chapter6-GIS

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Mapgis67教程_实用版

★ 70年代中后期实际应用阶段，商业化的GIS平台诞生，
标志着GIS技术基本走向了应用。如ArcView、ArcInfo等
★ 80年代大发展，GIS技术应用得到了空前的发展，很多国
家、机构、公司纷纷投巨资进行空间数据库的建设。1997年美国在空间信息中创造产值近100亿美元，美国地调局在跨世纪未来战略中，把集成GIS地理空间数据及开发数字产品列为其未来五年之中的主要业务，空间信息系统建设被认为是美
工程：对MAPGIS要素层的管理和描述的描述文件，
它提供了对GIS基本类型文件和图象文件的有机结合的描述。它可由一个以上的点、线、面文件组成。
点文件
点文件1
点文件2
…………
图层1 图层2
点图元1 …点…图…元…2
图…层…3
点图元m 点…图…元…n…
ቤተ መጻሕፍቲ ባይዱMapGis
地图数据结
工程
线文件
线文件1 线文件2
★ 关于GIS的概念，不同专业、不同的应用领域，对其理解也不相同。因此对GIS的定义也有不同，但目前比较为人们所接受的定义有：
★ 英国教育部（DOE）1987：
GIS是一种获取、存储、检查、操作、分析和显示地球空间数据的计算机系统。
★ 美国NCGIA（1988）：
为了获取、存储、检索、分析和显示空间定位数据而建立的计算机化的数据库管理系统。
第六章 MAPGIS简介及其应用
一、地理信息系统（GIS）简介二、 MAPGIS制图基本操作三、 MAPGIS空间分析简介
一、地理信息系统（GIS）简介
★ 地理信息系统:GIS ----Geographical Information System

arcgis第6章

6.4.1 色彩定义方式
ArcMap系统包含了种定义颜色的方式，分别为系统包含了5种定义颜色的方式分别为RGB 系统包含了种定义颜色的方式，（Red/Green/Blue）方式、HSV（Hue/Saturation/value ）方式、（ Intensity）方式、CMYK（Cyan/Magenta/Yellow/Black ）方式、（方式、方式和Name方式。方式。）方式、Gray Shade Ramp方式和方式和方式
第6 章
ArcMap样式和符号操作 ArcMap样式和符号操作
使用样式的优点在于：使用样式的优点在于：维护符号、颜色、图案、着色分布方法、维护符号、颜色、图案、着色分布方法、关系以及趋势等的制图标准；势等的制图标准；通过熟悉的样式，使用户地图间交流更为有效，使人通过熟悉的样式，使用户地图间交流更为有效，们浏览、理解和分析地图更加容易；们浏览、理解和分析地图更加容易；在创建地图或地图系列时，在创建地图或地图系列时，使用带有参照样式或样式组的地图模板更为省力；组的地图模板更为省力；使制图符号标准化，使制图符号标准化，因此当用户的地图被不同的印刷厂商出版或打印时，它们看起来是一样的。厂商出版或打印时，它们看起来是一样的。
6.3.1 制作点状符号
点符号可以分为四种：简单符号（），用可选点符号可以分为四种：简单符号（Simple），用可选），择的掩膜设置快速绘制的基本系列的形式；字符符号（择的掩膜设置快速绘制的基本系列的形式；字符符号（ Character），来源于），来源于字体；），来源于True Type字体；箭头符号（Arrow 字体箭头符号（），来源于来源于True Type字体；图片符号（Picture），单独字体；），单独），来源于字体图片符号（），图形。的bmp或emf图形。而无论通过哪种方式制作符号，都必须或图形而无论通过哪种方式制作符号，首先借助图式符号库的管理操作，生成新的点状符号。首先借助图式符号库的管理操作，生成新的点状符号。 1．生成新点状符号． 2．简单符号制作． 3．字符点状符号制作． 4．箭头点状符号制作． 5．图片点状符号制作．

GIS教程习题

第一章绪论第一章习题1．什么是信息、数据？信息与数据的关系是什么？信息（Information）是现实世界在人们头脑中的反映，是关于现实世界新的事实的知识。

信息以文字、数据、符号、声音、图像等形式记录下来，进行传递和处理，为人们的生产、建设、管理等提供依据。

数据（Data）是指对某一目标定性、定量描述的原始资料，指能被计算机进行处理的数字、文字、符号、图形、声音、图像以及它们能够转换成的形式。

信息与数据的联系和区别：（《地理信息系统——原理、方法和应用》P4）联系：信息与数据是不可分离的。

数据是信息的载体、表达，信息是数据的内涵，数据与信息是形与质的关系。

区别：●数据是客观对象的表示，是记录下来的某种可以识别的符号，与载荷它的物理介质有关，具有多种多样的形式，也可以加以转换；而信息是数据内涵的意义，不随载体的改变而改变，也可以离开信息系统而独立存在。

●数据是客观的原始事实，本身并没有意义，只有经过解释才有意义；而信息是数据处理的结果，是主观知识，不同人对于同一数据的理解，可得到不同信息。

2．信息、数据的特性是什么？地理信息特性有什么？（《地理信息系统——原理、方法和应用》P3）信息具有客观性、实用性、传输性、共享性4个主要特性：●客观性：任何信息都是与客观事实相联系的，这是信息的正确性和精确度的保证；●适用性：问题不同，影响因素不同，需要的信息种类是不同的；●传输性：信息可在信息发送者和接受者之间进行传输；●共享性：信息与实物不同，信息可传输给多个用户共享，而其本身并无损失，这为信息的并发应用提供可能性。

数据的基本特性：●选择性：侧面的取舍、存在方式的选择。

●可靠性（正确性）：任何描述是相对精确的，●时间性：体现了data的现势性●完备性：空间、时间、主题的完备性。

●详细性：指数据的分辨率，也就是可描述最细微差异的程度及最微小物体的大小。

详细性的对偶是综合性。

地理信息（Geographic Information）指与研究对象的空间地理分布有关的信息。

GIS算法-Chp6空间度量

空间查询
图形和属性的互查是最常用的查询，主要有两类：
1、按属性信息的要求来查询定位空间位置，称为“属性查图形”。如在中国行政区划图上查询人口大于4000万且城市人口大于1000万的省有哪些?称为SQL查询.
2、根据对象的空间位置查询有关的属性信息，称为“图形查属性”。如一般的GIS软件都提供一个“INFO”工具，让用户利用鼠标，用点选、画线、矩形、圆、不规则多边形等工具选中地物，并显示所查询对象的属性列表，可进行有关统计分析。
第六章空间度量算法
空间信息查询与量算
查询和定位空间对象，并对空间对象进行量算是GIS的基本功能之一，它是GIS进行高层次分析的基础。在GIS中，为进行高层次分析，往往需要查询定位空间对象，并用一些简单的量测值对地理分布或现象进行描述，如长度、面积、距离等。实际上，空间分析首先始于空间查询和量算，它是空间分析的定量基础。
空间查询
1、基于空间关系查询空间实体间存在多种空间关系，包括拓扑、距离、方
位等。如查找满足下列条件的城市：在京沪线的东部；距离京沪线不超过50公里; 城市人口大于100万; 城市区域面积5000平方公里.
空间查询
简单的点线面相互关系拓扑查询包括：面面查询:如与某个多边形相邻的多边形有哪些；面线查询:如某个多边形内包含哪些线；面点查询:如某个多边形内有哪些点状地物；线面查询:如某条线经过的多边形有哪些；线线查询:如与某条河流相连的支流有哪些；线点查询:如某条道路上有哪些桥梁，某条输电线上有哪些变电站；点面查询:如某个点落在那个多边形内；点线查询:如某个结点由哪些线相交而成；
空间信息量算
几何量算
1．长度线状物体的长度是最基本的形态参数之一，在矢量

地理信息系统课后思考题

《地理信息系统》思考题第一章绪论1、什么是地理信息系统?它与地图数据库有什么异同?与地理信息的关系是什么?2、地理信息系统由哪些部分组成?与其他信息系统的主要区别有哪些?3、地理信息系统的基本功能有哪些?基本功能与应用功能是根据什么来区分的?4、与其他信息系统相比，地理信息系统的哪些功能是比较独特的?5、地理信息系统的科学理论基础有哪些?是否可以称地理信息系统为一门科学?6、试举例说明地理信息系统的应用前景。

7、GIS近代发展有什么特点?《地理信息系统》思考题第二章地理信息系统的数据结构1、地理信息系统中的空间数据都包含哪些?2、通过实例说明GIS空间数据的基本特征及在计算机中的表示方法。

3、矢量数据与栅格数据的区别是什么？它们有什么共同点吗？4、矢量数据在结构表达方面有什么特色？5、矢量和栅格数据的结构都有通用标准吗？请说明。

6、栅格数据组织有哪些方法？7、栅格与矢量数据结构相比较各有什么特征？8、矢量与栅格一体化的数据结构有什么好处？9、属性数据的编码是必须的吗？10、简述空间数据的拓扑关系及其对GIS数据处理和空间分析有何重要意义？《地理信息系统》思考题第三章空间数据的处理1．GIS的数据源有哪些？2．请举例说明GIS对数据的质量要求。

3．纸张上的地图如何进入计算机系统？4．从地图上能得到GIS需要的所有数据吗？请举例说明。

5．如何发现进入GIS中的数据有错误？6．一般从扫描仪上直接得到的地图有什么问题？如何改正？7．如果两个作业小组各自从数字化仪上得到两张相邻图幅的地图数据在GIS中不能准确对接该怎么办？地图接边相关知识8．空间数据几何纠正的常用方法有哪些？9．假设一条矢量等高线上的点太过于密集了，如何减少占用系统的存储空间？你能给出多少方法？各有什么适用范围？10．栅格地图数据如何减少硬盘存贮空间？11．请简要说明通过扫描仪得到矢量地图数据的原理和过程。

12．空间数据的插值算法有什么用途？请举例说明。

GIS软件使用教程：创建和操作地图说明书

ContentsPreface ixAcknowledgments xiPart I Using and making mapsChapter 1 Introduction 1Tutorial 1-1 Opening and saving a map document 2Tutorial 1-2 Working with map layers 5Tutorial 1-3 Navigating in a map document 12Tutorial 1-4 Measuring distances 21Tutorial 1-5 Working with feature attributes 24Tutorial 1-6 Selecting features 29Tutorial 1-7 Changing selection options 30Tutorial 1-8 Working with attribute tables 36Tutorial 1-9 Labeling features 43Assignment 1-1 Analyze population by race in the top 10 US states 46Assignment 1-2 Produce a crime map 49Chapter 2 Map design 51Tutorial 2-1 Creating point and polygon maps using qualitative attributes 52 Tutorial 2-2 Creating point and polygon maps using quantitative attributes 62 Tutorial 2-3 Creating custom classes for a map 66Tutorial 2-4 Creating custom colors for a map 70Tutorial 2-5 Creating normalized and density maps 73Tutorial 2-6 Creating dot density maps 78Tutorial 2-7 Creating fishnet maps 80Tutorial 2-8 Creating group layers and layer packages 86Assignment 2-1 Create a map showing schools in New York City by type 92 Assignment 2-2 Create maps for military sites and congressional districts 93 Assignment 2-3 Create maps for US veteran unemployment status 95Chapter 3 GIS outputs 97Tutorial 3-1 Building an interactive GIS 97Tutorial 3-2 Creating map layouts 104Tutorial 3-3 Reusing a custom map layout 111Tutorial 3-4 Creating a custom map template with two maps 113Tutorial 3-5 Adding a report to a layout 119viGIS TUTORIAL FOR ARCGIS DESKTOP 10.8Tutorial 3-6 Adding a graph to a layout 121Tutorial 3-7 Building a map animation 123Tutorial 3-8 Using ArcGIS Online 128Assignment 3-1 Create a dynamic map of historic buildings in downtown Pittsburgh 128Assignment 3-2 Create a layout comparing 2010 elderly and youth population compositions in Orange County, California 130Assignment 3-3 Create an animation for an auto theft crime time series 131Part II Working with spatial dataChapter 4 File geodatabases 133Tutorial 4-1 Building a file geodatabase 133Tutorial 4-2 Using ArcCatalog utilities 136Tutorial 4-3 Modifying an attribute table 139Tutorial 4-4 Joining tables 142Tutorial 4-5 Creating centroid coordinates in a table 144Tutorial 4-6 Aggregating data 148Assignment 4-1 Investigate educational attainment 153Assignment 4-2 Compare serious crime with poverty in Pittsburgh 155Chapter 5 Spatial data 159Tutorial 5-1 Examining metadata 160Tutorial 5-2 Working with world map projections 162Tutorial 5-3 Working with US map projections 165Tutorial 5-4 Working with rectangular coordinate systems 167Tutorial 5-5 Learning about vector data formats 172Tutorial 5-6 Exploring raster basemaps from Esri web services 178Tutorial 5-7 Downloading raster maps from the USGS 181Chapter 6 Geoprocessing 185Tutorial 6-1 Extracting features for a study area 185Tutorial 6-2 Clipping features 190Tutorial 6-3 Dissolving features 192Tutorial 6-4 Merging features 195Tutorial 6-5 Intersecting layers 199Tutorial 6-6 Unioning layers 202Tutorial 6-7 Automating geoprocessing using ModelBuilder 208Assignment 6-1 Build a study region for Colorado counties 220Assignment 6-2 Dissolve property parcels to create a zoning map 222Assignment 6-3 Build a model to create a fishnet map layer for a study area 223Chapter 7 Digitizing 227Tutorial 7-1 Digitizing polygon features 228Tutorial 7-2 Digitizing line features 239Tutorial 7-3 Digitizing point features 245Tutorial 7-4 Using advanced editing tools 248Tutorial 7-5 Spatially adjusting features 255Assignment 7-1 Digitize police beats 259COnTEnTS viiAssignment 7-2 Use GIS to track campus information 261Chapter 8 Geocoding 263Tutorial 8-1 Geocoding data by ZIP Code 263Tutorial 8-2 Geocoding data by street address 268Tutorial 8-3 Correcting source addresses using interactive rematch 274Tutorial 8-4 Correcting street reference layer addresses 276Tutorial 8-5 Using an alias table 281Assignment 8-1 Geocode household hazardous waste participants to ZIP Codes 282Assignment 8-2 Geocode immigrant-run businesses to Pittsburgh streets 284Assignment 8-3 Examine match option parameters for geocoding 285Part III Analyzing spatial dataChapter 9 Spatial analysis 289Tutorial 9-1 Buffering points for proximity analysis 290Tutorial 9-2 Conducting a site suitability analysis 295Tutorial 9-3 Using multiple ring buffers for calibrating a gravity model 299Assignment 9-1 Analyze population in California cities at risk for earthquakes 308Assignment 9-2 Analyze visits to the Jack Stack public pool in Pittsburgh 310Chapter 10 ArcGIS 3D Analyst for Desktop 313Tutorial 10-1 Creating a 3D scene 314Tutorial 10-2 Creating a TIN from contours 315Tutorial 10-3 Draping features onto a TIN 320Tutorial 10-4 Navigating scenes 326Tutorial 10-5 Creating an animation 330Tutorial 10-6 Using 3D effects 332Tutorial 10-7 Using 3D symbols 335Tutorial 10-8 Editing 3D objects 339Tutorial 10-9 Using 3D Analyst for landform analysis 342Tutorial 10-10 Exploring ArcGlobe 348Assignment 10-1 Develop a 3D presentation for downtown historic sites 352Assignment 10-2 Topographic site analysis 354Assignment 10-3 3D animation of a conservatory study area 355Chapter 11 ArcGIS Spatial Analyst for Desktop 357Tutorial 11-1 Processing raster map layers 358Tutorial 11-2 Creating a hillshade raster layer 363Tutorial 11-3 Making a kernel density map 365Tutorial 11-4 Extracting raster value points 371Tutorial 11-5 Conducting a raster-based site suitability study 374Assignment 11-1 Create a mask and hillshade for suburbs 381Assignment 11-2 Estimate heart attack fatalities outside hospitals by gender 383Chapter 12 ArcGIS Network Analyst for Desktop 385Tutorial 12-1 Solving the “traveling salesperson” problem 386Tutorial 12-2 Building a TIGER-based network dataset 394viiiGIS TUTORIAL FOR ARCGIS DESKTOP 10.8Tutorial 12-3 Creating travel polygons 402Tutorial 12-4 Locating facilities 409Tutorial 12-5 Routing vehicles from depots to demand points 414Assignment 12-1 Geographic access to federally qualified health centers 421Assignment 12-2 Analyze visits to the Phillips public pool in Pittsburgh 423Assignment 12-3 Locate new farmers’ markets in Washington, DC 424Appendix Data source credits 427。

GIS课程各章节知识点

第一章导论第一节：•主要内容：数据与信息、地理信息与地理信息系统•基本概念和知识点：数据、信息、地理信息、地理信息系统的概念、数据与信息联系、信息的特点第二节：•主要内容： GIS 的基本构成•基本概念和知识点： GIS 的基本构成：系统硬件（ GIS 主机、 GIS 外部设备、 GIS 的网络设备）、系统软件（ GIS 专业软件、数据库软件、系统管理软件）、空间数据、应用人员、应用模型第三节：1 ．主要内容： GIS 的基本功能、应用功能2 ．基本概念和知识点： GIS 常见的基本功能（数据采集与编辑、数据存储与管理、数据处理和变换、空间分析和统计、产品制作和显示、二次开发和编程）、应用功能（资源管理、区域规划、国土监测、辅助决策）第四节1 ．主要内容： GIS 的发展透视2 ．基本概念和知识点： GIS 发展概况3 ．问题与应用（能力要求）：理解 GIS 发展趋势（ GIS 已成为一门综合性技术、产业化的发展势头强劲、 GIS 网络化已构成当今社会的热点、地理信息科学的产生和发展）第二章 GIS 的数据结构第一节：1 ．主要内容：地理空间及其表达2 ．基本概念和知识点：地理空间的概念、空间实体的表达、我国三种大地坐标系3 ．问题与应用（能力要求）：在计算机中空间实体的表达第二节：1 ．主要内容：地理空间数据及其特征2 ．问题与应用（能力要求）：掌握 GIS 的空间数据的类型（地图数据、影像数据、地形数据、属性数据、元数据）及其基本特征（空间特征、属性特征、时间特征），理解空间数据的拓扑关系及其意义（拓扑邻接、拓扑关联、拓扑包含），掌握空间数据的计算机表达第三节：1 ．主要内容：空间数据结构的类型2 ．基本概念和知识点：矢量数据结构、栅格数据结构、TIN数据结构、游程编码结构3 ．问题与应用（能力要求）：掌握矢量数据结构的定义及其类型（简单数据结构、拓扑数据结构、曲面数据结构）、栅格数据结构的定义及其类型（直接编码、链式编码、块码、游程编码结构、四叉树结构），掌握矢量与栅格数据结构的比较第四节1 ．主要内容：空间数据结构的建立2 ．基本概念和知识点：空间数据结构建立的定义、空间数据编码3 ．问题与应用（能力要求）：空间数据结构建立基本过程、矢量数据、栅格数据的获取方法第三章空间数据的处理第一节：1 ．主要内容：空间数据的坐标变换2 ．基本概念和知识点：几何纠正、投影转换（正解变换、反解变换、数值变换）3 ．问题与应用（能力要求）：理解空间数据的几何纠正、投影转换的定义及其意义。

地理信息系统英文教材

地理信息系统英文教材Introduction to Geographic Information Systems.Chapter 1: Overview of Geographic Information Systems (GIS)。

Geographic Information Systems (GIS) is an integratedset of hardware, software, and data that captures, stores, manages, analyzes, and presents all forms of geographically referenced information. GIS technology has revolutionized the way we understand and interact with the world by enabling the integration and visualization of spatial data with other forms of information.Chapter 2: The Components of GIS.GIS is composed of three main components: hardware, software, and data. Hardware refers to the computers and other devices used to run GIS software. Software is the set of tools and applications that enable users to create, edit,query, analyze, and visualize spatial data. Data is the core of any GIS, and it includes both spatial data (such as geographic coordinates) and non-spatial data (such as demographic information).Chapter 3: Spatial Data and Geospatial Databases.Spatial data is the foundation of GIS, and it represents the geographic features and relationships of the real world. Geospatial databases are specifically designed to store, manage, and retrieve spatial data efficiently. This chapter covers the different types of spatial data, such as vector and raster data, and the principles of geospatial databases.Chapter 4: GIS Software and Applications.GIS software enables users to create, edit, query, analyze, and visualize spatial data. This chapter introduces various GIS software packages, including desktop GIS, web GIS, and mobile GIS. It also covers the different types of GIS applications, such as urban planning,environmental monitoring, and transportation management.Chapter 5: Spatial Analysis and Modeling.Spatial analysis is a critical component of GIS, and it involves the examination of spatial patterns and relationships within a dataset. This chapter covers various spatial analysis techniques, including buffering, overlaying, and network analysis. It also introducesspatial modeling, which allows users to simulate andpredict spatial processes and outcomes.Chapter 6: GIS Data Visualization.Data visualization is a crucial aspect of GIS, as it enables users to communicate complex spatial information effectively. This chapter covers various visualization techniques, including maps, charts, and 3D models. It also discusses best practices for creating effective GIS visualizations.Chapter 7: Applications of GIS in Different Fields.GIS technology has found widespread applications in various fields, including urban planning, environmental science, transportation, health, and more. This chapter explores the specific applications of GIS in these fields and highlights the benefits and challenges of using GIS in each context.Chapter 8: Future Trends and Developments in GIS.GIS technology is constantly evolving, and new trends and developments are emerging. This chapter discusses some of the future trends in GIS, such as the increasing use of cloud computing, big data analytics, and artificial intelligence in GIS. It also explores the potential impact of these trends on GIS practice and research.In conclusion, this textbook provides a comprehensive introduction to Geographic Information Systems, covering the fundamentals of GIS, its components, spatial data and geospatial databases, GIS software and applications, spatial analysis and modeling, data visualization,applications in different fields, and future trends and developments. Through this textbook, students will gain a solid understanding of GIS technology and its applications, enabling them to effectively use GIS in various fields and contexts.。

牟乃夏ArcGIS教程：第6章空间数据的拓扑处理

多边形要素
3
6.1.3拓扑参数拓扑关系中存储了许多参数。如拓扑容差、等级、拓扑规则等。拓扑还包含有一个存储脏区域（已经编辑过的区域）、错误和异常的要素层，以此来保证拓扑数据的质量。 1.拓扑容差（topology tolerance）是不重合的要素顶点间的最小距离，它定义了顶点间在接近到怎样的程度时可以视为同一个顶点。位于拓扑容差范围内的所以顶点被认为是重合的并被捕捉到一起（图 1）。在实际应用中，拓扑容差一般是一段很小的实际地面距离。
【设置拓扑容差】对话框
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ห้องสมุดไป่ตู้
6.4.6更改坐标等级 1.更改等级数步骤为：打开【拓扑属性】对话框，切换到【要素类】选项卡，如图所示，在【等级数】文本框中输入新的等级数值（范围1-50），单击【确定】按钮，完成操作。 2.更改要素类的等级步骤为：打开【拓扑属性】对话框，切换到【要素类】选项卡如右图，选择要修改等级的要素类，在右侧【等级】下拉框，选择该要素类的新等级值，单击【确定】按钮，完成操作。
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3.拓扑规则（rules）通过定义拓扑的状态，控制要素之间存在的空间关系。在拓扑中定义的规则可控制一个要素类中各要素之间，不同要素类中各要素之间以及要素子类之间的关系。例如，“不能重叠”拓扑规则用于控制同一多边形要素类中或线要素类中要素之间的关系。如果两个要素存在重叠，重叠的几何部分会被标识出来并以红色显示，并在拓扑中存储为错误和异常，如图所示。另外ArcGIS10中增加了新的拓扑规则，详情参阅 ArcGIS10书第146-147页。
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6.错误与异常：错误（errors）以要素的形式存储在拓扑图层中，并且允许用户提交和管理要素不符合拓扑规则的情况。错误要素记录了发现拓扑错误的位置，用红色点、线、方块表示。其中，某些错误时数据创建与更新过程中的正常部分，是可以接受的，这种情况下可将该错误要素标记为异常（exceptions），用绿色点、线、方块表示。在拓扑图层存储了点、线、面三类错误要素。常见错误的具体表现形式为悬挂结点（dangle node）、伪结点（pseudo node）、碎屑多边形（sliver polygon）、不正规多边形（weird polygon）。

2024版《ArcGIS教程》PPT课件

01 ArcGISChapter软件背景及功能01020304用于城市空间布局、交通规划、公共设施选址等。

城市规划应用于环境监测、生态评估、自然保护区规划等。

环境保护支持灾害风险评估、应急响应、灾后重建等。

灾害管理用于精准农业、农业资源管理、农业气候分析等。

农业领域应用领域与案例01ArcGIS界面包括菜单栏、工具栏、图层窗口、属性窗口等部分。

020304常用操作习惯包括使用快捷键、定制工具栏、保存工作空间等。

图层管理是关键操作之一，涉及添加、删除、调整图层顺序和透明度等。

属性表编辑也是常用操作，用于查看和编辑空间数据的属性信息。

界面布局及操作习惯02数据管理与处理Chapter数据类型及格式支持栅格数据矢量数据以像素为单位的图像数据，支持GeoTIFF、ERDAS Imagine式。

属性数据导入数据导出数据数据转换030201数据导入与导出方法数据编辑与整理技巧编辑工具属性表编辑拓扑处理数据裁剪与合并03地图制作与可视化Chapter图层操作包括图层的添加、删除、重命名、调整顺序、设置可见性等基本操作，以及图层的属性设置、符号化、标注等高级操作。

图层概念图层是地图的基本组成单元，用于组织和管理空间数据，每个图层代表一种地理要素或现象。

图层属性图层属性包括空间范围、坐标系统、数据格式、字段信息等，可以通过图层属性窗口进行查看和修改。

地图图层概念及操作符号化表达方法符号类型ArcGIS提供了丰富的符号库，包括点符号、线符号、面符号等，用于表达不同地理要素的形状、颜色、大小等特征。

符号设置可以通过符号选择器选择合适的符号，也可以通过符号属性编辑器自定义符号的样式、颜色、大小等参数。

动态符号化根据地理要素的属性值动态设置符号的样式和颜色，实现地图的交互式表达。

01020304数据准备专题图设置专题图类型选择地图整饰专题图制作流程04空间分析功能介绍Chapter空间查询与统计方法空间查询空间统计空间插值缓冲区分析原理及应用缓冲区分析原理应用示例4. 结果分析与解释对叠加结果进行分析和解释，提取有用信息并应用于实际问题中。

(完整版)ArcGisChapter06

第六章回归拟合方程及其在城市与区域密度模型分析中的应用城市与区域研究首要的就是分析城市与区域的空间结构，尤其是人口密度分布特征。

如果把城市或区域作为一个经济系统来看，其供给方（劳动力）和需求方（消费者）都和人口挂钩，人口分布反映了经济活动的态势。

考察一定时段内城市或区域经济发展的空间模式，往往也是从分析人口分布模式的变化入手。

城市和区域的人口密度态势互为对应：中央商务区（CBD）是城市的中心，而城市本身又是区域的中心；城市人口密度从中央商务区向外递减，而区域中的人口密度则从中心城市向外递减。

城市和区域人口密度衰减模式的理论基础不同（参见第6.1节），但实证研究的方法却很类似，也互为借鉴。

本章试图寻找一种能够很好地描述密度分布模式的方程，并探讨如何通过这种方法来解释城市或区域增长模式。

方法上主要集中在方程回归分析以及相关的统计问题。

第6.1节介绍密度方程在城市和区域结构研究中的应用。

第6.2节介绍各种单中心结构方程。

第6.3节讨论单中心方程拟合中的一些统计问题，并介绍了非线性回归和加权回归法。

第6.4节探讨了多中心结构的各种假设及对应的方程形式。

第 6.5节是以芝加哥地区为例的应用（单中心与多中心，线性回归、非线性回归和加权回归）。

6.1刻画城市与区域结构的密度方程6.1.1城市密度方程研究自克拉克（Clark, 1951）开创性的经典研究工作以来，人们对城市人口密度方程的研究兴趣经久不衰，原因不单是获取数据比较方便。

城市人口密度方程揭示了城市的内部结构，而且又有坚实的经济理论作基础。

麦克唐纳（McDonald, 1989: 361）认为人口密度模型是城市一个至为重要的社会经济特征。

在1990年代早期，我还在俄亥俄州立大学上研究生时，选修了经济系唐纳德·哈伦（Donald Haurin）的城市经济学，当时就被城市人口密度分布的规律性所吸引，更为解释这种经验模型的经济理论模型所折服。

指数方程或克拉克模型是所有密度方程中使用最广的：9798brr ae D (6.1)这里，D r 是到城市中心（通常为中央商务区，即CBD ）距离为r 处的人口密度，a 为常数（或称CBD 截距），b 为密度斜率常数。

大量GIS文章及软件教程目录

大量GIS文章及软件教程目录以下是关于GIS文章及软件教程的大量目录：1.GIS入门教程-什么是GIS？-GIS的发展历史-GIS的应用领域2.GIS软件教程- ArcGIS软件教程-QGIS软件教程- MapInfo软件教程- Google Earth软件教程3.GIS数据处理教程-数据获取与处理-数据连接与关联-数据地理处理与分析4.GIS空间分析教程-空间距离分析-空间聚类分析-空间插值分析-空间相交与合并分析5.GIS矢量数据分析教程-矢量数据查询与选择-矢量数据空间关系-矢量数据属性统计-矢量数据符号化6.GIS栅格数据分析教程-栅格数据查询与选择-栅格数据空间关系-栅格数据运算与计算-栅格数据模型建立7.GIS空间插值教程-插值的概念与原理-插值算法的选择与应用-插值结果的分析与评估-插值在GIS中的应用8.地图制作与设计教程-地图设计原则与规范-地图制作方法与工具-地图样式与符号的设计-地图生成与输出9.GIS遥感数据处理教程-遥感数据获取与解译-遥感数据处理与分类-遥感数据融合与分析-遥感数据在GIS中的应用10.GIS网络分析教程-网络数据的建立与管理-路径分析与最短路径-网络服务与网络分析-网络分析在交通规划中的应用11.GIS地理建模教程-地理建模的概念与原理-地理建模方法与模型-地理建模在城市规划中的应用-地理建模在环境评价中的应用12.GIS空间数据可视化教程-空间数据可视化的方法与工具-空间数据可视化的技巧与效果-空间数据可视化在GIS中的应用-空间数据可视化与图形设计的结合。

冀教版七下地理第六章第二节知识点

冀教版七下地理第六章第二节知识点一、地理信息系统（GIS）的基本概念1.地理信息系统是一种通过计算机技术将地理信息封装、存储、管理、分析和展示的一种系统。

2.地理信息指的是与地球表面相关的各种信息，如地势高度、地表覆盖、地质构造、人口分布等等。

3.地理信息系统将地理信息与地图相结合，以数字化的方式进行处理和管理。

二、地理信息系统（GIS）的特点2.空间数据的特殊性：将地理空间信息与地图的图形和属性信息相结合，实现对地理信息的空间关系的分析和表达。

3.空间数据处理的复杂性：地理信息系统可以进行复杂的数据分析和模型计算，如路径分析、空间统计等。

4.空间数据展示的便捷性：地理信息系统可以将数据以图形的形式直观地展示出来，并且通过缩放、复制等操作进行灵活的数据显示与交互。

三、地理信息系统（GIS）的应用1.自然地理学应用：地理信息系统可以应用在地貌地形分析、气候变化监测、自然资源评估等方面，为科学研究提供支持。

2.社会经济学应用：地理信息系统可以应用在人口普查、城市规划、交通路线规划等方面，为社会经济发展提供决策支持。

3.农业应用：地理信息系统可以应用在农田土壤分析、农作物生长监测、农产品流通管理等方面，提高农业生产效率。

4.城市管理应用：地理信息系统可以应用在城市规划、市政设施管理、环境监测等方面，提高城市管理的科学性和高效性。

5.环境保护应用：地理信息系统可以应用在环境监测、生态保护区划、环境影响评估等方面，为环境保护提供技术支持。

四、地理信息系统（GIS）在中国的应用1.土地利用规划：地理信息系统可以应用在土地利用的调查与规划、土地资源评估与监测等方面，为土地管理和规划提供决策支持。

2.自然资源管理：地理信息系统可以应用在矿产资源开发与管理、水资源利用与保护等方面，为资源管理与保护提供科学依据。

3.环境保护与治理：地理信息系统可以应用在环境质量监测与评价、环境污染源控制与治理等方面，为环境保护与治理提供技术支持。

地理信息系统原理第六章课程实践报告

地理信息系统原理第六章课程实践报告下载温馨提示：该文档是我店铺精心编制而成，希望大家下载以后，能够帮助大家解决实际的问题。

文档下载后可定制随意修改，请根据实际需要进行相应的调整和使用，谢谢!并且，本店铺为大家提供各种各样类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，如想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by the editor. I hope that after you download them, they can help you solve practical problems. The document can be customized and modified after downloading, please adjust and use it according to actual needs, thank you!In addition, our shop provides you with various types of practical materials, such as educational essays, diary appreciation, sentence excerpts, ancient poems, classic articles, topic composition, work summary, word parsing, copy excerpts, other materials and so on, want to know different data formats and writing methods, please pay attention!地理信息系统原理第六章课程实践报告第一部分：引言地理信息系统（GIS）是一种集成地理数据、空间分析和地图制作的技术，并且在各行各业中都有着广泛的应用。

GIS应用Chapter 6

Chapter 6PreprocessingPreprocessing procedures are used to convert a dataset into a form suitable for permanent storage within the GIS database. Often, a large proportion of the data entered into a GIS requires some kind of processing and manipulation in order to make it conform to a data type, georeferencing system, and data structure that is compatible with the system. The end result of the preprocessing phase is a coordinated set of thematic data layers.The essential preprocessing procedures include:⏹format conversion;⏹data reduction and generalization;⏹error detection and editing;⏹merging of points into lines and of points and lines into polygons, whereappropriate;⏹edge matching;⏹rectification/registration;⏹interpolation; and⏹photointerpretation.We'll examine each of these in turn in this chapter. At the same time, many of these procedures are valuable at other stages in the end-to-end spatial analysis problem. We will point out these additional uses as they come up in later chapters. 6.1 Format ConversionFormat conversion covers many different problems, but can be discussed in terms of two families: conversion between different digital data structures and conversion between different data media. The former is the problem of modifying one data structure (such as those discussed in Chapter 4) into another. The latter typically involves converting source material such as paper maps, photographic prints, and printed tables into a useful computer-compatible form. Note that the reverse of data medium conversion -- changing the data stored within the digital database into maps, prints and tables -- is the problem of generating output products, which we discuss in Chapter 9.6. 1. 1 Data Structure ConversionsIn Chapter 4, we discussed several different data structures. There are many times when different datasets gathered for the same project are expressed in different data structures. Part of the cause of this lack of homogeneity is the nature of thedatasets themselves, some data structures being more suitable to some kinds of data than others. This problem is becoming more acute as increasing amounts of current data are created and maintained in various digital forms, while historical records are almost universally stored on paper or film. Another kind of problem that arises frequently is when we have raster datasets, such as digitized photography and multispectral scanner data (discussed in Chapter 10), and our GIS is based on a vector data structure. In these cases, we must be able to inter-convert between the data structures. Similar conversions will be required in order to develop final output products as well (see Chapter 9).The simplest forms of conversion are between members of a family of structures. For example, there are several common raster formats for raster data. Typical raster GIS datasets include arrays of elevation, rainfall, and classes of land cover. This is also the kind of data that is produced by multispectral scanners, which are common sensors on both aircraft and spacecraft platforms (some applications of these systems are discussed in Chapters 10 and 12). The data produced by such systems may be thought of as an array of brightness values for each wavelength band in the sensor. These systems generate datasets that are comparable to any other multivariate collection of raster data, including problems of geometric registration between the wavelength bands or between different dates of acquisition (Welch, 1985).There are, however, several ways to organize such datasets. Keeping each variable's data (for example, elevation and annual rainfall, or the different spectral channels from a multispectral scanner) as a separate array is one common method. This method is often called band sequential(BSQ), since each array is kept as a separate file on the magnetic disk or tape. In this case, one data file would contain the elevation array, and a separate file would contain rainfall values. A common alternative, called band interleaved by pixel(BIP), places all of the different measurements from a single pixel together. This organization may be thought of as a single array containing multivariate pixels (Figure 6.1). The first element in this second format would contain the elevation value for the pixel in the first row and column; the second element would contain the rainfall value for the same pixel.When operations on the data involve a single theme or layer at a time, a BSQ raster database can be the most efficient organization. This is because the specific theme of interest (or spectral channel) can be analyzed and manipulated as a physically independent entity. Conversely, when working with more than one data theme at a time, the BIP organization can be the most efficient. For example, consider a raster dataset with two themes: elevation data points and classes of forest cover. If a principal activity is to operate on a single data layer at a time, such as deriving slope from the elevation data points, the BSQ organization makes the elevation data directly available, without having to read the data files past land-use values. If, on the other hand, an analytic operation requires comparison of both themes on a pixel-by-pixel basis, such as finding the location of the highest elevation for each of several forest-type classes, the BIP organization makes good sense, as the values of the two themes for each pixel are adjacent in the database.The band interleaved by line (BIL) raster organization is a middle-ground between the extremes of BSQ and BIP. In this form, adjacent ground locations (in the row direction) for a single theme are adjacent in the data file, and subsequent themes are then recorded in sequence for that same line. In this way, the different themes corresponding to a row in the file are relatively near each other in the file. Thus, one expects that its performance on specified tasks will fall between the pure sequential or pure pixel-interleaved forms. This intermediate type of multivariate raster is used in some commercial raster systems.For those readers with a data processing background, we will briefly add to the complexity. There are two common physical data organizations for BIL-structured data. In one, the physical records hold all the themes from a single row in the array, ordered as described in the previous paragraph. Thus, the number of physical records is the same as the number of rows in the array. In the other common BIL format, a physical record corresponds to a single thematic category. Thus in this second case, the number of physical records in the dataset is the product of the number of rows in the array times the number of unique themes.The problem of converting between the different raster data formats described above is relatively simple. Typically, a portion of the data in one format is read into a memory-based storage array, and the appropriate pointers created to extract the data values in whatever sequence is required for the new format. Optimizing such conversion software on a given computer is straightforward. We discuss another family of data format complications in Chapter 10.There are a wide variety of problems that develop when converting datasets between different vector data structures. As we noted in Chapter 4, there are many different vector organizations. As a guiding principle, it is expensive to generate topological information when it is not explicitly present in the vector data structure. To illustrate this point, converting data from an arc-node organization to a relational one is very easy. As we observed in the last chapter, these are very similar data structures, in terms of the way topological information about spatial objects are organized. In effect, these two data structures are really storing the exact same semantic information, with a slightly different syntax. .At the other end of the spectrum, consider the problem of converting data in a whole polygon structure to an arc-node structure. In a dataset stored in whole polygon structure, there is very little explicitly identified topology. The list of nodes that form the boundaries of each individual polygon is stored. Consider just the problem of extracting the arc-node node list. We must go through the entire list of polygons, and create a list of the unique nodes. This might require a double-sort of all the points in the polygon file, and then a pass through the sorted list to identify the unique nodes. Creating the arc list requires another pass through the whole polygon file, this time cross- referencing edges of each polygon to the corresponding elements in the node list and generating the appropriate pointers. Furthermore, to identity all the polygons that border a given vector requires another complex sorting operation to identify all the shared edges.Converting vector data into a raster data structure is conceptuallystraightforward, although practically difficult. For point data elements, the cell or pixel in the raster array whose center is closest to the geographic coordinate of the point is coded with the attribute of the point. Thus, the elevation value from a surveyed benchmark is transferred to the raster cell whose location is closest to that of the original point. Of course, this operation usually changes the stored location of the point -- it is unlikely that the original point location exactly coincides with the center of a raster cell. This approach also ignores the problem of different objects occupying the same cell. Because of these important limitations, the conversion from vector to a raster data structure is not normally reversible: we cannot retrieve the original data points from the derived raster data without error. For some operations, this can be a fatal flaw.For linear data elements, the data structure conversion can be visualized by overlaying the vector or linear element on the raster array (see Figure 6.2a). The simplest conversion strategy would be to identify those raster elements that are crossed by the line, and to then code these cells with the attribute or class value associated with the line. For lines that are not oriented along the rows or columns of the array, the raster representation shows a stair-step distortion (see Figure 6.2, as well as the discussion of aliasing in Chapter 9). In this first discussion we have ignored the problem of specifying the thickness or width of rasterized line (Peuquet, 1981b).Polygons can be converted to a raster structure in two steps. First, the line segments that form the boundaries of the polygon can be converted as in the last example, producing what is sometimes called the skeleton or hull of the polygon. Second, those raster elements contained by the polygonal boundaries are recoded to the appropriate attribute value (Figure 6.2b).Converting raster data to a vector data structure can be a great deal more complex. To keep the discussion brief, we will first examine data where object boundaries are a single pixel wide, though this is rarely the case in real life. A simplistic approach for a trivial binary image (where the data are either in class 0 or class 1 and lines are only a single pixel wide) is illustrated in Figure 6.3. Consider that each raster cell can be represented by a point in its center. We can then draw vectors between all the non-zero 4-connected raster elements. Such an algorithm models all possible vectors as orthogonal vectors, parallel to the rows or columns of the raster. Further, this algorithm constrains all vectors to be of discrete lengths that are equal to an integer multiple of the raster spacing. Of course, vectors in real life do not have either of these restrictions.This approach will not be able to recover all vectors that have been converted to a raster. Consider the raster-coded vector in Figure 6.2a. Since the straight-line nature of this data element has been lost in the process of conversion, we will not be able to recover the straight line without ancillary information. However, there are algorithms that can be used to extract straight lines from raster data sets under restrictive circumstances, at an increased computational cost.To illustrate a somewhat more sophisticated approach to raster/vector conversion, let’s start with data that might have come from a flatbed digitizing tablet. The following table describes the (x-y) coordinates of three graphic objects, which wewill convert to a raster, and then attempt to convert back to vectors. By returning to a vector representation, we can begin to develop an understanding of the limitations of the algorithms.Object #l -- a vector, defined by connecting (x-y) points:1,6212,6212,511,51Object #2 -- a vector, connecting (x-y) points:16,5720,6027,5434,5731,4822,5215,52Object #3 -- a complex closed region, bounded by the closed (x-y) pattern:8,4621,4611,3815,4820,38These three objects -- two simple open paths and a complex closed region -- are plotted in Figure 6.4a. We'll explore this data by starting with these three vector objects, and converting them to a raster representation as in Figure 6.2. Converting these intermediate raster data back to a vector form, based on the 4-connected neighborhood approach described in Figure 6.3, gives us Figure 6.4b. Notice that the upper left object in the figures is perfectly reconstructed in a 4-connected algorithm, but the other two objects are badly distorted. In particular, elemental line segments between adjacent raster cells that are not parallel to the raster axes have disappeared.A more complete (but still simple) approach would be to search an 8- connected neighborhood around every raster element, searching for possible vector connections. In addition to the unit vectors orthogonal to the raster axes, this new algorithm permits diagonal vectors to be found. The output of this algorithm is shown in Figure 6.4c. The general characteristics of all three objects are retained. In this case, all the connections between the original points are preserved. However, this algorithm finds many connections between raster cell elements that were not in the original vector dataset.A further improvement, involving significantly more computation, would be to draw only diagonal lines between elements in any 3-by-3 8-connected region whenthey are not already connected by 4-connected orthogonal vectors. This additional rule gives us the objects in Figure 6.4d. Where two lines derived from the rasterized boundaries of the objects are close, this algorithm draws an extra line, compared to the original (Figure 6.4a). However, this is a relatively successful recovery of the original objects.Beyond these theoretical models, practical datasets require several additional functions. As Peuquet (1981a) explains, operational systems require two general functions for converting a raster to a vector dataset. The first, skeletonizing or thinning (Figure 6.5), is required because the input data are not generally as simple as those we have discussed: single pixels for point data and unit-width vectors for both vectors and polygon boundaries. Algorithms for determining the skeleton of an object are sometimes described as a peeling process, where the outside edges of thick lines are "peeled" away, ultimately leaving a unit-width vector. A symmetrical alternative approach is to expand the areas between lines, with the same ultimate goal. In either of these cases, the process is a sequence of passes through the data with each pass producing narrower vector outlines. A third alternative, the medial axis approach, is designed to directly identity the center of a line, by finding the set of interior pixels that are farthest from the outside edges of the original line.After the raster data have had such thinning operations applied, the vectors implicitly stored in the raster are extracted. The extraction process may be based on the models discussed above. Finally, the topological structure of the lines is determined, by recognizing line junctions and assembling the separate segments into connected vectors and polygons.Crapper (1984) presented an interesting analysis of the relations between vector and raster thematic data. His concern was understanding the accuracy of thematic maps. Consider an original dataset that is a map of photograph. Overlaying a grid on the original data and making assignments of thematic category, is relatively simple for cells in the interior of a homogeneous polygon. . The difficulty arises at the boundaries between polygons, where single grid cells cover more than one category. As we have mentioned, we could use a plurality rule to assign classes to the boundary cells, or alternatively, we could label them as a unique class. Crapper derives a relationship to estimate the number of boundary cells in this process, and thus gives us insight into the total area of boundary cells.6.1.2 Data Medium ConversionMost of the spatial data available today are not in computer-compatible formats. These include maps of many kinds and scales, printed manuscripts, and imagery (based on photographic processes, or generated by non-photographic instruments). Converting these materials into a format compatible with a digital geographic information system can be very expensive and time-consuming. According to the U.S. Geological Survey's Technology Exchange Working Group (U.S. Department of the Interior, 1985, p. iii):The digitizing of conventional cartographic data is perhaps the most resource intensivephage of constructing a digital cartographic data base or utilizing a geographic information system.The most common means of converting maps and other graphic data to a digital format is to use a digitizing tablet (Figure 6.6a). A digitizing tablet system consists of several parts, among which is a flat surface on which the map or graphic to be digitized is placed. This flat surface is typically from 1 to 20 square feet in area, and may be back-lit or even transparent to permit digitizing from transparencies. The user traces the features of interest with either a pen-like stylus or a flat cursor. The electronics in the tablet system convert the position of the stylus or cursor to a computer-compatible digital signal, with a typical precision of 100 to 1000 points per inch.There are several technologies used in commercial digitizing systems. Acoustic systems are relatively low in cost, and often able to work with large- format materials. Such systems use acoustic transducers to triangulate positions on the map or graphic. Electromagnetic and electrostatic systems are also available, and are generally preferred when high accuracy and precision are required. When the tablet has a cursor for tracing data elements, there are often buttons or switches on the cursor itself. This is particularly helpful, since it permits the analyst to select functions without moving from the map or graphic to a computer keyboard.When it is necessary to digitize a map or other graphic with very high precision, the dimensional stability of the medium can become important. Photographic films are considered very stable with respect to changes in temperature and humidity, with distortions below 0.2 percent (Wolfe, 1983). In contrast, paper shrinkage or expansion can range up to 3 percent, depending on paper type and thickness, as well as processing methods. These may be important considerations.When beginning a session with a digitizing tablet, the user must specify a number of attributes of the map, as well as the map's location on the digitizing tablet. Typically, the user will be prompted by the system for information about a map's scale and projection; menus with common choices can help the user to enter this information quickly and accurately (see Figure 6.7). After entering this information, the tablet or stylus is used to specify both georeferencing information (for example, by placing the cursor at locations known latitude/longitude) and a region of interest. For well-known map projections on most systems, these procedures permit any subsequent location of the stylus or cursor to be converted unambiguously into a geodetic location.One of the functions a user should be able to select is the mode of digitizing. In point mode, individual locations on the map (such as elevation benchmarks, road intersections, or water wells) can be entered by placing the cursor over the relevant location and pressing a button. In line mode, straight line segments (such as short segments along political boundaries, straight road sections, or lines of constant bearing on appropriate map projections) are entered by moving the cursor to one end of the line, pressing a button on the cursor, then moving to the other end and pressing a button again. The system automatically converts these two entered points into an appropriate vector. Digitizing curved line segments in this manner can be veryexacting work. In stream mode, the location of the cursor on the map surface is determined automatically at equal intervals of time, or after a specified displacement of the cursor (so that points are approximately evenly spaced; Nagy and Wagle, 1979). Stream mode is particularly useful when digitizing curved line segments, such as the boundaries of waterways. However, in stream mode it is often too easy to create very large data files, since data points are entered into the system so quickly. Further, stream mode can be very demanding on the operator.Neglecting the question of accuracy, digitizing tablets have finite resolution. We normally consider these devices as operating fundamentally for vector input, since we can ostensibly locate any point on the surface of the tablet. However, the finite limits on the precision of these devices provide an underlying raster-like limiting resolution element.Scan digitizing systems, generally called optical scanners or scanning densitometers, are typically larger and more expensive than digitizing tablets (Figure 6.6b). With many high-precision systems, the map (or graphic) to be digitized is attached to a drum. A light source and an optical detector are focused on the surface of the drum. The drum rotates at a constant speed around its major axis. Because of the rotation of the drum, the detector traces a line across the map, and the electronics in the associated computer system record numerical values that represent brightness (or color) on the map. This traced line across the map corresponds to a single row in a raster of data values. The detector then steps along the axis of the drum, in effect movingDIGPOL -- Digitize Polygons, Vectors, Points Version 7. 2.06.67Copyright (C) 1986 ERDAS, Inc. All rights reserved.Installation: U. of California (Santa Barbara) Remote sensing Unit--------------------------------------------------------------------------------------------------Enter Output filename: test1Setup NEW map or use PRSVIOUS setup? (N, P) [New] :Select type of coordinates to use:- UTM - Longitude / latitude- State Plane - Other(U, S,L,O) [UTM] : UTMSelect SCALE of this map:1) 1:24,000 (1”=2000 Feet) 5) 1:63,360 (1”=1 Mile)2) 1:25,000 6) 1:100,0003) 1:50,000 7) 1:125,0004) 1:62,500 8) 1:250,000 (1”=Approx 4 Miles)9) Other (i.e. 1: )? 4Enter UTM X of Reference Point ? 315360Enter UTM Y of Reference Point ? 1625475Digitize LEFT Reference CoordinateDigitize RIGHT Reference Coordinate* Digitize BOTTOM Reference Coordinate------------------------------------------------------------------------- Number of Digitized Points = 11Data Value = 10POLYGON ModeX, Y= 317141.25 1624303.25Use “A”, “B” or “C” on Keypa dto indicate new data value.Polygon Mode= AnnnAVector= BnnnB Point= CnnnCDigitize points by pressingbutton “1”. Back up by pressingthe “E” button. After the lastpoint, press button “2” .If “A”, “B” or “C” not ent ered,the previous data value is used.Continue digitizing polygonsusing buttons “1” and “2”.Signal end o f job with key “D”.Figure 6.7A digitizing menu. (Courtesy ERDAS, Inc. Copyright 1986. Used by permission.) Setting up a new session requires specifying the map coordinate system (UTM in this case), scale (1:62500), and then indicating a control point coordinate location. Left, right, and bottom reference coordinates then are indicated to calibrate the system for any relative rotation between the digitizer surface and the map's coordinate axes. Points, lines, and polygons are then digitized, with the user controlling the system from the 16-button cursor on the tablet.the detector to a new row in the raster, and the process repeats. In this way, the original map is converted to a raster of brightness values.An alternate mechanism uses a line array of photodetectors, which sweeps across the map in a direction perpendicular to the array axis (comparable to the system illustrated in Figure 10.4). Such a device has a much simpler mechanical design than the drum systems mentioned above. In modern systems of either type, the map or graphic to be scanned can be on the order of one meter square, and the scanning step size as small as 20 micrometers. Based on these extreme value, theresulting raster dataset for a one-meter-square map could contain 2.5 ×109pixels, before additional operations are begun. The resulting scanned raster of brightness (or color) may be used directly, or software can convert this initial raster dataset into other forms.A scanning system may be sensitive to 100 or more shades of brightness or color in the source document. These shades of brightness, along with information about spatial patterns in the raster, can be processed by the appropriate software so that the various graphic objects in the source documents can be distinguished. When required, the system may convert the raster dataset to a specified vector format (as discussed in section 6.1.1) as well as compress the dataset size in various ways.A normal sequence of steps in the use of a scanning digitizer would start with the actual scanning process. According to one set of figures, scanning a 24-by-36 inch document at 20 lines per millimeter takes 90 minutes. The raster files are then interactively edited, to ensure that the skeletonizing process accurately extracts the graphic elements in the original dataset. Next, the preprocessed raster data are converted to a vector dataset. The vector data are then structured, to build whatever composite elements (e.g., chains as connected line segments) and topological relations (e.g., containment and adjacency) are required in the ultimate datasets. Finally, the data are interactively edited for quality assurance requirements, and any additional attributes entered and verified.In many cases, there is tremendous redundancy in the raster digital data developed by an automated scanner. One way to store the data with less redundancy is to use run-length encoding, which we discussed in Chapter 4. For example, if we were to scan a page from this book, there would be a long sequence of identical white pixels from the row at the extreme top of the page, since there is no text at the top. Rather than placing many such sequences of identical pixel values in the data file, it is more efficient to use a run-length encoded file, where we store a single copy of the brightness value and the number of repeated pixels of this value.Map-like images are often successfully compressed in this way, due to two characteristics of maps: (1) there are generally few unique brightness (or color) values, and (2) there are often horizontally homogeneous sections. In contrasts this strategy rarely works as efficiently with photograph-like images, since the broader dynamic range and generally high texture of these data types provide a smaller opportunity for many horizontally homogeneous runs. A side benefit of a run-length encoded image is that this data structure explicitly codes for the boundaries in a raster array.Peuquet (1981a) presents case studies of several systems that have been used operationally for converting data from conventional maps to digital datasets. In these systems, the overall conversion process includes scanning the maps, which converts the continuous maps into a discrete raster, extracting the line segments and developing the topologically structured dataset.In section 6.8, we briefly mention more sophisticated means of extracting three-dimensional data from groups of photographs. These techniques are the domain of photogrammetry, and beyond the scope of this text.。

石大《地理信息系统》实习指导06叠合分析

第六章叠合分析一、实习目的1、理解ArcGIS空间分析功能；2、了解基于矢量数据和栅格数据基本空间分析的原理和操作；3、掌握栅格重分类(Raster Reclassify)、、面积制表（Tabulate Area）、分区统计(Zonal Statistic)、邻域统计（Neighborhood）等空间分析基本操作和用途。

4、为选择合适的空间分析工具求解复杂的实际问题打下基础。

二、实习准备2.1预备知识：2.1.1空间分析空间分析是基于地理对象的位置和形态的空间数据的分析技术，其目的在于提取空间信息或者从现有的数据派生出新的数据，是将空间数据转变为信息的过程。

空间分析赖以进行的基础是地理空间数据库。

空间分析是GIS的主要特征。

空间分析能力（特别是对空间隐含信息的提取和传输能力）是GIS区别与一般信息系统的主要方面，也是评价一个GIS的主要指标。

空间分析运用的手段包括各种几何的逻辑运算、数理统计分析，代数运算等数学手段。

空间分析可以基于矢量数据或栅格数据进行，具体是情况要根据实际需要确定。

2.1.2空间分析步骤空间分析实际上是一个地理建模过程，涉及以下基本步骤。

2.2实验数据：Ex6/data1：云南县界.shp ；Clip.shp；西双版纳森林覆盖.shp；西双版纳县界.shp；三、实验内容及步骤1.矢量数据叠合分析叠加操作是空间分析中使用最广泛和频繁的空间分析方法，其中涉及多种图形操作和属性叠加，能够获取2个图层的交叉信息。

1.1图层合并（Union）不同图层的Union操作，执行结果将保留所有产生的新图形和新属性（如图），是其他空间叠加操作的基础。

（1）在ArcMap 中加载数据西双版纳森林覆盖.shp和西双版纳县界.shp；（2）打开ArcToolbox，执行ArcToolbox→Analysis Tools→Overlay→“Union”命令；输入要素：依次添加“西双版纳森林覆盖”“西双版纳县界”两个图层；输出要素类：设置为Union.shp；（3）查看输出要素类：Union 的的属性表，并检查属性“Type”，其中为“Y”的表示有植被覆盖的区域，右键点击图层Union,修改属性－>符号（设置为唯一值图例，字段设置为TYPE）思考：勐海县的总面积是多少平方公里？其中有森林覆盖的区域面积是多少？没有森林覆盖的区域面积是多少？1.2 图层相交：（1）在ArcMap 中加载数据西双版纳森林覆盖.shp和西双版纳县界.shp；（2）执行ArcToolbox→Analysis Tools→Overlay→“Intersect”命令输出要素类InterSect，并与“Union”进行比较，并进一步思考这类操作适合求解哪一些现实问题。

GIS课程教案(第六章空间查询与空间分析)-2

§6-4 叠置分析
一、基于矢量数据的叠置分析
1、内容 1）点与多边形的叠置点与多边形的叠置点层与面层的叠置核心算法为判断点是否在多边形内。核心算法为判断点是否在多边形内。为判断点是否在多边形内 2）线与多边形的叠置线与多边形的叠置线与多边形的叠置是把一幅图(或一个数据层) 线与多边形的叠置是把一幅图(或一个数据层)中的多边形的特征加到另一幅图(或另一个数据层)的线上。特征加到另一幅图( 一个数据层)的线上。线与多边形叠置的算法就是线的多边形裁剪。线与多边形叠置的算法就是线的多边形裁剪。线的多边形裁剪 3）多边形与多边形的叠置多边形与多边形的叠置
地理信息系统原理
GIS
第六章空间查询与空间分析
（三）基于DEM的可视化分析基于DEM的可视化分析 DEM
1、剖面分析 1）意义：意义：
§6-3
DEM分析 DEM分析
常常可以以线代面，研究区域的地貌形态、轮廓形状、地势变化、地质构造、常常可以以线代面，研究区域的地貌形态、轮廓形状、地势变化、地质构造、斜坡以线代面特征、地表切割强度等。特征、地表切割强度等。如果在地形剖面上叠加其它地理变量，例如坡度、土壤、植被、土地利用现状等，如果在地形剖面上叠加其它地理变量，例如坡度、土壤、植被、土地利用现状等，叠加其它地理变量可以提供土地利用规划、工程选线和选址等的决策依据。可以提供土地利用规划、工程选线和选址等的决策依据。 2）绘制可在格网DEM或三角网DEM上可在格网DEM或三角网DEM上格网DEM DEM 进行。进行。已知两点的坐标A(x 已知两点的坐标A(x1，y1)，则可求出两点连线 B(x2，y2)，则可求出两点连线格网或三角网的交点，与格网或三角网的交点，并内交点上的高程，插交点上的高程，以及各交点之间的距离。然后按选定选定的垂之间的距离。然后按选定的垂直比例尺和水平比例尺，直比例尺和水平比例尺，按距高程绘出剖面图绘出剖面图。离和高程绘出剖面图。剖面图不一定必须沿直线绘剖面图不一定必须沿直线绘不一定曲线绘制。也可沿一条曲线绘制制，也可沿一条曲线绘制。

GIS 学习教程

地理信息系统理论与实践2012-10-11Geographical Information System1刘慧平2012.9课程安排●时间：51+3学时（2012年9月－2013年1月）●1GIS 基本原理（34－36学时）– 1.1概述：111Geographical Information System2• 1.1.1什么是GIS ？– 1.2GIS 原理：•1.2.1表达真实世界• 1.2.2GIS 软件功能• 1.2.3数据输入和预处理• 1.2.4网格数据空间分析• 1.2.5矢量数据空间分析– 1.3GIS 应用– 1.4GIS 设计与工程课程安排(cont.)●2.GIS 工具软件实践（12-?学时）–2.1ArcGIS –2.2SupermapGeographical Information System3●3GIS Project 实践（6学时）●4成绩：软件实践（30%）+期末成绩（70%，project+口试）第一讲什么是GIS ？What is GIS?2012-10-11Geographical Information System 4主要内容●什么是GIS ？●为什么用GIS ？Geographical Information System 5●GIS 如何产生？●GIS 组成●应用概述及发展趋势●GIS 产生的相关问题思考( cont.)：●The art, science, engineering, the technology associated with answering geographicalquestions is called Geographical Information Systems (GIS)GIS is a generic term Geographical Information System6Systems (GIS). GIS is a generic termdenoting the use of computers to create and depict digital representations of the Earth's surface. (Longley , Goodchild, 1999)什么是GIS ？●定义：–GIS：Aparticular form ofGeographical Information System7informationsystem applied togeographical data–系统：为实现共同目的，一组相互间有联系的整体。

GIS的基本操作-鲁教版选修七地理信息技术应用教案

GIS的基本操作-鲁教版选修七地理信息技术应用教案一、课程目标本课程旨在为学生带来GIS的基本操作知识，教导学生如何使用GIS软件进行数据的显示、查询以及数据分析。

通过本次课程，希望学生掌握基本的GIS软件使用技巧，并能够根据课堂所学知识，结合实际问题进行GIS数据处理。

二、教学内容本课程的主要教学内容如下：1. GIS的基础知识在本课程中，我们将介绍GIS的基础知识，包括GIS的定义、应用领域以及GIS的基础软件组成部分等内容，以帮助学生对GIS有更加全面的认识。

2. GIS软件的使用本课程将主要介绍GIS软件的使用，包括ArcGIS、QGIS等，以及它们的界面和主要功能模块。

3. GIS数据的处理和分析在本课程中，我们将着重介绍GIS数据的处理和分析，包括数据的读取、查询以及简单的空间分析等内容，并结合实际案例进行教学。

三、课堂教学方法为了便于学生更好地掌握课程内容，本课程采用多种教学方法，包括讲授、演示、实践等。

具体安排如下：1. 讲授教师将在课堂上进行讲授，包括GIS的基础知识、GIS软件的使用方法以及GIS数据处理和分析等方面的内容，以帮助学生快速掌握相关知识点。

2. 演示教师将通过演示的方式，向学生展示如何使用GIS软件完成数据处理和分析等工作。

此外，教师还将使用案例分析等教学方法，帮助学生更好地理解相关知识点。

3. 实践本课程还将设置实践环节，让学生动手完成GIS数据处理和分析等任务，并通过实践加深对相关知识点的理解和掌握。

四、教学设备和材料为了保证课程教学效果，本课程需要准备以下设备和材料：1. 计算机为了使学生能够更好地完成GIS数据处理和分析等任务，本课程需要配备相应的计算机设备。

2. GIS软件本课程所需的GIS软件包括ArcGIS和QGIS等，为了能够让学生真正掌握GIS操作技巧，建议对应软件安装在计算机中。

3. 相关资料为了加深学生对GIS的理解和掌握，本课程还需要提供相关的教学资料，如PPT课件、案例资料等。