GPA (Abbreviation of Geo-Processing Automation)
Refers to connecting a series of geographic data processing tools through specific logical relationships to build a model that can be processed automatically.
Model Builder
Used to create, execute, and manage GPA models, including the following parts:
Menu
Provides menu options for model building and operations.
Parameter Panel
Used to set and adjust the parameters of tool nodes.
Toolbox
Stores all available data processing and analysis tools, including: data import, data export, data processing, spatial analysis, spatial statistics, online analysis, etc. It also supports adding Python custom tools.
Modeling Canvas
A visual interface for dragging tools to build models. SuperMap iDesktopX 2025 redesigned the canvas interaction experience, seamlessly integrating output ports into tool/variable nodes. This design significantly reduces the number of nodes, minimizing visual clutter and making the logic clearer.
You can experience the new canvas style by checking the New GPA Canvas checkbox in the File tab -> Options group -> Experience function group.
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| Figure: New Canvas Style | Figure: Traditional Canvas Style |
Task Manager Window
Used to monitor and manage the execution of GPA models, including the following:
Task List
Lists all running and completed models.
Execution Progress
Displays the current execution progress and status of the model.
Execution Log
Records details during model execution, including error and warning messages.
GPA Model
A flowchart formed by connecting a series of GPA tools according to business logic, where the output of each tool serves as the input for the next. The model consists of the following elements:
Tool Node
A single tool within the model used to perform specific data processing tasks. In the new canvas style, the core functional modules of tool nodes are primarily data management, processing, analysis, etc. These nodes achieve data flow between nodes through data sources.
Variable Node
A node within the model that stores specified values, such as data connection information, real-time date, etc. In the new canvas style, the core functional modules of variable nodes are primarily variable definition, data import/export, etc. They support direct transfer of native values and provide a local file direct access channel, enabling data format conversion and improving data processing efficiency.
As shown in the figure below, green and blue are used to distinguish variable nodes and tool nodes.
You can experience the new canvas style by checking the New GPA Canvas checkbox in the File tab -> Options group -> Experience function group.
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| Figure: Node Classification in New Canvas Style | Figure: Node Classification in Traditional Canvas Style |
Parameters
Within tool nodes and variable nodes in the model, used to define input data parameters, functional parameters, and output result parameters.
Connector
A line in the model used to establish logical relationships between tools, supporting parameter transfer or control of execution order. When connecting two tools, specify the specific parameters of the tools to achieve parameter transfer from the output of the previous tool to the next. If "Pre-condition" is selected, it only defines the execution order without passing parameters, meaning the tool before the "Pre-condition" connector is executed first, followed by the tool after it.
Model Metadata
Used to describe the basic information of the model, including its scope of application, usage methods, etc., which is important for understanding and using the model.
Model Library
Used to centrally store and manage multiple models, supporting the addition of authorization codes to ensure safer and more controllable model sharing.
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