It is used to analyze the degree of dispersion or concentration of a group of elements near the Mean Center. Result Data is a circle with Mean Center of all sample data as the center and Standard Distance of All Data as the radius. The result circle represents the aggregation degree of All Data to Mean Center. The smaller the radius is, the higher the aggregation degree is.
Application case
- Distributions of multiple sample values can be compared. For example, in the field of crime analysis, criminal analysts can compare the intensity of assault and theft. Knowing the distribution of different types of crime may help police formulate responses to crime. The strategy of behavior. If the distribution of crime in a particular area is compact, a police vehicle near the center of the area may be sufficient. However, if the distribution is more dispersed, several police vehicles may be required to patrol the area at the same time in order to respond more effectively to crime.
- You can also compare the distribution of elements of the same type over different time periods. For example, a crime analyst can compare daytime thefts to nighttime thefts to see whether they are more dispersed or more compact during the day compared to at night.
- You can also compare feature distributions to static features. For example, you can measure and compare the distribution of emergency calls received by responding fire stations in an area over a period of several months to see which fire stations responded to a wider area.
Function entrance
- Spatial Statistical Analysis tab, Measuring Geographic Distributions, Standard Distance. （iDesktopX）
- Toolbox, Spatial Statistical Analysis, Measuring Geographic Distributions, Standard Distance. (iDesktopX)
Main parameters
- Source Data: Set the Vector Dataset to be analyzed, which supports three types of Dataset: point, line and surface.
- Ellipse size: It is used to set the level of the result circle, which is divided into three levels according to the data volume range contained in the result. The center point of the result will be different for different standard deviation levels.
- One Standard Deviational: The Result Range of the first standard deviation can contain about 68% of the centroid of the source data;
- Two Standard Deviations: The Result Range of the second standard deviation can include about 95% of the centroid of the source data;
- Three Standard Deviations: The Result Range of the third standard deviation can include about 98% of the centroid of the source data;
- Group Field: a field for classifying analysis elements. After classification, each group of objects will have a circle. Group Field can be of integer, date or string type. If the field value in the Group Field is blank, the element is excluded from the analysis.
- Weight Field: Set a numeric field as Weight Field, for example, use the field of the number of deaths in a terrorist attack as the Weight Field, and the result can reflect the spatial distribution of the event according to the severity of the terrorist attack.
- Mean Center: Set Reserved Fields of Result Data and Statistics Type of field value in the field list box.
- Result Settings: Set the Datasource and Dataset Name where the Result Data will be saved.
Explanation of results
The output result is Region Dataset, which is composed of circles with the radius of Standard Distance, where the face formed by each circle represents the dispersion or aggregation degree of the sampling data in Mean Center. Each circle object contains the following Property Fields, and the Result Dataset and Source Dataset are rendered as Group Fields in the map.
| Field | Attribute meaning |
|---|---|
| CircleCenterX | Center X coordinate |
| CircleCenterY | Y coordinate of the center of the circle |
| StandardDistance | Standard Distance |
| district_Group | Group Field |
The larger the radius of the Standard Distance circle is, the more discrete the data is; the smaller the radius of the Standard Distance circle is, the more aggregated the data is.
Instance
The following figure compares the dispersion of terrorist attacks in different regions in 2014, 2015 and 2016, and the change of the center of terrorist attacks. Green, orange and red in the figure respectively represent the results of 2014, 2015 and 2016. Taking South America as an example, the comparison results show that the degree of dispersion of terrorist attacks in 2015 is greater than that in 2014, while the degree of dispersion in 2016 is greater than that in 2015, indicating that the impact area of terrorist attacks is becoming larger.
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