中国正处于城镇化阶段的中后期,需要更合理规划城市,避免建设用地盲目扩展.元胞自动机(Cellular Automata,CA)因其易与其他模型耦合而被广泛用于土地利用变化模拟中,通过不同方法描述元胞状态能够实现不同目的的模拟预测,进而为国土空间规划提供科学指导.本文首先采用基于渗流模型的城市聚类算法(Percolation-based City Clustering Algorithm,PCCA),考虑系统临界特性将网格人口密度分布划分为高人口密度地区和低人口密度地区;然后结合土地利用覆盖数据中的建成环境分布,叠置得到顾及人口密度水平差异的建设用地分布格局,包括高人口密度城镇地区、低人口密度居民点和非居民点地区;进而利用人工神经网络(Artificial Neural Network,ANN)构建CA模型(PCCA-ANN-CA),对上述不同类型建设用地的扩展进行模拟.本文以京津冀区域为例分析发现,2020年高/低人口密度地区的划分阈值低于2010年,区域人口密度下降;对各地类县域数量占比分布进行冷热点分析,发现2010年低人口密度居民点分布热点区域集中在衡水、沧州、天津、唐山和秦皇岛南部,到2020年天津的热值有所下降;2010年高人口密度城镇地区热点区域集中在北京、天津,次热点区域集中在石家庄附近;2020年热点区域和次热点区域位置没有发生改变,但保定、张家口和承德的热值均有所下降;PCCA-ANN-CA不仅能够模拟非建设用地和建设用地之间的转换,还可以模拟不同类别建设用地之间的转换,利用人口密度水平差异细分建设用地属性,能避免模拟的新增建设用地过度集中于已有建设用地较多区域,使模拟结果与实际情况更相近.
Simulation of construction land expansion considering the difference of population density level
China is in the middle and late stage of urbanization,which requires more rational planning of cities to avoid blind expansion of construction land.Cellular Automata(CA)is widely used in the simulation of land use change because it is easy to be coupled with other models,and the description of cellular state through different methods can realize the simulation and prediction for different purposes,and then provide scientific guidance for spatial planning.In this paper,we firstly adopted the Percolation-based City Clustering Algorithm(PCCA),which considers the critical characteristics of the system,to divide the population density distribution into high population density areas and low population density areas;and then according to the built environment distribution in the land use and cover data,the construction land distribution pattern taking into account the difference of population density level was obtained,including high population density urban areas,low population density settlements and non-settlement areas;and then we used Artificial Neural Network(ANN)to construct a CA model(PCCA-ANN-CA)to simulate the expansion of the above categories of construction land.Taking the Beijing-Tianjin-Hebei urban agglomeration as an example,in this paper we found that the threshold for the division of high/low population density areas in 2020 is lower than that in 2010,and the regional population density decreases.By analyzing the proportion distribution of counties,it was found that the hot spots of low population density settlements are concentrated in Hengshui,Cangzhou,Tianjin,Tangshan and the south of Qinhuangdao in 2010,and the heat value of Tianjin decreases by 2020.In 2010,the hot spots in high population density urban areas were concentrated in Beijing and Tianjin,and the sub-hot spots were concentrated near Shijiazhuang.In 2020,the locations of hot spots and sub-hot spots did not change,but the calorific value of Baoding,Zhangjiakou and Chengde decreased.PCCA-ANN-CA can not only simulate the conversion between non-construction land and construction land,but also simulate the conversion between different types of construction land,and using the difference of population density level to subdivide the attributes of construction land can avoid the excessive concentration of simulated new construction land in the area with more existing construction land,so that the simulation results are more similar to the actual situation.
construction land expansionpopulation densityCellular Automatapercolation modelBeijing-Tianjin-Hebei region
万方数据
维普
