几何代数与GIS研究团队
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    几何代数与GIS研究团队

    虚拟地理环境教育部重点实验室(南京师范大学)

袁林旺,男,1973年11月生,江苏海安人,博士,教授,博士生导师,国家杰出青年基金获得者。1995年本科毕业于南京师范大学地理系,1998年和2001年分别获得南京师范大学地图学与遥感专业硕士学位和自然地理学专业博士学位。2009-2010年在美国德克萨斯大学达拉斯分校从事高访研究。入选教育部“新世纪优秀人才培养计划”、江苏省“青蓝工程中青年学术带头人”及首届校“百名领军人才培养计划”。曾获得第三届江苏省青年地理科技奖和江苏省优秀教学成果一等奖。兼任中国地理学会青年工作委员会副主任、江苏省遥感与地理信息系统学会地理信息科学与技术专业委员会主任、江苏省地理信息资源开发与利用协同创新中心执行主任、虚拟地理环境教育部重点实验室常务副主任、资源科学编委。主要从事GIS理论与方法研究,近年来主持国家自然科学基金、国家863课题等国家级课题5项,在IJGIS、TGIS、IEEE TKDE、EPB、Computers& Geosciences、中国科学、科学通报等刊物发表SCI、SSCI、EI收录论文30余篇。在科学出版社出版专著2本……
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研究方向

数据模型

包括:地理时空与几何代数空间的映射模式,地理过程连续-离散一体化表达模式,场景数据模型,地理规律驱动的GIS数据结构与索引。

计算模型

包括:新型GIS计算框架;计算模型算子库;分析流程统一的结构化模板;脚本化模板开发方法;算法解析优化与并行化。

分析模型

包括:地理模型模式的自适应集成与改造;动态模型的GIS计算嵌入;结构化特征模型的GIS计算嵌入;连续地理模型GIS计算嵌入。

基于GA的GIS系统

包括:系统架构;计算算子库;计算引擎;系统功能与截图;典型分析案例(三维城市案例、南极海地冰案例、应急疏散案例等)。

最新进展

10月 26,2020 发表评论 69,203 views

俞肇元教授在滁州学院作“高速公路交通流的量子模拟模型研究”报告

 

1.报告简介

2020年10月24日-10月25日,俞肇元教授参加滁州学院地理信息与旅游阮学院举办的地理信息感知与服务学术研讨会,并作题为“高速公路交通流的量子模拟模型研究”的学术报告。报告内容从经典交通流模拟模型存在的问题出发,介绍了量子交通模拟模型研究的缘起,提出了基于量子谐振子和量子随机游走的高速公路交通流模拟模型,构造了基于量子随机游走的非可观测的人文社会过程动态模拟方法,实现了近个体颗粒度城际高速公路交通流的精准模拟与情景预估。 阅读更多

10月 21,2020 发表评论 1,833 views

罗文副教授参加ENGAGE workshop at the CGI 2020 conference并作报告

2020年10月20日,罗文副教授同实验组其他老师以及部分研究生参加ENGAGE workshop at the CGI 2020 conference,并作题为“Geometric algebra-based multilevel declassification method for geographical field data”的学术报告,该报告讨论了GIS应用模式的多样性导致对多层GIS数据解密的需求,例如,必须对公共使用的数据进行解密以隐藏机密空间信息,还原过程不是常规加密方法那样的常见数据排列以及还原后的数据还应保留一般的地理空间特征。此外,当面对不同级别的机密性时,需要不同级别的还原。 阅读更多

10月 20,2020 发表评论 1,725 views

俞肇元教授参加2020地理信息技术创新大会并作相关报告

2020年地理信息技术创新大会于2020年10月13-16日在北京举行,由中国地理信息产业协会、中国测绘学会、中国区域科学协会、中国市长协会、北京大学地球与空间科学学院、武汉大学资源与环境科学学院、易智瑞信息技术有限公司等机构联合举办,大会关注全球地理信息技术的最新发展动向,关注中国地理信息技术和产品创新,关注各行业用户对地理信息技术的典型和创新性应用。大会将融合地理信息领域的学者、专家、企业、教育等多方力量,共同探讨地理信息技术对于促进人类世界信息交流和信息管理的创新价值。

俞肇元教授在线上进行报告,报告从城市信息模型的概念和内涵出发,探讨了基于全息地图技术的城市信息模型(CIM)的理论方法、关键技术和典型应用。总结了CIM应满足全空间、全流程、全要素和全用途的一体化表达、建模、分析和应用。建立了以统一时空框架、高维场景模型为基础的物理、人文社会、信息要素有机整合的CIM建模方法。介绍了基于全息地图技术的城市场景保真建模、声光电磁数据采集建模以及泛在信息智能解析与位置聚合的关键技术方法,研发了以可定制、可配置为核心的统一CIM平台,并给出了CIM在自然资源、规划房产和公共安全等领域的应用实践。 阅读更多

9月 23,2020 发表评论 2,467 views

袁林旺教授参加第255期双清论坛并作“面向陆海界面多尺度建模的数字孪生与虚拟实验”专题报告

2020年9月10至11日,国家自然科学基金委员会(以下简称自然科学基金委)第255期双清论坛在北京举办。袁林旺教授参加论坛并作“面向陆海界面多尺度建模的数字孪生与虚拟实验”专题报告,报告内容为:

数字孪生和虚拟实验技术通过在统一的时空框架下整合各类观测数据、模型模式,并利用虚拟仿真等手段,建立现实世界及其特征、过程和机制的数字化映射。海岸带可持续发展涉及跨圈层、多领域和多尺度要素相互作用,发展陆海界面多尺度建模的数字孪生技术和虚拟实验方法,可以在统一时空框架下汇聚多领域、多尺度的时空数据,进行跨圈层多模式耦合的集成建模和大数据智能分析,并提供具有真实感知和反馈控制的虚拟孪生海岸带多模态融合表达和多情景虚拟实验。研究可有效贯通面向海岸带可持续性建设的建模与模拟、诊断与归因、虚拟实验与可视化决策分析的全流程,为陆海统筹及其智能分析提供融数据环境建模环境-表达环境-协同环境于一体的综合支撑平台。

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成果展示

传感器网络行为语义分析

结构层次网络分析

多要素融合场景构建

高维时空数据特征解析

最新发表

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Irregular geographic spatio-temporal-field data have been rapidly accumulating; however, data organizations and operations for different irregular types are often segregated, leading to systematic drawbacks, such as interface expansion difficulty and high coupling codes in GIS implementations. The paper proposes a unified approach to organizing and operating irregular geographic spatio-temporal-field data. The proposed approach has two components, namely ‘concepts and definitions’, and ‘logical model’. The first component introduces the concept of primitive elements, which are formal sets of data points, to serve as the smallest building blocks in the data organization. We define the corresponding primitive elements for three prevalent irregularity types (including sparse, imbalanced, and heterogeneous). The second component utilizes object-oriented programming to support the implementation of various operators. Additionally, we develop the layered architecture to decouple data organization, operation, and visualization to assure low coupling among layers. For demonstrations, we conduct case studies to show the effectiveness of our approach. Additionally, we conduct experiments to new irregularity types and illustrate the flexibility and scalability of our approach. Comparisons with classic tensor methods and spatio-temporal analysis methods show that our approach has more comprehensive supports for different data types.

A tensor-based approach to unify organization and operation of data for irregular spatio-temporal fields Li et al. A tensor-based approach to unify organization and operation of data for irregular spatio-temporal fields.
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At small granularity (e.g., 10-minutes to hourly), expressway traffic volumes rely heavily on drivers' driving habits heterogeneity and decision randomness, making it challenging for accurate modeling. In this paper, we propose a small granularity simulation model named Small-Granularity Expressway Traffic Volumes with Quantum Walks (SGETV-QW). The proposed model adopts quantum walks to generate probability patterns of the exiting time of drivers from the expressway. Then, we refine and map the generated probability patterns to empirical traffic-volume data via a stepwise regression and quantify the modeling accuracy in both the time and frequency domain. We validate SGETV-QW for traffic volume data from seven stations along the Nanjing-Changzhou Expressway in China and compare it with Autoregressive Integrated Moving Average Model (ARIMA) and Long and Short-Term Memory (LSTM) networks. The results show that SGETV-QW improves the simulation accuracy at small granularity. In addition, traffic volumes simulated by SGETV-QW have almost the same frequency spectrum as observed traffic volumes. Finally, we conduct a sensibility analysis and show that SGETV-QW can adapt its parameters to model traffic volumes at different granularities.

Modeling Small-Granularity Expressway Traffic Volumes With Quantum Walks Yu et al. Modeling Small-Granularity Expressway Traffic Volumes With Quantum Walks.
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Lossy compression has been applied to the data compression of large-scale Earth system model data (ESMD) due to its advantages of a high compression ratio. However, few lossy compression methods consider both global and local multidimensional coupling correlations, which could lead to information loss in data approximation of lossy compression. Here, an adaptive lossy compression method, adaptive hierarchical geospatial field data representation (Adaptive-HGFDR), is developed based on the foundation of a stream compression method for geospatial data called blocked hierarchical geospatial field data representation (Blocked-HGFDR). In addition, the original Blocked-HGFDR method is also improved from the following perspectives. Firstly, the original data are divided into a series of data blocks of a more balanced size to reduce the effect of the dimensional unbalance of ESMD. Following this, based on the mathematical relationship between the compression parameter and compression error in Blocked-HGFDR, the control mechanism is developed to determine the optimal compression parameter for the given compression error. By assigning each data block an independent compression parameter, Adaptive-HGFDR can capture the local variation of multidimensional coupling correlations to improve the approximation accuracy. Experiments are carried out based on the Community Earth System Model (CESM) data. The results show that our method has higher compression ratio and more uniform error distributions compared with ZFP and Blocked-HGFDR. For the compression results among 22 climate variables, Adaptive-HGFDR can achieve good compression performances for most flux variables with significant spatiotemporal heterogeneity and fast changing rate. This study provides a new potential method for the lossy compression of the large-scale Earth system model data.

Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0) Yu et al. Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0).
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Due to the increasing complexity of GIS data and service modes, there is an urgent need for the next generation of GIS with new representation and computation methods. A number of spatiotemporal models, analytical and visualization methods, as well as system architectures have been proposed. However, previous studies failed to integrate basic geographical theories with latest computing technologies. Without a well-defined body of underlying theories, new models and methods are limited in scope and not able to meet the ultimate requirements of the next-generation GIS, which demands multidimensional, highly dynamic and semantic-rich representations and computational power. Geometric algebra (GA) provides an ideal tool for the expression and calculation of multidimensional geometric objects, and has proved to be effective for GIS representation and computation applications in our previous studies. We propose to use GA as the basic mathematical language for the establishment of the next-generation GIS. We present the framework of a GA-based next-generation GIS and describe the representation space, data structure, and computational models in this paper. A few issues that have not been sufficiently addressed by previous studies are discussed in detail with potential solutions proposed. These include multi-scale representations, modelling of geographic processes, simulation of geographic interactions, and multi-element modelling. The GA-based next-generation GIS uses an integrated structure consisting of a theoretical architecture, model for information expression, and computational methods. Implementation of the approach aims to improve GIS capacities in applications such as global spatiotemporal modelling and analysis, regional geographic modelling and simulation, smart city applications, and many others.

Towards the next-generation GIS: a geometric algebra approach Yuan et al. Towards the next-generation GIS: a geometric algebra approach.

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