https://github.com/geodacenter/geoda-lib
https://github.com/geodacenter/geoda-lib
Last synced: 12 months ago
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- Host: GitHub
- URL: https://github.com/geodacenter/geoda-lib
- Owner: GeoDaCenter
- Created: 2024-01-10T21:34:30.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-06T03:39:57.000Z (about 1 year ago)
- Last Synced: 2025-06-06T04:29:12.622Z (about 1 year ago)
- Language: TypeScript
- Homepage: https://geodacenter.github.io/geoda-lib/
- Size: 32 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# GeoDa WASM
GeoDa WASM is a WebAssembly module that provides spatial data analysis capabilities from the GeoDa C++ library for use in web browsers and Node.js applications.
## ✨ Features
@geoda/core
- 🗺️ Mapping
- 📊 Data Exploration
- ⚖️ Spatial Weights
- ⚡ Spatial Operations
@geoda/lisa
- 📐 Spatial Autocorrelation Analysis
@geoda/regression
- 📈 Spatial Regression
@geoda/clustering (WIP)
- 📍 Spatial Clustering
Dive deeper in our [API Overview](https://geodacenter.github.io/geoda-lib/api-overview)!
## 🚀 Installation
Get started in a flash with your favorite package manager:
```bash
npm install @geoda/core
```
Each package works independently, so you can install only the packages you need.
## 🛠️ Example
Here is a quick example of using GeoDaLib to apply Local Moran statistics on some example data:
```js
import {localMoran} from '@geoda/lisa';
// exmaple data
const data = [3.0, 3.0, 0.0, 9.0, 8.0, 8.5];
const neighbors = [[1], [0], [], [4, 5], [3, 5], [3, 4]];
const permutation = 99;
// call local moran
const result = await localMoran({data, neighbors, permutation});
```
## 🏗️ Development
### 📋 Prerequisites
- CMake (3.5 or higher)
- Emscripten
- Node.js (22.11.0 recommended)
- Yarn (4.0.0 recommended)
### 🛠️ Build Steps
1. Clone the repository
2. Build the WASM module:
```bash
cd js
yarn install
yarn wasm
```
3. Build the JavaScript wrapper:
```bash
yarn build
```
4. Run the tests:
```bash
yarn test
```
## 📜 License
This project is licensed under the MIT License. Authors: Luc Anselin and Xun Li.
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.