Esri Open-Source Spatial Data Science Project Template: Integrating Best Practices of Geography and Artificial Intelligence

Esri's cookiecutter-spatial-data-science is a project template specifically designed for spatial data science, integrating best practices from geography and artificial intelligence. It aims to solve problems researchers face when starting projects, such as disorganized structure, non-standard code, and poor reproducibility. This template is customized based on the cookiecutter framework, providing a standardized and scalable project starting point to help quickly build reproducible spatial data analysis projects.

Spatial data science is an interdisciplinary field between GIS and data science, which has gained attention in recent years with the development of AI. However, researchers often encounter pain points such as disorganized project structure, non-standard code organization, and poor reproducibility. As a leading GIS company, Esri launched this template to address these issues. It is based on the cookiecutter framework, deeply customized for the special needs of spatial data science, inheriting the advantages of data science templates and integrating professional features of geospatial processing.

1. Interdisciplinary Integration: Combine best practices from geography (coordinate reference system management, spatial format conversion, map visualization standards) and AI (data version control, experiment tracking, model management);
2. Reproducibility First: Ensure reproducibility through conda dependency management, DVC data version control, fixed random seeds, and automatic documentation generation;
3. Collaboration-Friendly: Clear structure reduces team communication costs, allowing researchers to focus on scientific problems.

• Data Management: The data/ directory is divided into raw (original, unmodifiable), processed (preprocessed), external (external references), and interim (intermediate results);
• Code Organization: The src/ directory uses modular design, including data (data processing), features (feature engineering), models (model training), and visualization (visualization);
• Notebooks: Use jupytext to convert .ipynb files to .py files for easier Git version control;
• Configuration Management: Hierarchical strategy (config/YAML for basic configuration, .env for sensitive information, command-line parameters for overriding).

• Coordinate Reference System (CRS) Management: Built-in CRS check and conversion tools to unify the coordinate system of datasets;
• Spatial Format Support: Natively supports vector (Shapefile, GeoJSON, etc.), raster (GeoTIFF, COG, etc.), and basic point cloud (LAS/LAZ) formats, providing a unified interface through GeoPandas and Rasterio;
• Map Visualization: Preset styles that meet academic standards for quickly generating professional maps.

• Spatial Cross-Validation: Integrates spatial K-fold and buffer cross-validation to solve spatial autocorrelation issues;
• Spatial Feature Engineering: Provides feature functions such as spatial lag, distance decay, and spatial clustering;
• Model Interpretability: Integrates SHAP and LIME tools to understand the spatial prediction patterns of models.

• Accelerated Project Launch: Build a complete project structure in a few minutes, saving design time;
• Improved Code Quality: Built-in code checking tools like flake8, black, and mypy;
• Enhanced Collaboration: Standardized structure lowers the threshold for team collaboration;
• Increased Credibility: Reproducibility improves recognition from research peers.

This template is an important infrastructure in the field of spatial data science. It lowers the threshold for project initiation through standardized structure, integration of best tools, and professional feature support. With the development of GeoAI, its value as a bridge between geography and AI will become more prominent, promoting the standardization of spatial data science methodologies. It is suitable for researchers in fields such as location intelligence, urban planning, environmental science, and public health, helping to improve research efficiency and quality.