Spatio-temporal Geographically Weighted Regression Analysis
Usage Instruction
The spatio-temporal geo-weighted regression is an extended and improved geo-weighted regression that is able to analyze spatial coordinate points with temporal attributes, solving the problem of total spatio-temporal non-smoothness of the model.
Application Scenarios
- To study the trends of urban housing in time and space.
- To study the factors of provincial economic development and their spatial and temporal patterns.
Parameter Description
| Parameter Name | Default Value | Parameter Definition | Parameter Type |
|---|---|---|---|
| Source Data | The specified vector dataset to be calculated. Can be point, line, or surface datasets | DatasetVector | |
| Explanation Fields | Explanatory field name. The explanatory variable is the independent variable, i.e., X inside the regression equation, which is used to model or predict the value of the dependent variable. | String | |
| Type of Kernel Function | GAUSSIAN | Kernel function type.ReferenceGeographically weighted regression analysis. | KernelFunction |
| Modeling Fields | Modeling fields. The dependent variable, i.e., the variable to be studied and predicted, supports only numeric fields. | String | |
| Time Field | Set the field that represents the time. | String | |
| Time Unit | Day | Set the time interval unit. The time distance is the difference between two records in the time field, and the time distance unit needs to be converted to the specified time distance unit, which supports Seconds, Minutes, Hours, Days, Weeks, Months and Years. For example, if the time interval is 60 minutes, the corresponding time distance is, Minutes: 60, Hours: 1, Days: 1/24, etc., that is, the unit can be selected according to the need, the smaller the time interval unit, the larger the time interval distance, and the larger or smaller the weight will be according to the different kernel functions set to react to the weights. | TimeSpanUnits |
| Bandwidth method | AICC | Bandwidth determination method. | BandWidthType |
| Bandwidth Type | FIXED | Bandwidth types, both fixed type bandwidth and variable type bandwidth are provided. | KernelType |
| Spatio-temporal geographically weighted regression prediction dataset (Optional) |
Spatio-temporal geographically weighted regression prediction dataset | DatasetVector | |
| The specified data source for the saved prediction result dataset (Optional) |
The specified data source for the saved prediction result dataset | Datasource | |
| Name of the specified prediction result dataset (Optional) |
Name of the specified prediction result dataset | String | |
| Field mapping of predicted data (Optional) |
Field mapping of the predicted data, indicating the correspondence between the field names of the predicted dataset and the field names maintained in the source dataset. If not set, all the explanatory fields in the source dataset must exist in the prediction dataset. This is filled in WebGPA as {"inputFieldName":"ABC","predictFieldName":"DEF"} | Object | |
| Result Datasource | The data source where the specified dataset of stored results is located | Datasource | |
| Resulting Dataset | Name of the specified result dataset | String |
Output Result
| Parameter Name | Parameter Definition | Parameter Type |
|---|---|---|
| Results of spatio-temporal geographically weighted regression analysis | Analysis results include: geographically weighted regression summary results and resultant datasets | GWRAnalystResult |
Resulting parameter definition is consistent with Geographically weighted regression analysis.