This project provides researchers with the capability to integrate three-dimensional (longitude, latitude, altitude) coordinates and environmental data sets in an urban block. Through calibration, 3D Kriging interpolation, and K-means automated clustering, it identifies hot zones and digitizes these zones to determine the weight and relationship of each hot zone.

The program runs in a Jupyter Notebook environment.

1. Please refer to the data format in data/data.
2. Set the relevant parameters in the data_cali file to obtain calibrated data after execution.
3. Set the relevant parameters in the kirking file to obtain 3D Kriging interpolated data after execution.
4. Set the relevant parameters in the kmeansAnd3DSpaceSyntax file to obtain the final results after execution.

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本專案提供研究者可將街廓中，立體(經度、緯度、高度)座標與環境數據資料集，透過校正、三維克里金插值、Kmeans自動化分群，找出熱區，並數位化熱區以找出各熱區權重與關係。

程式執行於jupyter notebook環境下。

1. 數據格式請參照 data/data。
2. 設定data_cali檔案內相關參數，執行後獲得校正後數據。
3. 設定kirking檔案內相關參數，執行後獲得三維克里金插值數據。
4. 設定kmeansAnd3DSpaceSyntax檔案內相關參數，執行後獲得最終結果。

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License

This project is licensed under the MIT License - see the LICENSE file for details.