On the design of formal theories of geographic space

This paper discusses the design of formal theories of geographic space for the application in Geographic Information Systems. GIS software is an implementation of formal theories of geographic space. The notions of formal theories are introduced and discussed in the context of examples from the GIS field.  Our approach is an application of the general framework of formal theories to the special class of theories of geographic space, in particular to the geometry of geographic space. A framework is introduced for characterizing and evaluating formal theories of geographic space and the process of their design. This is used to provide (1) a classification of formal theories of geographic space, (2) criteria of their adequacy, and (3) an evaluation of design decisions in the process of formalization.  The paper demonstrates the choices in the design of GIS and the dependencies between these choices. Considering the design space for theories underlying a GIS, we can see that current GIS are based on one choice: analytical geometry. Other designs are possible and a systematic exploration of alternative types of GIS, for example, based on constraints or based on stored spatial relations, becomes necessary. Received: 30 April 1997/Accepted: 8 March 1999     

线与面的空间拓扑关系组合推理

在原先所提出的空间拓扑关系组合描述的基础上,进一步完善了利用基本空间拓扑关系进行组合推理的方法,并建立了推理表,详细绘制了线与面的所有空间拓扑关系图。     

贝叶斯网络结构学习及其应用研究

阐述了贝叶斯网络结构学习的内容与方法,提出一种基于条件独立性(CI)测试的启发式算法。从完全潜在图出发,融入专家知识和先验常识,有效地减少网络结构的搜索空间,通过变量之间的CI测试,将全连接无向图修剪成最优的潜在图,近似于有向无环图的无向版。通过汽车故障诊断实例,验证了该算法的可行性与有效性。     

Comparison of landslide susceptibility mapping based on statistical index,certainty factors,weights of evidence and evidential belief function models

The main aim of this study was to produce landslide susceptibility maps using statistical index (SI), certainty factors (CF), weights of evidence (WoE) and evidential belief function (EBF) models for the Long County, China. Firstly, a landslide inventory map, including a total of 171 landslides, was compiled on the basis of earlier reports, interpretation of aerial photographs and supported by extensive field surveys. Thereafter, all landslides were randomly separated into two data sets: 70% landslides (120 points) were selected for establishing the model and the remaining landslides (51 points) were used for validation purposes. Eleven landslide conditioning factors, such as slope aspect, slope angle, plan curvature, profile curvature, altitude, distance to faults, distance to roads, distance to rivers, lithology, NDVI and land use, were considered for landslide susceptibility mapping in this study. Then, the SI, CF, WoE and EBF models were used to produce the landslide susceptibility maps for the study area. Finally, the four models were validated using area under the curve (AUC) method. According to the validation results, the EBF model (AUC = 78.93%) has a higher prediction accuracy than the SI model (AUC = 77.72%), the WoE model (AUC = 77.62%) and the CF model (AUC = 77.72%). Similarly, the validation results also indicate that the EBF model has the highest training accuracy of 80.25%, followed by SI (79.80%), WoE (79.71%) and CF (79.67%) models.     

Application of frequency ratio,weights of evidence and evidential belief function models in landslide susceptibility mapping

The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

3维GIS中位置关系的定性描述与推理

3维空间中位置关系的定性描述和推理是当前城市和矿山GIS界的重要理论课题之一,主要涉及如何用定性距离和方向关系来综合描述目标对象及相对于其参照物的空间位置关系的方法。本文基于投影系统划分空间方向区域,利用解析几何工具表达距离关系和方向关系,建立组合距离和方向关系来描述位置关系的概念框架,导出位置关系组合推理的一般表达式,并通过例子说明一些典型的位置关系定性推理过程和结果。     

Indexing large geographic datasets with compact qualitative representation

This paper develops a new mechanism to efficiently compute and compactly store qualitative spatial relations between spatial objects, focusing on topological and directional relations for large datasets of region objects. The central idea is to use minimum bounding rectangles (MBRs) to approximately represent region objects with arbitrary shape and complexity and only store spatial relations that cannot be unambiguously inferred from the relations of corresponding MBRs. We demonstrate, both in theory and practice, that our approach requires considerably less construction time and storage space, and can answer queries more efficiently than the state-of-the-art methods.     

The world,the computer,and the mind: how Andrew Frank helped make human language and cognition cornerstones of geographic information science

During the late 1980s and early 1990s, cognitive science was included as one of the key disciplines in the emerging multidisciplinary field of geographic information science (GIScience). One of the key proponents and popularizers of the study of human cognition as part of GIScience – and one of its major researchers – has been Andrew U. Frank. In this essay, we review the history of Andrew Frank’s role as an innovator and champion for cognitive GIScience, and summarize some of his research contributions in this domain. Taken along with his contributions to other areas of GIScience, this review shows that Andrew Frank has been one of the primary figures in modern GIScience, and among its very broadest and intellectually-diverse contributors.     

A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China

Reference values of erythrocyte sedimentation rate(ESR)are the key to interpret ESR blood test in clinic.The common local reference ESR values are more accuracy in blood test that are established with natural geographical factors by using the multiple linear regression(MLR)model and the artificial neural network(ANN).These knowledge-based methods have limitations since the knowledge domains of ESR and natural geographical factors are limited.This paper presents a new cases-depended model to establish reference ESR values with natural geographical factors and location using case-based reasoning(CBR)since knowledge domain of ESR and geographical factors is weak.Overall 224 local normal ESR values of China that calculated from 13623 samples were obtained,and the corresponding natural geographical factors and location that include altitude,sunshine hours,relative humidity,temperature,precipitation,annual temperature range and annual average wind speed were obtained from the National Geomatics Center of China.CBR was used to predict the unseen local reference ESR values with cases.The average absolute deviation(AAD),mean square error(MSE),prediction accuracy(PA),and Pearson correlation coefficient(r)between the observed and estimated data of proposed model is 33.07%,9.02,66.93% and 0.78,which are better than those of ANN and MLR model.The results show that the proposed model provides higher prediction accuracy than those of the artificial neural network and multiple linear regression models.The predicted values are very close to the observed values.Model results show significant agreement of cases data.Consequently,the model is used to predict the unseen local reference ESR with natural geographical factors and location.In spatial,the highest ESR reference areas are distributed in the southern-western district of China that includes Sichuan,Chongqing,Guangxi and Guizhou provinces,and the reference ESR values are greater than 23 mm/60 min.The higher ESR reference values are distributed in the middle part and northern-eastern of China which include Hubei,Henan,Shaanxi,Shanxi,Jilin and Heilongjiang provinces,and the reference ESR values are greater than 18 mm/60min.The lowest ESR reference values are distributed in the northern-western of China that includes Tibet and Xinjiang,and the reference ESR values are lower than 5 mm/60min.