stemflow

Spec sheet

Purpose

Stemflow is a Python toolkit for Adaptive Spatio-Temporal Exploratory Models (AdaSTEM), with typical use in daily abundance estimation from eBird citizen science surveys.Read 12 Oct 2026 · chenyangkang.github.io

Product

stemflow is a Python toolkit for Adaptive Spatio-Temporal Exploratory Models (AdaSTEM).Read 11 Oct 2026 · chenyangkang.github.io

Intended users

The introduction describes stemflow as a user-friendly Python package for modeling large spatiotemporal datasets, including ecological abundance estimation.Read 12 Oct 2026 · chenyangkang.github.io

Deployment

desktopRead 25 Sep 2026 · chenyangkang.github.io

Free plan

YesRead 25 Sep 2026 · chenyangkang.github.io

Integrations

The documented installation methods are pip, a GitHub beta install, and conda; examples also use XGBoost models and eBird survey data.Read 12 Oct 2026 · chenyangkang.github.io

License

The repository provides the software under the MIT License, which permits use, copying, modification, distribution, sublicensing, and sale subject to its stated conditions.Read 12 Oct 2026 · github.com

Base models

Users can supply scikit-learn-style BaseEstimator classes or use a scikit-learn-style Maxent model as a base model.Read 12 Oct 2026 · chenyangkang.github.io

Contributions and collaboration

The project welcomes pull requests and application-level research collaboration.Read 11 Oct 2026 · chenyangkang.github.io

Data import formats

pandas DataFrame; DuckDB (.duckdb); Parquet (.parquet)Read 25 Sep 2026 · chenyangkang.github.io

Data support

It supports continuous and categorical features, plus static and dynamic features.Read 11 Oct 2026 · chenyangkang.github.io

Features

It supports spatiotemporal modeling and prediction, overall feature importance calculation, and plotting spatiotemporal dynamics.Read 11 Oct 2026 · chenyangkang.github.io

Features and data

It supports feature importance calculations, plots of spatiotemporal dynamics, and continuous, categorical, static, and dynamic features.Read 12 Oct 2026 · chenyangkang.github.io

Indexing

The package supports user-defined 2D spatial indexing with CRS, 3D spherical indexing, and user-defined temporal indexing.Read 11 Oct 2026 · chenyangkang.github.io

Installation

The site gives pip and conda installation commands and a pip command for installing the latest GitHub beta.Read 11 Oct 2026 · chenyangkang.github.io

Memory handling

Lazy loading and database queries are supported to reduce memory load during parallel computing.Read 12 Oct 2026 · chenyangkang.github.io

Memory tools

Lazy loading and database queries are described as ways to reduce memory load during parallel computing.Read 11 Oct 2026 · chenyangkang.github.io

Model approach

It splits input data into spatial or spatiotemporal cells called stixels, trains each separately, and aggregates their predictions into an ensemble.Read 12 Oct 2026 · chenyangkang.github.io

Modeling approach

It splits input data with Quadtree or Sphere Quadtree, trains each spatiotemporal split separately, and aggregates the ensemble for prediction.Read 11 Oct 2026 · chenyangkang.github.io

Regression models

YesRead 25 Sep 2026 · chenyangkang.github.io

Resource note

The project says stemflow may consume large computational resources as data volume grows.Read 12 Oct 2026 · chenyangkang.github.io

Scripting support

YesRead 25 Sep 2026 · chenyangkang.github.io

Spatial and temporal indexing

It supports user-defined 2D spatial indexing, 3D spherical indexing, and user-defined temporal indexing.Read 12 Oct 2026 · chenyangkang.github.io

Support and collaboration

The project welcomes pull requests, application-level cooperation, and research collaboration.Read 12 Oct 2026 · chenyangkang.github.io

Supported tasks

It supports binary classification, regression, and hurdle regression for zero-inflated data.Read 12 Oct 2026 · chenyangkang.github.io

Tasks

Supported tasks include binary classification, regression, hurdle modeling, and spatial-only modeling.Read 11 Oct 2026 · chenyangkang.github.io

Typical use

The site describes daily abundance estimation using eBird citizen science survey data as a typical use.Read 11 Oct 2026 · chenyangkang.github.io

Visualization

SphereAdaSTEM produces an interactive Plotly gridding plot by default.Read 12 Oct 2026 · chenyangkang.github.io

Where Specifiction places it

• Statistical Analysis Software#81 of 1067.3