地理信息系统教程(汤国安,英文版)

Catalog

Chapter 1 Introduction

1.1 Geography

1.2 Information Systems

1.3 A Manual Geographic Information System

1.4 Applications

1.5 Geographical Concepts

1.6 The Four Ms

Chapter 2 Background and History

2.1 The Cartographic Process

2.2 Early History

2.3 The Modern Era

Chapter 3 The Essential Elements of a Geographic Information System: An Overview

3.1 GIS Functional Elements

3.2 Data in a GIS

Chapter 4 Data Structures

4.1 Raster Data Structures

4.1.1 Simple Raster Arrays

Chapter 1 Introduction

As in any other technical area, there can be a fair amount of technical vocabulary to learn before a student can be comfortable with the subject. Since this text is introductory by design, we will try to be consistent in our use of language. Much of the vocabulary of geographic information systems overlaps that of computer science and mathematics in general, and computer graphics applications in particular. We provide a glossary of technical terms at the end of this text, as a reference for the student.

1.1 Geography

Geography has been facetiously defined as that discipline which, when some use is found for it, is called something else. Slightly more serious scholars have defined geography as "what geographers do". The German philosopher Immanual Kant set geography in the context of the sciences by stating that knowledge could be subdivided into three general areas:

1. those disciplines that study particular objects or sets of objects and phenomena (such as biology, botany, forestry, and geology);

2. those disciplines that look at things through time (in particular, history); and

3. those disciplines that look at features within their spatial context(specifically, geographic disciplines) .

In a more classical sense, the word geography may be defined in terms of its constituent parts: geo and graphy. Geo refers to the Earth, and graphy indicates a process of writing; thus geography (in this literal interpretation) means writing about the Earth.

Another definition of geography focuses on man's relationship with the land. In their writings, geographers deal with spatial relationships. A key tool in studying these spatial relationships is the map. Maps present a graphic portrait of spatial relationships and phenomena over the Earth, whether a small segment of it or the entire globe.

It is interesting that in a survey conducted to determine what factors influenced people to adopt the profession of geography, an early interest in maps rated at the top of the list. There are many skills that people possess to a greater or lesser degree. If a person speaks well, he or she possesses fluency. If a person understands writing well, he or she possesses literacy. If a person understands numbers and quantitative concepts well, he or she possesses (at least in Great Britain) numeracy. Similarly, there is a special skill in the analysis of spatial patterns in two and three dimensions. This skill can be referred to as graphary. Although many individuals take this skill for granted, we all know those who have difficulty reading maps or interpreting aerial photographs. What these two activities have in common is the use of an essentially two dimensional view of geographic space, a view that helps the adept map-reader or photointerpreter to understand spatial relationships.

1.2 Information Systems

The function of an information system is to improve one's ability to make decisions. An information system is that chain of operations that takes us from planning the observation and collection of data, to storage and analysis of the data, to the use of the derived information in some decision-making process (Calkins and Tomlinson, 1977). This brings us to an important concept: a map is a kind of information system. A map is a collection of stored, analyzed data, and information derived from this collection is used in making decisions. To be useful, a map must be

Figure 1.1Simplified information system

able to convey information in a clear, unambiguous fashion, to its intended users.

A geographic information system(GIS) is an information system that is designed to work with data referenced by spatial or geographic coordinates. In other words, a GIS is both a database system with specific capabilities for spatially-referenced data, as well a set of operations for working with the data (see Figure 1.1). In a sense, a GIS may be thought of as a higher-order map.

As we shall see later, a modern GIS also stores and manipulates non-spatial data. Just as we have maps designed for specific tasks and users (road maps, weather maps, vegetation maps, and so forth), we can have GISs designed for specific users. The better we are able to understand the range of needs of a user, the better we will be able to provide the correct data and tools to that user.

A geographic information system can, of course, be either manual (sometimes called analog) or automated (that is, based on a digital computer). Manual geographic information systems usually comprise several data elements including maps, sheets of transparent materials used as overlays, aerial and ground photographs, statistical reports and field survey reports. These sets of data are compiled and analyzed with such instruments as stereo viewers, transfer scopes of various kinds, and mechanical and electronic planimeters. Calkins and Tomlinson (1977) point out that manual techniques could provide the same information as computer-aided techniques, and that the same processing sequences may occur. While this may no longer be entirely true, manual GISs have played an extremely important role in resource management and planning activities. Furthermore, there are still applications where a manual GIS approach is entirely appropriate. Although this text focuses on the technology, instrumentation, and utilization of geographic information systems that are automated, it is still helpful to examine a manual GIS first.

1.3 A Manual Geographic Information System

To introduce some of the language of geographic information systems with a simple first example, let's examine an application of a simple manual GIS. This GIS arises during the early steps in developing a site for a golf course. We assume for this discussion that a specific site is already under consideration. A planner has sought out and gathered together a group of existing datasets for the site. This group might

include a topographic map, a blue-line map of parcel boundaries from the local municipal planning agency, and an aerial photograph of the site (Figure 1.2). We refer to these three datasets - 2 maps and a photograph - as data layers or data planes.

The topographic map depicts several kinds of information. Elevation on the site is portrayed as a series of contour lines. These contour lines provide us with a limited amount of information about the shape of the terrain. Certain kinds of land cover are indicated by colors (often blue for water, green for vegetation) and textures or patterns (such as repeated patterns denoting wetlands). A number of kinds of man-made features are indicated, including structures and roadways, typically by lines and shapes printed in black. In many cases, the information on this map is five to fifteen years out of date, a common situation resulting from the rate of change of land cover in the area and the cycle of map updates. Each of these different kinds of information, which we may decide to store in various ways, is called a theme.

The map from the local planning agency provides us with additional and different kinds of information about the area. This map focuses principally on the infrastructure: legal descriptions of the proposed golf course property boundaries, existing and planned roadways, easements of different kinds, and the locations of existing and planned utilities such as potable water, electric and gas supplies, and the sanitary sewer system. The planning map is probably not at the same scale as the topographic map; the former is probably drawn at a larger scale than the latter. Furthermore, the two aren't necessarily based on the same map projection (see section 6.6.1). For a small area like our golf course, the approximate scale of the data is probably more important than the details of the map projection.

The aerial photograph is a rich source of data, particularly for an analyst with some background in image interpretation. A skilled interpreter may be able to detect patterns in soils, vegetation, topography, and drainage, based on the content of the photograph. Unfortunately, this photograph is probably of different scale than either of the two maps, and may have significant geometric distortions. The two maps attempt to be planimetric, that is, the horizontal spatial relationships between objects on the ground are correctly represented on the maps. The photograph, on the other hand, probably suffers from both the perspective distortion inherent in all photographs and from a non-vertical point of view.

A second step in developing plans for this site is to manipulate the three datasets so they can be used simultaneously. A cartographer or draftsperson is given the task of redrawing the municipal planning map and the topographic map onto plastic film, in such a way that the features on the new film-maps overlay their counterparts on the aerial photograph. This process, called registration, in effect causes the objects (buildings, roadways, and so forth) to move form their original locations in the planning map, so that they fall at the positions they are found in the photograph. Alternatively, the photograph could be manipulated in such a way that the visible features overlay the corresponding elements on the planning map, and then a transparency could be made. In any ease, this spatially registered set of data planes is now a useful geographic d atabase. Since the three sets of information have now been converted to overlay each other, further manipulations are much easier. Note that if the original aerial photography were chosen as the base data layer, the resulting database may have no simple relationship to a well-known geodetic coordinate system, such as latitude and longitude. However, for applications that cover a small area, this may not be a serious problem.

Once the individual data layers have been adjusted to a common view of the Earth's surface, there are a number of analytic operations we might make with this

manual geographic information system. The analyst begins by drawing some new features on another sheet of plastic that overlays the other data layers. For example, we might generate 25-meter-wide corridors at the edges of the property, and 10-meter-wide corridors around the existing roads and proposed utility locations. These newly derived regions might suggest some places that are unsuitable for development of large new facilities, and others that are particularly desirable due to proximity to needed utilities. As such, we might now be in a position to make preliminary decisions on the location of the club house, storage yards, access roads, and parking facilities.

Next, the planner lays a coarse grid over the database, and start marking the conjunction of topographic and hydrologic features and vegetation that are most suitable for fairways and tee-off areas. Existing waterways could be used as boundaries between areas on the golf course, while the locations of wooded areas could be considered as part of the course plan. Based on these preliminary decisions, we prepare a new data layer, which is a draft of the proposed course layout. By combining the tentative orientations of tees, fairways, and greens with the original topographic map, we could start to make calculations to estimate volumes of earth that must be moved to create the course. (Such cut-and-fill calculations are often considered the domain of civil engineering.) And once we have a tentative course layout, we can use a planimeter or map wheel to determine the length of each hole, which then provides us with the total course length (which is an important consideration for any golfer). Furthermore, based on a determination of the area of the holes, we can even begin to be able to calculate our needs for grass seed and fertilizer.

Overall, this process has involved a number of key steps. Several different kinds of spatial data were located, and then manipulated so that the important features in each were found at the same locations. Once these data were brought into a common geographic or spatial referencing system, it was possible to use them together, to develop a variety of types of derived information: the determination of potential corridors on the site, proposed locations for constructing facilities, and eventually, engineering estimates for earth moving equipment operators. As we will see, this is a very typical flow of data and information through a spatial data processing and analysis problem.

1.4 Applications

The number of data layers one needs to consider varies greatly from one application to another. Consider a more complex problem: deciding on the location of an airport. Some of the data layers or themes that a planner might require to site an airport include:

Administrative Infrastructure

Land Ownership Transportation Network

Government Jurisdiction Utility Corridors

Rights-of-Way Zoning Restrictions

Mining Claims

Existing and Use Biotic

Endangered Species Abiotic Vegetation Cover Surface Geology

Subsurface Geology Climatic

Surface Water Temperature

Subsurface Water Precipitation

Flood Plains Fog

Archaeological Sites Wind

Elevation Photoperiod

Geographic information systems are used in a wide variety of settings. Landscape architects have embraced the concepts behind GISs for many years, analyzing site suitability and developing capabilities of planning for a specified use (McHarg, 1969). Civil engineers and architects involved in developing large sites have comparable interests and techniques, including considerations of environmental impacts such as noise perception and obscuring or changing views. Forestry professionals use this technology for site mapping and management, and for pest and disease monitoring. City planners are using geographic information systems to help automate tax assessment, emergency vehicle routing, and maintenance of transportation facilities and public lands.

Environmental managers and scientists use these systems for such applications as maintaining an inventory of rare and endangered species and their habitats, and monitoring hazardous waste sites. In addition to these kinds of applications, military planners add several more: gauging the ability of heavy vehicles to traverse different kinds of terrain, and determining which sites on military bases which are suitable for various kinds of training exercises. We discuss in more detail a few of these varied kinds of applications in Chapter 12.

1.5 Geographical Concepts

Before proceeding further, we will introduce a number of terms in common usage (based in part on the brief discussion in Van Roessel, 1987). We will return to some of these in more detail in chapter 3.

Spatial objects are delimited geographic areas, with a number of different kinds of associated attributes or characteristics. The golf course discussed above is a spatial object: it is a specific area on the ground, with many distinct characteristics (such as land use, tax rate, types of vegetation, number of parking spaces, etc.). On the golf course are a number of other spatial objects, such as the greens and fairways.

A point is a spatial object with no area. The holes on our golf course represent points, even though they do in actuality cover a finite area. One of the key attributes of a point are its geodetic location, often represented as a pair of numbers (such as latitude-longitude, or northing-easting). There may be a range of data associated with a point, depending on the application. In our example, we may wish to record the number of the hole, as well as the date when a given hole on our golf course was placed on the green. The latter is useful so that we may remember to move the hole periodically to minimize wear on the green.

A line is a spatial object, made up of a connected sequence of points. Lines have no width, and thus, a specified location must be on one side of the line or the other, but never on the line itself. One important line in our example might indicate the out-of-bounds line between holes. Attributes we could attach to that line include the numbers of the holes that the line separates, and whether the line is indicated on the course by markers of a certain color. Nodes are special kinds of points, usually indicating the junction between lines or the ends of line segments.

A polygon is a closed area. Simple polygons are undivided areas, while complex polygons are divided into areas of different characteristics. Since our example golf course hole has interior objects, such as the sand trap and the green, it is

a complex polygon; since the sand trap is homogeneous (according to the available information in the figure), it is a simple polygon. Attached to the polygons on our golf course might be information about the length and area of each hole, and the kind and amount of seed and fertilizer used to maintain the fairways. Chains are special kinds of line segments, which correspond to a portion of the bounding edge of a polygon.

Figure 1.5 illustrates some of these different kinds of spatial objects, by focusing on one hole in our golf course. The boundary around the entire hole represents the boundary of a complex polygon. The location of the hole (or more specifically, its center) is a point. The 100-yard markers on either side of the fairway are certainly points, but since they form the ends of a line segment, we call them nodes. The portion of the out-of-bounds line that corresponds to the eastern edge of this hole would be considered a chain, since it corresponds to a portion of the polygon surrounding the entire hole.

We have already used the word scale in our discussions. By scale we mean the ratio of distances represented on a map or photograph to their true lengths on the Earth's surface. Scale values are normally written as dimensionless numbers, indicating that the measurements on the map and the earth are in the same units. A scale of 1:25000, pronounced one to twenty five thousand , indicates that one unit of distance on a map corresponds to 25,000 of the same units on the ground. Thus, one centimeter on the map refers to 25,000 centimeters (or 250 meters) on the Earth. This is exactly the same as one inch on the map corresponding to 25,000 inches (or approximately 2,080 feet) on the Earth. Note that scale always refers to linear horizontal distances and not measurements of area or elevation.

Th e terms small scale and large scale are in common use. A simple example helps to illustrate the difference. Consider a field 100 meters on a side. On a map of 1:10000 scale, the field is drawn l centimeter on a side. On a map of 1:1,000,000 scale, the field is drawn 0.1 millimeter on a side. The field appears larger on the 1:10000 scale map; we call this a large-scale map. Conversely, the field appears smaller on the1:1,000,000 scale map, and we call this a small-scale map. Said in another way, if we have a small area of the earth's surface on a page, we have a large-scale map; if we have a large area of the earth’s surface on a page we have a small -scale map.

An important concept when working with spatial data is that of resolution . Most dictionaries define resolution in such terms of “distinguishing the individual parts of an object.” For our purposes, however, we need a more specific definition. Tobler (1987) defines spatial resolution for geographic data as the content of the geometric domain divided by the number of observations, normalized by the spatial dimension. The domain , for two dimensional datasets like maps and photographs, is the area covered by the observations. Thus, for two-dimensional data, take the square root of the ratio to normalize the value. For example, if the area of the United States is approximately 6 million square kilometers, and there are 50 states, then the mean resolution element of a map portraying the states would be:

mean resolution element = ns observatio of number

area = approximately 346 km .

= 5010 626km x

This gives us a way of examining some spatial data, and calculating a representative value for the spatial resolution of the dataset. If we increase the number of observations, the mean resolution element decreases in size. Consider a map of the United States that indicates each of the 3141 counties:

square root of (6 x 2610 km /3141) =

314110 626km x = approximately 43 km .

When we have more information, the mean resolution element gets smaller;

we often call this a higher resolution dataset. Conversely, a lower resolution dataset will have fewer observations in an area, and thus, a larger mean resolution element. As we discuss in section 6.1.1, the size of the resolution element (sometimes abbreviated resel ) is related to the size of the objects we can distinguish in a dataset.

For interested readers, a good discussion of other important concepts,

including geometrical operations and relationships, may be found in Nagy and Wagle (1979).

1.6 The Four Ms

Our understanding of this planet has always been limited by our lack of

information, as well as our lack of wisdom and knowledge. For things too small to see, we have developed microscopes that can image down to the molecular level. At the other end of the continuum, for things that are (in a very real sense) too large to see, we have geostationary satellites that can take an image of an entire hemisphere. Geographic information systems are a means of integrating spatial data acquired at different scales and times, and in different formats.

Basically, urban planners, scientists, resource managers, and others who use

geographic information work in several main areas. They observe and measure environmental parameters. They develop maps which portray characteristics of the earth. They monitor changes in our surroundings in space and time. In addition, they model alternatives of actions and processes operating in the environment. These, then, are the four Ms: measurement, mapping, monitoring, and modeling (Figure 1.4). These key activities can be enhanced through the use of information systems technologies, and in particular, through the use of a GIS.

Geographic information systems have the potential for improving our

understanding of the world around us. Yet these systems do not lessen the need for quality data, nor will these systems do the work for us. The work we can do with a GIS is clearly dependent on the quality of data it contains. Thus, care must be taken to understand the potential sources and relative magnitudes of errors which may occur when gathering and processing spatial data. In addition, one must be cautious of the potential for misinterpretation of the information output from a GIS.

In interacting with a geographic information system, the user must not only

understand the application, but also the characteristics of the tool and the system itself. Like all advanced technologies, the kinds of spatial data processing systems we will discuss must be employed wisely, to keep us from fooling ourselves. The following chapters discuss the wise use of geographic information systems.

Chapter 2 Background and History

Geographic information systems evolved as a means of assembling and

analyzing diverse spatial data. Many systems have been developed, for land-use planning and natural-resource management at the urban, regional, state, and national levels of government agencies. Most systems rely on data from existing maps, or on data that can be mapped readily (Shelton and Estes, 1979).

The development of geographic information systems has its roots in at least

two overlapping areas: an interest in managing the urban environment (particularly in terms of planning and renewal), and a concern for the balancing competing uses of

environmental resources. Technology has played a critical role in addressing these concerns. If we look at John Naisbitt's 1984 work Megatrends, we can see why. Megatrends discusses new directions which are transforming our lives. In Naisbitt's words, none of the megatrends discussed “is more subtle, yet more explosive than . . . the megashift from an industrial to an information society.” This information society had its beginnings in 1956 and 1957. Indeed, the advances in communications and computer technology that facilitated the widespread dissemination of the ideas and concepts contained in Rachel Carson's book Silent Spring also provided the foundations and requirements that necessitated the construction of automated geographic information systems.

Today, environmental scientists and resource managers have access to more data than ever. Naisbitt (1984) estimates that scientific information is doubling every five years. The key to coping with this information explosion is the employment of systems- systems that will take the data, analyze it, store it, and then present it in forms that are useful. These are the requirements of an information system.

2.1 The Cartographic Process

According to Robinson and Sale (1969), cartography is often described as a meeting place of science and art. This science/art is fundamentally directed at communicating information to a user and is central to an understanding of the strengths and weaknesses of geographic information systems technology. Much of the material contained in this book is directly related to essential elements of the cartographic process, which involves a body of theory and practice that is common to all maps.

Maps are both a very important form of input to a geographic information system, as well as common means to portray the results of an analysis from a GIS. Like a GIS, maps are concerned with two fundamental aspects of reality: locations, and attributes at locations. Location represents the position of a point in two-dimensional space. Attributes at a location are some measure of a qualitative or quantitative characteristic, such as land cover, ownership, or precipitation. From these fundamental properties a variety of topologic and metric properties of relationships may be identified, including distance, direction, connectivity and proximity. As Robinson et al. (1984) observe, “a map is therefore a very powerful tool”. Indeed, maps are powerful tools for communicating spatial relationships. Following Robinson et al., maps:

?are typically reductions which are smaller than the areas they portray. As such,

each map must have a defined relationship between what exists in the area being represented, and the mapped representation. This relationship is of primary importance. Scale sets limits on both the type and manner of information that can be portrayed on a map.

?involve transformations. Often in mapping, we are faced with a need to

transform a surface which is not flat (such as a portion of the earth's surface).

In order to represent such a surface on a flat plane, map projections are employed (see section 6.6.l). Choice of a particular projection has an impact on how a given map may be used. Plane coordinate grids are often used on maps as systems of reference.

?are abstractions of reality. Maps are the cartographer's representation of

an area, and as such, display the data that the cartographer has selected for

a specific use. Thus, the information portrayed on a map has been classified

and simplified to improve the user's ability to work with the map.

?contain symbols which represent elements of reality. Few map symbols have

universally accepted meanings, but some maps use a standardized set of symbols.

?portray data using a variety of marks, including lines, dots, tones, colors,

textures, and patterns.

In addition to these basic characteristics of maps, the user of maps and other products of a geographic information system should understand the errors which may affect them. The sources of errors fall into three categories (Burrough, 1986): obvious sources, those resulting from natural variation and original measurement, and those arising through processing.

Obvious sources:

The source data may be too old to be of value.

The areal coverage of a given data type, within a given time frame, may not be complete.

The scale of the map may restrict the type, quantity, and quality of the data which may be presented.

The number of observations within the target area may not be sufficient to be able to determine the spatial patterns in the objects of interest.

Practical matters such as the time, funds, and staff which are available may not permit us to produce a product of the required characteristics.

Natural variation and the original measurement s:

Positional accuracy of the source data may not be sufficient, due to problems in the field data itself, instrument errors, and lack of rigor in the compilation process.

Attribute errors also may come from a variety of sources, including both mis-identification and compilation problems.

Processing:

Numerical errors may include round-off or dynamic-range errors in arithmetic computations.

Errors in logic may cause us to manipulate the data incorrectly, thus leading us to fool ourselves. Common problems in this area are associated with classification and generalization.

The above lists focus on errors in a single map sheet, or a single GIS data layer. When working with many layers at once, the separate layers may not be

completely compatible in terms of scale and accuracy, thus complicating the task of creating an accurate, final, analytic product. This, as well as other problems, such as developing efficient and cost-effective means for verifying the accuracy of map and GIS products, are active research areas.

2.2 Early History

Cartography is defined in the Multilingual Dictionary of Technical Terms in Cartography(Meynen, 1973) as “the art, science and technology of making maps together with their study as scientific documents and work of art.” Map-making per se can be traced back to the ancient cultures of Mesopotamia and Egypt. The earliest known map, a regional map imprinted in a clay tablet, dates from about 2500 B.C. Yet people must have been making maps much earlier than that. Simple arrangements of sticks or pebbles were probably used to illustrate geographic relationships long before clay tablets or papyrus came into vogue. A mound of dirt, a few pebbles, and a small furrow made with a stick, could have illustrated important game trails or berry-picking locations, and could thus have been the first analog geographic information system.

Parent and Church (1988) state that the origins of more sophisticated geographic information systems go back to early developments in cartography. They reference the mid-eighteenth-century production of the first accurate base maps as an important point in GIS development. As Parent and Church point out, until the development of high-quality base maps, the accurate graphic depiction of spatial attributes was not possible. These developments were followed by a rapid expansion of the use of thematic mapping. The idea of recording various layers of spatial data on a series of similar base maps was an established cartographic convention by the time of the American Revolutionary War (Harley et al., 1978). For example, a map by French military leader and cartographer Louis Alexandre Berthier (1753-1815) contained hinged overlays showing troop movements during the 1781 Siege of Yorktown (Rice and Browns, 1974).

In the early part of the nineteenth century, advances in both the physical and social sciences provided geographers with important intellectual tools for the analysis of spatial data. Such fields as statistical analysis, number theory, and advanced mathematics flourished. The first geologic maps of London and Paris appeared. The work of the distinguished German geographer Alexander Freiherr von Humbolt (1769-1859) became influential. The British census of 1825 produced a tremendous amount of data to be analyzed, and the science of demography soon evolved.

Church and Parent (1988) state that by 1835, technology (in particular, advanced cartographic techniques), social science, and social thought (specifically, concepts of environmental responsibility) had progressed to support new and improved levels of thematic mapping. However, it was the economic changes of the industrial revolution, according to Church and Parent, that provided the main catalyst for the early evolution of geographic information systems in this time period. The explosion in manufacturing, with the attendant demand for raw materials and labor, created the need for a new, extensive infrastructure, both social and industrial. Indicative of all these changes is a transportation study, completed in 1837, that first brought together technical, social, and scientific advances related to spatial data analysis, into a coherent whole. The Atlas to Accompany the Second Report of the Irish Railway Commissioners, appearing in 1838, consisted of a series of maps with a uniform base, depicting population, traffic flow, geology, and topography.

As this example indicates, cartographers realized some 150 years ago that a single map may not contain all the data required to satisfy a given information need. Indeed, the data may not exist in map form at all, but in graphs, text, or statistical tables. As researchers and resource managers began to ask more and more complex questions about their environment, their need for improved methods of processing spatially distributed data increased. This need lead to the beginnings of automated geographic data processing in the late 1800's.

Streich (1986) states that American statistician Herman Hollerith (1860-1929) was the father of automated geoprocessing. Hollerith adapted punched-card techniques, which had been used in France to program looms, to help process the information collected in the 1890 United States Census. Hollerith conceived the idea of punching raw demographic data onto cards and using machines to sort and collate this data. Streich goes on to state that". . . the move to 'electro-mechanical' data-processing technology for census tabulation characterizes a fundamental tenet of geoprocessing -- that a need exists to rapidly, accurately, and cost-effectively collect, analyze, and distribute spatially disposed information."

In 1936, Charles Colby's presidential address to the Association of American Geographers (Colby, 1936) laid out research challenges in geography. Among these challenges particular emphasis was placed on the development of quantitative approaches to map-based problems. In doing so,Colby set the stage for the modern era of geographic information systems.

2.3 The Modern Era

From these early beginnings, advances in computing, cartography, and photogrammetry laid the technological foundations for the automated geographic information systems that began to appear in the 1960s. The conceptual framework within which early geographic information systems were implemented involved individuals in many disciplines. Researchers and resource managers in diverse areas realized that there was a need for integrating data from a variety of sources, to manipulate the sets of data to analyze them, and then to be able to provide information for a resource planning and management decision process.

Three important factors helped lead to the creation of digital geographic information systems in the 1960's:

?refinements in cartographic technique,

?rapid developments in digital computer systems, and

?the quantitative revolution in spatial analysis.

These developments were very important in that they helped to provide analytic tools as well as stimulation to researchers and professionals in a variety of applications. In addition to these, we must not forget the advances in geographic thought that helped to bring about the modem GIS. Chrisman (1988) points out that the “cult of novelty and high technology”can blind us as to various disciplines' contributions to GIS development. As a geographer, he points to Sauer's early work in the upper peninsula of Michigan, as well as to the land-use research of Whillesey, Finch, and others that foreshadowed our current paradigm of stacking layers of thematic information. The roots of such map overlay may be traced back at least 100 years in the field of landscape architecture (Steinitz et al., 1976).

In 1969, Ian McHarg's Design with Nature was published. This work formalized the concept of land suitability/capability analysis (SCA). SCA is a technique in which data concerning land use in a locale being studied is entered into an analog or digital GIS. SCA programs are used to combine and compare data types

via a deterministic model, in order to produce a general plan map. If the model is carefully implemented, and suitable data is available, this map should be consistent with existing land-use classes and the constraints that are imposed by both natural and cultural features. Design with Nature is a seminal work, influencing the use of overlays of spatially referenced data layers in the resource planning and management decision process. McHarg's efforts in SCA have been followed by many articles in this area.

Resource management concerns spurred development of spatial data-processing systems in the U.S. government during the past two decades. A system called STORET was developed by the Public Health Service for storage of spatial information about water quality (Green, 1964). Another, called MIADS, was developed by the U.S. Forest Service for the analysis of recreation alternatives and hydrology (Amidon, 1964). The Census Bureau was also heavily involved in geocoding and automated spatial data processing at this time.

In the university community at this time, Harvard's Laboratory for Computer Graphics developed and made available a series of automated mapping and analysis programs. The University of Washington at Seattle also made important contributions, particularly in the areas of transportation analysis and urban planning and renewal (Gaits, 1969). Urban planning applications blossomed with the development of these kinds of tools; by 1968 thirty-five urban and regional planning agencies in the United States were using automated systems (Systems Development Corp., 1968).

The first system in the modern era to be generally acknowledged as a GIS was the Canada Geographic Information System or CGIS(Peuquet,1977). Roger Tomlinson (1982), involved in the design and development of the system, states that CGIS was designed specifically for the Agricultural Rehabilitation and Development Agency Program within the Canadian government. The main purpose of CGIS was to analyze Canadian Land Inventory data, which was being collected to find marginal lands. Therefore, in the broadest sense, the first geographic information system was developed to help with an environmental problem: rehabilitation and development of Canada's agricultural lands.

The Canadian Geographic Information System was implemented in 1964 (Deuker, 1979). This was one year after the first conference on Urban Planning Information Systems and Programs, a conference which led to the establishment of the Urban and Regional Information Systems Association. The New York Landuse and Natural Resources Information System was implemented in 1967, and the Minnesota Land Management Information System in 1969. In these early years, the costs and technical difficulties of implementing a GIS prevented all but national-and state-government agencies from developing these systems.

In 1977, a report issued by the United States Department of the Interior's Fish and Wildlife Service compares the selected operational capabilities of 54 GISs (USFWS, 1977). This survey, which is representative of several others conducted in the late 1970s, provides information on the hardware environment, programming language, documentation, and characteristics of the systems. This survey lists many GISs developed by federal and state agencies, as well as universities. However, it contained information on only a few commercial GISs. Even today, few commercial firms offer fully integrated geographic information systems. Streich (1986) estimates that there are ten commercial firms offering GISs on the open market.

Why is it that there are so few commercial firms offering geographic information systems, even today? There is no simple answer. A GIS is a complex hardware and software system, and requires considerable expertise in a variety of

geographic, computer science and systems engineering areas. Nevertheless, some GISs are being developed in the private sector. These commercially available systems provide a wide range of well-integrated GIS capabilities. As technology and scientific understanding improve, the development of geoprocessing systems becomes more and more open to commercial firms. Instead of being a large-user in-house activity, the development of geoprocessing systems will likely be taken over by commercial firms and made available to a variety of hardware environments, discipline interests, and goals.

In addition to the beginnings of commercial GIS development, the 1970s also saw significant developments in image processing and remote sensing systems, which often had some GIS functions. Such firms as the Environmental Systems Research Institute in California began operation. Image processing systems with some GIS elements were developed at the Jet Propulsion Laboratory and at the Purdue University laboratory for Applications of Remote Sensing. These latter systems incorporated GIS capabilities as the remote sensing community quickly realized that ancillary GIS data could play an important role in improving the accuracy of the interpretation of remotely sensed data.

Developments in remote sensing technology and applications during this decade spurred practical and theoretical work in the areas of geometrical corrections and registration. The coupling of map and image data also drove work in raster-vector data format conversion (see Chapter 6). Today there are a number of both commercial and public-domain image processing systems that possess sophisticated GIS capabilities; however, we still believe that a great deal more work needs to be done in terms of effectively integrating remotely sensed data into traditionally vector-based geographic information systems.

An interesting footnote to this brief history of geographic information systems comes from a conference held at the University of Calgary in 1982. The conference title was "Computer Assisted Cartography and Geographic Information Processing: Hope and Realism." In a session led by Roger Tomlinson, several individuals were critical of the operational status of the system. The criticisms revolved around the question of whether the system was meeting the needs of the users. Tomlinson responded that the system was originally designed for one specific class of users, who wished to analyze land inventory data and find marginal farms. This class of users had effectively disappeared, and there were now over 100 other users and a nine-month backlog of work. He concluded that this tremendous number of users and long backlog indicated that the system was successful. In response to Tomlinson's argument, it was noted that for many applications, a nine-month backlog was intolerable; when users cannot get their work done on the system, something is indeed not right.

The message we derive from this story is that problems will certainly arise as geographic information systems designed and implemented for a specific class of problems are used for other purposes. Users and system managers must guard against inappropriate use of systems and must establish priorities and long-range upgrade and migration policies to meet the needs of changing user communities and changing data.

In summary, the development of geographic information systems, in terms of both the underlying concepts and the technology, has drawn on the talent and experience of many researchers and investigators. It has grown out of concerns about the state of the physical and cultural environment, and it has been advanced by efforts in both the public and private sectors. Many early systems were developed to solve relatively narrow, specific kinds of problems. The past twenty years have seen an

explosion in the technological base for these systems, particularly in the areas of data processing and remote sensing systems. The 1980s have seen continued growth in GIS applications, significant system refinements, and a modest expansion of the commercial availability and applicability of geographic information systems.

While many operational systems may be limited in terms of the geographic area, the number of data types, and the modeling and analytic capabilities they can provide, they can perform many operations that only 25 years ago were considered unfeasible. One recent trend in the evolution of GIS technology is the inclusion of artificial intelligence into GIS design and operation. This topic was the subject of a workshop at a recent international symposium in Zurich, Switzerland (Smith, 1984). We will examine some of these far-reaching discussions in section 12.8. Let us now consider some of the generic components and functions of geographic information systems.

Chapter 3 The Essential Elements of a Geographic Information System: An Overview

As we said in the first chapter, an information system is fundamentally an end-to-end system, which deals with the flow of data and information from its primary sources to the derived information and its ultimate uses. Geographic information systems are designed to handle information regarding spatial locations. In this chapter, we will introduce the essential functional components of a GIS, and will discuss some key concepts in geography and geographic data processing.

3.1 GIS Functional Elements

There are five essential elements that a GIS must contain (Figure 3.l; based on the discussion in Knapp, 1978): data acquisition, preprocessing, data management, manipulation and analysis, and product generation. For any given application of a geographic information system, it is important to view these elements as a continuing process. We will introduce each of the elements in this chapter, and will examine each in greater detail later in this text. As a guiding principle, the analyst should develop an end-to-end model of the task at hand. Even when the precise details of the steps to be taken may depend on the results of intermediate calculations and analyses, an explicit outline of the process, like a working hypothesis in a scientific experiment, can be very valuable.

Data acquisition is the process of identifying and gathering the data required for your application. This typically involves a number of procedures. One procedure might 'be to gather new data by preparing large-scale maps of natural vegetation from field observations, or by contracting for aerial photography. Other kinds of surveys may be required to determine, for example, consumer satisfaction and preferences in different parts of a city to help locate new business offices. Other procedures for data acquisition may include locating and acquiring existing data, such as maps, aerial and ground photography, surveys of many kinds, and documents, from archives and repositories.

One must never underestimate the costs (in time as well as money) of the data-acquisition phase. A GIS is of no use to anyone until the relevant data have been identified and located. Furthermore, the accuracy (of the decisions reached through spatial analysis is limited by the accuracy and precision of the underlying datasets. We

often know too little about the underlying quality of many kinds of spatial data. At times, however, we may be forced to use maps and other datasets whose underlying quality is unknown. And without spending some effort ensuring that various datasets are not only relevant but also reliable, we run the risk of fooling ourselves.

Preprocessing involves manipulating the data in several ways so that it may be entered it into the GIS. Two of the principal tasks of preprocessing include data format conversion and identifying the locations of objects in the original data in a systematic way. Converting the format of the original data often involves extracting information from maps, photographs, and printed records (such as demographic reports) and then recording this information in a computer database. This process is a time-consuming and costly efforts for many organizations. This is particularly (and sometimes painfully) true when one calculates the costs of converting large volumes of data based on paper maps and transparent overlays, to an automated GIS based on computerized datasets. We will discuss aspects of the process in section 6.l.

A second key task of the preprocessing phase is to establish a consistent system for recording and specifying the locations of objects in the datasets. When this task is completed, it is possible to determine the characteristics of any specified location in terms of the contents of any data layer in the system. During these processes, it is very important to establish specific quality control criteria for monitoring the operations during the preprocessing phase so that the databases can be of maximum value to the user.

Data-management functions govern the creation of, and accession, the database itself. These functions provide consistent methods for data entry, update, deletion, and retrieval. Modern database management systems isolate the users from the details of data storage, such as the particular data organization on a mass storage medium. When the operations of data management are executed well, the users usually do not notice. When they are done poorly, everyone notices: the system is slow, cumbersome tease, and easy to disrupt. Under these latter circumstances, the smallest human and machine errors create large problems for both the users and the system operators. Data-management concerns include issues of security. Procedures must be in place to provide different users with different kinds of access to the system and its database. For example, database update may be permitted only after a control authority has verified that the change is both appropriate and correct.

Manipulation and analysis are often the focus of attention for user of the system. Many users believe, incorrectly, that this module is all this constitutes a geographic information system. In this portion of the system are the analytic operators that work with the database contents to derive new information. For example, we might specify a region of interest and request that the average slope of the area be calculated, based on the contours of elevation that have already been stored in the GIS database. Since no single system can encompass the complete range of analytic capabilities a user can imagine, we must have specific facilities to be able to move data and information between systems. For example, we may need to move data from our GIS to an external system where a particular numerical model is available, and then transport the derived results back into the spatial database inside the GIS. This kind of modularity, where other data processing and analysis systems can be linked to a GIS, is very valuable in many circumstances, and permits the system to be easily extended over time by pairing it with other analytic tools. When one speaks of geoprocessing, one is often focused on the manipulation and analysis components of a GIS.

Product generation is the phase where final outputs from the GIS are created. These output products might include statistical reports (such as a table listing the average population densities for each county in California, or a report indicating landowners who are delinquent in their property taxes), maps (for example, a presentation of the property boundaries of plots within a township that are owned by public agencies, or a map of a subdivision indicating where construction workers must be careful when digging due to the presence of underground pipes and cables), and graphics of various kinds (such as a set of bar charts that compare the acreage of different crop types in an area). Some of these products are soft copy images: these are transient images on television-like computer displays. Others, which are durable since they are printed on paper and film, are called hard copy. Increasingly, output products include computer-compatible materials: tapes and disks in standard formats for storage in an archive or for transmission to another system. The capability of taking the output of an analytic process, and placing it back into the geographic database for future analysis, is extremely important.

These essential components of a geographic information system are the same as those of any other information system. Let us compare this sequence of functional elements to a more conventional information system problem. Consider the steps that are taken in an automated system to manage employee records for a business. Information about the individuals must be gathered together, perhaps via a questionnaire and interview when the individual is hired. This is clearly the data acquisition phase. Then, because some of the information is inevitably expressed by different people in different ways (for example, some people will list their education as "through grade 12", while others will say "through high school"), the data must be put into a consistent vocabulary and format. Only after this preprocessing phase can the data be entered into the computer in a consistent form. Validation of the data entered into the system is a fundamental part of the preprocessing phase, to insure the accuracy of the resulting database.

Once the data have been converted into a consistent form and put in the computer database, we have accomplished a large fraction of the end-to-end task and often expended a large fraction of the end-to-end costs. Data management functions permit as to update the information when necessary (for example, when an employee completes an advanced degree), and to retrieve only the relevant information when required (as in a summary report of salaries for a particular division of the company). Various kinds of analytical operations can be run--perhaps using employee addresses to find out which employees live close to one another in an effort to encourage car pooling. Finally, we need to be able to develop statistical reports, graphics of many kinds, and other output products, such as documentation for management reviews of salary levels. These steps exactly parallel the five GIS components we will discuss in detail.

3.2 Data in a GIS

It is important to understand the different kinds of variables that can be stored in any information system. Nominal variables are those which are described by name, with no specific order. Categories of land use (such as parks, wilderness areas, residential districts, and central business districts) and trees (such as Eucalyptus calophylla, Pinus coulteri, and Quercus agrifolia) are different kinds of nominal variables. These are common in many kinds of thematic maps. Ordinal variables are lists of discrete classes, but with an inherent order. Classes of streams (first order, second order, and so forth; referring to the number of tributaries which contribute to

the stream) or levels of education (primary, secondary, college, post-graduate) are ordinal variables since the discrete classes have a natural sequence. Interval variables have a natural sequence, but in addition, the distances between the values have meaning. Temperature measured in degrees Celsius is an interval variable, since the distance between 10?C and 20?C is the same as the distance between 20?C and 30?C. Finally, ratio variables have the same characteristic as interval variables, but in addition, they have a natural zero or starting point. Since degrees Celsius is a measurement with an arbitrary zero point, the freezing point of pure water, it fails the latter test. Degrees Kelvin, since it is based on an absolute standard, is ratio variable. Per capita income, the fraction of the weight of a soil sample that passes through a specified sieve, and rainfall per month are common ratio variables.

In addition to these 4 kinds of data, there are two different classes of data found in most geographic information systems. Consider a simple object in space: a water well. From the point of view of a GIS, the primitive but essential piece of information to record about this water well is its location on the Earth -- a data value pair such as longitude and latitude, thus storing the simplest kind of spatial data. However, there may be a wide range of additional information which is required for many applications. This might include the depth of the well, the volume of water produced over a given period of time, dates of pump tests, and temporal sequences of measurements of dissolved and particulate matter in the water from the well. This second set of non-spatial or attribute data, which is logically connected to the spatial data, must not be forgotten. In many geographic information systems, there are tools to both store and manipulate the non-spatial data along with the spatial data. In some applications, as we will see, the volume of non-spatial data may actually be larger than the volume of the spatial data, and the logical connections between the spatial and non-spatial information may be very important.

A recent issue of The American Cartographer (January, 1988), the journal of the American Congress on Surveying and Mapping, proposes a standard for digital cartographic data. This standard is based on entities in the real world, and a mechanism to represent these entities in terms of objects in a database. Within this proposal is a set of definitions of spatial objects, which we now paraphrase to explain more of the vocabulary of geographic information systems. This brief discussion also expands on the comments in Chapter l about different kinds of spatial objects. One may divide the different kinds of spatial objects into three classes, based on spatial dimensions of the objects.

A 0-dimensional object is a point that specifies a geometric location. From a mathematician's perspective, a point is a primitive location with no areal extent. Points are used in a number of ways in both computer graphic and digital cartographic data, as well as in a geographic information system. They are commonly used to indicate features themselves, such as the exact center of the water well mentioned above, the end of a street, or the corner of a lot in a subdivision. Points are also used as a reserved position for a label (such as a place name) or a symbol (such as an airport or benchmark) on a map, or to carry information for the surrounding region (such as who owns the region, or the color to be used when the region is displayed). Points are also used to define more complex spatial objects, such as lines and areas.

The simplest 1-dimensional object is a straight line between two points. More complex forms of lines include connected sets of straight lines (determined by the sequence of points at which the path changes direction), curves which are based on mathematical functions, and lines whose direction is specified. Particular sets of mathematical functions are used to define curves in some disciplines, as in the

functional definition of the curve of a street used by a civil engineer. One advantage of a directed line segment is that we have a way to distinguish which end is the beginning of the line, and which end is the end. This may be particularly valuable in circumstances as diverse as the analysis of flow in pipes (perhaps indicating source and destination for flow in a potable water supply system) or models of population flow between countries. When the line segments carry information about direction, we are also able to distinguish the regions on the left and right sides of the line. As we shall see later, this can be very useful in a number of applications.

Finally, 2-dimensional objects are areas, which also come in many forms. In a particular application, we may refer to a bounded area, or focus on just the boundary, or just the region within the boundary. The description of the area itself is normally based on the geometry of the bounding line segments. The area may be either homogeneous or divided internally, as discussed in Chapter l. A distinction is often made between sets of two-dimensional bounded regions, and true three-dimensional surfaces. In some applications, an analysis based on a two-dimensional planimetric representation of the Earth may be completely sufficient. We focus on these kinds of applications in this introductory text.

The details of the connections between spatial objects, such as the information about which areas bound a line segment, is called topology. One of the distinguishing features of some geographic information system databases is that they have explicit mechanisms to store topology, as we shall see in Chapter 4.

Cowen (1987) discusses a geographic information system from several different points of view. The database approach stresses the ability of the underlying data structures to contain complex geographical data. The descriptions of spatial objects in the previous several paragraphs take this view. In Chapter 4 we examine a number of common alternatives to storing spatial data. The process-oriented approach focuses on the sequence of system elements used by an analyst when running an application -- the five components we discussed at the beginning of this chapter follow this view. Chapters 5 through 9 in this text represent such an approach. An application-oriented approach defines a GIS based on the kinds of information manipulated by the system and the utility of the derived information produced by the system. Chapter 12 presents a number of uses of these spatial data processing systems, and clearly emphasizes this view. A natural resources inventory system is an easily understood example of this approach. Finally, a toolbox approach emphasizes the software components and algorithms that should be contained in a GIS. We develop a number of details from this point of view of a GIS in Chapters 6 and 8. Each of these different points of view of a geographic information system is useful; we recommend that the reader consider the differences between them during the following discussions.

Chapter 4 Data Structures

There are a number of different ways to organize the data inside any information system. The choice of a particular spatial data structure is one of the important early decisions in designing a geographic information system. While very few of us will ever design a GIS from start to finish, knowledge of data structures is valuable from several points of view other than system design. Fundamentally, users must be aware of the characteristics of several different structures, since several different standard forms are commonly used, and the choice of a data structure can affect both data storage volume and processing efficiency. From another point of view,

when we are collecting our own data, we must make a choice of data structure for storage. Also, in an operational or research environment, it is often necessary to convert datasets between several different data structures, either to work with several kinds of data at the same time, or to import an unusual dataset into an existing system. It is very important to be able to understand how these conversions affect the underlying information itself.

As we stated in the introduction, each different type kind of spatial data or theme in a GIS is referred to as a data layer or data plane. In each of these data layers there are three primitive types of geometrical entities to encode (after Peucker and Chrisman, 1975): points, lines, and polygons or planes. Some authors make a distinction between the representation of a truly three-dimensional surface, such as elevation datasets, and a representation of space in two dimensions, such as legal boundaries of land ownership on a flat map. In this chapter, we will focus on the latter.

The essential function of the spatial data we store and manipulate is to subdivide the Earth's surface into meaningful entities or objects that can be characterized. In this way, the content of a spatial database is a model of the Earth. Points, such as the locations of oil and water wells, and lines, such as the centerlines of roadways or streams, are key elements of this breakdown into component parts. When we consider bounded regions, such as the borders of a subdivision or the edges of a reservoir, we often focus on the boundary lines, and call the enclosed regions polygons. These polygonal regions are not necessarily defined in the precise terms of geometry, where a polygon is ordinarily a planar figure bounded by a series of straight line segments. In spatial data processing, common usage relaxes the requirement that the bounding line segments be straight; we use the term polygon even when the boundaries are curved. We note, however, that not all GISs can work directly with curves as such, but more often permit a single line to have interior digitized points in addition to the end points. Many sophisticated applications have been developed around networks of lines, such as the network developed by the arteries of a transportation or communications system, or a variety of piping systems such as a sanitary sewer or a pressurized gas delivery system.

The above discussion concentrates on the geometry of the data. Equally important is the non-spatial or attribute data, which in some systems requires a greater amount of data storage. For a simple spatial object like a water well, the essential spatial information is the geodetic location of the well. The attribute data can include wide range of ancillary information about that well, including its depth, date of drilling, production volume, ownership, and so forth. Many geographic information systems have specialized capabilities for storing and manipulating the attribute data in addition to the spatial information.

4.1 Raster Data Structures

One of the simplest data structures is a raster or cellular organization of spatial data. In a raster structure, a value for the parameter of interest -- elevation in meters above datum, land use class from a specified list, plant biomass in grams per square meter, and so forth -- is developed for every cell in a (frequently regular) array over space. For example, in Figure 4.1, elevation in meters above mean sea level has been recorded at locations on a regular grid. The original data is from a topographic map, from which we have extracted the contour lines. The raster array of elevations is derived from these contour lines using procedures discussed in section 6.7. This kind of data structure is intuitive; we might imagine a survey team determining elevations

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操作地图 ArcMap提供了许多方法让你与地图进行交互操作。 浏览 地图可以让人们发现要素之间的空间关系。可以用刚才打开的地图查询市政大楼（City Hall）的位置，查看靠近学校的公园，或了解图书馆旁的街道的名称。 分析 可以通过向地图中添加图层获取新的信息和发现隐含的规律。例如，如果在Greenvalley地图中添加了人口统计信息，就可以用这张地图进行学区的划分或发现潜在的消费顾客。如果添加了地质或地表坡度的图层信息，就可以用这张地图确定可能发生山崩的危险地区。 显示结果 ArcMap可以打印地图，并能将其嵌入到其他文件或电子出版物中。用户可以迅速地组织数据制作成图，保存地图时，所设计的打印版面、符号、注记和图表都同时被保存。 ArcMap中包含了一大批创建和使用地图的工具。在本章后面的内容中，用户将使用其中的一些工具。定制 地图是一种很有效的工具。如果地图中包含了可对其进行编辑加工的工具，将有助于用户更快地完成工作。用户可以通过向工具条中添加或删除工具，或创建个性化的工具条，轻松地定制ArcMap的界面。这些经过定制的界面可以和地图一起保存。 用户也可以运用包含在ArcMap中的编程语言工具VBA （Visual Basic for Applications）来开发新的工具和创建界面。例如，运用VBA可开发一个工具，完成在一个选定区域内制作房屋地址数据表的功能。一旦设计出某种工具，把它和定制的工具条相关联，或把这个工具和地图存储在一起，其他人就能使用这个工具了。 编程 为了便于同地图进行交互操作，用户可以自行设计新界面，创建特殊要素类。ArcGIS是完全组件对象模型（COM）化的，开发人员可以使用任何一种与之兼容的编程语言来制作组件。如果需要更多关于定制ArcMap和ArcCatalog的信息，可以参阅《Exploring ArcObjects》一书。 浏览A RC C A TA LOG 和A RC M A P25

MAPGIS67教程(制图详细步骤讲解)

第1章概述与安装 1.1 概述 MAPGIS 是中国地质大学(武汉)开发的、通用的工具型地理信息系统软件。它是在享有盛誉的地图编辑出版系统MAPCAD 基础上发展起来的，可对空间数据进行采集，存储，检索，分析和图形表示的计算机系统。MAPGIS 包括了MAPCAD的全部基本制图功能，可以制作具有出版精度的十分复杂的地形图、地质图，同时它能对图形数据与各种专业数据进行一体化管理和空间分析查询，从而为多源地学信息的综合分析提供了一个理想的平台。 MAPGIS 地理信息系统适用于地质、矿产、地理、测绘、水利、石油、煤炭、铁道、交通、城建、规划及土地管理专业，在该系统的基础上目前已完成了城市综合管网系统、地籍管理系统、土地利用数据库管理系统、供水管网系统、煤气管道系统、城市规划系统、电力配网系统、通信管网及自动配线系统、环保与监测系统、警用电子地图系统、作战指挥系统、GPS 导航监控系统、旅游系统等一系列应用系统的开发。 1.2安装 1）系统要求： 硬件：CPU 486 以上、16M RAM、200M 硬盘、256 色以上显示器； 操作系统：Win9x、Win2000、WinNT 、WinXP或Win7系统； 输入设备：本单位主要使用的是GRAPHTEC—RS200Pro型扫描仪； 输出设备：本单位主要使用的是Canon—IPF700型出图打印机。 2) 硬件的安装： MAPGIS 硬件部分有加密狗，ISA 卡、PCI 卡三种，本单位主要为MAPGIS USB 软件狗，在确保机器BIOS 设置中USB 设备未被禁止的条件下，Windows 98 和Windows2000 自带的标准USB 驱动程序均可支持MAPGIS USB 软件狗工作。 3）软件的安装： MAPGIS 安装程序的安装过程为：找到MAPGIS 系统安装软件，双击SETUP 图标，系统自动安装软件，在WIN2000/NT/XP 下安装时，应先运行WINNT_DRV,提示成功后才可选择SETUP 开始MAPGIS 程序的安装； 对于MAPGIS6.1 及MAPGIS6.5，则无关键字和安装选择，但须根据实际需要选择安装组件。 从上述组件中选择实际运用中需要的选项，根据提示即可完成安装。

ArcGIS入门教程(1)——ArcMap应用基础

ArcGIS入门教程（1）——ArcMap应用基础 实验一 ArcMap应用基础 一、目的 通过实验操作，掌握ArcMap软件的基础操作，主要包括地图文档打开与保存、图层显示与数据查看、简单符号化、要素标识、注记添加、地图元素添加、地图排版与打印，对ArcMap软件的基础操作加以熟悉。 二、数据 （1）地图文档文件（airport.mxd）； （2）源数据文件（airport.gdb），其中各图层含义如下：“Schools”表示初级、中级、高级和私立学校的位置；“Runways”表示机场跑道的位置；“Arterials”表示主干道；“Cne165”表示噪声等值线；“Airport_area”表示计划的机场扩建区；“county”表示县界。 三、步骤 3.1 启动ArcMap 在开始菜单中找到ArcMap并单击打开，启动ArcMap，ArcMap启动界面如图1所示。 图1ArcMap启动界面 说明：打开ArcMap时，会弹出【ArcMap 启动】对话框。该对话框提供了几种启动ArcMap对话的选项。可以在左边目录中，打开一张最近打开过的地图文件。

3.2 打开地图文档 （1）点击主菜单中的【文件】→【打开】，启动【打开】对话框，在对话框中选择到需要打开的Mxd 地图文档，如图2所示。 图2 选择地图文档 说明：地图文档（.Mxd）一种ArcMap存储地图的形式，可以被用户显示、修改或者与其他用户共享。但地图文档（.Mxd）并不存储实际的数据，而是存储实际数据在硬盘上的指针和有关地图显示的信息。地图文件一般还存储了地图的其他信息，如地图的大小、所包含的地图元素（标题、比例尺等）。同时还需要注意的是，不同版本的Mxd文件是不同的，高版本可以兼容之前的版本，但是低版本却无法打开高版本的Mxd文件。 （2）点击【打开】，将选择的地图文档加载到ArcMap中，地图文档加载结果如图3所示，左侧为内容表，列出了可用来显示的地理图层；右侧为地图显示区。

mapgis67矢量化图的一般流程

图像处理的一般流程： 标准图框的生成--------格式的转化----------影像的校正-------画图-------图像的输出 一、图像的校正： 1、标准图框的生成：打开狗，双击图标-------实用服务-------投影变换 系列标准图框生成-----根据图幅号生成图框（K50E022012、K50E022013）

本图中使用地理坐标实线经纬网 图框模式简介： 地理坐标十字经纬网： ①在外图框用短线画地理坐标标记，用十字画经纬网并标记分秒的值； ②图幅外框写高斯坐标：在外图框写高斯坐标，用短线画地理坐标标记； ③单线内框：只画内图框； ④高斯坐标实线经纬网：外框写高斯坐标，用短线画地理坐标标记，图框内用实线画公里网； ⑤地理坐标实线经纬网：在外图框用短线画地理坐标标记，用实线画经纬网并标记分秒的值；

相同的方法生成K50E022013图框（自己练习） 2、将图片转换为Msi格式 图像处理----------------图像分析--------------- 文件------数据输入------

数据转换类型选择JPG格式，点击添加文件，点击需要转化的图像，打开 弹出“操作成功完成后！”点击确定 点击文件-----打开影像，看转化情况然后将其关闭 因此图跨越两个图幅，所以在校正前先应该将图框合并，为了方便校正，将图框添加一些必要的坐标信息

3、图框的编辑与合并 图像处理-------出入编辑-------点击确定-----从文件导入（因为图框有投影等地图参数信息），一直确定 点击左边空白处-----添加项目

右边空白处点击右键--------复位窗口，出现图框 （1）合并图框（只针对需要合并的图框）其他文件的合并也是相同的方法和步骤线文件的合并，将线文件处于编辑状态，并选中 点击右键------合并所选项-----

arcgis基础工具教程

1/数据的导入（添加） （1）点击添加数据 （2）点击小三角，找到你所需添加文件所在文件夹位置 （3）选中你所要添加的文件，添加，即可在内容列表看到你所添加进来的文件

2、数据的导出 右键需要导出图层，点击【数据】-【导出数据】，导出到所要放的文件夹，命名文件。点击保存即可，导出的图层会自动加载到左边内容列表

3.属性的标注 右键你所需图层，点击【属性】，切换到标注，点击标注此图层中的要素，标注字段选择你所需标注的字段，如地类名称，地类编码，行政村等 3、属性选择。例如选择河流名称为港边水的河流，右键图层，，点击【打开属性表】，点击【按属性选择】，双击河流名称，河流名称会出现在下面输入框里，点击【=】，点击【获取唯一值】，双击港边水，下面输入框里即就出现河流名称=港边水的字样，代表属性选中，点击应用，即选中

4、数据合并 数据合并需注意，要合并的数据必须同为面或者同为线，同为点，面和线，面和点，线和点都是不可以合并的，两个以上数据都可以合并，可以是两个、三个、四个，多个，点击【地理处理】-【合并】，输入所需合并的数据，此处合并11年和13年的数据，输出数据，选择你所需放的文件夹位置，命名输出的文件，点击确定，输出后的文件会自动加载到内容列表

5、数据筛选，打开属性表，按ctrl+f,即可打开查找，输入查找内容，文本匹配可选择任何部分，也可选择整个字段，可以选择仅搜索所选字段 6、字段添加。打开属性表，点击左上角按钮，点击【添加字段】，输入字段名称，选择类型，常用为文本型和双精度型，文本型要定义字段长度，双精度要定义精度和小数位数

(推荐下载)MAPGIS67教程(制图详细步骤讲解)

(完整word版)MAPGIS67教程(制图详细步骤讲解) 编辑整理： 尊敬的读者朋友们： 这里是精品文档编辑中心，本文档内容是由我和我的同事精心编辑整理后发布的，发布之前我们对文中内容进行仔细校对，但是难免会有疏漏的地方，但是任然希望（(完整word 版)MAPGIS67教程(制图详细步骤讲解)）的内容能够给您的工作和学习带来便利。同时也真诚的希望收到您的建议和反馈，这将是我们进步的源泉，前进的动力。 本文可编辑可修改，如果觉得对您有帮助请收藏以便随时查阅，最后祝您生活愉快业绩进步，以下为(完整word版)MAPGIS67教程(制图详细步骤讲解)的全部内容。

第1章概述与安装 1.1 概述 MAPGIS 是中国地质大学(武汉)开发的、通用的工具型地理信息系统软件。它是在享有盛誉的地图编辑出版系统 MAPCAD 基础上发展起来的,可对空间数据进行采集,存储,检索,分析和图形表示的计算机系统。MAPGIS 包括了 MAPCAD的全部基本制图功能，可以制作具有出版精度的十分复杂的地形图、地质图，同时它能对图形数据与各种专业数据进行一体化管理和空间分析查询，从而为多源地学信息的综合分析提供了一个理想的平台。 MAPGIS 地理信息系统适用于地质、矿产、地理、测绘、水利、石油、煤炭、铁道、交通、城建、规划及土地管理专业，在该系统的基础上目前已完成了城市综合管网系统、地籍管理系统、土地利用数据库管理系统、供水管网系统、煤气管道系统、城市规划系统、电力配网系统、通信管网及自动配线系统、环保与监测系统、警用电子地图系统、作战指挥系统、GPS 导航监控系统、旅游系统等一系列应用系统的开发。 1。2安装 1）系统要求: 硬件：CPU 486 以上、16M RAM、200M 硬盘、256 色以上显示器; 操作系统：Win9x、Win2000、WinNT 、WinXP或Win7系统； 输入设备:本单位主要使用的是GRAPHTEC—RS200Pro型扫描仪； 输出设备：本单位主要使用的是Canon—IPF700型出图打印机。 2）硬件的安装： MAPGIS 硬件部分有加密狗，ISA 卡、PCI 卡三种，本单位主要为 MAPGIS USB 软件狗，在确保机器 BIOS 设置中 USB 设备未被禁止的条件下，Windows 98 和 Windows2000 自带的标准 USB 驱动程序均可支持 MAPGIS USB 软件狗工作。 3）软件的安装: MAPGIS 安装程序的安装过程为：找到 MAPGIS 系统安装软件，双击SETUP 图标，系统自动安装软件，在 WIN2000/NT/XP 下安装时,应先运行 WINNT_DRV,提示成功后才可选择 SETUP 开始 MAPGIS 程序的安装； 对于 MAPGIS6。1 及 MAPGIS6。5，则无关键字和安装选择，但须根据实际需要选择安装组件。 从上述组件中选择实际运用中需要的选项，根据提示即可完成安装。

新手学习mapgis教程

------------------- 时磊5说----- - ---- ------- 新手学习mapgis教程 一、几个术语 图层：按照一定的需要或标准把某些相关物体组合在一起。可以把图层理解为一张透明薄膜， 每一层的图元在同一薄膜上，如水系力图层、铁路图层、地质界限图层、断层图层等。图层的分层有利于地图图元的管理，提高成图速度。 栅格图：即扫描的图像。 矢量图：即进行了数字化的图像，图中的每一个点都有相对的X和Y座标。 图元：图面上表示空间信息特征的基本单位，分为点、线（孤段）、多边形等三种类型。 点元：点图元的简称，有时称点。指其位置只有一组X和Y座标来控制。 它包括字符串（注释）、子图（专用符号）等。所有点保存在点文件中（*.wt）结点：指某线或孤段的端点或数条线或孤段的交点。 结点平差：使几条线或孤段成为共用一个结点的过程 线图元：地图中线状物的总称。如划线、省界、国界、地质界线、断层、水系、公路等。所有线图元都保存在线文件中（*.wl） 区图元（面图元）：由线或孤段组的封闭区域，可以以颜色和花纹图案填充。如湖泊、地层、岩体分布区等。所有区图元都保存在区文件中（*.wp ）。 工程：对一系列的点、线、面文件进行管理的描述性文件。 二、MAPGIS几种主要文件类型及后缀 .wp区（面）文件.pnt控制点文件 .wl线文件 .wt点文件 ?tif栅格文件 .rbm光栅求反后文件 .mpj工程文件 ?cln图例板文件 点文件（.wt）：包括文字注记、符号等。即在输入时，文字和符号都存在点 文件中。在机助制图时，文字注记称为注释（如各种标注等），符号称为子图（矿 点符号，泉符号等）。 线文件（.wl）:是由境界线、河流、航空线、海岸线等线状地物组成的图元。 面文件（.wp）:将各个行政区进行普染色后，就得到了区文件。在理论上，区是封闭的线组成的区域，因此区是基于线生成的。 工程（.mpj）:对一系列的点、线、面文件进行管理的描述性文件。其主要记录了各个文件的信息，如存放地、可编辑性等。（如何建立工程，见以后） 图形处理一输入编辑 一、输入编辑步聚 主要步骤为： 1将图件进件进行扫描，成灰度、或彩色、或二值。 2、建立相应的工作目录，即建立自已的工作文件夹。女口mapgis学习 3、将扫描的图像拷入到工作文件夹中， 4、将系统库（相当于\mapgis65\slib文件夹）拷到工作文件夹中。

VB+ArcGis Engine 开发零基础GIS程序框架教程

VB+ArcGis Engine开发零基础GIS程序框架教程 第一步配置环境和设计界面 环境：ArcGisEngine 9.1 + Microsoft Visual Basic 6.0 使用Engine控件：ESRI ToolbarControl, ESRITOCControl 、ESRILicenseControl、ESRIMapControl。 （按Ctrl+T调出部件面板，选中以下控件） 再从[工程]-[引用]添加一下引用：

界面布局（右侧大的MapcControl命名为MapControl1，为显示地图主界面。左下角的MapcControl命名为MapControl2，作为地图鹰眼。在工具栏里添加如图的几个按钮即可。其它再添加一个CommonDialog1和状态栏）：

在ESRI ToccControl和 ESRIToolbarControl属性里绑定控件EsriMapControl （buddy选择MapControl1）。 这样基本界面就布置好了。 第二步加载地图 代码为： '打开地图文档 On Error Resume Next Dim sFileName As String With CommonDialog1 .DialogTitle = "Open Map Document" .Filter = "Map Documents (*.mxd;*.pmf)|*.mxd;*.pmf" .ShowOpen If .FileName = "" Then Exit Sub sFileName = .FileName End With If MapControl1.CheckMxFile(sFileName) Then MapControl1.LoadMxFile sFileName

ArcGIS教程：路径分析

ArcGIS教程：路径分析 求解路径分析表示根据要求解的阻抗查找最快、最短甚至是最优的路径。如果阻抗是时间，则最佳路线即为最快路线。如果阻抗是具有实时或历史流量的时间属性，则最佳路径是对指定日期和时间来说最快的路径。因此，可将最佳路径定义为阻抗最低或成本最低的路径，其中，阻抗由您来选择。确定最佳路径时，所有成本属性均可用作阻抗。 可在路径分析中累积任意多个阻抗属性，但累积属性不会对沿网络计算路径造成任何影响。例如，如果选择时间成本属性作为阻抗属性，并且希望累积距离成本属性，最终仅会使用时间成本属性来优化解。求解过程中将累积并报告总距离，但此例中的路径并不是根据距离计算得出的。 查找通过一系列停靠点的最佳路径将遵照与执行其他网络分析相同的工作流。 一、路径分析图层 路径分析图层将存储路径分析的所有输入、参数和结果。 1、创建路径分析图层 要通过 Network Analyst 工具条创建路径分析图层，可以单击 Network Analyst > 新建路径。

创建新的路径分析图层后，该图层即会与它的五个网络分析类(停靠点、路径、点障碍、线障碍和面障碍)一起显示在Network Analyst 窗口中。 路径分析图层也会以名为“路径”的复合图层显示在内容列表中(如果地图文档中已经存在名称相同的路径，则会以路径 1、路径 2 等显示)。存在五种要素图层 - 停靠点、路径、点障碍、线障碍和面障碍。其中的每个要素图层都有默认的符号系统，您可在图层属性对话框中对这些默认的符号系统进行修改。 二、路径分析类 路径分析图层由五种网络分析类组成。 下面各部分概述了每个类及其属性。 1、停靠点类 该网络分析类用于存储路径分析中用作停靠点的网络位置。“停靠点”图层包含四种默认符号：已定位停靠点、未定位停靠点、有错误的停靠点和有时间冲突的停靠点。您可以在图层属性对话框中修改“停靠点”图层的符号系统，此对话框中包含停靠点的自定义符号系统类别，它位于 Network Analyst > 序列化的点中。 创建新的路径分析图层后，“停靠点”类为空。仅当将网络位置添加到该类后，它才不为空。创建路径至少需要两个停靠点。 2、停靠点属性 一些停靠点属性仅在定义起始时间或启用时间窗后才可用，其中，起始时间和时间窗均是路径分析图层的图层属性对话框的分析设置选项卡中的参数。 3、路径类 路径类存储通过分析生成的路径。与其他要素图层相同，它的符号系统也可通过图层属性对话框进行访问和更改。

mapgis的一些实用方法和处理技巧

mapgis的一些实用方法和处理技巧 一、如何将mapgis的图形插到word、excel、PowerPoint 中 首先点取mapgis菜单“其他-＞OLE拷贝”，接着打开word，点取“粘贴”。 Mapgis数据就复制到word文档里。 二、空心字格式 使用空心字时，字体采用相应字体编号的负数。如：-3表示黑体空心字。 三、合并区 1、可以在屏幕上开一个窗口，系统就会将窗口内的所有区合并，合并后区的图形参数及属性与左键弹起时所在的区相同。 2、也可以先用菜单中的选择区功能将要合并的区拾取到，然后再使用合并区功能实现。 3、还可以先用光标单击一个区，然后按住 CTRL 键，在用光标单击相邻的区即可。 四、翻转图形 在Mapgis中的其它下面整图变换中比例参数的X比例中输入法-1或Y比例中输入-1后确定。 五、CAD转化为MAPGIS 1．将CAD文件另存为2004/2000DXF格式。 2．在MAPGIS主程序中选择“文件转换”。 3．输入中选择转入DXF文件，确定并复位 4．保存点线文件（面无法转化） 六、MAPGIS转化为CAD 1．在MAPGIS主程序中选择“文件转换”。 2．分别装入点线文件，复位并全选。 3．输出中选择“部分图形方式输入DXF”全选并确定。 4．打开保存的DXF文件，用CAD复位显示图形，并改字体样式。 5．保存成CAD格式。 七、如何把JPG格式的转成ＭＳＩ格式 图象处理----------图象分析模块。在里面点：文件--------数据输入--------转换数据类型（选JPG）---------添加文件---------转换转换后的格式为mapgis的msi影像文件！转换为ＭＳＩ文件格式后再在输入编辑里，导入后矢量化。 八、在电脑里如何做剖面图，不用手画，而且精度更高！

MAPGIS67操作技巧汇总

MAPGIS6x平台3 1、输入编辑模块中，新建工程背景为黑色？3 2、把“公路”错画到“河流层”里，如何将它改到“公路”层？3 3、在MAPGIS中，如何同时输出三幅图？3 4、如何将矢量化后的图形文件上载到SQL Server数据库中？3 5、用户提供了线文件，在输入编辑中打开，却看不到，会有那些原因？3 6、如何将*wb格式的数据导入到Excel或Access中去？4 7、将图形文件的属性导入的Excel或Access表中与此类似4 8、镶嵌融合的过程应有这样几种认识4 9、误差校正中，选择采集文件时，为什么一个是点文件（方里网wt），一个则是线文件（标准wl）？4 10、USB狗的安装使用问题4 11、如何在wb表中装入图片？5 12、MAPGIS多用户版与MAPGIS网络版有何区别？5 13、MAPGIS数据转成AutoCAD数据？5 14、点、线、面工程数据，在输入编辑模块中，工程输入，看不到数据？6 15、AutoCAD数据转入MAPGIS6 16、MAPGIS中的大地坐标系解释6 17、为什么有些笔记本电脑无法使用MAPGIS软件狗？6 18、工程图例中的分类码和编码的用法6 19、图元参数中的“透明”是什么意思？6 20、完成自动节点平差后，为什么图形变化较大？7 21、为什么扫描的光栅文件，在编辑系统中打开时会提示内存不足或者不能正确显？7 22、做子图时为什么总是存不到子图库中？7 23、为什么打开线型库或子图库时，库中图元若隐若现？7 24、如何将MAPGIS的图形插入到word中？7 25、打印光栅文件时应该选用哪个打印机？7 26、如何使用TrueType字库输出PS或EPS文件？7 27、图形打印输出后为什么图形会放大？8 28、使用“WINDOWS打印”时为什么会有图元丢失？8 29、出现飞点怎么办？8 30、超大图形如何自动分幅打印图形？8 31、能否将图形形转为图像？8 32、为什么在输出PS或EPS时总是出现“打不开文件aiheadPs”提示？8 33、怎么发专色胶片？8 34、为什么HP500不能打印光栅文件？8 35、在输出模块中，对文件进行输出处理时，常会提示“非法操作”或“某图元出错”的信息。如何处理这些报错信息？8 36、如何将MAPGIS的图形数据成功转换为MAPINFO的图形数据？9 37、如何制作DXF文件转入MAPGIS的对照表？9 38、由ARC/INFO转到MAPGIS的文件为什么转回ARC/INFO时是空文件？10 39、如何重新整理图元的ID号？10 40、如何生成非标准图框？10 41、建地图库时如果有跨带现象情况如何处理？10

arcgis10.0__中文教程_

练习10 –ArcView GIS 3.3 网络分析：网络分析是GIS空间分析的重要组成部分，它的主要内容包括： ●寻找最佳行进路线，如：找出两地通达的最佳路径。 ●确定最近的公共设施，如：引导最近的救护车到事故地点。 ●创建服务区域，如：确定公共设施（医院）的服务区域。 通过对本实习的学习，应达到以下几个目的： ●加深对网络分析基本原理、方法的认识； ●熟练掌握ARCVIEW网络分析的技术方法。 ●结合实际、掌握利用网络分析方法解决地学空间分析问题的能力。 1．寻找最佳路径 (2) 2. 确定最近设施 (6) 3. 创建服务区域 (7) 软件准备：Arcview GIS 数据准备：街道图层：s_fran 医院图层：hospital.shp 事件位置：del_loc.shp 加载Arcview网络分析模块： 执行菜单命令：[Files]>>[Extension] 命令，在Extensions对话框中选中Network Analyst，单击OK，即装入Network Analyst空间分析扩展模块。 注：通过菜单命令[Network]>>[Show Problem Definition…] 可以查看当前网络分析问 题的定义。 运行ArcV iew GIS

11．寻找最佳路径 为邮递员设计最佳投递路线，该路线应是投递时的最短路线，并选择最有效率的投递顺序。具体的操作如下： (1)运行[开始]>>[程序]>>[ESRI]>>[ArcView GIS 3.3]>>[ArcV iew GIS3 .3] 在出现的对话框中选择[with a new View] 新建一个视图 (2)在视图界面下点击添加图层按钮(如下图中红色前头所示)可以从硬盘上添加 Shape文件 添加城市街道的网络线层面S_fran和投递点层面Del_loc。（见下图）。

MAPGIS教程(基础篇)

MAPGIS教程(基础篇)

基础篇 （ＭＡＰＧＩＳ） 广东友元国土信息工程有限公司编制 2009年7月 2

目录 第一讲MAPGIS 简介 (4) 一、几个术语 (4) 二、MAPGIS 几种主要文件类型及后缀 (5) 三、MAPGIS 总体结构 (5) 四、MAPGIS 安装 (6) 第二讲图形处理—输入编辑 (8) 一、输入编辑步聚 (8) 第三讲线、点的输入及编辑操作错误!未定义书签。 第四讲造区 (12) 一、检查线是否有错误 (12) 二、造区 (12) 三、需熟知的几种区操作 (13) 第五讲误差校正 (16) 第一步：采集较正控制点 (17) 第二步：数据较正 (18) 第六讲数据转换 (19) 第七讲标准图框-投影变换 (21) 一、投影系统及坐标系简介 (22) （一）、常用的投影类型 (22) （二）、坐标系（椭球参数） (22) （三）、高斯—克吕格投影 (22) 二、投影变换需注意问题： (23) 三、标准图框生成 (23) （一）已知图的四个角的经、纬度，生成 3

标准图框。 (23) （二）已知图的四个角的大地坐标，生成 标准图框 (27) 第八讲图形裁剪 (28) 第九讲建立工程 (29) 第十讲系统库编辑 (32) 符号拷贝 (34) 第十一讲图例板 (35) 第十一讲属性库 (40) 第一讲MAPGIS 简介 一、几个术语 图层：按照一定的需要或标准把某些相关物体组合在一起。可以把图层理解 为一张透明薄膜，每一层的图元在同一薄膜上，如水系力图层、铁路 图层、地质界限图层、断层图层等。图层的分层有利于地图图元的管 理，提高成图速度。 栅格图：即扫描的图像。 矢量图：即进行了数字化的图像，图中的每一个点都有相对的X 和Y 座标。 图元：图面上表示空间信息特征的基本单位，分为点、线（孤段）、多边形等三种类型。 点元：点图元的简称，有时称点。指其位置只有一组X 和Y 座标来控制。 它包括字符串（注释）、子图（专用符号）等。所有点保存在点文件中（*.wt）结点：指某线或孤段的端点或数条线或孤段的交点。 4

ArcGIS基本操作教程

Arcgis基本操作教程 （所有资料来自网络）

目录 1.配准栅格地图 (1) 1.1跟据图上已知点来配准地图 (1) 1.1.1选择标志性程度高的配准控制点 (1) 1.1.2从基础数据底图上获取控制点坐标 (1) 1.1.3增加Georeferncing 工具条 (2) 1.1.4加载需要配准的地图 (3) 1.1.5不选择Auto Adjust (3) 1.1.6在要配准的地图上增加控制点 (4) 1.1.7重复增加多个控制点检查残差 (5) 1.1.8更新地图显示 (5) 1.1.9保存配准图像 (6) 1.1.10增加有坐标的底图检验配准效果 (7) 1.2根据GPS观测点数据配准影像并矢量化的步骤 (9) 2.图形的失量化录入 (11) 2.1半自动失量化 (11) 2.1.1启动ArcMap (12) 2.1.2栅格图层的二值化 (12) 2.1.3更改Symbology设置 (13)

2.1.4定位到跟踪区域 (14) 2.1.5开始编辑 (14) 2.1.6设置栅格捕捉选项 (15) 2.1.7通过跟踪栅格像元来生成线要素 (16) 2.1.8通过跟踪栅格像元生成多边形要素 (18) 2.1.9改变编辑目标图层 (18) 2.1.10结束你的编辑过程 (20) 2.2批量矢量化 (20) 2.2.1启动ArcMap，开始编辑 (20) 2.2.2更改栅格图层符号 (21) 2.2.3定位到实验的清理区域 (22) 2.2.4开始编辑 (23) 2.2.5为矢量化清理栅格图 (23) 2.2.6使用像元选择工具来帮助清理栅格 (24) 2.2.7使用矢量化设置 (27) 2.2.8预览矢量化结果 (28) 2.2.9生成要素 (29) 2.2.10结束编辑过程 (31) 2.3手工数字化 (31) 2.3.1在ArcCatalog下新建一个空的shapefile： (31)

ArcGIS中文基础教程

第一章GIS 的概念和需求 理解GIS的三种角度： GIS是一个用于管理、分析和显示地理信息的系统。地理信息可以通过一系列地理数据集来表达。而地理数据集则通过使用简单的，普通数据结构来为地理信息建模。GIS包含了一套用以处理地理数据的综合工具。 我们可以从多个角度来理解地理信息系统是如何工作于地理信息的： 1．从空间数据库的角度看：GIS是一个包含了用于表达通用GIS数据模型（要素、栅格、拓扑、网络等等）的数据集的空间数据库。 2．从空间可视化的角度看：GIS是一套智能地图，同时也是用于显示地表上的要素和要素间关系的视图。底层的地理信息可以用各种地图的方式进行表达，而这些表现方式可以被构建成“数据库的窗口”，来支持查询、分析和信息编辑。 3．从空间处理的角度看：GIS是一套用来从现有的数据集获取新数据集的信息转换工具。这些空间处理功能从已有数据集提取信息，然后进行分析，最终将结果导入到数据集中。 这三种观点在ESRI ArcGIS中分别用ArcCatalog（GIS是一套地理数据集的观点）、ArcMap（GIS是一幅智能的地图）和ArcToolbox（GIS是一套空间处理工具）来表达。这三部分是组成一个完整GIS的关键内容，并被用于所有GIS 应用中的各个层面。 从空间数据库的角度： GIS是世界上独一无二的一种数据库――空间数据库（Geodatabase）。它是一个“用于地理的信息系统”。从根本上说，GIS是基于一种使用地理术语来描述世界的结构化数据库 这里我们来回顾一些在空间数据库中重要的基本原理。 地理表现形式 作为GIS空间数据库设计工作的一部分，用户要指定要素该如何合理的表现。例如，地块通常用多边形来表达，街道在地图中是中心线（centerline）的形式，水井表现为点等等。这些要素会组成要素类，每个要素类都有共同的地理表现形式。 每个GIS数据集都提供了对世界某一方面的空间表达，包括： 基于矢量的要素（点、线和多边形）的有序集合

MAPGIS67化探制图教程简介

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