Representing Complex Geographic Phenomena in GIS

Conventionally, spatial data models have been designed according to object- or field-based conceptualizations of reality. Conceptualization of complex geographic phenomena that have both object- and field-like properties, such as wildfire and precipitation, has not yet been incorporated into GIS data models. To this end, a new conceptual framework is proposed in this research for organizing data about such complex geographic phenomena in a GIS as a hierarchy of events, processes, and states. In this framework, discrete objects are used to show how events and processes progress in space and time, and fields are used to model how states of geographic themes vary in a space-time frame. Precipitation is used to demonstrate the construction and application of the proposed framework with digital precipitation data from April 15 to May 22, 1998, for the state of Oklahoma, U.S.A. With the proposed framework, two sets of algorithms have been developed. One set automatically assembles precipitation events and processes from the data and stores the precipitation data in the hierarchy of events, processes, and states, so that attributes about events, processes, and states are readily available for information query. The other set of algorithms computes information about the spatio-temporal behavior and interaction of events and processes. The proposed approach greatly enhances support for complex spatio-temporal queries on the behavior and relationships of events and processes.     

面向地理信息大数据的时空事件关系可视化分析框架

不同类型的地理信息大数据蕴含了丰富的时空关系,反映个体或者群体时空事件之间的变化模式,在资源、环境、交通等领域的智慧应用中起到关键性作用。然而,地理信息大数据拥有的多尺度和多语义特性,在挖掘地理信息大数据的时空关系时会存在许多困难。如何有效地构建多尺度多语义时空关系,并直观地表达这些时空关系是一个重大挑战。本文设计了一种时空事件关系可视化分析框架,通过定义事件间时空关系的描述,设计了一种多视图协同的可视化分析方法,结合时空关系的可视化协同交互,实现地理信息大数据时空事件可视分析。基于时空轨迹数据,通过试验说明该方法可以探寻时空事件及背景间的时空关系,实现时空事件的交互可视分析。     

现实与赛博空间数据相结合的城市活动事件时空建模

城市活动事件(如文化、娱乐、体育等事件)的规模与影响力是城市经济文化发展的重要体现,其发生的全过程对城市现实空间与赛博空间都会产生巨大影响,从现实空间与赛博空间对城市活动事件的演化感知、动态建模与时空分析,具有重要的理论研究与应用价值。提出了一种结合现实空间交通数据与赛博空间社交媒体数据的城市活动事件时空建模分析方法,从事件进行中的交通轨迹,探测识别与事件显著相关的城市时空区域和交通流,分析现实空间事件热度的时空变化;从事件发生全过程的社交媒体数据中,探测分析赛博空间事件热度的时空变化;通过将现实空间和赛博空间的融合,建立城市活动事件时空模型,刻画事件全过程中城市地理空间与城市行为空间的时空演变特征。以2015年周杰伦"魔天伦2.0"世界巡回演唱会(武汉站)事件为例,采用武汉市出租车GPS轨迹数据和微博数据,对演唱会的事前、事中、事后实现城市地理空间与行为空间全过程建模与时空演变分析,并与单一数据源事件刻画模型进行比较,结果显示本方法能更合理地结合现实空间和赛博空间刻画城市活动事件。     

The cognition of change: scaling deformations in mind and spatial theories

The dynamics of the earth and its inhabitants have become a core topic and focus in research in the spatial sciences. The spatio-temporal data avalanche challenges researchers to provide efficient and effective means to process spatio-temporal data. It is of vital importance to develop mechanisms that allow for the transition of data not only into information but also into knowledge. Knowledge representation techniques from artificial intelligence play an important role in laying the foundations for theories dealing with spatio-temporal data. Specifically, the advances in the area of qualitative spatial representation and reasoning (QSTR) have led to promising results. Categorical distinctions of spatio-temporal information identified by QSTR calculi potentially correspond to those relevant to humans. This article presents the first behavioral evaluation of qualitative calculi modeling geographic events associated with scaling deformations of entities, that is, changes in size by either expansion or contraction. Examples of such dynamics include a lake flooding its surroundings or an expanding oil spill in the ocean. We compare four experiments using four different semantic domains. Each domain consists of two spatially extended entities: one entity is undergoing scaling deformations while the other is static. We kept the formal QSTR characterization, which are paths through a topologically defined conceptual neighborhood graph, identical across all semantic domains. Our results show that for geographic events associated with scaling deformations (a) topological relations are not equally salient cognitively; (b) domain semantics has an influence on the conceptual salience of topological relations.     

A spatio-temporal model of housing prices based on individual sales transactions over time

A spatio-temporal model of housing price trends is developed that focuses on individual housing sales over time. The model allows for both the spatio-temporal lag effects of previous sales in the vicinity of each housing sale, and for general autocorrelation effects over time. A key feature of this model is the recognition of the unequal spacing between individual housing sales over time. Hence the residuals are modeled as a first-order autoregressive process with unequally spaced events. The maximum-likelihood estimation of this model is developed in detail, and tested in terms of simulations based on selected data. In addition, the model is applied to a small data set in the Philadelphia area.     

Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach

The expansion of satellite technologies makes remote sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time consuming. In this paper, we present an object-oriented methodology designed to handle multi-annual Satellite Image Time Series (SITS). This method has the objective to automatically analyse a SITS to depict and characterize the dynamic of the areas (the way that the land cover of the areas evolve over time). First, it identifies the spatio-temporal entities (reference objects) to be tracked. Second, the evolution of such entities is described by means of a graph structure and finally it groups together spatio-temporal entities that evolve similarly. The analysis were performed on three study areas to highlight inter (among the study areas) and intra (inside a study area) similarity by following the evolution of the underlying phenomena. The analysis demonstrate the benefits of our methodology. Moreover, we also stress how an expert can exploit the extracted knowledge to pinpoint relevant landscape evolutions in the multi-annual time series and how to make connections among different study areas.     

一种基于对象和快照的混合地表覆盖时空数据存储模型

时空数据模型是数据库和时空数据分析研究热点之一。由于时空数据变化类型多、分析计算复杂及应用范围广,一种观点认为建立通用的时空数据模型具有高度复杂性。本文针对地表覆盖变化时空数据建库和时空分析应用需求,提出了基于对象和快照的混合时空数据模型。该模型用面向对象描述地表覆盖的斑块对象时空过程,组织管理斑块对象时空事件和空间、属性信息,同时用快照描述地表覆盖整体时空分布,组织管理栅格快照,两者通过基于时间和空间位置的逻辑关联关系形成混合模型。模型在表达对象自身变化的时空事件类型基础上,增加了不同分类对象之间互相转变的时空事件表达。该模型既可以用于对地表覆盖历史观测数据组织存储建库,又可以用于支持时空统计分析、时空演变模拟和时空数据挖掘。通过对黑龙江地区地表覆盖时空数据建库、查询检索、整体格局演变分析和斑块变化时空过程检索与表达的试验,验证了模型的有效性。     

基于位置点统计的时空异常检测与分析

探讨了位置数据异常分析的一种新思路,给出了面向异常的时空表达模型,建立了基于区域位置点统计的异常检测和分析框架,探寻异常在事件影响下的时空发展趋势和分布特征。通过纽约两个月的出租车出行数据的实验分析验证了框架的有效性,并发现一些有趣的规律,为公共安全、交通管控应急、城市功能区分析等提供指导信息。     

Modeling Dynamic Polygon Objects in Space and Time: A New Graph-based Technique

The analysis of dynamic spatial systems requires an explicit spatio-temporal data model and spatio-temporal analysis tools. Event-based models have been developed to analyze discrete change in continuous and feature-based spatial data. In this paper, a spatio-temporal graph model is described that supports the analysis of continuous change in feature-based polygon spatial data. The spatio-temporal graph edges, called temporal links, track changes in polygon topology through space and time. The model also introduces the concept of a spatial-interaction region that extends a model's focus beyond short-term local events to encompass long-term regional events. The structure of the spatio-temporal graph is used to classify these events into five types of local polygon events and two types of spatial-interaction region events. To illustrate its utility, the model is applied to the ecological question of how patch size influences longevity in underwater plant communities in Chesapeake Bay, USA. Both a short-term local analysis and a longer-term regional analysis showed that patches of plants, or groups of patches, larger than one to two hectares in size were more likely to persist than smaller patches or groups of patches. Overall, the spatio-temporal graph model approach appears applicable to a variety of spatio-temporal questions.     

一种基于特征-语义的时空数据组织方法

在全面研究时空数据模型理论及构建方法的基础上,本文采用面向特征的数据模型构建方法结合时空语义思想,并引入无缝时空理念,探讨了一种基于特征的无缝时空数据模型构建方法。该模型将地理时空过程特征抽象为特征对象、特征事件、特征过程,不仅能够实现地理实体的空间数据组织及复杂拓扑分析,而且,通过描述特征事件、特征过程及其相互关系实现时空语义表达,进而实现海量信息存储以及高层次时空分析,也为通用性时空数据模型的构建提供了一种新思路。     

多视角监控视频中动态目标的时空信息提取方法

监控视频中动态目标的精准定位与跟踪作为计算机视觉领域中重要的研究方向,近年来已成为监控领域的研究热点。传统视频动态目标检测仅依赖图像特征数据,忽略了与地理坐标系精准匹配,特别是对于多个摄像机覆盖的区域,拍摄的角度不同,投影后形成的图像空间分辨率也不同,因此,难以满足智能监控在复杂的地理场景中全方位时空信息感知。本文提出一种多摄像头协同的视频监控图像与地理空间数据互映射模型构建方法来获取动态目标的轮廓和地理位置等时空信息,首先建立监控图像信息与地理空间数据的互映射关系,将观测角度不同、尺度不同和空间分辨率的监控图像置于同一坐标系下,并在此基础上通过融合Canny算子与背景减法来检测目标的边缘信息;然后采用质心偏移算法还原目标在该场景的实际位置,从而实现多角度下连续跟踪,提升地理场景的时空理解力和分析力,提高动态目标的精准定位与跟踪能力。     

基于特征的时空数据模型用于地籍变更的探讨

传统的基于专题地理分层的GIS数据模型在数据组织 ,数据共享及复杂的空间分析方面存在明显的不足 ,认为发展新一代的基于特征的时空模型成为必然。本文结合实践中地籍变更的特点 ,提出了基于特征变化状态的时空数据模型。该模型保持了地理现象的完整性 ,实现了时空专题信息的有机集成 ,有利于发展复杂的面向目标的定向分析。     

How fast is a cow? Cross‐Scale Analysis of Movement Data

Data representing the trajectories of moving point objects are becoming increasingly ubiquitous in GIScience, and are the focus of much methodological research aimed at extracting patterns and meaning describing the underlying phenomena. However, current research within GIScience in this area has largely ignored issues related to scale and granularity – in other words how much are the patterns that we see a function of the size of the looking glass that we apply? In this article we investigate the implications of varying the temporal scale at which three movement parameters, speed, sinuosity and turning angle are derived, and explore the relationship between this temporal scale and uncertainty in the individual data points making up a trajectory. A very rich dataset, representing the movement of 10 cows over some two days every 0.25 s is investigated. Our cross‐scale analysis shows firstly, that movement parameters for all 10 cows are broadly similar over a range of scales when the data are segmented to remove quasi‐static subtrajectories. However, by exploring realistic values of GPS uncertainty using Monte Carlo Simulation, it becomes apparent that fine scale measurement of all movement parameters is masked by uncertainties, and that we can only make meaningful statements about movement when we take these uncertainties into account.     

自然资源时空信息的技术内涵与研究方向

用时空信息说话,在三维空间研判,凭科学事实决策,既是提升自然资源管理水平、建设现代化治理体系的主要举措,也是测绘科技界面临的一项艰巨挑战。为此,应加强时空信息的技术创新,促进其与自然资源业务的深度融合,构建新一代的自然资源时空信息技术体系,提供高质量的时空信息、开展高层次时空分析和实施高水平的时空赋能。本文分析了自然资源管理对时空信息的主体技术需求,讨论了自然资源时空信息的技术内涵,提出了以“全面动态感知、系统精准认知、全域智慧管控”为主线的总体发展思路,展望了今后的研究方向。     

时空大数据时代的地理知识工程展望

伴随智慧城市的逐步建设,日益成熟的传感网技术使时空数据的获取变得容易。遥感影像、地图瓦片、视频监控、网络爬虫、社交平台等各类时空数据常有PB级的巨量数据存储,人类社会已全面进入时空大数据时代。然而,海量数据的骤增与信息增值机制的缺乏形成了鲜明的对比,仅仅依靠信息技术尚难以快速地产生地理知识。如何利用新兴的信息化技术手段,结合地理信息及地理分析手段,快速挖掘、产生和利用时空数据成为当前亟待解决的关键问题。地理知识工程围绕人类空间认知与复杂知识发掘技术,对大数据时代的智能化应用和政府决策有重要的意义。立足于时空大数据背景,展开地理知识工程的内涵、时代特点、结构组成及研究方向等论述。地理知识工程的发展将在以虚拟地理环境、数字地球、智慧城市为代表的新型地理信息系统推动下,促进传统地理信息系统向着符合人类认知特点和探索过程的方向转变。通过海量时空数据到地理知识的快速转化,最终将解决"地理数据爆炸但地理知识贫乏"这一重要难题,提升数据的使用效能。     

自然资源管理背景下的时空大数据平台建设

由于自然资源机构改革,测绘地理信息工作成为自然资源统一管理工作的组成部分。时空大数据平台如何在新的组织机构下发挥作用,是当前急需研究的问题。为此,本文在分析了自然资源管理的需求后,提出了时空大数据平台建设的技术路线和时空大数据服务自然资源管理的建设思路。     

大数据背景下的虚拟地理认知实验方法

如何构建新一代的虚拟地理认知实验研究范式,从海量人类活动和地理数据中挖掘人地交互过程中的新模式、新知识成为新一代虚拟地理认知实验需要解决的问题。本研究首先提出了在数据密集型科学研究范式下的虚拟地理认知实验框架,阐述了从环境心理学的视角,结合多源人类活动大数据、城市环境大数据构建实验平台的方法,进而支持地理知识工程的构建。其次,本文介绍了基于海量街景数据和对应的个体情感评分数据,利用深度学习的相关方法和统计模型来进行认知知识挖掘的案例。实验发现了可视域中与个体对场景的情感维度-压抑感具有较高相关性的一系列视觉要素,如植被、建筑、车辆等,并通过回归分析予以量化。     

地籍管理中时空数据模型研究

地籍信息系统是一种典型的时态地理信息系统,其技术关键就是在于用适当的时空数据模型来组织地籍数据库。本文简要介绍了几种主流的时空数据模型,重点从建模方法、时空数据组织和时空数据操作三个方面阐述了地籍管理领域时空数据模型的研究进展,探讨了存在的问题,提出了未来发展方向的建议。     

时空大数据时代的地图学

地图学研究的主战场是"地图",但不同历史时期的地图其信息源、主题、内容、载体、形式、制作方法和应用方式是不同的,当然它的全方位价值也就不同了。从科学史上的科学范式的变化来看,随着大数据时代的到来,如今已经步入"数据密集型"科学范式时代,地图学亦如此,具有明显的大数据科学的特征。所有大数据都是由包括人类活动在内的地理世界的任何事物和任何现象运动变化产生的,都具有空间和时间特性,当然也就离不开空间参照和时间参照。因此,大数据本质上就是时空大数据。自20世纪50年代末60年代初以来的现代地图学,即信息化时代的地图学,是以时空数据为对象的,其核心是时空数据处理与表达,但并没有像今天面对天空地海一体的大规模多源(元)异构和多维动态的数据流(或流数据),地图的实时动态性、主题针对性、内容复合性、载体多样化、表现形式个性化、制作方法现代化、应用泛在化等特征,是以往任何时期都无法比拟的,这就产生了地图学理论、技术和应用体系的巨大变化。而所有这些变化都正好发生在20世纪50年代末和60年代初以来的约60年间,故以本文纪念《测绘学报》创刊60周年。     

基于手机数据的城市内部就业人口流动特征及形成机制分析——以武汉市为例

城市内部就业人口流动作为城市群体的主要移动形式,分析其特征及形成机理对城市规划、交通预测等具有重要意义。基于武汉市手机信令数据,识别职住人口分布与流动,构建城市内部就业流动网络。运用网络分析、可达性计算、逻辑回归等方法,分析城市内部就业流动的特征及其形成机制。研究表明,武汉市内部就业流动在数量上分布不均衡,大量就业流动集中于少数街道间。在空间上,就业流动随距离、可达时间增加而减少,并依地形、文化形成若干联系紧密的就业社区;以就业流出地居住人口、流入地工作人口度量的就业势能是驱动就业流动的最主要因素,而文化差异、空间不邻近、可达性差阻碍就业流动的发生。此外,不同产业特色对就业流动影响不同,商业、科教阻碍就业外流,工业吸引外来就业。