MCCN Case Study 5 - Produce farm zone map

The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.The dataset contains input files for the case study (data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (result), and Jupyter Notebook (MCCN-CASE 5.ipynb)<b>Research Activity Identifier (RAiD)</b>RAiD: https://doi.org/10.26292/8679d473<b>Case Studies</b>This repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.<b>Case Study 5 - Produce farm zone map</b><b>Description</b>Use soil sample data and crop yield data to develop a zone map for a farm. This study demonstrates: 1) Loading heterogeneous data sources into a cube, and 2) Analysis and visualisation using pykrige and KMeans.<b>Data Sources</b>Use Llara-Campey data including yield values and soil maps to develop classification of farm area into contiguous zones of relatively self-similar productivity. Variables should include the minimum zone area and the maximum number of zone classes to return.This notebook can be delivered as a tool into which the user can load their own data in the form of spreadsheets containing points and associated values for the variables to take into account in the analysis. The requirement is either for comprehensive (raster) coverage for the area or of a set of point-based measurements for each variable (in which case a simple kriging or mesh interpolation will be applied).<b>Dependencies</b>This notebook requires Python 3.10 or higherInstall relevant Python libraries with: <b>pip install mccn-engine rocrate pykrige scikit-learn</b>Installing mccn-engine will install other dependencies<br>

MCCN项目旨在提供工具，助力农业领域理解作物与环境的关联，具体途径为辅助生成时空数据立方体（spatiotemporal data cube）。本仓库包含用于演示MCCN数据立方体组件功能的Jupyter笔记本（Jupyter Notebook）。

本数据集包含案例研究的输入文件（data目录）、RO-Crate元数据（ro-crate-metadata.json）、案例研究的结果（result目录），以及Jupyter笔记本MCCN-CASE 5.ipynb。

**研究活动标识符（Research Activity Identifier, RAiD）**：RAiD: https://doi.org/10.26292/8679d473

**案例研究**
本仓库包含以下案例研究的代码与示例数据。请注意，此处的分析仅用于演示软件功能，所得结果不应被视为具备科学或统计学意义。本项目未针对样本偏差进行任何处理，且示例数据的密度可能不足以支撑有效的分析。所有案例研究最终都会生成RO-Crate数据包，其中包含源数据、Jupyter笔记本及生成的输出文件，包括数据立方体的netcdf格式导出文件。

**案例研究5：生成农场分区地图**
**描述**
本研究利用土壤样本数据与作物产量数据，为农场生成分区地图。本研究演示了两项核心内容：1）将异构数据源加载至数据立方体；2）使用pykrige与KMeans进行分析与可视化。

**数据来源**
本研究使用Llara-Campey数据集（包含产量值与土壤地图），将农场区域划分为生产力相对相似的连续分区。需指定的参数包括最小分区面积与可返回的最大分区类别数。

本记事本可作为工具供用户使用，用户可加载自身数据，数据形式为包含分析所需变量的点位及其关联值的电子表格。本工具的输入要求为：要么覆盖研究区域的完整栅格数据，要么为每个变量提供一组点位测量数据（此时将应用简单克里金插值或网格插值）。

**依赖项**
本记事本要求使用Python 3.10及以上版本。可通过以下命令安装相关Python库：`pip install mccn-engine rocrate pykrige scikit-learn`。安装mccn-engine将自动安装其余依赖项。