标题:CoCA v2.1.0:基于元胞自动机模型的“土地-人口-经济”空间协同模拟平台

Title: CoCA v2.1.0: Spatial Cooperation Development Simulation Platform for "Land-Population-Economy" based on Cellular Automaton Model


CoCA v2.1.0,即基于元胞自动机模型的“土地-人口-经济”空间协同模拟平台,提供了离散型和连续性城市发展要素的模拟方法,并采用“层次递进”分步式动态驱动因素更新的策略,进行“土地-人口-经济”多要素空间协同模拟,为广大城市研究人员和城市规划人员提供帮助。
CoCA v2.1.0, Spatial Cooperation Development Simulation Platform for "Land-Population-Economy" based on Cellular Automaton Model, provides simulation methods for discrete and continuous urban development elements. It adopts a "hierarchical progression" step-by-step dynamic driving factor update strategy to carry out multi-element spatial coordination simulations of "land-population-economy". This model provides assistance to a wide range of urban researchers and city planners.

在CoCA v1.1.0 (https://www.urbancomp.net/archives/coca-v100)发布后,我们发现软件仍存在些许不足,如操作仍具有一定的难度、功能耗时过长、模拟精度较低等。为此,在v2.1.0版本中我们对相关功能进行了更新,重新设计了软件的界面,优化了部分功能模块。针对城市连续型要素模拟中的“灰度值”CA模型中城市中心点难以提取、模拟精度较低等问题,我们采用新的S曲线计算元胞密度值的方法替换了原始的Clark负指数模型方法,有效提高了人口、经济类连续性要素的模拟精度,也一定程度上降低了软件操作的复杂性。
Following the release of CoCA v1.1.0, We have found that the software still has some shortcomings, such as its operation being somewhat difficult, functions taking too long, and lower simulation accuracy. To address this, we have updated related features in version 2.1.0. For example: we have redesigned the software interface, optimized some function modules, and addressed issues in the "Density" CA model in the simulation of continuous urban elements where it is difficult to extract city center points and simulation accuracy is low. We replaced the original Clark negative exponential model method with a new method of calculating cell density values using an S-curve. This effectively improved the simulation accuracy of continuous population and economic elements and also reduced the complexity of software operation to a certain extent.

功能更新(Function update)


图 1 CoCA v2.1.0 主界面
Figure 1 User Interface(UI) for CoCA v2.1.0


图 2 总体发展概率计算界面
Figure 2 Calculation interface of overall development probability

Figure 2 shows the overall development probability calculation module, which is based on the machine learning algorithm of random forests. It calculates the overall development probability of this type of element in the research area. We have modified the calculation algorithm for this part. By reading the attribute table of input data to determine data types, it matches different ways to calculate development probabilities, solving the problem in previous versions where calculated development probability data could not be used when simulating population and economic elements.


图 3 采用S曲线算法的城市连续性要素变化模拟
Figure 3 Simulation of changes in continuous urban elements using the S-curve algorithm

Figure 3 shows the urban continuity element change simulation module, which implements the simulation of changes in population and economic continuity elements. In version 1.1.0, we used Clark's negative exponential model to calculate the current cell's corresponding urban development density. Clark pointed out that the population and economic density of a city tend to decay in a negative exponential form as the distance from the city center increases, so it is necessary to extract the city center operation. In version 2.1.0, we adopted the S-curve algorithm, which only requires inputting vector boundary data of the study area. The unit under study is a range boundary, which divides the city center and suburbs to match different areas' development trends, effectively improving simulation accuracy and simplifying operational steps.


图 4 城市发展要素分步式模拟
Figure 4 The step-by-step simulation of individual urban development elements

Figure 4 shows the step-by-step simulation module for urban development elements. This module uses a 'hierarchical progression' step-by-step dynamic driver update strategy for the simulation of single urban development elements. It has optimized the user interface and fixed issues in previous versions where it could not correctly identify the type of input data, and occasional crashes during model operation. After the update, the stability of this module has been improved.


图 5 设置密度模型参数
Figure 5 Setting density model parameters

Figure 5 shows the density model parameter module, which is the parameter setting interface for the density model in multi-element collaborative simulation. We have optimized the operation interface, added an input field for city vector boundary data, and modified the algorithm for calculating element density values in the model, simplifying the operation process of the model.


图 6 城市土地-人口-经济变化模拟
Figure 6 Simulating urban land-use, population, and economic changes

图6展示了CoCA v2.1.0软件在采用了S曲线算法后,进行“土地-人口-经济”多要素空间协同模拟的结果。
Figure 6 shows the results of the "land-population-economy" multi-element spatial collaborative simulation using the CoCA v2.1.0 software after adopting the S-curve algorithm.

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 2017(Download here: Latest supported Visual C++ Redistributable downloads | Microsoft Learn)

软件下载(Binary Download)

团队NAS下载地址-无密码版(Download Filepath from Team NAS - Password Free Version)

下载地址(Click here to download)

如果有其他需要,请联系姚尧老师( yaoy@cug.edu.cn )。

At present, the software is only for internal testing purposes within the team and collaborative teams, 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)


Manual in English:

软件著作权展示:CoCA v2.0(Software Copyright Display: CoCA v2.0)


  • Ongoing...
  • Tu W, Gao W, Li M, et al. Spatial cooperative simulation of land use-population-economy in the Greater Bay Area, China [J]. International Journal of Geographical Information Science: 1-26. ( 站内链接(Internal Link) )