GPA refers to connecting a series of geographic data processing tools through logical relationships to build a model that automates processing. This model is presented as a flowchart, where the output of one tool serves as the input for the next, achieving full-chain automation of data processing.

By programming and pipelining complex geographic data processing processes, GPA effectively avoids manual step-by-step operations and significantly improves work efficiency. It is suitable for batch data processing (such as multi-region analysis, periodic data updates, etc.), greatly enhancing productivity.

SuperMap iDesktopX 2025 has comprehensively upgraded the model-building canvas. Output ports are directly integrated into tool/variable nodes, drastically reducing redundant nodes, minimizing visual interference, and making data processing logic more intuitive and clear. The new canvas upgrade is mainly reflected in the following two aspects:

• Node classification: Tool nodes and variable nodes are reclassified to reduce redundant nodes.
• Data real-time preview: Supports directly viewing intermediate data processing results on the canvas to quickly verify workflow correctness.

Key Functions

• Modeling and workflow design

  • Supports operations like adding, deleting, connecting, moving, and scaling tools to flexibly build processing workflows.

  • Uses a visual drag-and-drop approach to intuitively design data processing logic.

• Check model

  • Automatically detects unfilled required parameters in the workflow to avoid runtime errors.

  • Provides error prompts to help quickly locate issues.

• Model execution and management

  • Single node execution: Allows executing a specific tool individually for debugging.

  • Partial execution: Specifies running from a particular node after executing up to it.

  • Full workflow execution: Runs the entire model with one click, automatically executing all tools in sequence.

  • Task Manager: Supports viewing execution progress, logs, and task status.

• Result overwrite

  • Check "overwrite results" in the top menu bar to overwrite result data with the same name during model execution. It supports overwriting datasets in the data source and local files. Note that some tools do not yet support overwriting; if result data with the same name exists, execution will fail or rename the resulting dataset by adding a suffix.

• Model saving and reuse

  • Supports saving the model to the workspace for subsequent modification and invocation.

  • Can be exported as a tool and integrated into the toolbox for use by other users or workflows.

• Model sharing

  • Local sharing: Export as a model file (*.xml) for easy distribution and migration.

  • Cloud publishing: Publish the model to the iServer service, supporting web-based editing, management, and execution to enable cross-platform collaboration.

Key Features

• Rich tools, flexible customization

  Provides two ways to integrate tools:

  • Directly call ready-made tools from the built-in toolbox, offering over 1200 tools covering data import, export, data conversion, data processing, spatial analysis, spatial statistics, etc.
  • Develop custom tools through Python scripts.
• Zero code, simple operation:
  • No programming required; build models by dragging and connecting tools.
  • The visual interface lowers the barrier to entry, making it suitable for non-technical users to get started quickly.
• One-click execution, automated operation:

  • After starting, the model executes automatically according to the workflow, reducing manual intervention.

  • Supports unattended operation for batch data processing tasks.

• Convenient sharing and multi-platform usage:

  • Can be exported as *.xml files for easy local migration and sharing.

  • Supports publishing to iServer to enable web-based invocation and management, enhancing team collaboration efficiency.

Related Content

Basic vocabulary

Create model

Manage model

Task Manager

Share model

Application examples