A Review of “Environmental Modelling: An Introduction”

The purpose of this study is to answer several important questions concerning the learning of categories of geographic information from maps. Categorization is a fundamental part of both learning and communication, and learning and communication are, in turn, central to any field of study. If categorization is a fundamental part of the learning process, then how people categorize information learned from maps must be of concern to cartographers and geographers. This study examines map reader categorization by having subjects in an experimental situation learn categories of maps. The primary variables in this study are the coherence of the categories and the duration of exposure to the categories. It was found that coherent categories cause subjects to rely on prior knowledge. The ability to use prior knowledge allows subjects to learn categories quickly, but it also causes them to make inaccurate assumptions about the category membership. This finding was not altered when subjects were given longer exposures to the category membership.     

Comparing Human and Neural Network Learning of Climate Categories

This study compares how humans and neural networks classify climate types. Human subjects were asked to classify climates from monthly temperature and precipitation patterns. To model their learning process, the same data were used to produce input vectors that trained a pattern associator neural network. Both human subjects and the neural network classified climates accurately after 10 rounds of supervised learning. The neural network successfully modeled the rate of human learning and the ability to learn specific climate categories. Moreover, the neural network weights used to classify climates correspond to distinct visual characteristics in temperature and precipitation. These results suggest that neural networks can model the formation of visual categories.     

Basic-Level Geographic Categories*

As we experience places, we learn about those places and generalize information into more abstract geographic categories. Rosch's basic-level theory argues that information known about objects is stored in our memories in a three-layered hierarchy. Data that could be used to test this theory in a geographic context was generated by having subjects make lists of activities, characteristics, and parts associated with 11 familiar geographic categories. An analysis of the distribution of information among the geographic categories confirmed two basic predictions of Rosch's theory. Significantly more information was stored in the basic-level geographic categories country, region, state, city, and neighborhood than in the superordinate category place. Significantly more information was not stored in subordinate categories home country, home region, home state, home city, and home neighborhood. The results suggest that geographic information is efficiently stored in memory so that much of what we know about geographic space is stored in basic-level categories that are both distinctive and informative.     

Executive Committee Meeting,Washington, D.C. January 28–29, 1972

This study compares how humans and neural networks classify climate types. Human subjects were asked to classify climates from monthly temperature and precipitation patterns. To model their learning process, the same data were used to produce input vectors that trained a pattern associator neural network. Both human subjects and the neural network classified climates accurately after 10 rounds of supervised learning. The neural network successfully modeled the rate of human learning and the ability to learn specific climate categories. Moreover, the neural network weights used to classify climates correspond to distinct visual characteristics in temperature and precipitation. These results suggest that neural networks can model the formation of visual categories.     

基于模糊集的土地利用变化自动发现方法探讨

传统的逐像素比较法忽略了地理对象的模糊性,缺乏区分噪声与真实变化信息的能力,难于处理空间数据的复杂性和不确定性。基于模糊集理论,根据邻域像元的空间位置关系确定变化的程度,并利用滑窗和景观指数表达比较不同时相遥感数据(TM、ETMI、KONOS)的分类专题图。试验结果表明,模糊集的描述更接近真实情况。     

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.     

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.     

Challenges related to interdisciplinary use of digital mapping technology in primary and lower secondary schools

The article reviews some learning objectives in the Norwegian core curriculum for four subjects in compulsory school education, namely science, social science, mathematics, and physical education. The subjects are examined with respect to maps, including map use, digital mapping tools, coordinate systems, and scales. Primary and lower secondary school pupils’ knowledge of maps was investigated before and after a group mapping project. Among three indicators, two showed statistically significant improvements in the pupils’ level of confidence when studying data with the aid of maps. The learning outcome is discussed in an inquiry-based and interdisciplinary pedagogical frame. In addition, the findings reveal that the four subjects have significantly different objectives and rates of progression regarding maps and map use. Based on the results of the group project, the author argues in favour of bringing the four curricula into more parallel learning curves. In order to facilitate interdisciplinary projects, he considers it would be interesting to coordinate the different subject curricula in the future, and recommends that attempts should be made to harmonise competence objectives that are part of several subjects in order to facilitate interdisciplinary projects in the next revision of the core curriculum in Norway.     

气候变化风险的新型分类

风险分类是对气候变化风险进行系统风险评估和管理的前提和基础。本文首先按部门和领域识别了主要的气候变化风险,然后采用国际风险管理理事会(International Risk Governance Council, IRGC)的新型风险分类体系(简单风险、复杂风险、不确定风险和模糊风险)进行分类:提出以"不确定性"作为分类依据;把IPCC第四次评估报告中描述不确定性的主要术语——"信度"和"可能性"作为分类特征参数,分别构建了四类风险的模糊隶属函数,根据最大隶属度原则从定量角度对气候变化风险进行分类;同时利用IPCC的两个定性指标"达成一致的程度和证据量"对定量分类方法进行补充,初步建立了气候变化风险的分类方法体系。获得的气候变化风险分类的初步结果,可以为风险管理机构选择不同类别的评估和管理方法进行风险研究提供科学依据。     

Tasks,Strategies, and Cognitive Processes Associated With Navigational Map Reading: A Review Perspective*

All geographers use and may create maps in their professional activities. While perceived by some as merely tools to present spatial information, maps are actually complicated communication mechanisms. Many geographers spend tremendous amounts of time learning about verbal and written communication (how to speak, listen, read critically, and write effectively). But to many, one of the basic tools of geographic communication, the map, is a general source of unknown. Many previous papers have focused on how to create effective maps; this paper focuses on how people read and use navigation maps. Everyone completes map‐reading tasks, uses (often different) strategies, and processes information through cognitive processing. Geographers may benefit from understanding how they, as well as their map‐viewing public, process the information contained on their maps. This paper addresses terms and issues addressed in navigational map reading, specifically including tasks and strategies as well as the governing cognitive processes.     

Knowledge discovery from soil maps using inductive learning

This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil maps, our study pays specific attention to the reduction of representation noise in soil maps. The data preprocessing step has exhibited an important role in obtaining greater accuracy. A specific method for sampling pixels based on modes of environmental histograms has proven to be effective in terms of reducing noise and constructing representative sample sets. Three inductive learning algorithms, the See5 decision tree algorithm, Naïve Bayes, and artificial neural network, are investigated for a comparison concerning learning accuracy and result comprehensibility. See5 proves to be an accurate method and produces the most comprehensible results, which are consistent with the rules (expert knowledge) used in producing the soil map. The incorporation of spatial information into the knowledge discovery process is found not only to improve the accuracy of the extracted knowledge, but also to add to the explicitness and extensiveness of the extracted soil-landscape model.     

Teaching Topographic Map Skills and Geomorphology Concepts with Google Earth in a One-Computer Classroom

Teaching high-school geomorphological concepts and topographic map reading entails many challenges. This research reports the applicability and effectiveness of Google Earth in teaching topographic map skills and geomorphological concepts, by a single teacher, in a one-computer classroom. Compared to learning via a conventional instructional method, students learning with Google Earth do not have different geomorphological concepts development because both settings enable students to learn with similar static representation. However, students learning with Google Earth improve topographic map skills significantly compared to the conventional instructional method. This is because of the 3D landscape visualization and prior knowledge connections available with Google Earth.     

学习型区域：面向全球化的区域发展

全球化、知识化已经影响到区域发展模式，一种新型区域-学习型区域日显强大的竞争力，本文基于地理学界、区域科学界关于学习经济、学习企业等的最新研究，总结了这种新的区域发展模式，探讨了营建学习型区域的途径，联系的内涵，学习型区域的基本特征等四个方面的内容，进而提出营建学习型区域，必须充分发挥区域的能动性以及利用区域的复杂网络效应。     

Further developments of a fuzzy set map comparison approach

Fuzzy set map comparison offers a novel approach to map comparison.The approach is specifically aimed at categorical raster maps and applies fuzzy set techniques, accounting for fuzziness of location and fuzziness of category, to create a similarity map as well as an overall similarity statistic: the Fuzzy Kappa. To date, the calculation of the Fuzzy Kappa (or K-fuzzy) has not been formally derived, and the documented procedure was only valid for cases without fuzziness of category. Furthermore, it required an infinitely large, edgeless map. This paper presents the full derivation of the Fuzzy Kappa; the method is now valid for comparisons considering fuzziness of both location and category and does not require further assumptions. This theoretical completion opens opportunities for use of the technique that surpass the original intentions. In particular, the categorical similarity matrix can be applied to highlight or disregard differences pertaining to selected categories or groups of categories and to distinguish between differences due to omission and commission.     

Competing space and place identity with landscape change analysis using Web GIS through Singapore historical maps (1828–2015)

The investigation of historical maps is fundamental to understanding the dynamics of landscape changes. In this study, we propose Web GIS tools as a way to compare historical maps efficiently for knowledge production. A key impetus of this article is to contribute to the ongoing efforts to broaden the appeal of ‘mixed‐methods research’ by bridging the epistemological and methodological gaps between GIS and qualitative methodologies in knowledge production. This article proposes a new visualization method for historical landscape change analysis by comparing multiple maps simultaneously with the mash‐up of Web GIS. Based on the analysis of exterior facts represented on the maps, four developmental footprints were investigated, namely, surviving place identity, disappeared place identity replaced by new identity, waning place identity overlapped with new identity, and waning identity by disconnected spatial relationships. To this end, the study argues that Web GIS applications have more potential in spatial knowledge production than traditional desktop GIS.     

A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping

This paper describes a hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping that generates fuzzy predictor patterns based on (a) knowledge-based fuzzy membership values and (b) data-based conditional probabilities. The fuzzy membership values are calculated using a knowledge-driven logistic membership function, which provides a framework for treating systemic uncertainty and also facilitates the use of multiclass predictor maps in the modeling procedure. The fuzzy predictor patterns are combined using Bayes’ rule in a log-linear form (under an assumption of conditional independence) to update the prior probability of target deposit-type occurrence in every unique combination of predictor patterns. The hybrid fuzzy WofE model is applied to a regional-scale mapping of base-metal deposit potential in the south-central part of the Aravalli metallogenic province (western India). The output map of fuzzy posterior probabilities of base-metal deposit occurrence is classified subsequently to delineate zones with high-favorability, moderate favorability, and low-favorability for occurrence of base-metal deposits. An analysis of the favorability map indicates (a) significant improvement of probability of base-metal deposit occurrence in the high-favorability and moderate-favorability zones and (b) significant deterioration of probability of base-metal deposit occurrence in the low-favorability zones. The results demonstrate usefulness of the hybrid fuzzy WofE model in representation and in integration of evidential features to map relative potential for mineral deposit occurrence.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

昆明市土地利用变化的强度分析与稳定性研究

在遥感、地理信息系统和统计定量分析结合的基础上,引进土地利用变化强度分析理论方法。作为一种解释性的数学框架,通过不同时间间隔的土地利用转移矩阵计算了每个时间间隔和每种地类的变化面积和变化强度。强度分析自上而下分为3个层次：间隔层次、地类层次和转变层次,将观察变化强度与平均变化强度比较,揭示不同层次的变化特征,并依次用快速的与缓慢的、活跃的与平稳的、来源与转变三对术语来描述。本文以昆明市的土地利用变化为例,深入阐述了不同层次的面积变化和强度变化,并对不同层次的稳定性进行了深入分析。结果表明：在间隔层次,2000年后昆明市的土地利用变化强度逐渐加大;在地类层次,建设用地的变化强度是最活跃的,林地的变化强度是最平稳的;在转变层次,增加的建设用地主要来源于耕地,减少的林地主要转变为草地。强度分析在土地利用变化过程分析中具有系统性的优势,对深入挖掘土地利用变化信息、理解土地利用变化过程具有重要作用。     

Map-based multicriteria analysis to support interactive land use allocation

This article focuses on the use of map-based multicriteria analysis to develop a negotiation support tool for land use allocation. Spatial multicriteria analysis is used to make explicit trade-offs between objectives and to provide guidance and feedback on the land use changes negotiated by the participants. Digital maps are the means of communication among workshop participants, and an interactive mapping device (the ‘Touch table’) is used as the interface. Participants are informed about the relevant trade-offs on the map and use this information to change the land use maps. The approach is tested during a negotiation session as part of the land use planning process of the Bodegraven polder, a peat meadow area in the Netherlands.