Directional Distribution can reflect the spatial characteristics of elements, such as distribution center, dispersion trend and diffusion direction. This method uses Mean Center as the starting point to calculate the standard deviation of X and y coordinates to define the axis of the ellipse, so the ellipse is called the standard deviation ellipse.
Application case
- Mapping the distribution of a group of criminal acts can determine the relationship between the act and specific elements (a series of bars or restaurants, a specific street, etc.).
- Mapping groundwater well samples for specific contamination can indicate how the toxins are spreading, which can be useful when deploying mitigation strategies.
- A comparison of the size, shape, and overlap of the ellipses for each racial or ethnic region can provide insight related to racial quarantine or ethnic quarantine.
- Drawing an ellipse of an outbreak over time models the spread of the disease.
Function entrance
- Spatial Statistical Analysis tab-> Measuring Geographic Distributions-> Directional Distribution. （iDesktopX）
- Toolbox-> Spatial Statistical Analysis-> Measuring Geographic Distributions-> Directional Distribution. (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 ellipse, 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 that classifies analysis features. After classification, each group of objects will have an ellipse. 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, if a traffic accident grade field is used as Weight Field, the result ellipse can reflect not only the spatial distribution of accidents, but also the severity of traffic accidents.
- 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 is a Region Dataset, where each Ellipse contains the following Property Fields, and the flat field of the Result Dataset and the Group Field of the Source Dataset are rendered in the map.
| Field | Attribute meaning |
|---|---|
| CircleCenterX | Center X coordinate |
| CircleCenterX | Y coordinate of the center of the circle |
| SemiMajorAxis | Major semi-axis |
| SemiMinorAxis | Short semi-axis |
| RotationAngle | The direction of the ellipse (the angle between the semi-major axis and the direction of true north) |
| district_Group | Group Field |
| flat | Elliptical flattening |
- The long semi-axis reflects the direction of greater dispersion, and the short semi-axis reflects the direction of higher aggregation.
- The larger the difference between the values of the long and short semi-axes (the larger the oblateness), the more obvious the directionality of the data. On the contrary, the closer the long and short semi-axes are, the less obvious the directivity is.
- If the long and short semi-axes are exactly the same, it is equivalent to a circle, which means that there is no directional feature.
Instance
Case data: Click here to download the case data . After downloading, unzip it for use.
The distribution trend of Medical points in each district and county of a city obtained through Directional Distribution analysis is shown in the figure below. The standard deviation ellipse flattening (0.8) of Daxing District is the largest, which indicates that the distribution of medical points in this area has the most obvious direction.
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