信息库建设中的数据字典标准化生产

在信息库的建设中,对原有数据库的整合改造是保证实现信息共享的关键问题。本文基于《信息库-数据字典规范》,将XML模式应用于数据字典的标准模板,设计了一个标准化、实用化的数据字典采集器,达到规范化、自动化采集数据字典的目的,不仅方便数据库的维护,而且可以利用数据字典对数据库结构作检查,保证各参建单位数据库整合改造结果的一致性和协调性。     

Remote sensing image classification with GIS data based on spatial data mining techniques

Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification. Two learning granularities are proposed for inductive learning from spatial data, one is spatial object granularity, the other is pixel granularity. We also present an approach to combine inductive learning with conventional image classification methods, which selects class probability of Bayes classification as learning attributes. A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data. Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning. Comparing with the results produced only by Bayes classification, the overall accuracy increased by 11% and the accuracy of some classes, such as garden and forest, increased by about 30%. The results indicate that inductive learning can resolve spectral confusion to a great extent. Combining Bayes method with inductive learning not only improves classification accuracy greatly, but also extends the classification by subdividing some classes with the discovered knowledge.     

Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers

Abstract

This study examines the potentials of remotely sensed data, GIS and some machine learning classifiers and ensemble techniques in the investigation of the non-linear relationship between malaria occurrences and socio-physical conditions in the Dak Nong province of Viet Nam. Accuracy assessment was determined with Receiver Operating Characteristic (ROC) curve and pair t-test. The results showed that the area under ROC of Random Subspace ensemble model performed better than the other models based on statistical indicators. Comparing pair t-test with Area Under Curve values showed a slight difference of about 1%. Therefore ensemble techniques had significantly improved the performance of the base classifier. However, the performances might vary according to geographic locations. It is concluded that the machine learning classifiers combined with remotely sensed data and GIS is promising for malaria vulnerability mapping, and the derived maps can be used as a fundamental basis for programmes on spatial disease control.     

Semantic Web and service computation in GIScience applications: a perspective and prospective

Semantic Web and Web services have been two prominent themes in the computer science and IT mainstream for more than a decade. While both of these themes have been evolving, some geographers and GIScientists have been trying to introduce and adopt such new technologies into GIScience research and development. This paper reviews the state-of-the-art and the main constraints of semantic Web and Web services technologies and their applications in GIScience. Unless some fundamental problems within both semantic Web and Web services can be resolved, such technologies will be difficult to match the needs in the GIScience community. Besides logics, more sciences and theories have to be introduced into this research front in order to break through the long-term bottleneck in semantic Web and service computation in general.     

Scale dependent effects in resource selection by crop-raiding Japanese macaques in Niigata Prefecture,Japan

A resource selection function is one that yields values proportional to the probability of use of a resource unit. This quantity is influenced by the heterogeneity of landscape structures, which occurs over multiple spatial scales. To provide input into wildlife management strategies, we investigated the scale dependency and functional responses of Japanese macaques using multiple scale analysis. The multiple buffers with radii of 100, 500, 1000, 1500, 2000, and 2500 m were defined as the spatial scale. Crop damage was predicted at the within-home range scale, using the Random Forests algorithm with environmental variables linked to resource selection of Japanese macaques. Sixteen environmental variables were defined, covering aspects of landscape configuration, human disturbance, topography, and adopted countermeasures. Crop damage was most accurately predicted within a buffer zone of 1000 m, although radii exceeding 1000 m were also highly accurate. Although the importance of variables differed among spatial extents, the functional responses for each environmental variable were independent of spatial extent. These results suggest that the limiting factors of crop damage depend on spatial extent, while functional responses in resource selection remain constant across spatial extents. We also compared a multi-scale gradient map with a typical binary map to demonstrate the uncertainty in damage predictions at different spatial scales. Our results may aid wildlife management planning, for which differences in resource selection across different spatial scales are critically important.     

Extracting urban landmarks from geographical datasets using a random forests classifier

ABSTRACT

Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods.     

Introduction: Learning as a Geographical Process

In this introduction to the Focus Section “Learning as a Geographical Process,” I provide a context for the four articles that follow, by means of (1) making explicit the threefold rationale for this initiative; (2) relating this initiative with previous geographical scholarship on the problematic of learning; (3) highlighting the significance of the self-referential character of our work; (4) providing a brief outline of the articles that follow; and (5) pointing out the important fact that both “learning” and “geographical process” constitute semantically rich categories and that relating the two involves a many-to-many type of logical mapping.     

Self-Organized Cognitive Maps

This paper argues cognitive mapping is a learning process that can be simulated by a self-organizing neural network. The learning of city locations was considered in two studies. One study focused on the learning of city locations on four continents. Results indicated the neural network aligned the cities producing systematic errors similar to those in human cognitive maps. A second study had a neural network learn a biased sample of city locations in the United States. Results indicated a non-linear relationship between cognitive and physical distances. Self-organized cognitive maps naturally produce this non-linear relationship when information from more than one scale is mapped into one space.     

论变化环境下流域管理的知识创新

提出全球气候变化与世界社会经济动荡等变化环境下流域可持续发展的科学内涵、制约因素以及解决途径。以自然与社会协同进化的复杂系统作为变化环境下流域概念的抽象表征,以科研能力与管理能力作为流域可持续发展的重要影响因素,以流域管理中科学研究与管理实践的相互作用机理为出发点,分析当前流域管理中科学研究与管理实践的种种错位,提出适应变化环境的流域管理知识创新机制,为提高流域科研水平、充分发挥科学在流域可持续发展中的作用提供理论支撑。     

Comparison of daily streamflow forecasts using extreme learning machines and the random forest method

ABSTRACT

Daily streamflow forecasting is a challenging and essential task for water resource management. The main goal of this study was to compare the accuracy of five data-driven models: extreme learning machine (basic ELM), extreme learning machine with kernels (ELM-kernel), random forest (RF), back-propagation neural network (BPNN) and support vector machine (SVR). The results show that the ELM-kernel model provided a superior alternative to the other models, and the basic ELM model had the poorest performance. To further evaluate the predictive capacities of the five models, the estimations of low flow and high flow in the testing dataset were compared. The RF model was slightly superior to the other models in predicting the peak flows, and the ELM-kernel model showed the highest prediction precision of low flows. There was no single model that showed obvious advantages over the other models in this study. Therefore, further exploration is required for the hydrological forecasting problems.