Extraction of Row and Column from Matrix:.The result table will include three fields: unique source ID(UniqueID), unique adjacent feature ID (NeighborsID), weight (Weight). Generate Attribute Table : Checking the checkbox will covert the binary file (.swmb) into a table file, and you need to specify a name for the table and a datasource to save it.File Path : Specify a name and output path.Whenever you want to work with polygon features that represent administrative boundaries, you may want to choose this option. ![]() The spatial weight matrix normalization will convert the weights so that they are between 0 and 1, creating a relative (rather than absolute) weighting scheme. This reduces the deviation caused by the feature having a different number of adjacent features. The tool “Spatial Weights Matrix Standardization” is often used in conjunction with Fixed Distance adjacent features and is almost always used for adjacent features based on face-contiguous. After selecting Spatial Weights Matrix Standardization, each weight is divided by the sum of the rows (the sum of the weights of all adjacent features). Spatial Weights Matrix Standardization : Spatial weight matrix normalization is recommended when the distribution of features may deviate due to sampling design or applied polymerization schemes.About the instructions of the two distances, please refer to Basic Vocabulary of Spatial Statistical Analysis. Measure Distance Method : Two kinds of distance methods are adopted: Euclidean distance and Manhattan distance.Adjacent Number : Specify a positive integer which is the number of adjacent features nearest to the target feature.The affection of far features decrease with the exponent. Inverse Distance Power Exponent : This exponent is used for controlling the importance of distance values.A positive value means the two features whose distance is less than this value are adjacent. “0” means every feature is an adjacent feature and no distances are adopted. Break Distance Tolerance : “-1” denotes calculating and adopting the default distance, and ensures every feature has at least one feature adjacent.The features within the specific fixed distance range have the same weight (1). This model does not apply to the big dataset. It takes every feature as the adjacent feature of other features. Undifferentiated Region: This model is a combination of Inverse Distance and Fixed Distance.The tool is useful for the cases where some special features are far away from other features, and the number of fixed adjacent features are more important. K Nearest Neighbors: K features nearest to the target feature are involved in the calculation of target feature (the weight is 1), and other features will be excluded (the weight is 0).This model applies to the continuous data. The affection decreases much more with the distance. Inverse Distance Square: Similar to the model “Inverse Distance”.The affection decreases with the distance. All features will effect the target features. Inverse Distance: Every feature is considered adjacent with all other features.Polygon Adjacent (Node/Common Edges/Intersect): This model applies to polygons with adjacent points, adjacent ot intersecting borders.Polygon Adjacent (Common Edges/Intersect): this model applies to polygons with adjacent or intersecting borders.Fixed Distance: this model applies to points, or polygons with big differences in size.The more vivid the model is, the more accurate the results are. The field values must be unique for every element.Ĭoncept Model : The selected model must reflect the intrinsic relationships among elements. Unique ID Field : The uique ID field associates with the elements acquired by running the feature.The supportive types of datasets include: point, line, region. Source Data : Specify a dataset which the result spatial weights matrix file will be generated from. ![]() ![]()
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