Appreciative GIS and Strength‐Based Community Change

Problem‐solving is embedded deeply in the digital DNA of GIS. Most projects involving GIS 1.0 and 2.0 raise community weaknesses, failures and other problems and use them to motivate change. The projects often create a future that does not differ greatly from the past or last long. Although looking into community problems is important and sometimes necessary, the steps of problem‐solving do not always leave people feeling happy. To inspire communities to create change based on their strengths and achievements, and to provide better participant experience, this article develops the concept of a new form of GIS called Appreciative GIS (AGIS). AGIS are grounded in the affirmative premise that every community, however challenged, is gifted with certain assets, potentials and strengths which, when effectively exploited, can take that community to a better and sustainable future. Based on Earth's internal structure, the article develops a useful layered community model where crust, mantle and core strata represent the community's body, life‐draining and life‐giving elements in that order. The model helps explain the different views of the community from current GIS and AGIS perspectives. A Total GIS (TGIS) cube is also developed and used to indicate that current GIS commonly focus on elements of the mantle or what we do not have, what we cannot do and what makes us weak. AGIS, on the other hand, dig up the core and cultivate what we have, what we can possibly do and what makes us strong. In a significant departure from the linear approach of problem‐solving, the article suggests implementing AGIS through a 7G cyclical method. The article contends that although AGIS may not be the whole enchilada, they can serve as a beacon of hope and a powerful source of inspiration especially for individuals and groups who are perceived or made to perceive themselves as dysfunctional, weak or poor.     

互动式教学用于地理信息系统课程的方法研究

介绍了互动式用于GIS教学,即"都市规划与空间信息技术实习"的授课之中,采取降低教师授课比重、由教师设计各种GIS实际应用的专题,让学生从问题提出、资料搜集、集体讨论、自我发现与纠正错误概念等一系列环节,循序渐进地获取GIS相关知识与技能;而教师在课程教学中所扮演的角色,从知识的提供者转变为解决问题的引导者,让学生通过独立思考与讨论来寻找问题的答案。为检验这种教学改革的效果,笔者进行了问卷调查,共取得44份有效问卷。问卷分析结果表明,大多数学生对本科教学效果持肯定意见,多数学生对于本课程所花费的学习时间高于其他课程。此外,绝大多数学生掌握了GIS的基本操作,增加了实际应用的信心,而且对于GIS系列后续课程产生兴趣。     

GIS and Intelligent Agents for Multiobjective Natural Resource Allocation: A Reinforcement Learning Approach

An important component of natural resource management is determining how to allocate resources within a landscape to different stakeholders in a manner that satisfies multiple objectives. Developing decision making tools for assisting natural resource allocation is a challenging endeavor as stakeholders' objectives typically exist at varying spatial scales, their actions are defined by the spatial constraints in which they operate, and the spatial distribution of resources can be altered due to system disturbances. The nature of such challenges suggests the need for a geographic approach that can investigate these spatial complexities in order to generate a suitable set of solutions. The objective of this study is to develop and evaluate an Intelligent Agent Model for multiobjective natural resource allocation. The model integrates agent-based modeling in a GIS environment with reinforcement learning – a heuristic method for generating, evaluating, and improving multiobjective decision making solutions. The model is implemented by simulating a forest management scenario in which agents that represent forest companies learn how to harvest trees in a manner that maximizes economic return while minimizing the adverse ecological impact to the surrounding landscape. In addition, the model simulates forest disturbances of varying frequencies and intensities to determine how disturbance events affect the decision-making ability of agents. The model is validated to demonstrate that it can provide practical solutions to natural resource decision making.     

GIS空间数据库系统误差控制的黑箱方法

ＧＩＳ建库数据中系统误差的控制是当前ＧＩＳ数据质量研究领域的重要方向之一。本文提出一种直接估计系统误差综合效应的黑箱方法,而传统的仿射变换将成为该法的特例。文中首先阐述黑箱方法的基本原理,然后根据多元统计学工具建立黑箱校正模型,最后给出模型辨识和参数辨识的具体方法。     

UML‐Based Approach to Developing a CityGML Application Domain Extension

Recently a national 3D standard was established in the Netherlands as a CityGML Application Domain Extension (called IMGeo). In line with the Dutch practice of modeling geo‐information, the ADE is developed using a model driven approach. The classes are designed in UML and automatically mapped to GML schema. The current OGC CityGML specification does not provide rules or guidance on correctly modeling an ADE in UML. This article fills this gap by studying how CityGML can be extended for specific applications starting from the UML diagrams. Six alternatives for modeling ADEs in UML are introduced and compared. The optimal alternative is selected and applied to obtain the national 3D standard. The approach was extensively discussed with international experts, who were members of both SIG3D and other working groups. As a consequence the approach was adopted by the SIG3D, the Special Interest Group 3D which, among other things, work on the 3D standard CityGML in cooperation with OGC. Therefore the approach contains many issues that can be generalized and reused by future domain extensions of CityGML. To further support this, the article formulates a model‐driven framework to model CityGML ADEs. Open issues are described in the conclusions.     

Visualising Crime Clusters in a Space‐time Cube: An Exploratory Data‐analysis Approach Using Space‐time Kernel Density Estimation and Scan Statistics

For an effective interpretation of spatio‐temporal patterns of crime clusters/hotspots, we explore the possibility of three‐dimensional mapping of crime events in a space‐time cube with the aid of space‐time variants of kernel density estimation and scan statistics. Using the crime occurrence dataset of snatch‐and‐run offences in Kyoto City from 2003 to 2004, we confirm that the proposed methodology enables simultaneous visualisation of the geographical extent and duration of crime clusters, by which stable and transient space‐time crime clusters can be intuitively differentiated. Also, the combined use of the two statistical techniques revealed temporal inter‐cluster associations showing that transient clusters alternatively appeared in a pair of hotspot regions, suggesting a new type of “displacement” phenomenon of crime. Highlighting the complementary aspects of the two space‐time statistical approaches, we conclude that combining these approaches in a space‐time cube display is particularly valuable for a spatio‐temporal exploratory data analysis of clusters to extract new knowledge of crime epidemiology from a data set of space‐time crime events.     

Exploring Mobility Indoors: an Application of Sensor‐based and GIS Systems

The popularization of tracking devices, such as GPS, accelerometers and smartphones, have made it possible to detect, record, and analyze new patterns of human movement and behavior. However, employing GPS alone for indoor localization is not always possible due to the system's inability to determine location inside buildings or in places of signal occlusion. In this context, the application of local wireless networks for determining position is a promising alternative solution, although they still suffer from a number of limitations due to energy and IT‐resources. Our research outlines the potential for employing indoor wireless network positioning and sensor‐based systems to improve the collection of tracking data indoors. By applying various methods of GIScience we developed a methodology that can be applicable for diverse human indoor mobility analysis. To show the advantage of the proposed method, we present the result of an experiment that included mobility analysis of 37 participants. We tracked their movements on a university campus over the course of 41 days and demonstrated that their movement behavior can be successfully studied with our proposed method.     

A GIS Toolbox for Measuring and Mapping Person‐Based Space‐Time Accessibility

This article presents a toolbox to compute and map person‐based accessibility indicators, based on classical time geography concepts. The intent is to provide GIS and urban planning practitioners with a user‐friendly and easily customizable tool. While it relies on well‐known concepts, the toolbox implements a major innovation in person‐based accessibility assessment by taking into account opening hours when measuring the accessibility of urban facilities. The toolbox can be downloaded from http://bit.ly/1h6yg5Z .     

Simulating Spatial Dynamics and Processes in a Retail Gasoline Market: An Agent‐Based Modeling Approach

Simulating the dynamics and processes within a spatially influenced retail market, such as the retail gasoline market, is a highly challenging research area. Current approaches are limited through their inability to model the impact of supplier or consumer behavior over both time and space. Agent‐based models (ABMs) provide an alternative approach that overcomes these problems. We demonstrate how knowledge of retail pricing is extended by using a ‘hybrid’ model approach: an agent model for retailers and a spatial interaction model for consumers. This allows the issue of spatial competition between individual retailers to be examined in a way only accessible to agent‐based models, allowing each model retailer autonomous control over optimizing their price. The hybrid model is shown to be successful at recreating spatial pricing dynamics at a national scale, simulating the effects of a rise in crude oil prices as well as accurately predicting which retailers were most susceptible to closure over a 10‐year period.     

Spatio‐Temporal Building Population Estimation for Highly Urbanized Areas Using GIS

Detailed population information is crucial for the micro‐scale modeling and analysis of human behavior in urban areas. Since it is not available on the basis of individual persons, it has become necessary to derive data from aggregated census data. A variety of approaches have been published in the past, yet they are not entirely suitable for use in the micro‐scale context of highly urbanized areas, due mainly to their broad spatial scale and missing temporal scale. Here we introduce an enhanced approach for the spatio‐temporal estimation of building populations in highly urbanized areas. It builds upon other estimation methodologies, but extends them by introducing multiple usage categories and the temporal dimension. This allows for a more realistic representation of human activities in highly urbanized areas and the fact that populations change over time as a result of these activities. The model makes use of a variety of micro‐scale data sets to operationalize the activities and their spatio‐temporal representations. The outcome of the model provides estimated population figures for all buildings at each time step and thereby reveals spatio‐temporal behavior patterns. It can be used in a variety of applications concerning the implications of human behavior in urban areas.     

A Back‐Propagation Neural Network‐Based Approach for Multi‐Represented Feature Matching in Update Propagation

Spatial data infrastructures, which are characterized by multi‐represented datasets, are prevalent throughout the world. The multi‐represented datasets contain different representations for identical real‐world entities. Therefore, update propagation is useful and required for maintaining multi‐represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real‐world entities and the detection of changes in updated datasets. Using polygon features of settlements as examples, this article addresses these key problems and proposes an approach for multi‐represented feature matching based on spatial similarity and a back‐propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in contrast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one‐to‐one matching but also to one‐to‐many and many‐to‐many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to demonstrate the weight differences in different data contexts and to measure the performance of the BPNN‐based feature matching approach.     

基于描述逻辑本体的GIS多重表达

多重表达是地理信息弹性表达和弹性存取的一个内在要求,它实质上提供了一个多尺度、多应用主题的数据集成机制。多重表达的建模不能仅限于数据库中多重几何特征的一致性表达,还必须支持不同语义粒度、不同应用主题下语义特征的弹性描述。基于形式化本体的地理信息建模更贴近于认知模型,还有助于语义表达以及基于语义的信息集成和共享。通过经典描述逻辑中具体域以及context的扩展,满足区域拓扑和上下文语义形式化描述的需要;给出一个基于描述逻辑的多表达地理本体方案,该方案能够为数据库中几何信息和语义信息的弹性表达提供一个统一的基于逻辑的模型理论基础。     

GIS辅助下的环青海湖地区草地蝗虫生境分类研究

草地蝗虫是危害中国广大草原地区的主要昆虫之一,针对草地蝗虫的生态特征,实现草地蝗虫生境类型的划分,是草地蝗灾管理迫切需要解决的课题之一.以环青海湖地区为例,使用TM影像数据和DEM,草地类型图等地理辅助数据,以及GPS定位的野外调查资料,从遥感图像处理/地理数据/专家知识一体化的角度出发,应用基于知识的方法进行环青海湖地区草地蝗虫生境类型的分类研究,其分类总精度为84.23%.研究表明,克氏针茅草原,芨芨草草原,紫花针茅草源是草地蝗虫多发的生境类型.占整个研究区总面积的29.09%,这为进一步加强环青海湖地区草地蝗虫的管理提供科学依据.     

面向行业应用的《摄影测量学》教学改革初探——以安徽科技学院GIS专业为例

《摄影测量学》在GIS类专业人才培养中占据核心地位,为适应其行业发展需求和前沿进展,培养"应用、素质、创新"型的摄影测量人才。本文以安徽科技学院GIS专业《摄影测量学》教学改革为例,探讨了教学内容体系的灵活定制,教学手段的丰富优化与考核方式的定量细化。最后总结了教学改革经验,简要分析了教学改革的实效,为相关院校的同类型教学改革提供参考。     

基于Web2.0的课程网络教学平台建设——以地理信息系统概论为例

在Web 2.0技术飙发电举的今天,高校教育体制和教学模式也深受信息化影响,并逐渐走向教育信息化。本文从河南大学地理信息系统概论课程教学改革出发,在现有教学模式基础上,对Web 2.0在高校课程教学平台构建中的可行性进行探讨。通过对传统教学资源的整理挖掘,讨论了地理信息系统概论网络教学平台建设的模式和内容,提出了一套新的地理信息系统概论网络教学平台建设方案,探索了新的教学模式和资源库应用机制。     

Building Agent‐Based Walking Models by Machine‐Learning on Diverse Databases of Space‐Time Trajectory Samples

We introduce a novel scheme for automatically deriving synthetic walking (locomotion) and movement (steering and avoidance) behavior in simulation from simple trajectory samples. We use a combination of observed and recorded real‐world movement trajectory samples in conjunction with synthetic, agent‐generated, movement as inputs to a machine‐learning scheme. This scheme produces movement behavior for non‐sampled scenarios in simulation, for applications that can differ widely from the original collection settings. It does this by benchmarking a simulated pedestrian's relative behavioral geography, local physical environment, and neighboring agent‐pedestrians; using spatial analysis, spatial data access, classification, and clustering. The scheme then weights, trains, and tunes likely synthetic movement behavior, per‐agent, per‐location, per‐time‐step, and per‐scenario. To prove its usefulness, we demonstrate the task of generating synthetic, non‐sampled, agent‐based pedestrian movement in simulated urban environments, where the scheme proves to be a useful substitute for traditional transition‐driven methods for determining agent behavior. The potential broader applications of the scheme are numerous and include the design and delivery of location‐based services, evaluation of architectures for mobile communications technologies, what‐if experimentation in agent‐based models with hypotheses that are informed or translated from data, and the construction of algorithms for extracting and annotating space‐time paths in massive data‐sets.     

Using the Newly‐created ILE DBMS to Better Represent Temporal and Historical GIS Data

This article introduces a type of DBMS called the Intentionally‐Linked Entities (ILE) DBMS for use as the basis for temporal and historical Geographical Information Systems. ILE represents each entity in a database only once, thereby mostly eliminating redundancy and fragmentation, two major problems in Relational and other database systems. These advantages of ILE are realized by using relationship objects and pointers to implement all of the relationships among data entities in a native fashion using dynamically‐allocated linked data structures. ILE can be considered to be a modern and extended implementation of the E/R data model. ILE also facilitates storage of things that are more faithful to the historical records, such as gazetteer entries of places with imprecisely known or unknown locations. This is difficult in Relational database systems but is a routine task using ILE because ILE is implemented using modern memory allocation techniques. We use the China Historical GIS (CHGIS) and other databases to illustrate the advantages of ILE. This is accomplished by modeling these databases in ILE and comparing them to the existing Relational implementations.     

Comparing Student Performance in Online and Blended Sections of a GIS Programming Class

Educators and non‐educators alike commonly assume that online courses cannot match the effectiveness of their traditional, face‐to‐face counterparts. This article describes a comparative study of the performance and study habits of two groups of students in a course in Geographic Information Systems (GIS) software programming and customization: one delivered online to adult professionals working asynchronously; the other delivered in a blended, online/face‐to‐face environment to upper‐level undergraduates in residence. Course grades show that the undergraduate cohort underperformed in comparison to the online cohort, in spite of the fact that their version of the course included face‐to‐face lecture and lab time in addition to the online course materials. To explore differences in the study habits of the two groups, all students were asked to keep a diary of the time they devoted to coursework. Information from these diaries suggest that a student's ability to meet learning objectives is less dependent on the course content delivery mechanism than it is on the student's motivation, maturity and time management skills.     

A Pattern‐Based Approach for Matching Nodes in Heterogeneous Urban Road Networks

This article presents an approach to hierarchical matching of nodes in heterogeneous road networks in the same urban area. Heterogeneous road networks not only exist at different levels of detail (LoD), but also have different coordinate systems, leading to difficulties in matching and integrating them. To overcome these difficulties, a pattern‐based method was implemented. Based on the authors' previous work on detecting patterns of divided highways, complex road junctions, and strokes to eliminate the LoD effect of road networks, the proposed method extracts the local networks around each node in a road network and uses them as the matching units for the nodes. Second, the degree of shape similarity between the matching units is measured using a Minimum Road Edit Distance based on a transformation. Finally, the proposed method hierarchically matches the nodes in a road network using the Minimum Road Edit Distance and eliminates false matching nodes using M‐estimators. An experiment involving matching heterogeneous road networks with different LoDs and coordinate systems was carried out to verify the validity of the proposed method. The method achieves good and effective matching regardless of differences in LoDs and road‐network coordinate systems.     

An Automated Approach for Land Cover Classification Based on a Fuzzy Supervised Learning Framework

This paper proposes an automatic framework for land cover classification. In majority of published work by various researchers so far, most of the methods need manually mark the label of land cover types. In the proposed framework, all the information, like land cover types and their features, is defined as prior knowledge achieved from land use maps, topographic data, texture data, vegetation’s growth cycle and field data. The land cover classification is treated as an automatically supervised learning procedure, which can be divided into automatic sample selection and fuzzy supervised classification. Once a series of features were extracted from multi-source datasets, spectral matching method is used to determine the degrees of membership of auto-selected pixels, which indicates the probability of the pixel to be distinguished as a specific land cover type. In order to make full use of this probability, a fuzzy support vector machine (SVM) classification method is used to handle samples with membership degrees. This method is applied to Landsat Thematic Mapper (TM) data of two areas located in Northern China. The automatic classification results are compared with visual interpretation. Experimental results show that the proposed method classifies the remote sensing data with a competitive and stable accuracy, and demonstrate that an objective land cover classification result is achievable by combining several advanced machine learning methods.