发展时态GIS的可操作性时空数据模型

当前GIS广泛采用的空间数据模型不足以满足时态GIS发展的需要。该文探讨时态GIS所需的、可操作性时空数据模型,并且以土地利用和交通发展的时空相互作用为例,说明如何设计和实施相关的时空数据模型,用于支持发展时态GIS。     

A data model for moving regions of fixed shape in databases

Moving object databases are designed to store and process spatial and temporal object data. An especially useful moving object type is a moving region, which consists of one or more moving polygons suitable for modeling the spread of forest fires, the movement of clouds, spread of diseases and many other real-world phenomena. Previous implementations usually allow a changing shape of the region during the movement; however, the necessary restrictions on this model result in an inaccurate interpolation of rotating objects. In this paper, we present an alternative approach for moving and rotating regions of fixed shape, called Fixed Moving Regions, which provide a significantly better model for a wide range of applications like modeling the movement of oil tankers, icebergs and other rigid structures. Furthermore, we describe and implement several useful operations on this new object type to enable a database system to solve many real-world problems, as for example collision tests, projections and intersections, much more accurate than with other models. Based on this research, we also implemented a library for easy integration into moving objects database systems, as for example the DBMS Secondo (1) (2) developed at the FernUniversität in Hagen.     

Disciplinary structure and development strategy of information geography in China

Journal of Geographical Sciences - The advent of the information era has resulted in exceptional advances in geographic science. The domain of geographic science has expanded from traditional...     

信息地理学学科体系与发展战略要点

信息时代的到来极大地促进了地理科学的发展,地理科学的研究已从传统的自然地理空间、人文地理空间拓展到了信息地理空间,催生了信息地理学的发展,并逐渐形成了地理遥感科学、地理信息科学和地理数据科学3个分支学科。在《中国学科及前沿领域发展战略研究（2021—2035）》地理科学的学科规划背景下,本文梳理了信息地理学的形成、定义和学科体系,重点阐述了信息地理学的学科发展战略布局、优先领域发展目标和重点方向。以期本文有助于促进遥感、地理信息科学与技术的发展和应用回归地理科学,进一步强化地理科学研究,使其更加系统化、科学化和现代化,促进地理科学的整体发展。     

Estimating the prediction performance of spatial models via spatial k-fold cross validation

In machine learning, one often assumes the data are independent when evaluating model performance. However, this rarely holds in practice. Geographic information datasets are an example where the data points have stronger dependencies among each other the closer they are geographically. This phenomenon known as spatial autocorrelation (SAC) causes the standard cross validation (CV) methods to produce optimistically biased prediction performance estimates for spatial models, which can result in increased costs and accidents in practical applications. To overcome this problem, we propose a modified version of the CV method called spatial k-fold cross validation (SKCV), which provides a useful estimate for model prediction performance without optimistic bias due to SAC. We test SKCV with three real-world cases involving open natural data showing that the estimates produced by the ordinary CV are up to 40% more optimistic than those of SKCV. Both regression and classification cases are considered in our experiments. In addition, we will show how the SKCV method can be applied as a criterion for selecting data sampling density for new research area.     

地理背景的不确定性对时空行为模式分析的影响——基于西宁市的实证研究

伴随着城市研究新数据源与方法论的兴起,从时空间整合的角度反思和修正传统的概念与方法,成为时空行为研究重要发展趋势。地理背景作为城市研究的核心变量,其界定方式不确定性如何影响对个体行为作用效应的分析结果,已成为地理学的新方法论问题。在已有文献的基础上,本文基于西宁市的实证研究,尝试验证地理背景不确定性对行为模式分析的影响,并进一步探讨多个行为变量与其相互关系以及时间维度差异。通过比较两类地理背景范围（仅考虑居住地建成环境的地理背景范围、综合考虑居住地与家外活动地建成环境的地理背景范围）,分析对个体行为作用效应结果的差异性。研究发现：如果仅以居住地作为地理背景范围,关注其中建成环境对行为影响时,可能会夸大“家”的作用,而综合考虑居住地与活动地的地理背景范围时,更为接近于“真实”的地理背景,建成环境对家外活动时长、出行时长和活动空间有一定的解释力,且能够捕捉到一些重要的影响因素并修正有悖于常识的结论。同时地理背景不确定性对个体行为作用效应的分析结果影响还表现出较明显的工作日/休息日差异,表明地理背景存在时间维度的不确定性。研究结论在一定程度上揭示了时空行为研究在重新审视传统城市地理学概念、探索日常生活视角的度量方法与研究范式方面的有效性。     

环境遥感对地理学研究方法的拓展

环境遥感对于地理科学研究方法的支持作用,体现为使地理科学研究从定性到定量,对地球表层系统物质地信息和能量信息的双重同步考察等。而地综合分析客一的加强,规范化的“信息合”技术应用以及以地理环境遥感信息分析处理为核心的地理制图和地理信息系统技术应用则是环境遥感的发展对地理科学研究理念的一系列拓展。     

Mark on the globe: a quest for scientific bases of geographic information and its international influence

David M. Mark published his first journal article in 1970. Since then, he has written or coauthored more than 220 publications over a period of 40 years as of 28 May 2012. Based on data from Web of Science (WoS) and Google Scholar, Mark’s publications have been cited over 7410 times by researchers in more than 80 countries or regions as of 28 May 2012, when this paper was first prepared. The geographic extent of Mark’s scholarly influence is truly global. An examination of his 20 most cited articles reveals that his work in diverse areas as digital elevation models, geomorphology, geographic cognition, and ontology of the geospatial domain enjoyed a lasting impact worldwide.     

Analysis of positional uncertainty of road networks in volunteered geographic information with a statistically defined buffer-zone method

Volunteered geographic information (VGI) is crowdsourced information that can enrich and enhance research and applications based on geo-referenced data. However, the quality of VGI is of great concern, and positional accuracy is a fundamental basis for the VGI quality assurance. A buffer-zone method can be used for its assessment, but the buffer radius in this technique is subjectively specified; as result, different selections of the buffer radius lead to different positional accuracies. To solve this problem, a statistically defined buffer zone for the positional accuracy assessment in VGI is proposed in this study. To facilitate practical applications, we have also developed an iterative method to obtain a theoretically defined buffer zone. In addition to the positional accuracy assessment, we have derived a measure of positional quality, which comprises the assessment of positional accuracy and the level of confidence in such assessment determined with respect to a statistically defined buffer zone. To illustrate and substantiate the theoretical arguments, both numerical simulations and real-life experiments are performed using OpenStreetMap. The experimental results confirm the high significance of the proposed statistical approach to the buffer zone-based assessment of the positional uncertainty in VGI.     

Coupling maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists

Modeling the geographic distribution of tourists at a tourist destination is crucial when it comes to enhancing the destination’s resilience to disasters and crises, as it enables the efficient allocation of limited resources to precise geographic locations. Seldom have existing studies explored the geographic distribution of tourists through understanding the mechanisms behind it. This article proposes to couple maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists in order to facilitate disaster and crisis management at tourist destinations. As one of the most popular tourist destinations in the United States, San Diego was chosen as the study area to demonstrate the proposed approach. We modeled the tourist geographic distribution in the study area by quantifying the relationship between the distribution and five environmental factors, including land use, land parcel, elevation, distance to the nearest major road and distance to the nearest transit stop. The geographic distribution’s dependency on and sensitivity to the environmental factors were uncovered. The model was subsequently applied to estimate the potential impacts of one simulated tsunami disaster and one simulated traffic breakdown due to crisis events such as a political protest or a fire hazard. As such, the effectiveness of the approach has been demonstrated with specific disaster and crisis scenarios.     

A case study of model selection and parameter inference by maximum likelihood with application to uncertainty analysis

One of the uses of geostatistical conditional simulation is as a tool in assessing the spatial uncertainty of inputs to the Monte Carlo method of system uncertainty analysis. Because the number of experimental data in practical applications is limited, the geostatistical parameters used in the simulation are themselves uncertain. The inference of these parameters by maximum likelihood allows for an easy assessment of this estimation uncertainty which, in turn, may be included in the conditional simulation procedure. A case study based on transmissivity data is presented to show the methodology whereby both model selection and parameter inference are solved by maximum likelihood.