ip地址有几种分类

Posted 2023-04-24

参考技术A

最初设计互联网络时，为了便于寻址以及层次化构造网络，每个IP地址包括两个标识码（ID），即网络ID和主机ID。同一个物理网络上的所有主机都使用同一个网络ID，网络上的一个主机（包括网络上工作站，服务器和路由器等）有一个主机ID与其对应。Internet委员会定义了5种IP地址类型以适合不同容量的网络，即A类~E类。

其中A、B、C3类（如下表格）由InternetNIC在全球范围内统一分配，D、E类为特殊地址。

1、A类IP地址

一个A类IP地址是指， 在IP地址的四段号码中，第一段号码为网络号码，剩下的三段号码为本地计算机的号码。如果用二进制表示IP地址的话，A类IP地址就由1字节的网络地址和3字节主机地址组成，网络地址的最高位必须是“0”。A类IP地址中网络的标识长度为8位，主机标识的长度为24位，A类网络地址数量较少，有126个网络，每个网络可以容纳主机数达1600多万台。

A类IP地址 地址范围1.0.0.0到127.255.255.255[1]  （二进制表示为：00000001 00000000 00000000 00000000 - 01111110 11111111 11111111 11111111）。最后一个是广播地址。

A类IP地址的子网掩码为255.0.0.0，每个网络支持的最大主机数为256的3次方-2=16777214台。

2、B类IP地址

一个B类IP地址是指，在IP地址的四段号码中，前两段号码为网络号码。如果用二进制表示IP地址的话，B类IP地址就由2字节的网络地址和2字节主机地址组成，网络地址的最高位必须是“10”。B类IP地址中网络的标识长度为16位，主机标识的长度为16位，B类网络地址适用于中等规模的网络，有16384个网络，每个网络所能容纳的计算机数为6万多台。

B类IP地址地址范围128.0.0.0-191.255.255.255[3]  （二进制表示为：10000000 00000000 00000000 00000000----10111111 11111111 11111111 11111111）。 最后一个是广播地址。

B类IP地址的子网掩码为255.255.0.0，每个网络支持的最大主机数为256的2次方-2=65534台。

3、C类IP地址

一个C类IP地址是指，在IP地址的四段号码中，前三段号码为网络号码，剩下的一段号码为本地计算机的号码。如果用二进制表示IP地址的话，C类IP地址就由3字节的网络地址和1字节主机地址组成，网络地址的最高位必须是“110”。C类IP地址中网络的标识长度为24位，主机标识的长度为8位，C类网络地址数量较多，有209万余个网络。适用于小规模的局域网络，每个网络最多只能包含254台计算机。

C类IP地址范围192.0.0.0-223.255.255.255[3]  （二进制表示为: 11000000 00000000 00000000 00000000 - 11011111 11111111 11111111 11111111）。

C类IP地址的子网掩码为255.255.255.0，每个网络支持的最大主机数为256-2=254台。

ArcGIS中的几种分类方法

参考技术A Equal Interval classification - GIS Wiki | The GIS Encyclopedia

    The equal interval classification method divides attribute values into equal size ranges.Unlike quantile classification（分位数分类法）, the number of records that fall into each category (or bin) will differ. Equal Interval Classification in GIS - GIS Geography

    相等间隔会将属性值的范围划分为 若干个大小相等的子范围 。您可以指定间隔数，ArcGIS 将基于值范围自动确定分类间隔。例如，如果为取值范围为 0-300 的字段指定三个类，ArcGIS 将创建三个类，其取值范围分别为 0–100、101–200 和 201–300。

    ArcGIS PRO文档：Equal interval is  best applied to familiar data ranges, such as percentages and temperature . This method emphasizes the amount of an attribute value relative to other values. For example, it shows that a shop is part of the group of shops that make up the top one-third of all sales. （相等间隔最适用于常见？的数据范围，如百分比和温度。这种方法强调的是某个属性值相对于其他值的量。例如，它可显示某个商店为一组商店的一部分，而该组商店的销售额占总销售额的三分之一。）

    维基百科：Equal interval is useful when distribution of the data has a rectangular shape in the histogram（数据的分布在直方图中呈矩形，也就是说分布均匀） . However, in geography, equal interval is most common when the classification units are nearly equal in size.

       One advantage of using equal interval classification is that the steps to compute the intervals can easily be completed using a calculator or pencil and paper. A second advantage is that when the results of this classification are projected onto a map they are easily interpreted. Another advantage is that the legend limits contain no missing values or gaps. This permits faster map interpretation, but might create confusion concerning the bounds of each class.

    The main disadvantage of this classification type is that it fails to consider how data are distributed along the number line（没有考虑数据是如何沿着数轴分布的，对可视化效果不友好，可能会出现大量同一色块的分布） . For example, the map to the right shows the percentage of total homes in Arkansas which are mobile homes. There are many areas that fall into the two lower percentages, leaving most of the state the two shades of green. If a different classification was used, the data displayed in the map could be shown more effectively.

    Use defined interval to specify an interval size to define a series of classes with the same value range. For example, if the interval size is 75, each class will span 75 units. The number of classes, based on the interval size and maximum sample size, is determined automatically. The interval size must be small enough to fit the minimum number of classes allowed, which is three.

     Quantile - GIS Wiki | The GIS Encyclopedia

    In a quantile classification , each class contains an equal number of features . （每一个类别中的包含被分类对象的数目相等）

    A quantile classification is well suited to linearly distributed data . Quantile assigns the same number of data values to each class. There are no empty classes or classes with too few or too many values. 比如，分位数分类法适用于区分人口密度这类在其范围内均匀分布的数据

    Using the quantile classification method gives data classes at the extremes and middle the same number of values. Each class is equally represented on the map and the classes are easy to compute. Quantile classification is also very useful when it comes to ordinal data . Ordinal Data: Definition, Analysis and Examples

    When using quantile, classification gaps can occur between the attribute values . These gaps can sometimes lead to an over-weighting of the outlier in that class division  [3] .

    Another disadvantage is that if the number of classes is not correctly created two areas with the same value can end up in different groups.（由于组内的数目是确定的，有相同属性的对象可能被分到不同的组内）  For example, imagine you have data for the number of fast food restaurants in each county for 21 counties and you want to divide the counties into 7 groups with 3 counties in each group. If 4 counties each have exactly 10 fast food restaurants one of those counties will be classified in a different group, because there are only 3 counties per group, despite the values being the same.

Jenks Natural Breaks Classification - GIS Wiki | The GIS Encyclopedia

    With natural breaks classification (Jenks) , classes are based on natural groupings inherent in the data. Class breaks are created in a way that best groups similar values together and maximizes the differences between classes（类内差异小，类间差异大）.  The features are divided into classes whose boundaries are set where there are relatively big differences in the data values.

    The method reduces the variance within classes and maximizes the variance between classes.It is also known as the goodness of variance fit (GVF) , which equals the subtraction of SDCM (sum of squared deviations for class means) from SDAM (sum of squared deviations for array mean). （该方法减少了类内的方差，并使类间的方差最大化。它也被称为 方差拟合优度(GVF) ，等于SDCM(类均值的平方偏差和)减去SDAM(数组均值的平方偏差和)）

   Jenks classification is not recommended for data that have a low variance.  不适用于数据方差很小的情况下

    Natural breaks are data-specific classifications and not useful for comparing multiple maps built from different underlying information.

    Because natural breaks classification places clustered values in the same class, this method is good for mapping data values that are not evenly distributed.

Geometric Interval Classification - GIS Wiki | The GIS Encyclopedia

    The geometrical interval classification scheme creates class breaks based on class intervals that have a geometric series. The geometric coefficient in this classifier can change once (to its inverse) to optimize the class ranges. The algorithm creates geometric intervals by minimizing the sum of squares of the number of elements in each class. This ensures that each class range has approximately the same number of values in each class and that the change between intervals is fairly consistent.

    此算法专门用于 处理连续数据 。这是相等间隔、自然间断点分级法 (Jenks) 和分位数间的折衷方法。其在突出显示中间值变化和极值变化之间达成一种平衡，因此生成的结果外形美观、地图内容详尽

    This classification method is useful for visualizing data that is not distributed normally, or when the distribution is extremely skewed. 这种分类方法对于显示 非正态分布的数据 或当 数据的分布极其倾斜时 非常有用。

    The Geometrical intervals classification is better than quantiles for visualizing prediction surfaces, which often do not have a normal data distribution. Geometric interval works best when the data is spread over a large area and is not well distributed. 

    标准差分类方法用于显示 要素属性值与平均值之间的差异 。ArcMap 可计算平均值和标准差。将使用与标准差成比例的等值范围创建分类间隔 - 间隔通常为 1 倍、1/2 倍、1/3 倍或 1/4 倍的标准差，并使用平均值以及由平均值得出的标准差。

    通过强调平均值以上和以下的值，标准差分类有助于显示哪些位置高于或低于平均值。

    Use this classification method when it is important to know how values relate to the mean , such as population density in a given area, or comparing foreclosure rates across the country. For greater detail in your map, you can change the class size from 1 standard deviation to 0.5 standard deviation.