Spatial Autocorrelation, which measures the distance between features, reflects the degree of spatial clustering through the z-value, and the statistically significant peak z-score indicates the distance that promotes the spatial process to cluster the most. The peak distance is usually used by functions with "Distance Range" or "Distance Radius" parameters. It is very important to select an appropriate distance when doing something like Hotspot Analysis or Density Analysis; An appropriate distance can be obtained through Incremental Spatial Autocorrelation analysis.
Function entrance
- Spatial Statistical Analysis tab-> Analysis Mode-> Incremental Spatial Autocorrelation. （iDesktopX）
- Toolbox, Spatial Statistical Analysis, Analysis Mode, Incremental Spatial Autocorrelation. (iDesktopX)
Main parameters
- Source Data: Set the Vector Dataset to be analyzed, which supports three types of Dataset: point, line and surface.
- Evaluation Field: Set the Property Field value of the analysis element involved in the analysis. The field value should be a variety of values. If the attribute value of All Objects is 1, it cannot be solved. Only numeric fields are supported.
- Start Distance: It refers to the start distance of Incremental Spatial Autocorrelation analysis, which can be determined according to the aggregation of data. If no starting distance is given, the default is the minimum distance at which each feature in the Dataset has at least one neighbor. If you have location outliers in your Dataset, this distance may not be the most appropriate starting distance.
- Incremental Distance: Incremental Spatial Autocorrelation the interval distance of each analysis, that is, the second analysis will use the start distance plus the Incremental Distance for analysis. If No IncrementalDistance is given, the Average Nearest Neighbor distance or (Md-B)/C is used (where Md is the Maximum threshold distance, B is the starting distance, and C is the maximum threshold distance). C is that numb of distance segments). The algorithm ensures that the calculation is always performed based on the specified number of distance segments, and that the Max Distance segments are not so large that some features have all other features or almost all other features as their neighbors.
- Incremental Distance Segments: The Incremental Spatial Autocorrelation specifies the number of times to analyze the Dataset. The value range is: 2 ~ 30.
- Measure Distance Method: The Measure Distance method uses Euclidean distance and Manhattan distance. Detail Description for Euclidean Distance and Manhattan Distance. Refer to the Basic Vocabulary of Spatial Statistical Analysis .
- Spatial Weights Matrix Standard ization: Spatial Weights Matrix Standard ization is recommended when the distribution of features may deviate due to sampling design or imposed aggregation scheme. When you select a Spatial Weights Matrix Standard ization, each weight is divided by the sum of the rows (the sum of the weights of all adjacent features). Weighting of Spatial Weights Matrix Standard ization is typically used in conjunction with fixed distance neighboring features, and is almost always used for neighboring features based on face adjacency. This reduces the bias that occurs when an element has a different number of adjacent elements. The Spatial Weights Matrix Standard ization will scale all weights between 0 and 1, creating a relative (rather than absolute) weight scheme. You may want to select the Spatial Weights Matrix Standard ization "option whenever you are working with a face feature that represents an administrative boundary.
Explanation of results
Analyst Result is tabular data, and the table is drawn in the statistical chart window. The table includes six fields: IncrementalDistance, Morans, Expected Value, Variance, Z Score, and P Value. The Z score reflects the degree of spatial clustering, and the statistically significant peak Z score represents the most obvious distance that promotes the clustering of the spatial process. The peak distance of the Z score is typically the appropriate value used with the "Distance Range" or "Distance Radius" parameters.
Instance
There is a microblog login data of a city, and we want to study the hot spots and aggregation of the spatial distribution of the login points, and use the login number of each location as an evaluation field to conduct clustering research from two aspects of space and number of people, and we can get the corresponding results through Hotspot Analysis or Kernel Density Analysis. Prior to this, the appropriate distance value is obtained through the Incremental Spatial Autocorrelation "analysis. The Incremental Spatial Autocorrelation Analyst Result is as follows:
As shown in the figure above, when the Incremental Distance is 700, the Z value is the largest, indicating that 700 is suitable as a distance radius for Hotspot Analysis of microblog login data.
Related topics