2160247 地理信息系统导论(中英文)(2011)
地理信息系统导论
地理信息系统导论
地理信息系统（GIS）是一种能够收集、储存、管理、分析、模拟和显示地理空间信息的计算机系统。
它使用地理数据（如坐标、地图等）将地理空间位置与其他数据关联起来，从而使具有地理空间信息的记录变得可视化、定量化、可检索及可分析性质，为管理者进行地理空间运筹和决策提供便捷的模拟和可视化工具。
GIS系统定义了一组数据及其关联的数据库管理系统，可为各类社会经济学及环境学应用提供决策支持。
GIS可以分析各类地理信息数据，如人口分布、自然资源利用、污染物分布等，并用地图来表示不同分析结果。
地理信息系统导论chap02
Figure 2.2 The geographic c o o r di n at e system.
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Figure 2.3 Al o n g i t u d e reading i s r e p r e s e n t e d by a on t h e l e f t , and a l a t i t u d e reading i s r e p r e s e n t e d by b on t h e r i g h t . Both longitude and l a t i t u d e readings are angular measures.
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Figure 2.1 The top map shows the i n t e r s t a t e highways in Idaho and Montana based on d i f f e r e n t coordinate systems. The bottom map shows t h e connected i n t e r s t a t e networks based on t h e same coordinate system.
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Figure 2.8 The c e n t r a l meridian i n t h i s s e ca n t case t r a n s v e r s e Mercator p r o j e c t i o n has a s c a l e f a c t o r of 0.9996. The two standard l i n e s on e i t h e r s i d e of th e central meridianБайду номын сангаасhave a scale factor of 1.0.
地理信息系统概论笔记
地理信息系统概论（修订版）第一章导论第一章导论§1 地理信息系统基本概念1．1数据与信息数据（data）是信息(information)的表达，而信息是数据的内容。
数据是未经加工的原始材料，地理信息系统的设计和建立，首先是收集数据和处理数据。
数据是通过数字化或记录下来可以可以被鉴别的符号，不仅数字是数据，而且文字、符号、图象也是数据，数据本身没有意义；信息是对数据的解释、运用与解算，数据即使是经过处理以后的数据，只有经过解释才有意义，才成为信息。
就本质而言数据是客观对象的表示，而信息则是数据内涵的意义，只有数据对实体行为产生影响时才成为信息。
信息是用数字、文字、符号、语言等介质来表示事件、事物、现象等的内容、数量或特征，以便向人们（或系统）提供关于现实世界新的事实的知识，作为生产、管理和决策的依据。
信息的特点：客观性、适用性、传输性、共享性。
1．2地理信息与地理信息系统地理信息是指表征地理圈或地理环境固有要素或物质的数量、质量、分布特征、联系和规律等的数字、文字、图象和图形的总称。
地理信息属于空间信息，其位置的识别是与数据联系在一起的，这是地理信息区别于其他类型信息的一个最显著的标志。
地理信息系统（Geographical Information System）是由计算机硬件、软件和不同的方法组成的系统，该系统设计支持空间数据的采集、管理、处理、分析、建模和显示，以便解决复杂的规划和管理问题。
（美国联邦数字地图协调委员会（FICCDC）关于GIS的定义）§2 GIS的基本组成GIS一般包括以下5个主要部分：系统硬件、系统软件、空间数据、应用人员和应用模型。
2．1系统硬件1、GIS主机：包括大型、中型、小型机，工作站/服务器和微型计算机，其中各种类型的工作站/服务器成为GIS的主流。
2、GIS外部设备：包括各种输入（如图形数字化仪、图形扫描仪、解析和数字摄影测量设备等）和输出设备（如各种绘图仪、图形显示终端和打印机）。
地理信息系统导论课程
1．地理信息系统的基本概念
数据包含原始事实，信息是把数据处理成有 意义的和有用的形式。
人的知识、经验作用到数据上，可以得到信息， 而获得信息量的多少，与人的知识水平有关*。 （图1-1）
1．地理信息系统的基本概念
图1-1：数据和信息
1．地理信息系统的基本概念
1．1．3地理信息和地理数据 地理数据 是指表征地理圈或地理环境固有
3 决策支持系统能从管理信息系统中获得信息， 帮助管理者制定好的决策。该系统是一组处理数 据和进行推测的分析程序，用以支持管理者制定 决策。它是基于计算机的交互式的信息系统，由 分析决策模型、管理信息系统中的信息、决策者 的推测三者相组合达到好的决策效果。
4 人工智能和专家系统是能模仿人工决策处理过 程的基于计算机的信息系统。专家系统扩大了计 算机的应用范围，使其从传统的资料处理领域发 展到智能推理上来。专家系统由五个部分组成： 知识库、推理机、解释系统、用户接口和知识获 得系统。
1．地理信息系统的基本概念
1．2信息系统及其类型
系统是具有特定功能的、相互有机联系的许多要素所 构成的一个整体。对计算机而言，系统是为实现某些 特定的功能，由必要的人、机器、方法或程序按一定 的相关关系联系起来进行工作的集合体，内部要素之 间的相互联系通过信息流实现。系统的特征由构成系 统的要素及其相互之间的联系方式所决定。
了或正在发生变化的地理现象。
3．地理信息系统的功能概述
• 4）模式（Patterns） • 该类问题是分析与已经发生或正在发生事件
有关的因素。地理信息系统将现有数据组合 在一起，能更好地说明正在发生什么，找出 发生事件与哪些数据有关。 • 5）模型（Models） • 该类问题的解决需要建立新的数据关系以产 生解决方案。
地理信息系统专业英语(全书翻译)
地理信息系统专业英语(全书翻译)
引言
本书是一本关于地理信息系统（Geographic Information System，简称GIS）专业英语的全书。
本书旨在帮助研究GIS的学生和从业
人员提高他们的英语听说读写技能，使他们能够流利地进行专业交
流和文献阅读。
全书内容包括以下几个部分：
第一部分：地理信息系统基础
本部分介绍了地理信息系统的基本概念和原理，包括地理数据、地图投影、地理空间分析等内容。
通过研究本部分的内容，读者可
以了解GIS的基础知识，并掌握相关的专业英语表达。
第二部分：地理信息系统应用领域
本部分介绍了地理信息系统在不同应用领域的具体应用，包括
土地利用规划、城市规划、环境保护等。
读者可以了解不同领域中
的GIS应用案例，并研究相关的专业英语表达。
第三部分：地理信息系统技术与工具
本部分介绍了地理信息系统的常用技术和工具，包括GIS软件、地理数据库、数据采集与处理等。
读者可以了解不同的GIS技术和
工具，并研究相关的专业英语表达。
第四部分：地理信息系统发展趋势与挑战
本部分介绍了地理信息系统的发展趋势和挑战，包括云计算、
大数据、人工智能等新技术对GIS的影响。
读者可以了解GIS领域的最新发展动态，并研究相关的专业英语表达。
结论
本书通过全面介绍地理信息系统的相关知识，帮助读者提高英
语水平和专业素养。
读者通过学习本书，可以更好地理解和应用地
理信息系统，并与国际同行进行有效的交流。
地理信息科学导论讲义-2011年春季
地理信息科学导论讲义（适用于本科地理信息系统专业）赵军西北师范大学地理与环境科学学院2007年6月2010年2月修订2011年2月第二次修订目录第一章绪论 (1)第一节本课程讲授的主要内容、目的和要求 (1)一、对《地理信息科学导论的理解》 (1)二、本课程的主要内容 (1)三、教学目的 (2)第二节本课程的特点和学习方法 (2)一、本课程的特点 (2)二、学习方法 (2)三、教学安排 (2)第二章地理信息科学形成的基础 (3)第一节地理信息科学形成的技术背景 (3)一、遥感技术 (3)二、全球定位技术 (3)三、地理信息系统 (3)四、计算机技术 (4)五、网络技术 (4)六、通讯技术 (4)第二节地理信息科学形成的科学基础 (4)一、地理学 (4)二、地图学 (5)三、测绘学 (5)四、信息科学 (5)五、系统科学 (6)第三节社会需求与应用背景 (6)第三章地理信息科学相关科学知识 (8)第一节系统论与系统科学 (8)一、系统的基本概念 (8)二、系统的结构与功能 (8)三、系统的数学描述 (9)四、系统的分类 (9)五、系统科学概述 (9)第二节信息论与信息科学 (13)一、有关信息的基本概念 (13)二、信息传递模型 (14)三、信息的度量 (15)四、信息论概述 (17)第三节地理认知与地理思维 (17)一、地理信息的流场理论 (17)二、地理信息的形成机理 (18)三、地理信息认知理论 (19)四、地理信息解译与反演理论 (22)第四章遥感科学与技术 (23)第一节遥感概述 (23)一、遥感的概念 (23)二、遥感的特点 (23)第二节遥感分类 (24)一、按遥感平台分类 (24)二、按探测的电磁波段分类 (24)三、按信息获取方式分类 (24)四、遥感的其他分类 (24)第三节遥感技术系统 (24)一、遥感平台 (24)二、传感器 (26)三、遥感数据传输和接收系统 (26)四、遥感数据处理系统 (26)五、图像解译与应用 (26)第四节遥感技术新进展 (27)一、高分辨率遥感技术 (27)二、高光谱遥感技术 (27)三、遥感非遥感数据融合技术 (28)第五节遥感应用 (29)一、大面积农作物的估产 (29)二、精准农业 (29)三、森林草地火情监测 (29)第五章全球定位系统 (30)第一节卫星定位系统基本知识 (30)一、基本概念 (30)二、定位与导航方法 (30)三、GPS导航的特点 (31)第二节GPS组成与工作原理 (31)一、GPS组成 (31)二、GPS定位原理 (32)三、GPS定位的主要误差来源 (33)四、RTK GPS技术 (33)第三节北斗卫星导航系统 (34)一、中国卫星定位系统发展概况 (34)二、北斗一号定位原理 (34)第四节全球定位系统应用型 (35)一、GPS提供的服务类型 (35)二、GPS在科学研究领域的应用 (35)三、GPS在工程技术领域的应用 (35)四、GPS在军事技术领域的应用 (36)第六章地理信息系统 (38)第一节地理信息系统概述 (38)一、基本概念 (38)二、地理信息系统的基本组成 (38)三、地理信息系统基本功能 (39)第二节地理空间数据及其计算机表达 (39)一、地理空间 (39)二、空间数据计算机表示的基本思想 (39)三、矢量数据结构 (39)四、栅格数据结构 (41)五、图形与图像一体化数据结构 (42)第三节地理规律与信息系统 (43)一、地理复杂现象与地理系统 (43)二、地理系统与地理系统工程 (43)三、地理系统工程与地理信息系统 (43)第七章地理信息技术体系 (44)第一节地理信息技术体系与天地人机一体化网络系统 (44)一、地理信息技术体系 (44)二、地理信息技术的集成 (44)三、天地人机一体化网络系统 (45)第二节遥测信息系统 (46)一、地面实测台站的数据 (46)二、地面数据通信与传输 (46)三、地面数据处理 (47)第三节专家信息系统 (47)一、专家信息系统的基本概念 (47)二、专家系统的结构 (47)三、地理知识形式化的方法 (47)第四节管理信息系统 (49)一、现代管理和管理信息系统 (49)二、地理系统工程的管理 (49)三、地理系统工程的管理信息系统 (50)四、管理科学的规划模型 (50)第五节决策信息系统 (50)一、现代决策科学的理念 (50)二、决策信息系统的结构 (51)三、对策科学的博弈模型 (52)第八章地球系统科学与地理信息科学 (53)第一节地球系统科学 (53)一、地球系统概述 (53)二、地球系统科学的产生 (53)三、地球系统科学研究内容与方法 (55)第二节地理信息科学 (59)一、地理信息科学的形成与发展 (59)二、地理信息科学的研究对象和内容 (61)三、地理信息科学的学科性质和定义 (62)四、地理信息科学的学科体系 (63)五、瓦伦纽斯计划简介 (64)第一章绪论第一节本课程讲授的主要内容、目的和要求一、对本课程名称（地理信息科学，introduction to geographical information science）的理解1.地理据《辞海》地理词条，有三层含义：①山川土地之形势；②地址或位置；③地球表面各种自然现象和人文现象以及它们之间的相互联系和区域分异（同地理学）。
地理信息系统概论
DOCS SMART CREATE
地理信息系统概论
DOCS
01
地理信息系统的基本概念
与定义
地理信息系统的发展历程与背景
20世纪60年代初期
• 地理信息系统（GIS）的概念诞生
• USGS（美国地质调查局）和加拿大地理信息系统（CGIS）的研
究和应用
20世纪70年代
• GIS技术迅速发展，广泛应用于城市规划、资源管理等领域
• 数据显示：将地理空间数据可视化
地理信息系统（GIS）是一种
• 软硬件结合的系统
• 用于采集、存储、管理、分析和显示地理空间数据的工具
地理空间数据
• 描述地理现象的空间位置和属性的数据
• 包括矢量数据、栅格数据、点云数据等
地理信息系统的主要应用领域
01
土地资源管理
• 土地资源调查、评价、规划、利用和保
• 用于输入地理空间数据பைடு நூலகம்
• 用于输出地理空间数据
• 用于存储地理空间数据
行GIS软件和数据处理
的设备
的设备
的设备
• 包括台式机、工作站、
• 如键盘、鼠标、数字化
• 如打印机、投影仪、显
• 如硬盘、光盘、U盘等
服务器等
仪、GPS接收器等
示器等
地理信息系统的主要软件技术
数据处理软件
⌛️
• 用于地理空间数据的处
• 制定环境保护措施和政策
• 制定污染源治理措施和政策
地理信息系统的交通与物流应用
交通规划
• 分析交通需求和交通网络
• 制定交通规划和建设方案
物流管理
• 分析物流需求和物流网络
地理信息系统导论资料
地理信息系统导论笔记● 空间数据是指用来表示空间实体的位置、形状、大小及其分布特征诸多方面信息的数据，它可以用来描述来自现实世界的目标，它具有定位、定性、时间和空间关系等特性。
空间数据是一种用点、线、面以及实体等基本空间数据结构来表示人们赖以生存的自然世界的数据。
● 拓扑：数学的一个分支，GIS 中用拓扑来确保要素的空间关系能明确地表达。
拓扑学(topology)是研究几何图形或空间在连续改变形状后还能保持不变的一些性质的学科。
它只考虑物体间的位置关系而不考虑它们的形状和大小。
[1] 拓扑英文名是T opology ，直译是地志学，最早指研究地形、地貌相类似的有关学科。
● 矢量数据主要是指城市大比例尺地形图。
此系统中图层主要分为底图层、道路层、单位层，合理的分层便于进行叠加分析、图形的无逢拼接以实现系统图形的大范围漫游。
矢量数据一般通过记录坐标的方式来尽可能将地理实体的空间位置表现的准确无误，显示的图形一般分为矢量图和位图。
● 矢量数据模型用点及其x 、y 坐标系来构建空间要素。
基于矢量的要素是作为空间不连续的几何对象来看待。
● 栅格数据是按网格单元的行与列排列、具有不同灰度或颜色的阵列数据。
每一个单元（像●数字化错误可以通过数据编辑消除，数据编辑是数据库建设的一部分。
●为了完成一个GIS项目的数据库建设，必须输入、核实和管理属性数据。
属性数据通常用关系数据库管理。
●数据探查是以数据为中心的查询和分析。
数据探查可以是一个GIS操作或是一个执行数据分析的指令。
数据查询可以帮助用户弄清数据中的大概趋势，更好理解数据集，关注数据集间的可能关系。
目的是更好的理解数据并帮助阐明研究的问题和设想。
●地理可视化的功能与数据探查相似，只是它是面向地图的。
●模型是现象或系统的简化表达。
●地理信息系统（GIS）可输入、存储、查询、分析和显示地理数据的计算机系统●地图投影从球面地理格网到平面坐标系的转换过程●GIS建模用GIS对空间数据建模的过程●面向对象的数据模型，用对象来组织空间数据的数据模型。
地理信息系统导论
2．栅格数据结构及其编码
• 2．2．2面积占优法 • 以占矩形区域面积最大的地物类型或现象特性
决定栅格单元的代码，在图7-5所示的例子中， 显见B类地物所占面积最大，故相应栅格代码 定为B。面积占优法常用于分类较细，地物类 别斑块较小的情况。
2．栅格数据结构及其编码
• 2．2．3重要性法 • 根据栅格内不同地物的重要性，选取最重要
1．空间数据库
图7-3：矢量结构和栅格结构
1．空间数据库
• 1．4．1矢量模型 • 在矢量模型中，现实世界的要素位置和范
围可以采用点、线或面表达，与它们在地 图上表示相似，每一个实体的位置是用它 们在坐标参考系统中的空间位置（坐标） 定义。点、线和多边形用于表达不规则的 地理实体在现实世界的状态。 • 矢量模型中的空间实体与要表达的现实世 界中的空间实体具有一定的对应关系。
1．空间数据库
• 1．4．2栅格模型 • 在栅格模型中，地理实体的位置和状态是用它
们占据的栅格的行、列来定义的。每个栅格的 大小代表了定义的空间分辨率。由于位置是由 栅格行列号定义的，所以特定的位置由距它最 近的栅格记录决定。栅格的值表达了这个位置 上物体的类型或状态。采用栅格方法，空间被 划分成大量规则格网，而且每个栅格取值可能 不一样。空间单元是栅格，每一个栅格对应于 一个特定的空间位置，如地表的一个区域，栅 格的值表达了这个位置的状态。
1．空间数据库
• 1．1．2 两者共同之处 • 两者都是以计算机为核心的信息处理系统，都具
有数据量大和数据之间关系复杂的特点，也都随 着数据库技术的发展在不断的改进和完善。
1．空间数据库
• 1．2 空间数据库 • 1．2．1 数据库的概念 • 数据库就是为一定目的服务，以特定的数据存储
地理信息系统导论
地理空间技术、纳米技术和生物技术被美国劳工部列为三大新兴产业。
地理信息系统（GIS）是用于采集、存储、查询、分析和显示地理空间数据的计算机系统。
GIS组成：硬件、软件、专业人员、基础设施、模型（方法）。
GIS的作用：空间数据输入、属性数据管理、数据显示、数据探查、数据分析、GIS建模。
CGIS 60-80年代，加拿大地理信息系统。
国外软件：ArcGis,Mapinfo,Autodesk map 国内软件：SuperMap（超图）MapGis,吉奥之星。
地理空间数据是具有地理参照的。
地球表面的空间要素是以地理坐标系统为参照，用经纬度值来表示的。
而这些要素在地图上显示时，他们通常是基于投影坐标系统，用x，y表示。
地理关系数据模型将空间要素的空间数据和属性数据分别储存。
两者通过要素ID连接起来。
近年来，基于对象的数据模型将几何形状和属性存储在唯一系统中。
栅格数据模型使用格网和格网像元来表示如高程、降水等连续要素。
投影是将数据及从地理坐标转成投影坐标。
重新投影是从一种投影坐标转成另一种投影坐标。
经纬网——球面坐标。
投影——平面坐标。
1.PARAMETER[“False-Nothing”0.0] PARMETER[“Scale-Factor”,0.9996]高斯投影PARAMETER[“Latitude-Of-Origin”,0.0] 回点在赤道上即时投影可以根据不同坐标系统显示其数据集。
软件包使用现有投影文件并自动将数据集转换成通用坐标系统。
即时投影不是真的改变数据集的坐标系统。
即时投影存储在数据框里，不能改变、代替原始数据信息。
1.矢量数据模型用点、线、面和体等几何对象来表示简单的空间要素。
2.第一代Auto CAD .DXF非拓扑，文件格式；第二代ArcInfo Coverage COV 拓扑，文件格式，地理关系数据模型；第三代ArcInfo Shapefile .SHP非拓扑，文件和数据库，地理关系数据模型，存储点线面数据；第四代。
1.地球信息科学导论
中国海洋大学本科生课程大纲课程属性：公共基础/通识教育/学科基础/专业知识/工作技能，课程性质：必修、选修一、课程介绍1.课程描述（中英文）：本课程为地球信息科学与技术专业的先导性课程，是构筑学生专业知识体系基本框架的基础课程，是本专业的必修课程。
本课程主要介绍地球信息科学的发展历史、学科内涵、基础理论、地学实体多要素信息的度量、感知和综合分析处理等内容，使学生较全面了解现代地球信息科学的发展、基本知识和时空观念，激发学生对地球信息科学的兴趣。
This course will introduce the fundamental knowledge structure of geo-informatics. It provides students with history and development of geo-informatics and basic theory related to geo-informatics. Topics will include how to scale and acquire the temporally and spatially varying information of the geo-entities, how to integrate and process the information and how to express the information with model construction. The students will understand the recent development in theory and technology of geo-informatics.2.设计思路：地球信息科学与技术专业是以地球科学和信息科学为基础，综合集成卫星遥感、全球定位和地理信息系统等技术，对地学实体信息进行感知采集、集成融合、处理分析、- 1 -表达建模的新兴交叉型学科，具有突出的综合性和交叉性。
《地理信息系统原理(双语)》课程教学大纲
《地理信息系统原理(双语)》课程教学大纲Course syllabus of “The Principles of Geographic InformationSystem”二、教学目的与任务Purpose and Task（一）目的Purpose本课程适用于本科地理信息科学专业、测绘专业、遥感科学与技术以及市规划专业等。
学时至少为48学时，其中包含40学时的理论讲授和8学时的演示验证性实验教学。
本课程设置有一定比例的实践教学环节。
通过本课程的学习，使得学生了解地理信息系统的发展历史、主要应用领域及GIS的基本理论和空间分析方法等。
This course is mainly for undergraduate students in GIS, Surveying and Mapping, Remote Sensing Science and Technology, as well as city plan. The main purpose of this course is to help students to understand the basic concepts, principles and spatial analysis of GIS. This course has total credit hours of 48 including 40 hours classroom teaching and 8 hours’ practical experiments in lab. This course is a basic professional course for GIS specialty. The outcomes for students from this course are the understanding of the basic concepts of GIS, major data structures, data sources and data processing methods, as well as principles and methods for spatial analysis. （二）任务Task本课程的教学任务对GIS技术的基本概念、空间参照、空间关系、空间数据结构、空间数据库、空间数据的获取、编辑和处理以及典型的空间分析功能如缓冲区分析、叠加分析等，通过本课程的学习，，使学生了解本专业的前沿发展现状和趋势，具有扎实的测绘学科基本理论和工程专业理论与技术知识，同时具有运用工程基础知识和本专业基本理论知识解决问题的能力，为学生日后学习其他课程奠定基础。
地理信息系统中英文对照外文翻译文献
中英文对照外文翻译(文档含英文原文和中文翻译)A Survey on Spatio-Temporal Data WarehousingAbstractGeographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis,to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community. However, not much has been done in providing moving object databases with OLAP functionality. In the first part of this paper we survey theSOLAP literature. We then move to Spatio-Temporal OLAP, in particular addressing the problem of trajectory analysis. We finally provide an in-depth comparative analysis between two proposals introduced in the context of the GeoPKDD EU project: the Hermes-MDC system,and Piet, a proposal for SOLAP and moving objects,developed at the University of Buenos Aires, Argentina.Keywords: GIS, OLAP, Data Warehousing, MovingObjects, Trajectories, AggregationINTRODUCTIONGeographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning (Rigaux, Scholl, & V oisard, 2001; Worboys, 1995). Spatial information in a GIS is typically stored in different so-called thematic layers (also called themes). Information in themes can be stored in data structures according to different data models, the most usual ones being the raster model and the vector model. In a thematic layer, spatial data is annotated with classical relational attribute information, of (in general) numeric or string type. While spatial data is stored in data structures suitable for these kinds of data, associated attributes are usually stored in conventional relational databases. Spatial data in the different thematic layers of a GIS system can be mapped univocally to each other using a common frame of reference, like a coordinate system.These layers can be overlapped or overlayed to obtain an integrated spatial view.On the other hand, OLAP (On Line Analytical Processing) (Kimball,1996; Kimball & Ross, 2002) comprises a set of tools and algorithms that allow efficiently querying multidimensional databases, containing large amounts of data, usually called Data Warehouses. In OLAP, data is organized as a set of dimensions and fact tables. In the multidimensional model, data can be perceived as a data cube, where each cell contains a measure or set of (probably aggregated) measures of interest. As we discuss later, OLAP dimensions are further organized in hierarchies that favor the data aggregation process (Cabibbo & Torlone, 1997). Several techniques and algorithms have been developed for query processing, most of them involving some kind of aggregate precomputation (Harinarayan, Rajaraman, & Ullman, 1996).The need for OLAP in GISDifferent data models have been proposed for representing objects in a GIS. ESRI () first introduced the Coverage data model to bind geometric objects to non-spatial attributes that describe them. Later, they extended this model with object-oriented support, in a way that behavior can be defined for geographic features (Zeiler,1999). The idea of the Coverage data model is also supported by the Reference Model proposed by the Open Geospatial Consortium (). Thus, in spite of the model of choice,there is always the underlying idea of binding geometric objects to objects or attributes stored in (mostly) object-relational databases (Stonebraker & Moore, 1996). In addition, query tools in commercial GIS allow users to overlap several thematic layers in order to locate objects of interest within an area, like schools or fire stations.For this, they use indexing structures based on R-trees (Gutman, 1984).GIS query support sometimes includes aggregation of geographic measures, for example, distances or areas (e.g., representing different geological zones). However, these aggregations are not the only ones that are required, as we discuss below.Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps, probably in different thematic layers. In this sense, OLAP and GIS vendors are increasingly integrating their products (see, for instance,Microstrategy and MapInfo integration in /, and /). In this sense, aggregate queries are central to DSSs. Classical aggregate OLAP queries (like “total sales of cars in California”), and aggregation combined with complex queries involving geometric components (“total sales in all villages crossed by the Mississippi river and within a radius of 100 km around New Orleans”) must be efficiently supported. Moreover, navigation of the results using typical OLAP operations like roll-up or drill-down is also required. These operations are not supported by commercial GIS in a straightforward way. One of the reasons is that the GIS data models discussed above were developed with “transactional” queries in mind. Thus, the databases storing nonspatial attributes or objects are designed to support those (nonaggregate) kinds of queries. Decision support systems need a different data model, where non-spatial data, probably consolidated from different sectors in an organization, is stored in a data warehouse. Here,numerical data are stored in fact tables built along several dimensions.For instance, if we are interested in the sales of certain products in stores in a given region, we may consider the sales amounts in a fact table over the three dimensions Store, Time and Product. In order to guarantee summarizability (Lenz & Shoshani, 1997), dimensions are organized into aggregation hierarchies. For example, stores can aggregate over cities which in turn can aggregate into regions and countries. Each of these aggregation levels can also hold descriptive attributes like city population, the area of a region, etc. To fulfill the requirements of integrated GIS-DSS, warehouse data must be linked to geographic data. For instance, a polygon representing a region must be associated to the region identifier in the warehouse. Besides, system integration in commercial GIS is not an easy task. In the current commercial applications, the GIS and OLAP worlds are integrated in an ad-hoc fashion, probably in a different way (and using different data models) each time an implementation is required, even when a data warehouse is available for non-spatial data.An Introductory Example. We present now a real-world example for illustrating some issues in the spatial warehousing problematic. We selected four layers with geographic and geological features obtained from the National Atlas Website (). Theselayers contain the following information: states, cities, and rivers in North America, and volcanoes in the northern hemisphere (published by the Global V olcanism Program - GVP). Figure 1 shows a detail of the layers containing cities and rivers in North America, displayed using the graphic interface of the Piet implementation we discuss later in the paper. Note the density of the points representing cities (particularly in the eastern region). Rivers are represented as polylines. Figure 2 shows a portion of two overlayed layerscontaining states (represented as polygons) and volcanoes in the northern hemisphere.There is also non-spatial information stored in a conventional data warehouse. In this data warehouse, dimension tables contain customer,stores and product information, and a fact table contains stores sales across time. Also, numerical and textual information on the geographic components exist (e.g., population, area), stored as usual as attributes of the GIS layers.In the scenario above, conventional GIS and organizational data can be integrated for decision support analysis. Sales information could be analyzed in the light of geographical features, conveniently displayed in maps. This analysis could benefit from the integration of both worlds in a single framework. Even though this integration could be possible with existing technologies, ad-hoc solutions are expensive because,besides requiring lots of complex coding, they are hardly portable. To make things more difficult, ad-hoc solutions require data exchange between GIS and OLAP applications to be performed. This implies that the output of a GIS query must be probably exported as members in dimensions of a data cube, and merged for further analysis. For example, suppose that a business analyst is interested in studying the sales of nautical goods in stores located in cities crossed by rivers. She could first query the GIS, to obtain the cities of interest. She probably has stored sales in a data cube containing a dimension Store or Geography with city as a dimension level. She would need to“manually” select the cities of interest (i.e., the ones returned by the GIS query) in the cube, to be able to go on with the analysis (in the best case, an ad-hoc customized middleware could help her). Of course, she must repeat this for each query involving a (geographic) dimension inthe data cube.Figure 1. Two overlayed layers containing cities and rivers in North America.On the contrary, GIS/Data warehousing integration can provide a more natural solution. The second part of this survey is devoted to spatio-temporal datawarehousing and OLAP. Moving objects databases (MOD) have been receiving increasing attention from the database community in recent years, mainly due to the wide variety of applications that technology allows nowadays. Trajectories of moving objects like cars or pedestrians, can be reconstructed by means of samples describing the locations of these objects at certain points in time. Although thereFigure 2. Two overlayed layers containing states in North America and volcanoes in thenorthern hemisphere.exist many proposals for modeling and querying moving objects, only a small part of them address the problem of aggregation of moving objects data in a GIS (Geographic Information Systems) scenario. Many interesting applications arise, involving moving objects aggregation, mainly regarding traffic analysis, truck fleet behavior analysis, commuter traffic in a city, passenger traffic in an airport, or shopping behavior in a mall. Building trajectory data warehouses that can integrate with a GIS is an open problem that is starting to attract database researchers. Finally, the MOD setting is appropriate for data mining tasks, and we also comment on this in the paper. In this paper, we first provide a brief background on GIS, data warehousing and OLAP, and a review of the state-of-the-art in spatial OLAP. After this, we move on to study spatio-temporal data warehousing, OLAP and mining. We then provide a detailed analysis of the Piet framework, aimed at integrating GIS, OLAP and moving object data, and conclude with a comparison between this proposal, and the Hermes data cartrridge and trajectory datawarehouse developed in the context of the GeoPKDD project (Information about the GoePKDD project can be found at http://www.geopkdd.eu).A SHORT BACKGROUNDGISIn general, information in a GIS application is divided over several thematic layers. The information in each layer consists of purely spatial data on the one hand, that is combined with classical alpha-numeric attribute data on the other hand (usually stored in a relational database). Two main data models are used for the representation of the spatial part of the information within one layer, the vector model and the raster model. The choice of model typically depends on the data source from which the information is imported into the GIS.The Vector Model. The vector model is used the most in current GIS (Kuper & Scholl, 2000). In the vector model, infinite sets of points in space are represented as finite geometric structures, or geometries, like, for example, points, polylines and polygons. More concretely, vector data within a layer consists in a finite number of tuples of the form (geometry, attributes) where a geometry can be a point, a polyline or a polygon. There are several possible data structures to actually store these geometries (Worboys, 1995).The Raster Model. In the raster model, the space is sampled into pixels or cells, each one having an associated attribute or set of attributes. Usually, these cells form a uniform grid in the plane. For each cell or pixel, the sample value of some function is computed and associated to the cell as an attribute value, e.g., a numeric value or a color. In general, information represented in the raster model is organized intozones, where the cells of a zone have the same value for some attribute(s). The raster model has very efficient indexing structures and it is very well-suited to model continuous change but its disadvantages include its size and the cost of computing the zones.Spatial information in the different thematic layers in a GIS is often joined or overlayed. Queries requiring map overlay are more difficult to compute in the vector model than in the raster model. On the other hand, the vector model offers a concise representation of the data, independent on the resolution. For a uniform treatment of different layers given in the vector or the raster model, in this paper we treat the raster model as a special case of the vector model. Indeed, conceptually, each cell is, and each pixel can be regarded as, a small polygon; also, the attribute value associated to the cell or pixel can be regarded as an attribute in the vector model.Data Warehousing and OLAPThe importance of data analysis has increased significantly in recent years as organizations in all sectors are required to improve their decision-making processes in order to maintain their competitive advantage. We said before that OLAP (On Line Analytical Processing) (Kimball, 1996; Kimball & Ross, 2002) comprises a set of tools and algorithms that allow efficiently querying databases that contain large amounts of data. These databases, usually designed for read-only access (in general, updating isperformed off-line), are denoted data warehouses. Data warehouses are exploited in different ways. OLAP is one of them. OLAP systems are based on a multidimensional model, which allows a better understanding of data for analysis purposes and provides better performance for complex analytical queries. The multidimensional model allows viewing data in an n-dimensional space, usually called a data cube (Kimball & Ross,2002). In this cube, each cell contains a measure or set of (probably aggregated) measures of interest. This factual data can be analyzed along dimensions of interest, usually organized in hierarchies (Cabibbo & Torlone, 1997). Three typical ways of OLAP tools implementation exist: MOLAP (standing for multidimensional OLAP), where data is stored in proprietary multidimensional structures, ROLAP (relational OLAP), where data is stored in (object) relational databases, and HOLAP (standing for hybrid OLAP, which provides both solutions. In a ROLAP environment, data is organized as a set of dimension tables and fact tables, and we assume this organization in the remainder of the paper.There are a number of OLAP operations that allow exploiting the dimensions and their hierarchies, thus providing an interactive data analysis environment. Warehouse databases are optimized for OLAP operations which, typically, imply data aggregation or de-aggregation along a dimension, called roll-up and drill-down, respectively. Other operations involve selecting parts of a cube (slice and dice) and reorienting the multidimensional view of data (pivoting). In addition to the basic operations described above, OLAP tools provide a great variety of mathematical, statistical, and financial operators for computing ratios, variances, ranks,etc.It is an accepted fact that data warehouse (conceptual) design is still an open issue in the field (Rizzi & Golfarelli, 2000). Most of the data models either provide a graphical representation based on the Entity- Relationship (E/R) model or UML notations, or they just provide some formal definitions without user-oriented graphical support. Recently, Malinowsky and Zimányi (2006) propose the MultiDim model. This model is based on the E/R model and provides an intuitive graphical notation. Also recently, Vaisman (Vaisman, 2006a, 2006b) introduced a methodology for requirement elicitation in Decision Support Systems, arguing that methodologies used for OLTP systems are not appropriate for OLAP systems.Temporal Data WarehousesThe relational data model as proposed by Codd (1970), is not wellsuited for handling spatial and/or temporal data. Data evolution over time must be treated in this model, in the same way as ordinary data. This is not enough for applications that require past, present, and/or future data values to be dealt with by the database. In real life such applications abound. Therefore, in the last decades, much research has been done in the field of temporal databases. Snodgrass (1995) describes the design of the TSQL2 Temporal Query Language, an upward compatible extension of SQL-92. The book, written as a result of a Dagstuhl seminar organized in June 1997 by Etzion, Jajodia, andSripada (1998), contains comprehensive bibliography, glossaries for both temporal database and time granularity concepts, and summaries of work around 1998. The same author (Snodgrass, 1999), in other work, discusses practical research issues on temporal database design and implementation.Regarding temporal data warehousing and OLAP, Mendelzon and Vaisman (2000, 2003) proposed a model, denoted TOLAP, and developed a prototype and a datalog-like query language, based on a (temporal) star schema. Vaisman, Izquierdo, and Ktenas (2006) also present a Web-based implementation of this model, along with a query language, called TOLAP-QL. Eder, Koncilia, and Morzy (2002) also propose a data model for temporal OLAP supporting structural changes. Although these efforts, little attention has been devoted to the problem of conceptual and logical modeling for temporal data warehouses. SPATIAL DATA WAREHOUSING AND OLAPSpatial database systems have been studied for a long time (Buchmann, Günther, Smith, & Wang, 1990; Paredaens, Van Den Bussche, & Gucht, 1994). Rigaux et al. (2001) survey various techniques, such as spatial data models, algorithms, and indexing methods, developed to address specific features of spatial data that are not adequately handled by mainstream DBMS technology.Although some authors have pointed out the benefits of combining GIS and OLAP, not much work has been done in this field. Vega López,Snodgrass, and Moon (2005) present a comprehensive survey on spatiotemporal aggregation that includes a section on spatial aggregation. Also, Bédard, Rivest, and Proulx (2007) present a review of the efforts for integrating OLAP and GIS. As we explain later, efficient data aggregation is crucial for a system with GIS-OLAP capabilities.Conceptual Modeling and SOLAPRivest, Bédard, and Marchand (2001) introduced the concept of SOLAP (standing for Spatial OLAP), a paradigm aimed at being able to explore spatial data by drilling on maps, in a way analogous to what is performed in OLAP with tables and charts. They describe the desirable features and operators a SOLAP system should have.Although they do not present a formal model for this, SOLAP concepts and operators have been implemented in a commercial tool called JMAP, developed by the Centre for Research in Geomatics and KHEOPS, see /en/jmap/solap.jsp. Stefanovic, Han, and Koperski (2000) and Bédard, Merret, and Han (2001), classify spatial dimension hierarchies according to their spatial references in: (a) non-geometric;(b) geometric to non-geometric; and (c) fully geometric. Dimensions of type (a) can be treated as any descriptive dimension (Rivest et al., 2001). In dimensions of types (b) and (c), a geometry is associated to members of the hierarchies. Malinowski and Zimányi (2004) extend this classification to consider that even in the absence of several related spatial levels, a dimension can be considered spatial. Here, a dimension level is spatial if it is represented as a spatial data type (e.g., point, region), allowing them to link spatial levels through topological relationships (e.g., contains, overlaps). Thus, a spatial dimension is a dimension that contains at least one spatial hierarchy. A critical point inspatial dimension modeling is the problem of multiple-dependencies, meaning that an element in one level can be related to more than one element in a level above it in the hierarchy. Jensen, Kligys, Pedersen, and Timko (2004)address this issue, and propose a multidimensional data model for mobile services, i.e., services that deliver content to users, depending on their location.This model supports different kinds of dimension hierarchies, most remarkably multiple hierarchies in the same dimension, i.e., multiple aggregation paths. Full and partial containment hierarchies are also supported. However, the model does not consider the geometry, limiting the set of queries that can be addressed. This means that spatial dimensions are standard dimensions referring to some geographical element (like cities or roads).Malinowski and Zimányi (2006) also propose a model supporting multiple aggregation paths. Pourabbas (2003) introduces a conceptual model that uses binding attributes to bridge the gap between spatial databases and a data cube. The approach relies on the assumption that all the cells in the cube contain a value, which is not the usual case in practice, as the author expresses. Also, the approach requires modifying the structure of the spatial data to support the model. No implementation is presented.Shekhar, Lu, Tan, Chawla, & Vatsavai (2001) introduced MapCube, a visualization tool for spatial data cubes. MapCube is an operator that, given a so-called base map, cartographic preferences and an aggregation hierarchy, produces an album of maps that can be navigated via roll-up and drill-down operations.Spatial Measures. Measures are characterized in two ways in the literature, namely: (a) measures representing a geometry, which can be aggregated along the dimensions; (b) a numerical value, using a topological or metric operator. Most proposals support option (a), either as a set of coordinates (Bédard et al., 2001; Rivest et al., 2001; Malinowski & Zimányi, 2004; Bimonte, Tchounikine, & Miquel, 2005), or a set of pointers to geometric objects (Stefanovic et al., 2000). Bimonte et al. (Bimonte et al., 2005) define measures as complex objects (a measure is thus an object containing several attributes). Malinowski and Zimányi (2004) follow a similar approach, but defining measures as attributes of an n-ary fact relationship between dimensions.Damiani and Spaccapietra (2006) propose MuSD, a model allowing defining spatial measures at different granularities. Here, a spatial measure can represent the location of a fact at multiple levels of (spatial) granularity. Also, an algebra of SOLAP operators is proposed.Spatial AggregationIn light of the discussion above, it should be clear that aggregation is a crucial issue in spatial OLAP. Moreover, there is not yet a consensus about a complete set of aggregate operators for spatial OLAP. We now discuss the classic approaches to spatial aggregation. Han et al. (1998) use OLAP techniques for materializing selected spatial objects, and proposed a so-called Spatial Data Cube, and the set of operations that can be performed on this data cube. The model only supports aggregation of spatial objects.Pedersen and Tryfona (2001) propose the pre-aggregation of spatial facts. First, they pre-process these facts, computing their disjoint parts in order to be able to aggregate them later. This pre-aggregation works if the spatial properties of the objects are distributive over some aggregate function. Again, the spatial measures are geometric objects.Given that this proposal ignores the geometries, queries like “total population of cities crossed by a river” are not supported. The paper does not address forms other than polygons, although the authors claim that other more complex forms are supported by the method, and the authors do not report experimental results.With a different approach, Rao, Zhang, Yu, Li, and Chen (2003), and Zhang, Li, Rao, Yu, Chen, and Liu (2003) combine OLAP and GIS for querying so-called spatial data warehouses, using R-trees for accessing data in fact tables. The data warehouse is then exploited in the usualOLAP way. Thus, they take advantage of OLAP hierarchies for locating information in the R-tree which indexes the fact table.Although the measures here are not only spatial objects, the proposal also ignores the geometric part of the model, limiting the scope of the queries that can be addressed. It is assumed that some fact table, containing the identifiers of spatial objects exists. Finally, these objects happen to be points, which is quite unrealistic in a GIS environment, where different types of objects appear in the different layers. Some interesting techniques have been recently introduced to address the data aggregation problem. These techniques are based on the combined use of (R-tree-based) indexes, materialization (or preaggregation) of aggregate measures, and computational geometry algorithms.Papadias, Tao, Kalnis, and Zhang (2002) introduce the Aggregation Rtree (aR-tree), combining indexing with pre-aggregation. The aR-tree is an R-tree that annotates each MBR (Minimal Bounding Rectangle) with the value of the aggregate function for all the objects that are enclosed by it. They extend this proposal in order to handle historic information (see the section on moving object data below), denoting this extension aRB-tree (Papadias, Tao, Zhang, Mamoulis, Shen, and & Sun, 2002). The approach basically consists in two kinds of indexes: a host index, which is an R-tree with the summarized information, and a B-tree containing time-varying aggregate data. In the most general case, each region has a B-tree associated, with the historical information of the measures of interest in the region. This is a very efficient solution for some kinds of queries, for example, window aggregate queries (i.e., for the computation of the aggregate measure of the regions which intersect a spatio-temporal window). In addition, the method is very effective when a query is posed over a query region whose intersection with the objects in a map must be computed on-thefly,and these objects are totally enclosed in the query region. However, problems may appear when leaf entries partially overlap the query window. In this case, the result must be estimated, or the actual results computed using the base tables. In fact, Tao, Kollios, Considine, Li,and Papadias (2004), show that the aRB-tree can suffer from the distinct counting problem, if the object remains in the same region for several timestamps.时空数据仓库的调查摘要地理信息系统已被广泛应用于不同的应用领域，包括经济，生态和人口统计分析，城市和路线规划。
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天津大学《地理信息系统导论》课程教学大纲课程代码：2160247 课程名称：地理信息系统导论学 时： 40 学 分： 2学时分配： 授课：24 上机：16授课学院： 计算机科学与技术学院适用专业： 计算机科学与技术先修课程： 数据库原理一．课程的性质与目的地理信息系统近年来成为支持地理学及其相关学科发展的一项重要技术，为本科生开设这门课程，目的是通过本课程的学习，使学生了解地理信息系统的产生背景、功能、应用领域及发展方向；掌握GIS的基本概念、GIS的数据结构、GIS数据输入存储编辑方法、GIS空间分析方法、GIS产品等知识点；懂得如何利用GIS去解决实际问题的思路。
了解地理信息系统在各个学科和社会中的应用。
二．教学基本要求该课程注重地理信息系统的基础理论，全面系统讲述地理信息系统的技术体系，重点突出地理信息系统的基础理论、技术与应用。
地理信息系统是一门理论性和实践都很强的学科，要求学生在掌握基本理论与方法的同时，加强实验和实践。
该课程力求将地理空间基础理论、地理信息系统技术方法和地理信息系统的实践融为一体，使学生在学习地理信息系统技术方法的同时，掌握与地理信息系统的技术实现和方法应用有关的基础理论，从而使学生能够真正领会和把握作为现代高科技的地理信息系统的科学性、技术性和实践性。
三．教学内容课程内容：第一部分：基本概念1. 导论第二部分：空间数据与空间信息2.空间数据模型3.空间参照系与GIS中的数据第三部分：GIS的功能4.空间数据获取与管理5.空间分析6.空间数据表现第四部分：GIS的应用7.GIS平台、应用与工程8.GIS展望课程设计：和ESRI和SuperMap公司联合制定课题，各同学分组完成课程设计。
四．学时分配教学内容 授课 上机 实验 实践 实践(周)导论 2空间数据模型 4空间参照系与GIS中的数据4空间数据获取与管理 4空间分析 4空间数据表现 2GIS平台、应用与工程 2GIS展望 2课程设计 16 总计： 24 16 五．评价与考核方式本课程期末考试不采用试卷考试的形式，具体考核形式如下：1、GIS功能开发大作业。
设置空间数据采集整理与录入、基本GIS功能开发、数字校园功能、其它GIS系统开发等类别，每一类设若干个作业题目完成具体功能和指标，由1-2位同学完成。
占总成绩65%。
2. GIS综述性报告。
教师提供20-40篇GIS前沿学术论文和GIS相关学术网站，由学生结合课堂所学基本理论知识，写一遍综述论文，让学生进一步了解GIS的前沿知识。
占总成绩35%。
六．教材与主要参考资料1、陈述彭等，地理信息系统导论，科学出版社，1999。
2、邬伦等，地理信息系统——原理、方法和应用，科学出版社，2001。
3、Paul A. Longley, Michael F. Goodchild等，地理信息系统（上下卷），电子工业出版社，2004。
4、《地理信息系统原理》，黄杏元等编著，高等教育出版社，2001年5、《地理信息系统原理与方法》，吴信才等编著，电子工业出版社，2002年6、龚健雅，地理信息系统基础, 科学出版社, 2001。
制定人：审核人：批准人：批准日期：年月日TU Syllabus for Geographical Information SystemCode:2160247 Title: Geographical Information System Semester Hours:40Credits:2Semester Hour Structure Lecture ：24 Computer Lab ：16 Experiment ： Practice ： Practice (Week)：Offered by: School of Computer Science and Technology for: Computer Science and Technology Prerequisite:1. ObjectiveThis course requires students to learn the basic theory of GIS ，master the GIS technology with the knowledge of GIS. Every group develops a GIS application.2. Course DescriptionGIS basic theories and technologies were taught in this lesson. Application of GIS were stood out. In order to make students to grasp technologies and relational theories of GIS best, basic theories of geographical space, GIS methods and applications of GIS were united. This lesson was divided into seven parts. It’s concepts, functions, history and actuality were introduced in the first part. The second part mainly taught students relational knowledge of cartology. Geographical space, spatial data, structures and quality of model and metadata were discussed in the third part. The fourth part taught students how to deal with spatial data. The fifth part introduced knowledge about spatial model. How to express spatial data of GIS were taught in the sixth part. In the last part, examples of GIS applications were discussed.3. TopicsChapter one ：Basic concepts 1. IntroductionChapter two：Spatial data and spatial information2.Spatial data models3. Spatial Reference System and GIS dataChapter three：GIS functions4. Spatial data acquisition and management5. Spatial Analysis6. The performance of spatial dataChapter four：GIS applications7. GIS platform, application and engineering projects8. GIS ProspectsPractice：Every group of students develop a GIS project about the actual industry needs of esri and supermap with some guidance from ESRI and SuperMap.4. Semester Hour StructureTopics Lecture ComputerLab.Experiment PracticePractice(Week)Introduction 2Spatial data models 4Spatial Reference Systemand GIS data4Spatial data acquisitionand management4Spatial Analysis 4The performance ofspatial data2GIS platform, applicationand engineering project2GIS Prospects 2Practice16Sum: 405. Grading1、project results ：develop the basic functions of GIS，Meet this project’s all demand of ESRI or SuperMap, Accounted for 65% of the Grading.2. GIS Summary Report：the teacher offers 20-40 GIS papers and related websites，every students write a report about new trends of GIS, Accounted for 35% of the Grading.6. Text-Book & Additional Readings1、Chen shupeng，Introduction to Geographic Information System，Science Press，1999。
2、Wu lun，Geographic information systems - principles, methods and applications，Science Press，2001。
3、Paul A. Longley, Michael F. Goodchild，Geographic Information System，Electronics Industry Press，2004。
4、Huang Xingyuan，principles of Geographic Information System， Higher Education Press，20015、Wu Xincai Wu，Geographic information systems - principles, methods，Electronics Industry Press，20026、Gong Jianya，Basic of Geographic information systems, Science Press , 2001Constitutor:Reviewer:Authorizor:Date:。