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 betwteen 10C and 20C is the same as the distance between 20C and 30C. 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 mathematicians 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 Cha

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地理信息系统中的数据

了解可以存储在任何信息系统中的不同类型的变量很重要。名义变量是以名称描述的变量，没有特定的顺序。土地利用类别（如公园，荒野地区，住宅区和中央商务区）和树木（如桉树，樟子松，栎树等）是不同的名义变量。这些在许多专题地图中很常见。有序变量是离散类的列表，但具有固有的顺序。水流的类别（一阶，二阶等等;参考有助于水流的支流数）或教育水平（小学，中学，大学，研究生以后）是序数变量，因为离散类具有自然序列。间隔变量具有自然的顺序，但是这些值之间的距离也是有意义的。以摄氏度测量的温度是间隔变量，因为10℃和20℃之间的距离与20℃和30℃之间的距离相同。最后，比率变量与区间变量具有相同的特征，但是它们具有自然的零点或起始点。由于摄氏度是任意零点的测量，因此纯水的凝固点的测试最后失败。开尔文，因为它是一个绝对的标准，是比率变量。人均收入，通过指定筛网的土壤样品重量的分数，以及每月降雨量都是常见的比例变量。

除了这四种数据之外，在大多数地理信息系统中都有两种不同的数据类型。考虑一个简单的空间物体：一个水井。从地理信息系统的角度来说，记录关于这个水井的原始但重要的信息是它在地球上的位置——数据值对，如经度和纬度，从而存储最简单的空间数据。然而，许多应用可能需要广泛的附加信息，这可能包括井的深度，在给定时间段内产生的水的体积，泵测试的日期，以及来自井的水中溶解和颗粒物质的时间序列。第二组非空间或属性数据（与逻辑上连接到空间数据）不能被遗忘。在许多地理信息系统中，存在与空间数据一起存储和操纵非空间数据的工具。如我们将看到的，在一些应用中，非空间数据的数量实际上可能大于空间数据的量，并且空间和非空间信息之间的逻辑连接是非常重要的。

美国最近一期《制图学家》（1988年1月）中，美国测绘大会期刊提出了数字制图数据的标准。该标准是基于现实世界中的实体，以及根据数据库中的对象来表示这些实体的机制。 在这个提案中，是一套空间对象的定义，我们现在用这个解释来解释更多的地理信息系统的词汇。这一简要讨论还扩大了第一章关于不同种类的空间物体的评论。可以根据对象的空间维度将不同类型的空间对象划分为三类。

0维对象是指定几何位置的点。从数学家的角度来说，一个点是没有区域范围的原始位置。积分在计算机图形和数字制图数据以及地理信息系统中以多种方式使用，它们通常用于指示特征本身，例如上述水井的确切中心，街道的末端或细分区域的一角。积分也用作地图上的标签（例如地名）或符号（如机场或基准）的保留位置，或用于携带周边地区的信息（例如拥有该地区的人员或显示区域时使用的颜色）。点也用于定义更复杂的空间对象，如行和区域。

最简单的一维对象是两点之间的直线。更复杂的线条形式包括连线的直线组（由路径改变方向的点序列确定），基于数学函数的曲线以及指定方向的线。特定的数学函数集用于定义某些学科的曲线，如在土木工程师使用的街道曲线的功能定义中。有线线段的一个优点是我们有一种方法来区分哪一端是线的开头，哪一端是结束。这对于管道中的流量分析（可能指示饮用水供应系统中的流量来源和目的地）或国家之间的人口流动模式的情况可能特别有价值。当线段携带有关方向的信息时，我们也可以区分线的左侧和右侧的区域。如以后我们将看到的，这在许多应用中可能非常有用。

最后，二维对象也是多种形式的区域。在一个特定的应用中，我们可能会指向一个有界区域，或者仅仅是边界上的焦点，或者只是边界内的区域。区域本身的描述通常基于边界线段的几何形状。该区域可能是内部均匀的或分开的，如第1章所述。通常在二维有界区域集合和真实的三维表面之间进行区分。在某些应用中，基于地球的二维平面图表示的分析可能是完全足够的。我们在这个介绍性文本中专注于这些类型的应用程序。

空间对象之间的连接的细节，例如关于绑定线段的区域的信息，称为拓扑。一些地理信息系统数据库的一个显着特征是它们具有明确的存储拓扑的机制，如第4章所示。

考恩（1987）从几个不同的角度讨论了地理信息系统。数据库方法强调基础数据结构包含复杂地理数据的能力。前几段的空间对象的描述就是这样看的。在第4章中，我们研究了存储空间数据的许多常用备选方案。以流程为导向的方法着重于运行应用程序时分析人员使用的系统元素的顺序——本章开头讨论的五个组件遵循此视图。本文第5至9章代表了这种方法。面向应用的方法根据系统操纵的信息种类和系统生成的派生信息的实用性来定义GIS。第12章介绍了这些空间数据处理系统的一些用途，并且明确地强调了这一观点。自然资源清查系统是这种方法的一个容易理解的例子。最后，工具箱方法强调了应该包含在GIS中的软件组件和算法。我们从第6章和第8章的GIS的角度开发了一些细节。地理信息系统的这些不同观点都是有用的;我们建议读者在以下讨论中考虑它们之间的差异。

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