Rank-based strategies for cleaning inconsistent spatial databases

A spatial data set is consistent if it satisfies a set of integrity constraints. Although consistency is a desirable property of databases, enforcing the satisfaction of integrity constraints might not be always feasible. In such cases, the presence of inconsistent data may have a negative effect on the results of data analysis and processing and, in consequence, there is an important need for data-cleaning tools to detect and remove, if possible, inconsistencies in large data sets. This work proposes strategies to support data cleaning of spatial databases with respect to a set of integrity constraints that impose topological relations between spatial objects. The basic idea is to rank the geometries in a spatial data set that should be modified to improve the quality of the data (in terms of consistency). An experimental evaluation validates the proposal and shows that the order in which geometries are modified affects both the overall quality of the database and the final number of geometries to be processed to restore consistency.     

Implementing a new data model for simulating processes

The paper describes the development of a new methodological approach for simulating geographic processes through the development of a data model that represents a process. This methodology complements existing approaches to dynamic modelling, which focus on the states of the system at each time step, by storing and representing the processes that are implicit in the model. The data model, called nen, focuses existing modelling approaches on representing and storing process information, which provides advantages for querying and analysing processes. The flux simulation framework was created utilizing the nen data model to represent processes. This simulator includes basic classes for developing a domain specific simulation and a set of query tools for inquiring after the results of a simulation. The methodology is prototyped with a watershed runoff simulation.     

SIMPLE VALIDATORY TOOLS FOR JUDGING THE PREDICTIVE PERFORMANCE OF PARAFAC AND THREE-WAY PLS

The methods PARAFAC and three-way PLS are compared with respect to their ability to predictreversed-phase retention values.Special attention is paid to simple validatory tools,the meaning and useof which are explained.The simple validatory tools consist of percentages of explained variation in the training set and thosethat can be calculated with the use of markers.These markers are special(reference)solutes,the retentionvalues of which are used to gain information about a new object for which predictions are wanted.Different validatory tools can be calculated with the use of these marker retention values:percentagesof used variation and mean sum of squared residuals after applying the model to these marker retentionvalues.The validatory tools are evaluated on their power to estimate their test set counterparts:thepercentages of explained variation in the test set and mean sum of squared prediction errors in the test set.Two different data sets from reversed-phase chromatography are used to evaluate the validatory tools.The first data set has a high signal-to-noise ratio and is measured under the same measurementconditions.The second data set has a low signal-to-noise ratio and is measured under differentmeasurement conditions.Some of the simple validatory tools seem to have relevance to their test setcounterparts,even in the case of the second data set.     

荒漠生态系统定位观测方法与指标体系探讨

对许多涉及生态过程重大科学问题的认知,使长期生态学的研究在国际上受到越来越多的重视。长期定位观测也是系统地了解荒漠生态系统过程,获取基础数据的主要手段和有效途径,但是国内已有的定位观测站点的观测指标各成体系,不利于数据比较和信息共享。本文按照继承性、系统性、代表性和可行性的原则制定了比较完整可行的适合我国荒漠生态系统定位观测,包括水、土、气、生的综合指标体系,以期为荒漠生态系统定位观测工作的规范化打下一个坚实的基础。     

The Technology of Religion: Mapping Religious Cyberscapes

This article combines geographical studies of both the Internet and religion in an analysis of where and how a variety of religious practices are represented in geotagged Web content. This method provides needed insight into the geography of virtual expressions of religion and highlights the mutually constitutive, and at times contradictory, relationship between the virtual and material dimensions of religious expression. By using the spatialities of religious practice and contestation as an example, this article argues that mappings of virtual representations of material practices are important tools for understanding how online activities simultaneously represent and reproduce the material world.     

Clustering of temporal event processes

A temporal point process is a sequence of points, each representing the occurrence time of an event. Each temporal point process is related to the behavior of an entity. As a result, clustering of temporal point processes can help differentiate between entities, thereby revealing patterns of behaviors. This study proposes a hierarchical cluster method for clustering temporal point processes based on the discrete Fréchet (DF) distance. The DF cluster method is divided into four steps: (1) constructing a DF similarity matrix between temporal point processes; (2) constructing a complete linkage hierarchical tree based on the DF similarity matrix; (3) clustering the point processes with a threshold determined by locating the local maxima on the curve of the pseudo-F statistic (an index which measures the separability between clusters and the compactness in clusters); and (4) identifying inner patterns for each cluster formed by a series of dense intervals, each of which contains at least one event of all processes of the cluster. The contributions of the article are: (1) the proposed DF cluster method can cluster temporal point processes into different groups and (2) more importantly, it can identify the inner pattern of each cluster. Two synthetic data sets were created to illustrate the DF distance between temporal point process clusters (the first data set) and validate the proposed DF cluster method (the second data set), respectively. An experiment and a comparison with a method based on dynamic time warping show that DF cluster successfully identifies the preconfigured patterns in the second synthetic data set. The cluster method was then applied to a population migration history data set for the Northern Plains of the United States, revealing some interesting population migration patterns.     

A GeoAgent‐based framework for knowledge‐oriented representation: Embracing social rules in GIS

While current Geographic Information Systems (GISs) can represent observational spatial data well, they have limited capabilities in representing some non‐observational social elements and goal‐driven behaviours that can be important factors in a wide range of geographic issues. Such social components may include laws, regulations, polices, plans, culture, and customs, as well as their relations and interactions with the geographic environment at different scales. Getting beyond traditional data‐centred approaches, this research presents a knowledge‐oriented strategy in order to address these issues within a GIS context. We incorporate two major conceptual elements. First, extending from conventional agent notions and their geographic applications, geographic agents (GeoAgents) are considered as a basic representation component to specifically address social rules and goal‐driven behaviours that impact the Earth and environmental systems. Second, in order to incorporate GeoAgents with current space–time representation, a new conceptual representation framework, called ‘fields, objects, time, GeoAgents, and relations’ (FOTAR), is introduced to address the cross‐scale processes of both social and natural interactions. A Java‐based prototype, GeoAgent‐based Knowledge System (GeoAgentKS), is described to implement this framework by integrating agent technologies with multiple data and knowledge representation techniques, such as expert systems, concept maps, mathematical models, and geospatial databases. The application of this prototype in a case study is also presented, investigating scale‐dependent human–environment interactions under different emergency situations for community water systems in Central Pennsylvania, USA. In this case study, a systematic set of methodologies of knowledge acquisition, representation, and confirmation for constructing GeoAgents' knowledge bases by using expert systems were explored to formalize high‐level knowledge and social behaviours in the FOTAR‐based representations. The results show that the proposed conceptual representation framework is achievable at both implementation and application levels, and the prototype tool is demonstrated to be valuable in facilitating knowledge sharing, policymaking, municipal management, and decision‐making, especially for real‐world emergency management.     

Automatic classification of dome instabilities based on Doppler radar measurements at Merapi volcano, Indonesia: Part I

Monitoring lava dome instabilities is crucial to efficiently monitor active dome building volcanoes. The Doppler radar technique provides a unique opportunity to gather information about the number of instability events occurring at the growing dome and about the dynamic processes that take place during different types of instabilities. So far, three different kinds of processes have been identified: sliding material, gravitational break-offs and explosive outbursts. In addition, Doppler radars provide rain measurements, which can be used to investigate possible correlations between rainfall and dome activity. Two radar systems have been installed at Merapi volcano in October 2001 and January 2005 to continuously monitor dome instabilities. Due to the large number of instability events that occur during times of high activity, manual processing and analysis of instability events is not practical for monitoring purposes. Therefore, an automatic classification system has been developed, which is capable of identifying different kinds of instabilities as well as rainfall. Two different kinds of classifier models have been applied: (1) neural network and (2) K-nearest-neighbour classifier model. Both classify Doppler spectra according to the underlying dynamic process, that is, rain, sliding material, gravitational break-off or explosive outburst. The classifiers are able to identify disturbances, which have no physical source, but are merely artefacts from the radar device itself. Because radar events are sequences of Doppler spectra, a rule set has been defined, which finally determines the event class. All classifiers have been trained and tested on independent data sets to estimate the classification performance. The overall classification rate is about 90 per cent. Discrimination of instabilities and non-volcanic events reaches about 98 per cent accuracy.     

On Methods of Teaching Geography

This study investigates the location and varying spatiality of questions in geography textbooks. The results show that study questions posed in page margins address the three components of spatial thinking—concepts of space, using tools of representation, and processes of reasoning—more than questions in other locations within the text. Three practices are particularly recommended to help students develop spatial thinking skills through answering textbook questions: (1) utilize page-margin questions; (2) select questions around spatial representations; and (3) focus on higher-level cognitive processes.     

An inconsistency measure of spatial data sets with respect to topological constraints

An inconsistency measure can be used to compare the quality of different data sets and to quantify the cost of data cleaning. In traditional relational databases, inconsistency is defined in terms of constraints that use comparison operators between attributes. Inconsistency measures for traditional databases cannot be applied to spatial data sets because spatial objects are complex and the constraints are typically defined using spatial relations. This paper proposes an inconsistency measure to evaluate how dirty a spatial data set is with respect to a set of integrity constraints that define the topological relations that should hold between objects in the data set. The paper starts by reviewing different approaches to quantify the degree of inconsistency and showing that they are not suitable for the problem. Then, the inconsistency measure of a data set is defined in terms of the degree in which each spatial object in the data set violates topological constraints, and the possible representations of spatial objects are points, curves, and surfaces. Finally, an experimental evaluation demonstrates the applicability of the proposed inconsistency measure and compares it with previously existing approaches.     

An inductive modelling procedure based on Bayes' theorem for analysis of pattern in spatial data

Abstract

This paper describes an inductive modelling procedure integrated with a geographical information system for analysis of pattern within spatial data. The aim of the modelling procedure is to predict the distribution within one data set by combining a number of other data sets. Data set combination is carried out using Bayes’ theorem. Inputs to the theorem, in the form of conditional probabilities, are derived from an inductive learning process in which attributes of the data set to be modelled are compared with attributes of a variety of predictor data sets. This process is carried out on random subsets of the data to generate error bounds on inputs for analysis of error propagation associated with the use of Bayes’ theorem to combine data sets in the GIS. The statistical significance of model inputs is calculated as part of the inductive learning process. Use of the modelling procedure is illustrated through the analysis of the winter habitat relationships of red deer in Grampian Region, north-east Scotland. The distribution of red deer in Deer Management Group areas in Gordon and in Kincardine and Deeside Districts is used to develop a model which predicts the distribution throughout Grampian Region; this is tested against red deer distribution in Moray District. Habitat data sets used for constructing the model are accumulated frost and altitude, obtained from maps, and land cover, derived from satellite imagery. Errors resulting from the use of Bayes’ theorem to combine data sets within the GIS and introduced in generalizing output from 50 m pixel to 1 km grid squares resolution are analysed and presented in a series of maps. This analysis of error trains is an integral part of the implemented analytical procedure and provides support to the interpretation of the results of modelling. Potential applications of the modelling procedure are discussed.     

A metaheuristic approach for efficient and effective sketch-to-metric map alignment

Sketching as a natural mode for human communication and creative processes presents opportunities for improving human–computer interaction in geospatial information systems. However, to use a sketch map as user input, it must be localized within the underlying spatial data set of the information system, the base metric map. This can be achieved by a matching process called qualitative map alignment in which qualitative spatial representations of the two input maps are used to establish correspondences between each sketched object and one or more objects in the metric map. The challenge is that, to the best of our knowledge, no method for matching qualitative spatial representations suggested so far is applicable in realistic scenarios due to excessively long runtimes, incorrect algorithm design or the inability to use more than one spatial aspect at a time. We address these challenges with a metaheuristic algorithm which uses novel data structures to match qualitative spatial representations of a pair of maps. We present the design, data structures and performance evaluation of the algorithm using real-world sketch and metric maps as well as on synthetic data. Our algorithm is novel in two main aspects. Firstly, it employs a novel system of matrices known as local compatibility matrices, which facilitate the computation of estimates for the future size of a partial alignment and allow several types of constraints to be used at the same time. Secondly, the heuristic it computes has a higher accuracy than the state-of-the-art heuristic for this task, yet requires less computation. Our algorithm is also a general method for matching labelled graphs, a special case of which is the one involving complete graphs whose edges are labelled with spatial relations. The results of our evaluation demonstrate practical runtime performance and high solution quality.     

Towards an integration of process measurements, archive analysis and modelling in geomorphology — the Kärkevagge experimental site, Abisko area, northern Sweden

The analysis of Holocene geomorphic process activity demands long–term data sets, which are available for the Kärkevagge catchment due to 50 years of intensive geomorphologic field studies. This data set is used in combination with additional field measurements, remote sensing and digital elevation model (DEM) analysis to provide input data for modelling Holocene valley development. On the basis of this information, geomorphic process units (GPUs) are defined by means of GIS modelling. These units represent areas of homogeneous process composition that transfer sediments. Since the data base enables the quantification of single processes, the interaction of processes within the units can also be quantified. Applying this concept permits calculation of recent sediment transfer rates and hence leads to a better understanding of actual geomorphic landscape development activity. To extrapolate these data in time and space the process–related sediments in the valley are analysed for depth and total volume, primarily using geophysical methods. In this fashion the validity of measured process rates is evaluated for the Holocene time scale. Results from this analysis are exemplified in a cross–profile showing some of the principal sediment units in the valley. For example, the measured modern rates on a slush torrent debris fan seem to represent the Holocene mean rate. This approach should also be suitable for revealing Holocene geomorphic landscape development in terms of climate change.     

Towards an integration of process measurements, archive analysis and modelling in geomorphology — the Kärkevagge experimental site, Abisko area, northern Sweden

The analysis of Holocene geomorphic process activity demands long–term data sets, which are available for the Kärkevagge catchment due to 50 years of intensive geomorphologic field studies. This data set is used in combination with additional field measurements, remote sensing and digital elevation model (DEM) analysis to provide input data for modelling Holocene valley development. On the basis of this information, geomorphic process units (GPUs) are defined by means of GIS modelling. These units represent areas of homogeneous process composition that transfer sediments. Since the data base enables the quantification of single processes, the interaction of processes within the units can also be quantified. Applying this concept permits calculation of recent sediment transfer rates and hence leads to a better understanding of actual geomorphic landscape development activity. To extrapolate these data in time and space the process–related sediments in the valley are analysed for depth and total volume, primarily using geophysical methods. In this fashion the validity of measured process rates is evaluated for the Holocene time scale. Results from this analysis are exemplified in a cross–profile showing some of the principal sediment units in the valley. For example, the measured modern rates on a slush torrent debris fan seem to represent the Holocene mean rate. This approach should also be suitable for revealing Holocene geomorphic landscape development in terms of climate change.     

基于局部聚类的网络Voronoi图生成方法研究

提出一种将网络约束下的Voronoi和空间聚类相结合的方法,通过构造局部的聚类分析方法对网络边进行加权,根据实际的点过程性质可以把权重定义为加权或者乘权,进行标准化后与道路段本身长度融合进行计算,依此生成网络Voronoi图,以期理解城市街道的空间特性。以武汉市江汉区为例,对城市网格管理系统产生的城市事件进行算法验证,结果表明,该方法提供了一种灵活的网络约束下的服务区域划分工具,可用于基于网络空间点过程影响下的服务区划分,也可用于系统性地定量刻画城市管理的动态特性。     

Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation

The wider uptake of GIS tools by many application areas outside GIScience means that many newer users of GIS will have high-level knowledge of the wider task, and low-level knowledge of specific system commands as given in reference manuals. However, these newer users may not have the intermediate knowledge that experts in GI science have gained from working with GI systems over several years. Such intermediate knowledge includes an understanding of the assumptions implied by the use of certain functions, and an appreciation of how to combine functions appropriately to create a workflow that suits both the data and overall goals of the geographical analysis task.

Focusing on the common but non-trivial task of interpolating spatial data, this paper considers how to help users gain the necessary knowledge to complete their task and minimise the possibility of methodological error. We observe that both infometric (or cognitive) knowledge and statistical knowledge are usually required to find a solution that jointly and efficiently meets the requirements of a particular user and data set. Using the class of interpolation methods as an example, we outline an approach that combines knowledge from multiple sources and argue the case for designing a prototype ‘intelligent’ module that can sit between a user and a given GIS.

The knowledge needed to assist with the task of interpolation is constructed as a network of rules, structured as a binary decision tree, that assist the user in selecting an appropriate method according to task-related knowledge (or ‘purpose’) and the characteristics of the data sets. The decision tree triggers exploratory diagnostics that are run on the data sets when a rule requires to be evaluated. Following evaluation of the rules, the user is advised which interpolation method might be and should not be considered for the data set. Any parameters required to interpolate the particular data set (e.g. a distance decay parameter for Inverse Distance Weighting) are also supplied through subsequent optimisation and model selection routines. The rationale of the decision process may be examined, so the ‘intelligent interpolator’ also acts as a learning tool.     

地理环境因素对伊洛河流域森林景观的影响

地理环境因素是引起区域森林景观动态变化的主要原因。运用景观生态学的基本原理,借助于地理信息系统技术,分析了自1983年以来伊洛河流域洛宁县森林景观的动态变化。结果显示,森林景观类型总面积呈增加趋势,从1983年的960.34km²增加到1999年的1175.00km²。景观空间动态变化上,森林景观类型转化为非森林景观类型部分主要位于海拔500~1000m的低山区,海拔大于1000m的地区森林景观类型变化较小,而非森林景观类型转化为森林景观类型主要集中在海拔高度小于500m的塬陵区和川涧区。通过建立森林景观类型动态变化与相应高程、坡度、距离居民点中心的远近和与森林边缘距离之间的相互关系,分析了与上述地理环境因素之间的相关性,表明森林景观类型动态变化与上述地理环境因素之间有显著相关关系。     

过程与格局的关系及其在区域景观生态规划中的应用

景观生态学基本原理被广泛应用于景观设计和区域生态规划中,生态过程与生态格局之间的关系是区域生态规划面对的重要问题。在回顾景观生态学生态过程与生态格局关系认识过程的基础上,提出二者之间"过程为体,格局为用"的哲学关系。"体"即根本和内在的东西,"用"为"体"的外在表现。面对具体的规划区域时,分析问题的切入点和解决问题的着眼点都是"格局",但是规划的根本出发点和目的都是"过程"。围绕二者关系主线,建立了一个基于生态过程的区域景观生态格局规划方法框架,主要包括3个阶段的研究工作:分析区域历史景观格局演变过程,确定面向生态过程需求的生态格局以及制定提升生态系统功能的对策措施。     

人地系统远程耦合的研究进展与展望

在越发紧密关联的全球化世界中,如何实现可持续发展日益需要跨系统思维和超区域政策。这首先要求致力于可持续发展研究的学科要提升自身对跨区域资源环境问题的洞察力,因此就有必要推动地理学传统研究框架的适应性创新和变革。远程耦合作为聚焦于“远距离人类与自然耦合系统之间社会经济与环境相互作用”的理论框架,有极大的潜力和优势来促进这一理论创新进程。为了缩小国内外在远程耦合领域的研究差距,论文基于文献研究和笔者对远程耦合框架的理解,从远程耦合的理论建构、经验证据、重点研究领域和研究方法4个方面介绍了该理论框架及其应用进展,并提出3个方面的展望来激发新的研究。综述表明,由于远距离人类活动不断增长以及大尺度自然过程与人类活动的相互作用,在远程连接、全球化和城市化维度上均存在大量远程耦合的经验证据;目前学术界对远程耦合框架的应用主要集中于生态系统服务、远程耦合的社会经济和环境影响及土地变化科学3个方面;同时得益于理论操作化的进展,目前开展远程耦合研究已具备较好的方法支持。基于此,论文认为,为应对新兴的现实科学命题,基于远程耦合框架的地理学研究应当在人地关系网络系统的理论创新、远程耦合动力机制和远程耦合的调控工具集3个方面实现突破。