing factors was as follows:science and technology level>administrative power>openness>market forces.This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region.

ST-Map:an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

Data Augmentation and Random Multi-Model Deep Learning for Data Classification

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML models.The increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen examples.Deep learning(DL)models have a lot of parameters,and they frequently overfit.Effectively,to avoid overfitting,data plays a major role to augment the latest improvements in DL.Nevertheless,reliable data collection is a major limiting factor.Frequently,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in batches.In this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for classification.We present a methodology for using Generative Adversarial Networks(GANs)to generate images for data augmenting.Through experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency.Experimenting across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

Spatiotemporal variations of evapotranspiration and reference crop water requirement over 1957-2016 in Iran based on CRU TS gridded dataset

Agriculture needs to produce more food to feed the growing population in the 21st century.It makes the reference crop water requirement(WREQ)a major challenge especially in regions with limited water and high water demand.Iran,with large climatic variability,is experiencing a serious water crisis due to limited water resources and inefficient agriculture.In order to overcome the issue of uneven distribution of weather stations,gridded Climatic Research Unit(CRU)data was applied to analyze the changes in potential evapotranspiration(PET),effective precipitation(EFFPRE)and WREQ.Validation of data using in situ observation showed an acceptable performance of CRU in Iran.Changes in PET,EFFPRE and WREQ were analyzed in two 30-a periods 1957-1986 and 1987-2016.Comparing two periods showed an increase in PET and WREQ in regions extended from the southwest to northeast and a decrease in the southeast,more significant in summer and spring.However,EFFPRE decreased in the southeast,northeast,and northwest,especially in winter and spring.Analysis of annual trends revealed an upward trend in PET(14.32 mm/decade)and WREQ(25.50 mm/decade),but a downward trend in EFFPRE(-11.8 mm/decade)over the second period.Changes in PET,EFFPRE and WREQ in winter have the impact on the annual trend.Among climate variables,WREQ showed a significant correlation(r=0.59)with minimum temperature.The increase in WREQ and decrease in EFFPRE would exacerbate the agricultural water crisis in Iran.With all changes in PET and WREQ,immediate actions are needed to address the challenges in agriculture and adapt to the changing climate.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.

A Bayesian multi-model inference methodology for imprecise momentindependent global sensitivity analysis of rock structures

Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.

Spatial Distribution Characteristics and Influencing Factors of Traditional Villages in Northern Guangxi Based on Spatiotemporal Big Data and Spatial Syntax

Public space as an extension of private living spaces carries the different social life and customs of human settlement.To analyze the spatial distribution characteristics of traditional villages in northern Guangxi based on spatial syntax and its influencing factors,this paper analyzed and compared the spatial structure and morphology of traditional villages in northern Guangxi by using the theory of spatial syntax and linguistics as the quantitative analysis method of spatial syntax,and verified the feasibility of expanding the application of spatial syntax,finally,the generality and characteristics of the spatial structure and form of traditional villages in northern Guangxi were put forward.Protection has been implemented.According to the comprehensibility data in this paper,the comprehensibility of the village 1 in northern Guangxi is 0.52,the village 2 is 0.40,the village 3 is 0.35,the village 4 is 0.48,the village 5 is 0.55 and the village 6 is 0.50.It showed that in the selected 6 village samples,except for the 3 ones in northern Guangxi,the local space of the other 3 villages could better perceive the overall space,which also reflected the overall space permeability of most traditional villages in northern Guangxi was good.

Spatiotemporal characteristics and water budget of water cycle elements in different seasons in northeast China

In this paper, we study the spatiotemporal characteristics of precipitable water, precipitation, evaporation, and watervapor flux divergence in different seasons over northeast China and the water balance of that area. The data used in this paper is provided by the European Center for Medium-Range Weather Forecasts （ECMWF）. The results show that the spatial distributions of precipitable water, precipitation, and evaporation feature that the values of elements above in the southeastern area are larger than those in the northwestern area; in summer, much precipitation and evaporation occur in the Changbai Mountain region as a strong moisture convergence region; in spring and autumn, moisture divergence dominates the northeast of China; in winter, the moisture divergence and convergence are weak in this area. From 1979 to 2010, the total precipitation of summer and autumn in northeast China decreased significantly; especially from 1999 to 2010, the summer precipitation always demonstrated negative anomaly. Additionally, other elements in different seasons changed in a truly imperceptible way. In spring, the evaporation exceeded the precipitation in northeast China; in summer, the precipitation was more prominent; in autumn and winter, precipitation played a more dominating role than the evaporation in the northern part of northeast China, while the evaporation exceeded the precipitation in the southern part. The Interim ECMWF Re-Analysis （ERA-Interim） data have properly described the water balance of different seasons in northeast China. Based on ERA-Interim data, the moisture sinks computed through moisture convergence and moisture local variation are quite consistent with those computed through precipitation and evaporation, which proves that ERA- Interim data can be used in the research of water balance in northeast China. On a seasonal scale, the moisture convergence has a greater influence than the local moisture variation on a moisture sink, and the latter is variable slightly, generally as a constant. Likewise, in different seasons, the total precipitation has a much greater influence than the evaporation on the moisture sink.

Spatiotemporal Variations and Controls on Anthropogenic Heat Fluxes in 12 Selected Cities in the Eastern China

Spatiotemporal variations of anthropogenic heat flux(AHF)is reported to be associated with global warming.However,confined to the low spatial resolution of energy consumption statistical data,details of AHF was not well descripted.To obtain high spatial resolution data of AHF,Defense Meteorological Satellite Program/Operational Linescan System(DMSP/OLS)nighttime light time-series product and Moderate Resolution Imaging Spectroradiometer(MODIS)satellite monthly normalized difference vegetation index(NDVI)product were applied to construct the human settlement index.Based on the spatial regression relationship between human settlement index and energy consumption data.A 1-km resolution dataset of AHF of 12 selected cities in the eastern China was obtained.Ordinary least-squares(OLS)model was applied to detect the mechanism of spatial patterns of AHF.Results showed that industrial emission in selected cities of the eastern China was accountable for 63%of the total emission.AHF emission in megacities,such as Tianjin,Jinan,Qingdao,and Hangzhou,was most significant.AHF increasing speed in most areas in the chosen cities was quite low.High growth or extremely high growth of AHF were located in central downtown areas.In Beijing,Shanghai,Guangzhou,Jinan,Hangzhou,Changzhou,Zhaoqing,and Jiangmen,a single kernel of AHF was observed.Potential influencing factors showed that precipitation,temperature,elevation,normalized different vegetation index,gross domestic product,and urbanization level were positive with AHF.Overall,this investigation implied that urbanization level and economic development level might dominate the increasing of AHF and the spatial heterogeneousness of AHF.Higher urbanization level or economic development level resulted in high increasing speeds of AHF.These findings provide a novel way to reconstruct of AHF and scientific supports for energy management strategy development.

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Spatio-temporal heterogeneous data is the database for decisionmaking in many fields,and checking its accuracy can provide data support for making decisions.Due to the randomness,complexity,global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions,traditional detection methods can not guarantee both detection speed and accuracy.Therefore,this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks.Firstly,the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology.Secondly,design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy.Finally,the proposed method is compared with three methods,ARIMA,T-GCN,and STGCN,in real scenarios to verify its effectiveness in terms of detection speed,detection accuracy and stability.The experimental results show that the RMSE,MAE,and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree,which are 13.82/12.08,2.77/2.41,and 16.70/14.73,respectively.Also,it detects the shortest time of 672.31/887.36,respectively.In addition,the evaluation results are the same under different time periods of processing and complex topology environment,which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.

基于多源数据的长江流域1982—2022年骤旱事件时空演变

骤发性干旱(简称骤旱)是一种突发性高且强度大的极端干旱现象,会对农业生产和生态系统构成严重威胁。近年来,长江流域骤旱频发,然而其骤旱时空演变格局及规律尚不明晰。本研究基于GLEAM、GLDAS和ERA5-Land数据,以标准化蒸发胁迫比及其变化值作为识别指标,开展1982—2022年长江流域骤旱识别,全面分析长江流域骤旱空间分布和时间演变特征;并鉴于2022年旱情的严重性和特殊性,重点分析该年长江流域骤旱事件。研究结果表明:①在空间分布上,长江流域上游的金沙江水系和中下游的大型水库湖泊骤旱发生频率最高且强度最大;②在时间演变上,骤旱发生频率、平均持续时间和强度均在长江流域整体上呈现出非显著上升趋势,而有显著变化趋势的区域在2001年前后表现出明显的趋势反转现象;③2022年夏季受极端高温热浪影响,长江流域遭遇大规模骤旱事件,具有波及范围广、持续时间长的特点,且骤旱在空间上呈现出从上游向下游传递的态势。

自然资源“三全”调查监测技术体系研究与实践

为统筹整合遥感数据源,统一实施地类变化信息提取,优化各项调查监测和业务管理的业务流,探索建立“全范围地类监测、全流程变化跟踪、全业务数据支撑”的“三全”调查监测体系。整合“天空地网”多源协同数据获取、变化监测要素智能提取、三维时空场景建模与管理、自然资源监测智能化服务平台等关键技术,形成标准统一、手段智能、业务联通、先进实用的自然资源统一调查监测技术体系,更有力地支撑生态文明建设和自然资源管理。

基于知识的目标关系分析挖掘技术

在战场目标价值分析和打击目标排序分析过程中,为了构建敌方作战目标体系,需要分析战场目标间的关联关系。轨迹和部署数据中隐藏大量信息,提出了一种从轨迹和部署数据中挖掘出感兴趣的目标关系类型信息的方法,所提方法对轨迹部署数据进行时空聚类,从聚类结果提取目标;对聚类目标使用频繁项挖掘算法分析挖掘满足一定支持度的有关联关系的目标,再根据构建的关系类型知识库或关系规则,分析目标间的具体关系类型。所提方法能对积累的目标历史轨迹部署数据分析挖掘出目标间的关联关系,挖掘出目标潜在的关系类型可为后续构建目标体系提供关系数据。

一种基于时间序列分解和时空信息提取的云服务器异常检测模型

异常检测是维护云数据中心性能的一项重要任务。云数据中心中运行着大量的云服务器以实现各种云计算功能。由于云数据中心的性能取决于云服务的正常运行,因此检测和分析云服务器中的异常至关重要。为此,该文提出一种基于时间序列分解和时空信息提取的云服务器异常检测模型。首先,提出带时空信息提取模块的双向Wasserstein生成对抗网络算法(BiWGAN-GTN),该算法在具有梯度惩罚的双向Wasserstein生成对抗网络(BiWGAN-GP)算法的基础上,将生成器与编码器替换为由图卷积网络(GCN)与时间卷积网络(TCN)组成的时空信息提取模块(GTN),实现对数据空时信息的提取;其次,提出半监督BiWGAN-GTN算法来识别多维时间序列中的异常,以在训练过程中避免异常数据侵入的风险并增强模型鲁棒性。最后设计多通道BiWGAN-GTN算法-MCBiWGAN-GTN以实现降低数据复杂度并提升模型学习效率的目标。利用带有自适应噪声完全集合经验模态分解(CEEMDAN)算法将时序数据分解,然后将不同的分量送入对应通道下的BiWGAN-GTN算法中训练。在真实世界云数据中心数据集Clearwater和MBD上采用精确率、召回率和F1分数这3个性能指标验证了该文所提模型的有效性。实验结果表明,MCBiWGAN-GTN在这两个数据集上的性能稳定并优于所比较的方法。

时空大数据在突发公共卫生事件应急指挥中的应用与挑战

时空是人类生存和发展一切活动的基础,时空大数据是同时具有时间和空间维度的、与地理位置有关的大数据,是突发公共卫生事件应急指挥与控制的重要支撑。针对我国在新冠肺炎疫情应急响应中出现的各种问题,总结时空大数据在新冠肺炎疫情应急响应中的应用。在此基础上分析我国突发公共卫生事件应急指挥系统存在的问题、时空大数据在突发公共卫生事件应急指挥中面临的挑战。提出面向突发公共卫生事件的时空大数据应急指挥技术框架,以期深化时空大数据在突发公共卫生事件领域的应用。

社会感知数据表征下的历史文化街区地方感变迁——以南锣鼓巷(2008—2022年)为例

以北京南锣鼓巷历史文化街区近15年的变迁实践为研究对象,根据2008—2022年社会感知网络数据特征,分析其地方感变化的阶段性差异,以此构建南锣鼓巷地方感变迁的概念模型。首先,建立历史文化街区评价专属词库,基于扎根理论,从专属词库的文本中编码获得地方感的3个维度:地方认知、地方依赖和地方认同,通过对历年高频词主类目占比变化曲线、突现词特点、相关语义网络和南锣鼓巷商业业态占比等数据分析,结合社会生态系统适应性循环理论,提取特征差异明显的4个阶段:2008—2009年的稳定期、2010—2014年的生长期、2015—2018年的重构期以及2019—2022年的调整期。最后从资本的角度对历史文化街区的地方感变迁进行分析,指出经济资本需要综合社会资本与文化资本才能更好地适应发展,在循环中维持动态平衡。

基于卷积长短时记忆网络的短时公交客流量预测

针对传统的短时客流预测方法没有考虑到时序特征中跨时段客流之间的相似性问题,提出一种改进k-means聚类算法与卷积神经网络和长短时记忆网络相结合的短时客流量预测模型k-CNN-LSTM。通过k-means算法对跨时段时序数据进行聚类,使用间隔统计确定k值,构建交通流矩阵模型,采用CNN-LSTM网络处理具有时空特征的短时客流。该模型能够对具有空间相关性的数据进行较为准确的预测。使用真实数据集对模型进行检验和参数调优,实验结果表明:k-CNN-LSTM模型较其他模型有相对较高的预测精度。

呼吸道传染病时空传播风险精细化评估系统构建与应用

目的开发多源城市大数据支持下的城市内部呼吸道传染病时空传播风险精细化评估系统,为城市内部实施更加精准的疾病防控提供决策支持。方法本研究面向流感等典型的呼吸道传染病,融合传染病、人群活动、地理环境等多源大数据,以500米网格为基本空间单元,通过德尔菲法构建高精度传播风险评估模型;通过构建基于人口流动网络的传播动力学模型,实现高精度的呼吸道传染病时空传播模拟,推演未来30天城市内部疾病发展的时空趋势;选取传染病传播高风险区域作为关键的空间防控节点,利用时空传播模拟模型精细化评估不同关键节点防控的效果。结果以深圳市为例,构建了呼吸道传染病时空传播风险精细化评估系统。结论本研究提供了一套面向大型城市内部区域的呼吸道传染病时空传播风险精细化评估系统,能够支持传染病时空扩散趋势的研判,并且能够在疫情发展的不同阶段快速预判高危风险区域防控措施的效果,为呼吸道传染病精准防控提供应急响应的技术和系统储备。

智慧城市时空大数据平台建设与实践——以宜兴市为例

在新一代信息技术与城市现代化的深度融合下,智慧城市的建设显得尤为重要。城市时空大数据平台是智慧城市的基础支撑。目前的智慧城市普遍存在时空数据单一、数据更新周期长、信息孤岛现象严重等问题。智慧宜兴时空大数据平台建设项目构建统一的时空基准,将各类信息资源集中于一体,引入基于HMM模型的中文地名地址引擎构建技术,并提出一种基于深度学习的电子地图快速更新方法。该平台可为用户提供大量的时空数据和分析服务,具有明显的社会效益和推广价值,为创建“智慧宜兴”奠定了坚实的基础。

融合Transformer和卷积LSTM的轨迹分类网络

为了减少原始轨迹数据的噪声,充分提取轨迹的时空特征,提高基于轨迹数据的交通模式分类精度,提出一种融合堆叠降噪自编码器、Transformer和卷积长短期记忆网络的轨迹分类网络(networks fusing stacked denoising auto-encoder, Transformer and ConvLSTM,SDAETC)。通过堆叠降噪自编码器减少原始轨迹数据中的噪声;利用结合了Transformer的递归图自编码器,提取到更为丰富的时间特征,同时利用特征图自编码器提取空间特征;改进卷积长短期记忆网络,充分提取轨迹中的时空特征,并与提取到的时间特征和空间特征相融合,从而实现交通模式分类。实验结果表明,提出的SDAETC与基线模型相比,在GeoLife和SHL数据集上的准确率分别提升了1.8%和2%。此外,消融实验结果和模型训练时间分析表明,引入堆叠降噪自编码器、Transfomer和ConvLSTM虽然增加了时间消耗,但是对分类精度有积极贡献。