标题:UrbanVCA v2.2.0(完全版): 基于真实地块的城市土地利用变化模拟和预测系统

Title: UrbanVCA v2.2.0 (Full version): Actual Land-parcel-based Urban Land-use Simulation and Prediction System

在地理智能(GeoAI)快速发展背景下,UrbanVCA是一款基于真实地块和矢量元胞自动机(Vector-based CA)的城市土地利用变化模拟和预测系统。该系统同时支持城市内的土地利用相互转换和城市用地扩张过程 (Yao et al. 2017, Zhai et al. 2020)。
UrbanVCA is a GeoAI-based software for the simulation and prediction of urban development and land-use change process by using vector-based cellular automata. UrbanVCA supports the simulation and prediction of both land use interchange and urban land use expansion processes within the city (Yao et al. 2017, Zhai et al. 2020).


介绍 (Introduction)

在UrbanVCA v1.5.0(https://www.urbancomp.net/archives/urbanvcav-1-5 发布后,我们发现软件仍存在许多的不足,比如功能耗时过长、软件接口过多、操作仍具有一定的难度等。为此,在V2.0版本中,我们重新优化了软件所有界面与各项功能模块,极大提升了软件的运行速度,并最大程度上降低了操作的复杂性,让VCA模型更易于操作。
After the release of UrbanVCA v1.5.0 (https://www.urbancomp.net/archives/urbanvcav-1-5), we found that there are still many deficiencies in the software, such as functions that take too long, too many software interfaces, and still It has certain difficulty and so on. To this end, in the V2.0 version, we reoptimized all interfaces and functional modules of the software, which greatly improved the running speed of the software, reduced the complexity of operations to the greatest extent, and made the VCA model easier to operate.

功能更新 (Function update)


图 1 UrbanVCA主界面
Figure 1 UrbanVCA main interface

图 2 土地利用重分类界面
Figure 2 Land use reclassification interface

The interface can be used to reclassify and numerically code land use types for subsequent software manipulation.

图 3 动态地块分裂界面
Figure 3 Dynamic block splitting interface

The interface optimizes the original dynamic block splitting algorithm, fixes the bug that some blocks cannot be split, and the updated dynamic block splitting algorithm is significantly more efficient.

图 4 地块匹配界面
Figure 4 Parcel matching interface

This interface realizes the automatic matching of land use types before and after the land use change of vector parcels, and the resulting data attribute list will automatically generate fields: ID, before, simulated, after, Pr, area, centerX, centerY, Pg0, Pg1...Pgn, N0, N1...Nn. They respectively represent: the serial number of the parcel ID, the type of the parcel before the land use change, the type of the parcel after the change of the land use simulation, the type of the parcel after the change of the land use, the restriction factor, the area of the parcel, the type of the parcel The coordinates of the center of parcel X, the coordinates of the center of mass of the parcel Y, the overall development probability of the parcel developing into the 0th land use type, the overall development probability of the parcel developing into the first land use type... The overall development probability of the parcel developing into the nth land use type Probability, parcels are affected by the neighborhood effect of the 0th land use type, parcels are affected by the neighborhood effect of the 1st land use type... parcels are affected by the neighborhood effect of the nth land use type. The list of properties automatically generated by the data is as follows:

(The figure comes from QGIS)

All subsequent operations will be based on the above fields, and users can view and modify each parameter at any time.

图 5 总体发展概率计算界面
Figure 5 Calculation interface of overall development probability

Based on machine learning algorithms such as random forest, neural network and logistic regression, this module can mine and dynamically display the overall development probability at the plot level. The attribute list of the result file will also automatically generate the overall development probability field value, which is convenient for users to view and modify . Users can also import Pg files externally or modify the overall development probability value directly based on GIS software. In addition, users can view the relevant parameters of each model training process in the log status bar.

图 6 UrbanVCA模拟界面
Figure 6 UrbanVCA simulation interface

This module can set the radius of neighborhood effect, the number of simulation iterations, the restricted development area, and the setting of conversion rules. At the same time, the function of setting the conversion area of various land use types has been added, and various precisions such as FoM, PA, UA, Kappa, and OA are added. evaluation indicators.

图 7 附加条件的UrbanVCA模拟界面
Figure 7 UrbanVCA simulation interface with additional conditions



Considering that the current research involves more and more ways of constructing vector cellular automata models, many studies have considered multiple additional factors on the basis of the original cellular automata models:


Among them, Pg is the overall development probability, Pr is the limiting factor, Ω is the neighborhood effect, RA is the random factor, and Factor_x is the Xth additional factor.
Therefore, in order to facilitate researchers to carry out related research, our team designed and developed the UrbanVCA model with additional factors based on the original model.

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

软件下载 (Binary Download)

点此下载软件(software download)
It contains software, test data and manuals in both Chinese and English.
点此下载演示视频(demo video download)

*Bug fix in version 2.1: Fix an abnormal area count problem that may be caused by the uninitialization of plot area count in Markov chain function.
*2.2版本修复bug:优化附加因子的 UrbanVCA 模型模拟功能。
*Bug fix in version 2.2: Optimizing the UrbanVCA Model Simulation Function for Additional Factors.