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

5月 19,2023 发表评论 11,986 views

俞肇元教授等参加第十八届中国地理信息科学理论与方法学术年会

2023年5月19日至21日,由中国地理信息产业协会地理信息科学理论与方法工作委员会主办的第十八届中国地理信息科学理论与方法学术年会在桂林主办,俞肇元教授、赵彬如讲师共同参加了会议。

5月 18,2023 发表评论 169 views

俞肇元教授参加第四届军事大数据论坛

2023年5月18日至19日, 俞肇元教授参加了由军事科学院在北京举办的第四届军事大数据论坛 ,论坛以“数据制胜:军事大数据综合治理与融合应用”为主题,采取主旨报告、论文交流、成果展示等形式,开展大数据理论、技术和实践应用等方面的学术思想碰撞,展示军内外大数据研究成果,以推动军事大数据事业创新发展。

5月 12,2023 发表评论 267 views

俞肇元教授参加第十届全国地图学与地理信息系统学术大会并作报告

  1. 报告简介

2023年5月12至14日, 由中国地理学会地图学与地理信息系统专业委员会、中国测绘学会地图学与地理信息系统专业委员会等举办的第十届全国地图学与地理信息系统学术大会在西安召开,俞肇元教授参并作了“泛地图对象空间理论研究进展”的汇报。介绍了泛地图对象空间理论是在三元空间下重新构建的地图学理论框架,它将地图对象从二元空间扩展到三元空间。泛地图对象空间理论主要分为地球圈层空间、自然地理空间、人文地理空间、信息地理空间等表达空间分类和自然要素、人文要素、信息要素等表达对象分类。 阅读更多

4月 21,2023 发表评论 233 views

俞肇元教授参加BIMSA讨论班并作报告

2023年4月21日, 由清华大学求真书院举办的BIMSA讨论班在线上举行, 俞肇元教授参加了线上腾讯会议并作了“城市交通流建模、模拟优化的量子模型关键技术”的报告。汇报了基于量子模型进行城市交通流建模、模拟与优化研究的最新进展,尝试为城市交通流系统建模和模拟提供新的思路等。

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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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