A Hybrid Geocoding Methodology for Spatio‐Temporal Data

As tools for collecting data continue to evolve and improve, the information available for research is expanding rapidly. Increasingly, this information is of a spatio‐temporal nature, which enables tracking of phenomena through both space and time. Despite the increasing availability of spatio‐temporal data, however, the methods for processing and analyzing these data are lacking. Existing geocoding techniques are no exception. Geocoding enables the geographic location of people and events to be known and tracked. However, geocoded information is highly generalized and subject to various interpolation errors. In addition, geocoding for spatio‐temporal data is especially challenging because of the inherent dynamism of associated data. This article presents a methodology for geocoding spatio‐temporal data in ArcGIS that utilizes several additional supporting procedures to enhance spatial accuracy, including the use of supplementary land use information, aerial photographs and local knowledge. This hybrid methodology allows for the tracking of phenomenon through space and over time. It is also able to account for reporting inconsistencies, which is a common feature of spatio‐temporal data. The utility of this methodology is demonstrated using an application to spatio‐temporal address records for a highly mobile group of convicted felons in Hamilton County, Ohio.     

A Spatio‐Temporal Graph Model for Marine Dune Dynamics Analysis and Representation

Defining a model for the representation and the analysis of spatio‐temporal dynamics remains an open domain in geographical information sciences. In this article we investigate a spatio‐temporal graph‐based model dedicated to managing and extracting sets of geographical entities related in space and time. The approach is based on spatial and temporal local relations between neighboring entities during consecutive times. The model allows us to extract sets of connected entities distant in time and space over long periods and large spaces. From GIS concepts and qualitative reasoning on space and time, we combine the graph model with a dedicated spatial database. It includes information on geometry and geomorphometric parameters, and on spatial and temporal relations. This allows us to extend classical measurements of spatial parameters, with comparisons of entities linked by complex relations in space and time. As a case study, we show how the model suggests an efficient representation of dunes dynamics on a nautical chart for safe navigation.     

A UML‐based Representation of Spatio‐Temporal Evolution in Road Network Data

Geographic features change over time, this change being the result of some kind of event. Most database systems used in GIS are relational in nature, capturing change by exhaustively storing all versions of data, or updates replace previous versions. This stems from the inherent difficulty of modelling geographic objects and associated data in relational tables, and this is compounded when the necessary time dimension is introduced to represent how these objects evolve. This article describes an object‐oriented (OO) spatio‐temporal conceptual data model called the Feature Evolution Model (FEM), which can be used for the development of a spatio‐temporal database management system (STDBMS). Object versioning techniques developed in the fields of Computer Aided Design (CAD) and engineering design are utilized in the design. The model is defined using the Unified Modelling Language (UML), and exploits the expressiveness of OO technology by representing both geographic entities and events as objects. Further, the model overcomes the limitations inherent in relational approaches in representing aggregation of objects to form more complex, compound objects. A management object called the evolved feature maintains a temporally ordered list of references to features thus representing their evolution. The model is demonstrated by its application to road network data.     

CAUSTA: Clifford Algebra‐based Unified Spatio‐Temporal Analysis

Introducing Clifford algebra as the mathematical foundation, a unified spatio‐temporal data model and hierarchical spatio‐temporal index are constructed by linking basic data objects, like pointclouds and Spatio‐Temporal Hyper Cubes of different dimensions, within the multivector structure of Clifford algebra. The transformation from geographic space into homogeneous and conformal space means that geometric, metric and many other kinds of operators of Clifford algebra can be implemented and we then design the shortest path, high‐dimensional Voronoi and unified spatial‐temporal process analyses with spacetime algebra. Tests with real world data suggest these traditional GIS analysis algorithms can be extended and constructed under Clifford Algebra framework, which can accommodate multiple dimensions. The prototype software system CAUSTA (Clifford Algebra based Unified Spatial‐Temporal Analysis) provides a useful tool for investigating and modeling the distribution characteristics and dynamic process of complex geographical phenomena under the unified spatio‐temporal structure.     

Spatio‐Temporal Building Population Estimation for Highly Urbanized Areas Using GIS

Detailed population information is crucial for the micro‐scale modeling and analysis of human behavior in urban areas. Since it is not available on the basis of individual persons, it has become necessary to derive data from aggregated census data. A variety of approaches have been published in the past, yet they are not entirely suitable for use in the micro‐scale context of highly urbanized areas, due mainly to their broad spatial scale and missing temporal scale. Here we introduce an enhanced approach for the spatio‐temporal estimation of building populations in highly urbanized areas. It builds upon other estimation methodologies, but extends them by introducing multiple usage categories and the temporal dimension. This allows for a more realistic representation of human activities in highly urbanized areas and the fact that populations change over time as a result of these activities. The model makes use of a variety of micro‐scale data sets to operationalize the activities and their spatio‐temporal representations. The outcome of the model provides estimated population figures for all buildings at each time step and thereby reveals spatio‐temporal behavior patterns. It can be used in a variety of applications concerning the implications of human behavior in urban areas.     

Integrated Spatio‐temporal Data Mining for Forest Fire Prediction

Forests play a critical role in sustaining the human environment. Most forest fires not only destroy the natural environment and ecological balance, but also seriously threaten the security of life and property. The early discovery and forecasting of forest fires are both urgent and necessary for forest fire control. This article explores the possible applications of Spatio‐temporal Data Mining for forest fire prevention. The research pays special attention to the spatio‐temporal forecasting of forest fire areas based upon historic observations. An integrated spatio‐temporal forecasting framework – ISTFF – is proposed: it uses a dynamic recurrent neural network for spatial forecasting. The principle and algorithm of ISTFF are presented, and are then illustrated by a case study of forest fire area prediction in Canada. Comparative analysis of ISTFF with other methods shows its high accuracy in short‐term prediction. The effect of spatial correlations on the prediction accuracy of spatial forecasting is also explored.     

基于事件-状态的地理时空数据模型研究

分析了事件与状态的关系,对基态修正模型加以改进,根据事件发生的频繁程度及每次数据量更新的大小,建立历史基态,对于历史基态间的地理实体数据只记录其相对于历史基态的变化部分.既克服了基于快照时空数据模型产生的大量冗余数据,又克服了基态修正模型随着修正次数的增加,历史重构需要大量的复杂计算的缺陷,具有历史数据恢复快的优势.     

面向交通事件管理的GIS-T数据模型

从交通事件管理角度出发,归纳总结交通事件管理对GIS-T数据模型7个方面的功能需求,在此基础上提出面向交通事件管理的GIS-T数据模型,并利用UML对模型进行构建、分析和描述。     

以事件为核心的面向对象时空数据模型

GIS中时态问题的解决不仅要在数学上寻求方法的支持，更应该在现实世界的时空现象中去探求其内在的变化规律。事件是驱动时空数据发生变化的根本原因，但每一次事件对数据对象的影响总是局限于一定的范围，这内在地表现为对象的属性之间的时空内聚性并决定了时态数据管理的对象粒度。时空数据模型的设计必须参照这一规律才能在冗余与效率之间取得平衡。在简要分析现有的几种典型时空数据模型的基础上，根据上述观点设计了一种以事件为核心的面向对象时空数据模型。房产信息系统的设计与实现证明，该模型对于时空数据管理效果较好，为进一步抽取变化的模式和预测决策奠定了基础。     

Improving Representation of Land‐use Maps Derived from Object‐oriented Image Classification

Object‐oriented (OO) image analysis provides an efficient way to generate vector‐format land‐cover and land‐use maps from remotely sensed images. Such image‐derived vector maps, however, are generally presented with congested and twisted polygons with step‐like boundaries. They include unclassified polygons and polygons with geometric conflicts such as unreadable small areas and narrow corridors. The complex and poorly readable representations usually make such maps not comply well with the Gestalt principle of cartography. This article describes a framework designed to improve the representation by resolving these problematic polygons. It presents a polygon similarity model integrating semantic, geometric and spectral characteristics of the image‐derived polygons to eliminate small and unclassified polygons. In addition, an outward‐inward‐buffering approach is presented to resolve the narrow‐corridor conflicts of a polygon and improve its overall appearance. A case study demonstrates that the implementation of the framework reduces the number of the polygons by 32% and the length of the polygon boundaries by 20%. At the same time, it does not cause distinct changes the distribution of land‐use types (less than 0.05%) and the overall accuracy (decreased only 0.02%) as compared with the original image‐derived land‐use maps. We conclude that the presented framework and models effectively improve the overall representation of image‐derived maps without distinct changes in their semantic characteristics and accuracy.     

面向相似材料模型的数据处理软件的研究

工业测量系统具有高精度、简单便捷、应用广泛等优点,但在开采沉陷方面的应用尚处于研究的初级阶段。数字工业近景摄影测量系统是工业测量系统中的一种,基于该系统的相似材料模型观测方法是开采沉陷模拟研究领域一项重大的技术革新,但工业测量系统的海量数据、数据间的关系等一系列的问题急需解决,因此面向相似材料模型的观测数据处理软件的开...     

地理现象时空逻辑过程建模方法

为了有效的对地理现象进行回溯和预测,必须对地理现象的发生过程进行准确描述,时空过程模型是一种有效的方法。在研究时空过程概念模型的基础上,将概念层发展到逻辑层,研究了地理现象时空逻辑过程建模方法,使用UML–RT标准化逻辑建模技术,解决了实时信号传输和并行处理问题,并且引入π-演算来解决面向过程系统的信号传输和可移植性问题,提出了地理现象时空过程模型的逻辑架构。     

Object‐oriented tracking of thematic and spatial behaviors of urban heat islands

Modeling thematic and spatial dynamic behaviors of urban heat islands (UHIs) over time is important for understanding the evolution of this phenomenon to mitigate the warming effect in urban areas. Although previous studies conceptualized that a UHI only has a single life cycle with spatial behaviors, a UHI can be detected to appear and disappear several times periodically in terms of thematic and spatial integrated behaviors. Such multiple behaviors have not yet been illustrated with proof or evidence. This study conceptualizes a UHI as an object which has thematic and spatial behaviors simultaneously and proposes several graphs to depict periodic life‐cycle transitions triggered by behaviors. The conceptualized behaviors have been modeled and implemented in an object‐relational database management system and temperature readings collected from numerous weather stations were interpolated as temperature images per hour. The results of this study indicate that the model could track the spatial and thematic evolution of UHIs continuously and reveal their periodical patterns and abnormal cases.     

面向全时段查询的移动对象时空数据模型研究

大量移动对象时空数据的有效管理是移动空间信息服务的一个新兴的研究内容。本文提出一个面向全时段查询的移动对象时空数据模型,并给出基本的模型定义;探讨移动对象过去、现在和将来的运动位置估算方法,这些方法包括延时法、滑动平均法、三次指数法等;研究了移动对象的状态更新策略,提出一个适应于更新时间间隔的变动阈值法的移动对象的状态更新策略,该策略采用时间滑动窗口形式建立移动对象状态更新间隔的一元指数回归方程,通过求取回归参数获得移动对象状态更新趋势,利用该参数动态调整状态更新闽值。最后,给出实验模拟结果,并对今后的研究工作进行了展望。     

Spatio‐temporal prediction of land surface temperature using semantic kriging

Spatio‐temporal prediction and forecasting of land surface temperature (LST) are relevant. However, several factors limit their usage, such as missing pixels, line drops, and cloud cover in satellite images. Being measured close to the Earth's surface, LST is mainly influenced by the land use/land cover (LULC) distribution of the terrain. This article presents a spatio‐temporal interpolation method which semantically models LULC information for the analysis of LST. The proposed spatio‐temporal semantic kriging (ST‐SemK) approach is presented in two variants: non‐separable ST‐SemK (ST‐SemK_NSep) and separable ST‐SemK (ST‐SemK_Sep). Empirical studies have been carried out with derived Landsat 7 ETM+ satellite images of LST for two spatial regions: Kolkata, India and Dallas, Texas, U.S. It has been observed that semantically enhanced spatio‐temporal modeling by ST‐SemK yields more accurate prediction results than spatio‐temporal ordinary kriging and other existing methods.     

一种基态修正模型的改进模型

介绍了各种常用的时空数据模型,并重点介绍了基态修正模型及其扩展模型,分析比较了各种基态修正模型的扩展模型的优缺点,在此基础上提出了一种新的基态修正模型的改进模型,并分析了该模型的优缺点。     

Spatio‐temporal land use multi‐objective optimization: A case study in Central China

Much effort has been applied to the study of land use multi‐objective optimization. However, most of these studies have focused on the final land use scenarios in the projected year, without considering how to reach the final optimized land use scenario. To fill this gap, a spatio‐temporal land use multi‐objective optimization (STLU‐MOO) model is innovatively proposed in this research to determine possible spatial land use solutions over time. The STLU‐MOO is an extension of a genetic land use multi‐objective optimization model (LU‐MOO) in which the LU‐MOO is generally carried out in different years, and the solutions at year T will affect the solutions at year T + 1. We used the Wuhan agglomeration (WHA) as our case study area. The STLU‐MOO model was employed separately for the nine cities in the WHA, and social, economic, and environmental objectives have been considered. The success of the experiments in the case study demonstrated the value and novelty of our proposed STLU‐MOO model. In addition, the results also indicated that the objectives considered in the case study were in conflict. According to the results, the optimal land use plan in 2050 can be traced back to 2040, 2030, and 2020, providing a series of Pareto solutions over the years which can provide spatio‐temporal land use multi‐objective optimization solutions to support the land use planning process.     

地理信息的时态性分析及时空数据模型的研究

地理信息系统中 ,时间、空间和属性是信息的三种基本成分 ,理想的地理信息系统应支持信息的时态性 ,对时空数据进行统一的模拟和管理。由于当前的地理信息系统软件难以处理时态现象 ,时空数据模型已成为GIS领域的一个研究热点。本文首先分析了地理信息的时态性 ,之后讨论了当前时空数据模型的研究现状 ,在此基础上 ,提出了一种面向对象时空数据模型 ,并从三个方面描述了该模型的数据结构 ,包括时空对象结构、时空数据组织方式和时空数据存贮方式     

时态GIS理论及其数据模型初探

时态GIS(Geography Information System)是GIS中的一个新兴的研究领域。传统的GIS记录的只是这个不断变化的世界的某一时刻的影像,这类GIS亦称为静态GIS。如果在GIS中引入时间这个与空间同等重要的因素,或者说是在三维GIS的基础上,增加时间维或者时间变量,就形成了时态GIS。目前对于时空关系模型的研究就是当前时态GIS研究的重点之一。时态GIS数据模型的实现方法主要有两种:一是扩展传统的关系模型,二是采用面向对象的方法。当前时态GIS的研究取得了一些进展,但仍有许多不足之处。本文将结合实际,对未来完善时态GIS模型及系统建设提出一些需求方面的意见和想法。