CoVCA v1.0.0：基于矢量元胞自动机模型的“土地-人口-经济”城市空间协同模拟平台
CoVCAv1.0.0: "Land-population-economy" urban space collaborative simulation platform based on vector-based cellular automata model
简介(introduction)：
CoVCA v1.0.0是基于矢量元胞自动机模型的“土地-人口-经济”城市空间协同模拟平台，该平台结合了密度元胞自动机和矢量元胞自动机，通过多要素协同模拟，采用“层次递进”分步式动态驱动因素更新的策略，能够更加精细地捕捉城市发展要素的微小变化，揭示了不同城市要素之间的相互作用和协同效应，可以用于广泛的城市研究的应用程序。
CoVCA v1.0.0 is a "landuse-population-economy" urban spatial synergistic simulation platform based on vector cellular automaton models, which combines density cellular automaton and vector cellular automaton, and simulates multiple factors in a coordinated manner. It adopts a "hierarchical progressive" step-by-step dynamic strategy for updating driving factors, which can capture the subtle changes of urban development elements more finely and reveal the interactions and synergistic effects between different urban elements. It can be used as an application program for a wide range of urban research.
功能介绍(Function introduction)：
CoVCA基于地理空间数据，集成了土地利用、人口和经济数据的协同模拟功能，并采用“分层递进”策略进行动态驱动更新，以实现多因素空间协同模拟。
CoVCA, based on geospatial data, integrates the collaborative simulation function of land use, population and economic data, and uses the "hierarchical progressive" strategy to carry out dynamic driver updates to achieve multi-factor spatial collaborative simulation.
CoVCA提供了栅格重采样、矢量动态地块分割、土地利用数据匹配、矢量数据栅格化和栅格图像标准化等数据预处理功能。
CoVCA provides data preprocessing functions for raster resampling, vector dynamic plot segmentation, land use data matching, vector data to raster image and raster image normalization.
CoVCA提供了土地利用变化的模拟模型，可以完成整体发展概率的计算，准确推断和评估土地利用变化。
CoVCA provides a simulation model of land use change, which can complete the calculation of the overall development probability, accurately infer and evaluate the land use change.
CoVCA提供了城市要素连续变化的模拟功能，包括城市发展的初始密度状态计算、整体密度发展概率计算、连续变化模拟和精度评估。
CoVCA provides continuous change simulation functions of urban elements, including initial density state calculation of urban development, overall density development probability calculation, continuous change simulation and accuracy evaluation.
CoVCA结合了密度元胞自动机和矢量元胞自动机。用户不仅可以使用城市矢量地块进行模拟操作，还可以结合人口和经济数据进行多因素协同模拟，并获得相应的精度。
CoVCA combines density cellular automata and vector cellular automata. Users can not only use the city vector land to simulate the operation, but also join the population and economy for multi-factor collaborative simulation, and obtain the corresponding precision.
模型介绍(Model introduction)
现有研究的问题(Problems for existing research)
- 现有大多数模拟模型注重模拟单个城市发展要素的变化，而忽略了发展过程中多个城市要素之间的协同作用。
- 现实中的地块往往都是不规则的多边形，传统的栅格元胞自动机模型无法很好的模拟真实的土地利用分布。
- Most of the existing simulation models focus on simulating the change of a single urban development factor, but ignore the synergistic effect of multiple urban factors in the development process.
- The real land plots are often irregular polygons, and the traditional raster cellular automata model can not simulate the real land use distribution well.
模型优势(Model Advantage)
该模型基于元胞自动机模型实现了“土地-人口-经济”三者的空间协同模拟，提供了离散型和连续性城市发展要素的模拟方法，该方法可以有效揭示不同城市要素之间的相互作用和协同效应，更加精细地捕捉城市发展要素的微小变化。
Based on the cellular automata model, the model realizes the spatial collaborative simulation of "land-population-economy" and provides a simulation method of discrete and continuous urban development elements, which can effectively reveal the interaction and synergistic effect between different urban elements and capture the small changes of urban development elements in a more precise manner.
图 1 模型总体流程图
Figure 1 Model general flowchart
模型充分结合灰度元胞自动机模型和矢量元胞自动机模型，采用“层次递进”的思路，考虑了真实地块、人口、经济三者之间的交互影响，共同迭代，依次作为训练数据去影响下一次模拟的结果，实现协同模拟发展。
The model fully combines gray level cellular automata model and vector cellular automata model, adopts the idea of "hierarchical progression", considers the interaction effects among real land mass, population and economy, and iterates together to influence the results of the next simulation as training data in turn, so as to achieve collaborative simulation development.
图 2 “层次迭代”流程图
Figure 2 "Hierarchical iteration" flowchart
研究发现，多要素协同模拟相较于单要素模拟，在城市精细化演变中，可以感知土地自身发展的模态，掌握土地演变的规律；对居住用地、工业用地、商业用地、公共管理用地等都具备良好的感知能力，呈现出更加优秀的土地利用的模拟结果。
It is found that compared with the single factor simulation, the multi-factor collaborative simulation can perceive the mode of land development and master the law of land evolution in the fine urban evolution. It has good perception ability of residential land, industrial land, commercial land, public management land, etc., and presents better simulation results of land use.
图 3 土地利用模拟结果
Figure 3 Land use simulation result
功能界面(Functional interface)
图 4 CoVCA启动界面
Figure 4 User Interface(UI) for CoVCA
图 5 导入矢量文件界面
Figure 5 UI for import vector files
图 6 导入栅格文件界面
Figure 6 UI for import raster files
图 7 矢量动态地块分裂界面
Figure 7 UI for DLPS Split
图 8 土地利用数据匹配功能
Figure 8 UI for Parcel Match
图 9 栅格图像归一化功能
Figure 9 UI for Raster Normalization
图 10 栅格图像重采样功能
Figure 10 UI for Raster Resample
图 11 总体发展概率计算
Figure 11 UI for Calculating Pg
图 12 UrbanVCA模拟模块
Figure 12 UI for UrbanVCA
图 13 城市发展初始密度状态计算功能
Figure 13 UI for Initialization Density State of Urban Development
图 14 计算城市总体密度发展概率
Figure 14 UI for Calculate Overall Development Probability (Density)
图 15 城市要素连续性变化模拟
Figure 15 UI for Continuity-based Mechanism CA
图 16 城市单要素（真实矢量地块）模拟
Figure 16 UI for Step-wise Synergetic Simulation VCA
图 17 城市多要素协同变化模拟
Figure 17 UI for Step-wise Synergetic Simulation VCA (Multi-factor)
软硬件系统需求 (Software and Hardware System Requirements)
- 内存 >= 4GB (RAM >= 4GB)
- 硬盘空间 >= 3GB (Hard Disk Space >= 3GB)
- Windows 8.1及以上版本 (Windows 8.1/10 or above)
- Visual C++ Redistributable 2019 (Download here: Latest supported Visual C++ Redistributable downloads | Microsoft Learn)
软件下载(Binary Download)：
公开下载
下载地址（Click here to download）- CoVCA v1.0.0
目前软件仅供团队内部测试使用，如果需要请联系姚尧老师（ yaoy@cug.edu.cn ）。
At present, the software is only for internal testing purposes within the team, so there is a password for publicly downloading the software installation package. If needed, please contact Dr. Yao Yao （ yaoy@cug.edu.cn ）.
软件使用说明书(Software Manual)
中文说明书：
Manual in English:
项目资助（Fundings）
本研究得到了以下项目的支持：科技部国家重点研发计划项目[2023YFB3906803]；自然资源部城市国土资源监测与仿真重点实验室开发基金资助课题 [KF-2023-08-04]；以及自然资源部陆表系统与人地关系重点实验室开放基金 [LBXT2023YB03]。
The project was supported by the National Key Research and Development Program of China [2023YFB3906803]; the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources [KF-2023-08-04]; and the Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China [LBXT2023YB03].
Q.E.D.