ArcGIS中的几种分类方法

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 intervalto 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.

ArcGIS按位置选择出来的结果少了解决方法

1、打开Arcmap应用程序，选择——按位置选择。

2、示例：点、线位置关系选择结果。

3、示例：点、面位置关系选择结果。

4、示例：点、线和面的位置关系选择结果。

5、添加“距离”关系的结果。

6、要素之间的位置关系选择的所有方法。

1、打开arcgis，加入数据后，打开arctoolbox工具箱，找到汇总统计数据。所示。

2、d出窗口所示。加入数据，选择统计字段。可以多个选择，如面积、长度、个数等可统计字段。

3、选择字段后，进行下步 *** 作，选择统计类型所示选择的是SUM求和统计，还有其他的统计方式。

4、然后选择保留的分类字段，所示，这里进行统计的是各地类的面积，所以选择了地类名称字段作为分类字段。

5、选择好后确定。最后得出一个表格，所示，按地类分类统计表就出来了。这个适合放在模型中进行 *** 作，如连接关联、导出表格、数据分析等 *** 作。

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