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.

A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity

Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.

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.

Spatio-temporal Changes and Associated Uncertainties of CENTURYmodelled SOC for Chinese Upland Soils, 1980-2010

Detailed information on the spatio-temporal changes of cropland soil organic carbon(SOC) can significantly contribute to the improvement of soil fertility and mitigate climate change. Nonetheless, information and knowledge on the national scale spatio-temporal changes and the corresponding uncertainties of SOC in Chinese upland soils remain limited. The CENTURY model was used to estimate the SOC storages and their changes in Chinese uplands from 1980 to 2010. With the Monte Carlo method, the uncertainties of CENTURY-modelled SOC dynamics associated with the spatial heterogeneous model inputs were quantified. Results revealed that the SOC storage in Chinese uplands increased from 3.03(1.59 to 4.78) Pg C in 1980 to 3.40(2.39 to 4.62) Pg C in 2010. Increment of SOC storage during this period was 370 Tg C, with an uncertainty interval of –440 to 1110 Tg C. The regional disparities of SOC changes reached a significant level, with considerable SOC accumulation in the Huang-Huai-Hai Plain of China and SOC loss in the northeastern China. The SOC lost from Meadow soils, Black soils and Chernozems was most severe, whilst SOC accumulation in Fluvo-aquic soils, Cinnamon soils and Purplish soils was most significant. In modelling large-scale SOC dynamics, the initial soil properties were major sources of uncertainty. Hence, more detailed information concerning the soil properties must be collected. The SOC stock of Chinese uplands in 2010 was still relatively low, manifesting that recommended agricultural management practices in conjunction with effectively economic and policy incentives to farmers for soil fertility improvement were indispensable for future carbon sequestration in these regions.

数字经济赋能减污降碳效应——基于国家级大数据综合试验区的准自然实验

以数字经济引领绿色发展,是实现经济效益和环境效益双重提升的重要契机。本文基于国家大数据综合试验区这一准自然实验,选取2006—2022年281个城市面板数据为研究样本,运用多期双重差分模型、中介效应模型等方法,从多维度实证研究数字经济的减污降碳效应及作用机制。结果表明:数字经济能够促进城市污染排放和碳排放协同降低,即存在减污降碳效应,在经过系列稳健性检验后结论仍成立。数字经济发展通过促进环境规制强度、产业结构升级和提升能源利用效率,从而提升城市的减污降碳水平。在区域示范类和基础设施统筹发展类试点城市、中西部城市、资源型城市以及外围城市,数字经济的减污降碳效应更明显。本文的实证研究为数字经济助推全面绿色发展提供一定参考。

Dynamic evolution trend of comprehensive transportation green efficiency in China:From a spatio-temporal interaction perspective

It is urgent and important to explore the dynamic evolution in comprehensive transportation green efficiency(CTGE)in the context of green development.We constructed a social development index that reflects the social benefits of transportation services,and incorporated it into the comprehensive transportation efficiency evaluation framework as an expected output.Based on the panel data of 30 regions in China from 2003-2018,the CTGE in China was measured using the slacks-based measure-data envelopment analysis(SBM-DEA)model.Further,the dynamic evolution trends of CTGE were determined using the spatial Markov model and exploratory spatio-temporal data analysis(ESTDA)technique from a spatio-temporal perspective.The results showed that the CTGE shows a U-shaped change trend but with an overall low level and significant regional differences.The state transition of CTGE has a strong spatial dependence,and there exists the phenomenon of“club convergence”.Neighbourhood background has a significant impact on the CTGE transition types,and the spatial spillover effect is pronounced.The CTGE has an obvious positive correlation and spatial agglomeration characteristics.The geometric characteristics of the LISA time path show that the evolution process of local spatial structure and local spatial dependence of China’s CTGE is stable,but the integration of spatial evolution is weak.The spatio-temporal transition results of LISA indicate that the CTGE has obvious transfer inertness and has certain path-dependence and spatial locking characteristics,which will become the major difficulty in improving the CTGE.

STGI:a spatio-temporal grid index model for marine big data

Marine big data are characterized by a large amount and complex structures,which bring great challenges to data management and retrieval.Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database,this paper proposes a spatio-temporal grid index model(STGI)for efficient optimized query of marine big data.A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data.Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models.Through theoretical analysis and experimental verification,the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency,such as marine big data.

基于模糊DEMATEL-超效率DEA的可再生能源发电技术综合效益评估方法

“双碳”目标下,各类可再生能源发电技术发展迅速,综合权衡不同可再生能源发电方案的综合效益对可再生能源的优化设计具有重要意义。综合考虑经济效益、环境效益、能源效益和社会效益4个层面,提出了一种基于模糊决策试验和评价实验(decision making trial and evaluation laboratory,DEMATEL)与超效率数据包络分析(data envelopment analysis,DEA)模型的可再生能源发电技术综合效益评估方法。该方法分为投入-产出指标体系构建和综合评估2个阶段。首先,利用三角直觉模糊数处理模糊评价信息,将其与DEMATEL相结合量化各指标之间相互影响关系,基于指标间逻辑分析结果建立投入-产出评估指标体系。然后,基于超效率DEA模型对各可再生能源发电方案进行评估排序,结合投入冗余和产出不足分析结果给出各方案的针对性改善建议,以期为进一步选择和确定可再生能源产业发展战略提供参考。最后以某省10类可再生能源发电单元为研究对象,基于所提研究方法进行综合评估和分析,并与多准则妥协解排序法和熵权法进行对比分析,验证了所提方法的有效性。

考虑数据稀疏性的图书推荐协同过滤算法仿真

图书推荐算法易忽略数据稀疏性问题,导致推荐结果与用户感兴趣内容之间存在较大的偏差。在考虑数据稀疏性的基础上提出一种图书推荐协同过滤算法,对数据预处理,通过对用户和用户之间综合信任度分析,利用分布估计算法对用户兴趣建模;构建用户兴趣簇类集,划分用户兴趣,从中选择出与检索对象最接近的邻居;计算邻近项目得分,按照从大到小的顺序排列,排名靠前的资源项即为图书推荐结果。实验结果表明,所提方法在推荐500本图书时,用时在12s内,且降低了平均绝对误差和均方根误差,实现了最精准的图书推荐。

“双碳”背景下钢铁新产线选址的环境结合度评价——以中国宝武为例

为准确测度“双碳”(即碳达峰、碳中和的合称)背景下具体工业门类选址与环境体系的结合发展实效,通过研究钢铁新产线的选址取向及环境关切转变,提出新产线选址的环境结合度评价框架。通过数据包络分析(Data Envelopment Analysis,DEA)模型计算环境-经济效率,通过动态综合评价模型揭示时间序列上效率的变化特征,以构建一套钢铁新产线选址的环境结合度评价方法,进而对中国宝武钢铁集团部分生产基地开展实证分析。评价结果为上海宝山基地的环境结合度最高。研究表明:环境结合度是“双碳”背景下钢铁新产线向既有基地选址时主要的环境关切,而环境结合度的评价框架和评价方法可有效表征连续时间序列上既有基地与所在城市环境体系的互促共进态势,对大型钢企未来选址新产线具有一定指导作用。

大数据时代统计学专业创新人才培养质量的综合评价模型

为了给大数据时代统计学专业创新人才提供质量评价参考,选取知识水平、创新能力、综合素养以及就业情况为一级指标,结合层次分析法和熵权法对我校统计学专业创新人才培养质量进行评价,确定创新人才培养质量的评价指标体系,构建综合评价模型,定量评价大数据时代统计学专业创新人才的培养质量。评价结果表明,本评价模型可行、有效。

基于多传感器数据关联的综合传动装置服役状态辨识方法

针对综合传动装置在恶劣环境下传感器数据跳变、可靠性差,导致依赖单一传感器信息带来的状态判断虚警率高、服役状态难以准确辨识的问题,提出一种基于多传感器数据关联的综合传动装置服役状态辨识方法。通过时间窗均值关联网络,充分考虑关联传感器数据在各时间段的关联程度,可以有效表征复杂工况下传感器数据的关联关系。在时间窗关联度计算方法的基础上进一步构造了误差反向传播(Back Propagation,BP)数据映射模型,完成对关键传感器数据的映射。采用变分模态分解和样本熵(Variational Mode Decomposition-Sample Entropy,VMD-SE)方法对数据进行预处理;利用所提方法计算各传感器数据间的关联性,选取出相关性高的数据;将相关性高的数据输入构造出的BP数据映射模型进行映射。油压数据的案例验证结果表明,时间窗关联度计算方法能准确地衡量传感器数据间的关联性,BP数据映射模型输出的数据能够良好地表征关键传感器数据,二者结合能够有效提升服役状态判断的准确性。

Data-Driven Approaches for Spatio-Temporal Analysis:A Survey of the State-of-the-Arts

With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.

时空数据影响下耕地空间布局优化模型仿真

由于耕地空间布局不仅需要考虑土地质量和生产潜力,还需要综合考虑环境保护、水文特征等因素影响,增加了空间布局规划的复杂性。为此,提出一种时空数据随机性影响下耕地空间布局规划方法。根据栅格空间数据和线性加权方法构建耕地布局规划模型,通过耕地质量自相关性计算耕地各指标数值,结合最优组合赋权法确定评价体系权重,利用增量自相关分析,归一化处理耕地指标数值,基于物元矩阵计算耕地地质环境的经典域与节域,获取耕地地质环境评价的综合关联程度,实现耕地空间布局规划。实验结果表明,所提方法时空数据随机性特征识别能力更强,耕地分类精度为94%,耕地空间布局规划效果好,空间集约性高,较小的耕地区域占地面积的NNI指数高达0.634。

An interactive 4D spatio-temporal visualization system for hydrometeorological data in natural disasters

Dynamic visualization of multidimensional hydrometeorological data is vital for decision-makers to catch situational awareness and command an emergency response in natural disasters.Nevertheless,few software tools can comprehensively visualize hydrometeorological data in different scales,dimensions,and time.In this paper,an interactive 4D spatio-temporal visualization system based on a virtual globe is proposed.Voxel-based data model and multi-level index are adopted to organize the field data in a unified data structure.Meanwhile,it is resampled in both spatial and temporal dimensions in memory to prepare smooth data stream for rendering.Ten field models,including large-scale volume rendering and adaptive streamline rendering,are accelerated and integrated to display the field data collaboratively.The profile analysis and eddy tracking improve user experience in interactively exploring specific scenes.The system is tested against both large-scale meteorological data in the atmosphere and small-scale hydrological data at the surface,using typhoon landfall and riverine flood,respectively.The results demonstrate the applicability and efficiency of the system to dynamically visualize hydrometeorological data.

东盟数据出境标准合同立法及其现实启示

随着《促进和规范数据跨境流动规定》的正式出台,我国个人信息出境标准合同的适用范围、文本内容得到进一步优化。东盟与我国设立数据出境标准合同规则的时间较为接近,且同处《区域全面经济伙伴关系协定》(简称“RCEP”)的规范之下,针对《东盟跨境数据流动示范合同条款》(简称“MCCs”)的比较研究具有重要意义。通过对MCCs立法意图、规则内容和法律效力等方面的分析,可以发现我国与东盟在数据跨境立法的价值取向、与RCEP的衔接度、标准合同的内容上有所不同。为促进中国与东盟之间的数据安全自由流动,我国未来应细化数据传输工具操作路径,加强双方数据跨境规则互认,并进一步完善个人信息保护影响评估制度。

基于地理模型和多源遥感的林草生态综合监测方法——以日照市莒县为例

随着生态系统保护工作的不断推进,林草生态系统成为人们关注的重点问题。分析林草生态监测现状可确定目前林草生态监测存在的问题。针对当前林草生态综合监测过程,由于遥感图像分析能力不足,造成监测结果精度不足等问题,提出基于地理模型和多源遥感的林草生态综合监测方法。采集遥感图像,完成3D标定及预处理工作。通过纹理特征提取与支持向量机算法,对林草生态环境遥感影像进行判读。应用多源遥感技术构建地理加权回归模型,实现林草地图斑监测。将图斑带入马尔科夫矩阵模型中,获取林草生态综合变化监测结果。至此,基于地理模型和多源遥感的林草生态综合监测方法设计完成。通过实验证实,此方法的遥感图像分类精度以及监测精度均优于当前方法。

DRG改革下构建医院精细化质量管理平台的路径分析

疾病诊断相关分组(diagnosis related groups,DRG)是现代化管理理念融入医院管理体系的重要组成,尤其是近些年,医院精细化管理研究持续深化。DRG改革与实施的核心体现在对疾病进行分组归类,并应用统计控制理论展开精细化管理。医院精细化质量管理平台的建设是医院管理模式创新与管理质量提高的必然选择。文章以DRG改革下构建医院精细化质量管理平台的路径研究为中心,采用理论研究法与文献分析法,从多维度对医院精细化质量管理平台构建的必要性进行梳理,并结合医院精细化管理现状积极对管理创新路径进行探索。在此基础上,进一步优化医院精细化管理模式,辅助医院有效控制资源消耗,减少不必要的费用支出等,以期为医院未来发展与管理提供参考。

国家大数据综合试验区对数字经济的影响研究--基于双重差分的实证分析

设立大数据试验区能够有效畅通数据资源的开放和共享,有助于提升数字技术创新水平,也为发展数字经济营造良好的氛围。采用2011—2020年我国31个省级面板数据,实证研究大数据试验区对数字经济的影响及其内在机理。研究结果表明,建立大数据试验区能够有效推进区域数字经济的发展,改善数字创新水平。异质性实证结果显示,大数据试验区对东部区域省份的数字经济推动效果要好于中西部地区,且该试点政策的跨区域试验区产生的政策协同效应要优于区域类试验区产生的政策针对效应。空间效应表明,大数据试验区对本区域和相邻区域数字经济发展都能够产生积极显著的空间溢出效应。在此基础上,提出完善网络基础设施、增加大数据试点数量和加强试验区协同发展的政策建议。