Knowledge Discovery from Area–Class Resource Maps: Capturing Prototype Effects

This paper presents a knowledge discovery approach to extracting knowledge from area–class resource maps. Prototype theory forms the basis of the approach which consists of two major components: (1) a scheme for organizing knowledge used in categorizing geographic entities which allows for the modeling of indeterminate boundaries and non–uniform memberships within categories; and (2) a data mining method using the Expectation Maximization (EM) algorithm for extracting such knowledge from area–class maps. A case study on knowledge discovery from a soil map demonstrates the details of the approach. The study shows that knowledge for classifying geographic entities with indeterminate boundaries is embedded in area–class maps and can be extracted through data mining; and that continuous spatial variation of geographic entities can be better modeled if the knowledge discovery process retains knowledge of within-class variations as well as transitions between classes.     

数据仓库及其在城市规划决策支持系统中的应用探讨

在分析传统决策支持系统在城市规划决策应用中存在的问题的基础上 ,初步提出一种基于数据仓库的城市规划决策支持系统的基本框架 ,探讨了该系统建立中数据仓库的数据组织、数据挖掘、知识发现方法等关键技术问题 ,并进一步阐述城市规划决策支持系统的建立方法 ,最后以荆州市环境规划为例 ,说明数据仓库在城市规划决策支持系统中的具体应用。     

The Role of Knowledge Representation in Geographic Knowledge Discovery: A Case Study

With the advent of massive, heterogeneous geographic datasets, data mining and knowledge discovery in databases (KDD) have become important tools in deriving meaningful information from these data. In this paper, we discuss how knowledge representation can be employed to significantly enhance the power of the knowledge discovery process to uncover patterns and relationships. We suggest that geographic data models that support knowledge discovery must represent both observational data and derived knowledge. In addition, knowledge representation in the context of KDD must support the iterative and interactive nature of the knowledge discovery process to enable the analyst to iteratively apply, and revise the parameters of, specific analytical techniques. Our approach to knowledge representation and discovery is demonstrated through a case study that focuses on the identification and analysis of storms and other related climate phenomena embedded within a spatio‐temporal data set of meteorological observations.     

An Evidential Reasoning Approach to Land Degradation Evaluation: Dempster-Shafer Theory of Evidence

This study explores the combination of expert and local knowledge for an integrated evaluation of land degradation in southern Mauritania. The expert knowledge uses two imprecise information and uncertainty management tools, Fuzzy measure theory and Dempster‐Shafer theory, mediated by expert knowledge, to integrate data collected using local knowledge. Fuzzy measures were found to be an efficient way of handling uncertainty of data compiled from local environmental knowledge and uniformly scaling all land degradation indicator variables to the same numerical range of values. The two techniques allowed scaling and integration of 13 evidential themes characterizing southern Mauritania. The cartographic outputs of this study show the potential of Fuzzy measure theory and Demspter‐Shafer theory as powerful complementary environmental resource management tools. On a scale of 0.0 to 1.0, the maps indicate the degree of degradation. The seriousness of land degradation decreases from settlements, roads, and watering points outward. As such, they constitute an excellent guide for resource allocation and rehablitation plans.     

基于海量 POI 数据的城市小学教育资源信息的提取与分析

通过网络爬虫获取海量POI数据具有巨大的应用前景。文中利用改进后的两步移动搜索法,计算居民区的教育资源可达性,评价以市为单位的小学教育资源的状况,将各市的GDP与教育资源可达性进行相关性分析,评价各省经济发展与城市教育发展的相关程度,为政府部门提供决策依据。同时,研究POI数据的预处理方法和分类方法。     

领域专家知识及其在空间数据挖掘中的作用

根据空间数据挖掘的需要，提出把领域专家知识分为3类，即，属性划分规则(Attribute Partition Rule)、概念层次树(Concept Hierarchical Trees)和约束条件(Constraints)。文中详细介绍了领域专家知识和领域专家在空间数据挖掘各个阶段的作用。领域专家知识的表示方法也是一个非常值得重视的问题，文中给出了常用的表示模型。     

Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference

As mapping is costly and labor‐intensive work, government mapping agencies are less and less willing to absorb these costs. In order to reduce the updating cycle and cost, researchers have started to use user generated content (UGC) for updating road maps; however, the existing methods either rely heavily on manual labor or cannot extract enough information for road maps. In view of the above problems, this article proposes a UGC‐based automatic road map inference method. In this method, data mining techniques and natural language processing tools are applied to trajectory data and geotagged data in social media to extract not only spatial information – the location of the road network – but also attribute information – road class and road name – in an effort to create a complete road map. A case study using floating car data, collected by the National Commercial Vehicle Monitoring Platform of China, and geotagged text data from Flickr and Google Maps/Earth, validates the effectiveness of this method in inferring road maps.     

数据挖掘和知识发现的技术方法

数据挖掘是指从数据库中提取出隐含的、先前不知道的有用知识的过程。本文从数据挖掘和知识发现的概念出发 ,总结了数据挖掘常采用的技术方法 ,并从数据挖掘采用的技术方法的角度对数据挖掘进行分类 ,指出每种技术适合挖掘出哪些知识 ,比较每种技术的优缺点 ,同时还对几个数据挖掘的应用进行了阐述。     

基于空间数据挖掘技术的农用地产能核算数据库的应用研究

为提高农用地产能核算数据库的利用效率,以空间关联规则为基础,在河北省土地产能核算数据库的应用中得到提高河北省农业用地普查结果的利用效率的几条知识规则。利用现有软件,使用GIS软件进行空间数据预处理及空间关系计算。属性数据使用access管理,而关联规则挖掘算法直接应用Clementine。通过数据选取、数据预处理、数据变换、数据挖掘算法实施以及模式解释与评估,从空间数据库中提取用户感兴趣的空间模式与特征、空间与非空间数据的普遍关系及其他一些隐含在数据中的普遍的数据特征,从而得到提高土地产能核算成果利用效率的规律。     

Satellite data and geographic information systems for fire and resource management in the Canadian arctic

Abstract

A method of analyzing remotely sensed data, a geographic information system, and an intelligent fire management system have been developed to provide integrated resource data for fire and other resources management. Natural and cultural features were digitized from 1:50,000 topographic maps using a geographic information system (GIS) to cover the 29 communities below the tree line in the western Canadian Arctic. Landsat Thematic Mapper data covering the same area were classified into land cover or fuel types. Detailed information on each fire such as location, area burned, date of discovery, fire number, fire zone, fire class and source of ignition was obtained and added to each map sheet as attribute data. A generalized vegetation cover map using NOAA AVHRR data was also obtained. The Intelligent Fire Management Information System (IFMIS) integrates relational data bases, geographic information display, and expert systems. It also has a spatial analysis procedure for forest fire preparedness planning. Linking the weather to the forest fuels through the Fire Weather Index system (FWI) and the Fire Behaviour Prediction System (FBPS), fire danger and fire behaviour are calculated and displayed, cell‐by‐cell. Values‐at‐risk and fire suppression resources are used in the dispatching and planning component of the system. The planning component allows the user to evaluate the coverage of fire suppression resources under the prevalent forecast fire behaviour conditions. Through the integration of data from the above systems, a set of maps were created which were used to analyze fire behaviour potential, identify fire hazards, and provide a basis for settlement protection strategies within the context of other land use activities such as wildlife harvesting and recreational activities.     

Change detection of surface mining activity and reclamation based on a machine learning approach of multi-temporal Landsat TM imagery

Being able to quantify land cover changes due to mining and reclamation at a watershed scale is of critical importance in managing and assessing their potential impacts to the Earth system. In this study, a remote sensing-based methodology is proposed for quantifying the impact of surface mining activity and reclamation from a watershed to local scale. The method is based on a Support Vector Machines (SVMs) classifier combined with multi-temporal change detection of Landsat TM imagery. The performance of the technique was evaluated at selected open mining sites located in the island of Milos in Greece. Assessment of the mining impact in the studied areas was based on the confusion matrix statistics, supported by co-orbital QuickBird-2 very high spatial resolution imagery. Overall classification accuracy of the thematic land cover maps produced was reported over 90%. Our analysis showed expansion of mining activity throughout the whole 23-year study period, while the transition of mining areas to soil and vegetation was evident in varying rates. Our results evidenced the ability of the method under investigation in deriving highly and accurate land cover change maps, able to identify the mining areas as well as those in which excavation was replaced by natural vegetation. All in all, the proposed technique showed considerable promise towards the support of a sustainable environmental development and prudent resource management.     

Gully boundary extraction based on multidirectional hill‐shading from high‐resolution DEMs

The gully boundary, which distinguishes gully from non‐gully areas, is a significant geomorphologic feature for research on gully development and gully erosion. This study presents a new method based on multidirectional hill‐shading maps, which identify the gully (i.e., shadow area) from inter‐gully (i.e., non‐shadow area). These shadows obtained from various illumination azimuths are merged; consequently, the border of the shadows, which is the gully boundary line, can be achieved. In this process, two key parameters, namely, altitude and azimuth of light, affect the accuracy of gully boundary extraction. The experiments in Yaojiawan area of China show that the method of average median slope of all sampling profiles across the gully boundary is effective and practical for light altitude selection. Moreover, the six azimuths are sufficient for gully boundary extraction in a loess hilly area. The application in the Madigou area indicates the replicability and rationality of this method. A comparison with the positive‐negative terrain and slope variation method confirms a higher accuracy of gully boundary extraction by the proposed method in terms of visual interpretation, length, and contour‐matching difference with reference to manually digitalized results. Accuracy assessment indicates that the proposed method is applicable for gully boundary extraction based on high‐resolution DEMs.     

Georelief Transfiguration in Areas Affected by Open‐cast Mining

Open‐cast mining activity causes the largest georelief transfigurations all over the world. Large localities in north‐west Bohemia (the Czech Republic) are affected. In this area, we focus on a very significant case: the royal town Most, which has been turned into a lake. The main aim of this article is the reconstruction of the original georelief in different time periods together with analysis showing the process of landscape devastation. The workflow and analysis is based on precise elevation data obtained from aerial photographs and old maps. The georelief development is reconstructed using the digitized contour lines contained in Derived state‐map 1:5,000 (SMO5) from the years 1953, 1972, and 1980; maps of the 3^rd Military Survey (year 1936); and digital surface models (DSM) extracted, using the pixel correlation method, from aerial images (year 1953 and 2008). The most important results of the analysis are digital terrain models showing the evolution of the landscape which may be used for many purposes in landscape development analysis, historical applications, visualization or landscape reclamation.     

基于数据挖掘的基坑监测关联性分析及联合预报研究

基坑监测数据量较大,且蕴含大量待发掘的有效信息。而数据挖掘则是从海量,数据信息中探寻内在规律及有效信息的技术。对此,提出将数据挖掘技术应用于基坑监测数据分析中,并利用其进行监测因素关联性研究;在顾及因素关联性基础上,对时间序列法进行改进,提出多因素时间序列法进行监测数据预测,并通过工程实例对此方法进行验证。     

空间数据发掘和知识发现的框架

探讨和提出了空间数据发掘和知识发现的理论技术框架，包括空间数据发掘和知识发现的定义与描述、理论框架、从空间数据库中可发现的知识类型及其应用、数据发掘与知识发现方法、空间知识发现系统的结构及开发方法等，最后探讨了空间数据发掘和知识发现的发展方向。     

A Spatial Analysis System for Integrating Data,Methods and Models on Environmental Risks and Health Outcomes

Integrating data on health outcomes with methods of disease mapping and spatially explicit models of environmental contaminants are important aspects of environmental health surveillance. In this article, we describe a modular, web‐based spatial analysis system that uses GIS, spatial analysis methods and software services delivered over computer networks to achieve this end. The Environmental Health Surveillance System (EHSS) is a prototype system that is designed to serve three purposes: a secure environment for producing maps of disease outcomes from individual‐level data while preserving privacy; an automated process of linking environmental data, environmental models, and GIS tasks like geocoding for the purposes of estimating individual exposures to environmental contaminants; and mechanisms to visualize the spatial patterns of disease outcomes via Web‐based mapping interfaces and interactive tools like Google Earth.     

DBSCAN空间聚类算法及其在城市规划中的应用

空间聚类是空间数据挖掘和知识发现的主要方法之一。DBSCAN算法可以从带有“噪声”的空间数据库中发现任意形状的聚类,是一种较好的聚类算法。本文介绍了DBSCAN算法的基本概念和原理,并应用GIS二次开发组件MapObjects予以了实现。然后,本文将该算法应用于城市规划中,对某城市中小学和商业网点等公共设施的分布进行了聚类分析,并根据聚类结果对城市规划设计规范中的某些条款进行了讨论。     

遥感图像在土地利用更新中的应用研究——以里下河地区防汛信息系统(LXFPGIS)为例

以里下河地区防汛信息系统为例 ,阐述了利用 TM遥感图像进行 GIS土地利用更新的试验研究。采用了最大似然分类法和模糊分类法两种监督分类方法进行分类试验。提出了两种提高训练区采样效率的基于 GIS本底数据的采样方法 ,即半人工采样方法和全自动分类区直方图提取法。对上述方法分别做了土地利用分类试验 ,对分类结果进行了分析比较 ,并作出了相应的结论。     

Remote sensing and geoinformation center of Mongolia: A brief outlook

Abstract

A GIS based approach is proposed for the integration of three thematic maps viz. geomorphology, drainage density and slope using fuzzy logic for the assessment of ground water resource potential of a soft rock terrain of Midnapur District, West Bengal, India. The geomorphology and drainage density maps of the area are prepared from IRS‐1B LISS‐II data, and the slope map is obtained from the contours depicted on the topographic map of Survey of India. Each feature of all the thematic maps is assigned with individual fuzzy set values within a range between 0 to 1 according to their relative importance in the prediction of ground water occurrence. The maps are then integrated through fuzzy operation to model the ground water potential zone of the study area. The evolved model while verified with surface geophysical results is found to be in good agreement.     

A novel method for discovering spatio-temporal clusters of different sizes,shapes, and densities in the presence of noise

The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery. In this paper, a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors (STSNN) is proposed to detect spatio-temporal clusters of different sizes, shapes, and densities in spatio-temporal databases with a large amount of noise. The concepts of windowed distance and shared nearest neighbor are utilized to define a novel spatio-temporal density for a spatio-temporal entity with definite mathematical meanings. Then, the density-based clustering strategy is employed to uncover spatio-temporal clusters. The spatio-temporal clustering algorithm developed in this paper is easily implemented and less sensitive to density variation among spatio-temporal entities. Experiments are undertaken on several simulated data-sets to demonstrate the effectiveness and advantage of the STSNN algorithm. Also, the real-world applications on two seismic databases show that the STSNN algorithm has the ability to uncover foreshocks and aftershocks effectively.