基于CA-ABM模型的福州城市用地扩张研究

以中国海西地区重要门户福州市为研究区,结合其地理位置多层次约束性条件,以地理加权回归模型作为元胞自动机(CA)层的转换规则,同时以2000-2015年多期LandsatTM/ETM+影像的城市用地情况为参照,借助GIS空间分析技术,对CA和多智能体(ABM)相耦合的城市用地扩张模型进行改进。然后利用传统的和改进后的CA-ABM模型,多角度、多层次地模拟福州市2000年、2005年、2010年、2015年城市用地扩张在微观格局上的变化。结果表明,传统的和改进后的CA-ABM模型的整体精度均在80%以上,模拟结果具有较强的可信度;改进的 CA-ABM模型模拟的点对点总体精度和Kappa系数均高于传统的CA-ABM模型,而且模拟结果更加接近实际的城市用地扩张分布情况。结论可为平衡城市化进程和合理规划城市用地提供重要的理论技术支撑。     

Urban land expansion and its driving factors of mountain cities in China during 1990–2015

Land expansion of mountain cities in China is not systematically studied yet. This study identified 55 major mountain cities at and above prefecture level, and analyzed the land expansion characteristics and driving forces, based on visually interpreted data from TM images in 1990, 2000, 2010 and 2015. From 1990 to 2015, total built-up land area of the mountain cities increased by 3.87 times, 5.56% per year. The urban land growth was apparently accelerated after 2000, from 4.35% per year during 1990–2000 increased to 6.47% during 2000–2010 and 6.2% during 2010–2015. Compared to the urban population growth, the urban land expansion rate was 44% higher. As a result, the urban land area per capita increased, but it was still within the government control target, and also was much lower than the average of all cities in China. Urban development policy, changes to administrative divisions, GDP and population growth, and road construction were identified as the major driving forces of land expansion. Terrain conditions were not found a relevance to the urban land expansion rate during 1990–2015, but had a significant impact on the layout and shape, and also probably on the urban land efficiency.     

Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones

ABSTRACT

Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (CA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified CA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, China from 2013 to 2019. The proposed model is applied to future simulation in 2019–2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs.     

基于PLUS模型的乌鲁木齐市生态服务价值权衡协同探究

土地利用/覆被变化将干扰生态系统的稳定供给能力,进而对生态系统服务和可持续发展构成威胁。以乌鲁木齐市为研究区,基于斑块生成土地利用模拟（Patch-generating land use simulation,PLUS）模型,模拟了2030年多情景下乌鲁木齐市的土地利用变化,并结合当量因子法、空间自相关分析等方法估算并预测了1990—2030年乌鲁木齐市生态系统服务价值（Ecosystem service value, ESV）及其权衡协同关系。结果表明：（1）1990—2020年乌鲁木齐市土地利用呈现“两增四减”变化趋势。2030年在惯性发展情景下,除林地外其他地类皆遵循历史演变规律;在生态保护情境下,建设用地面积萎缩,生态用地大幅增加;在综合开发情境下,建设用地面积有所增加的同时生态用地面积也处于上升状态;在重点开发情景下,城市建设用地扩张最为剧烈,耕地面积退化严重。（2）1990—2020年乌鲁木齐市ESV整体上呈现“V”型下降态势,共减少59.59×108元。2030年在生态保护情景下和综合开发情景下ESV处于增加趋势,在惯性发展情景和重点开发情景下ESV出现减少趋势。（3）2...     

Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques

Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making.     

基于分县尺度的2020-2030年中国未来人口分布简

选取1982年、1990年、2000年和2010年人口普查数据,运用Logistic模型系统预测了2020年和2030年中国分县人口规模,定量分析了未来中国人口分布的基本布局、各地区人口增减变化以及城市群人口集聚度变化。研究认为：①2020-2030年中国未来人口空间分布的总体格局不会发生根本改变,东南半壁人口比例会有所减少,西北半壁人口比例会有所增加,但增减变化在0.1%~0.3%之间;②2010-2020年中国有1641个分县单元人口将仍呈增加趋势,占地规模和相应人口都在全国3/4水平,人口增加仍是主要特征;2020-2030年中国人口增加的分县单元将大幅减少到598个,人口减少地区占地规模和相应人口将占3/5以上,人口减少成为普遍现象。③中国21个城市群地区人口总量将由2010年的7.81亿增加到2020年的8.68亿和2030年的9.17亿,相应的人口集聚度也将由2010年的1.99逐步提高到2020年的2.17和2030年的2.33,城市群地区人口集聚规模和集聚程度在逐步提高,人口集聚态势更加明显。     

长江中游城市群空间结构演变历程与特征

城市群空间结构反映了城市群中城市的等级结构、职能结构和联系形态,代表了城市群在一定时间范围内的扩张模式与发展特征。本文选取中国首个获批复的国家级城市群——长江中游城市群作为研究对象,收集其1990—2019年的LULC、Landsat等图像资料以及城镇人口等数据,通过空间数据库构建、方格网系统建立、城镇扩张程度计算、扩张玫瑰图构建,从宏观、微观层面研究了长江中游城市群1990—2019年的空间结构演变历程及特征,并按照历史发展特征及规律,对2025年、2030年的发展态势进行情景分析。结果表明：① 1990—2019年长江中游城市群的空间结构经历了“三中心”到“一核双心多组团”,再到“双核多心多组团”模式的历程。作为“双核”的武汉城市圈、长株潭城市群已发展为巨型核心,在2020—2030年期间将继续扩张,成为面积达2000 km²和1500 km²的超巨型核心。而南昌规模相对较小,将继续作为辐射江西的区域发展中心。目前已形成了襄阳、宜昌、荆州、常德、衡阳、九江、上饶等7个发展次中心,在未来有望进一步增加。② 在发展历程中,逐渐形成了长江沿线、武汉—长沙、武汉—南昌、南昌—长沙4条城镇化发展主轴和多条发展次轴,但由于缺乏强有力的边界枢纽和桥头堡,轴线对城市群的辐射带动作用有待进一步提升。③ 城市群发展存在一些问题,包括武汉城市圈与长株潭城市群的核心极化作用过强并出现连绵发展趋势、江西缺乏强有力发展核心、城镇建成区过度集中导致环境问题等。亟待从控制核心扩张速度、培育新的区域中心、强化轴线功能等方面进行调控和优化。     

Urban construction land demand prediction and spatial pattern simulation under carbon peak and neutrality goals: A case study of Guangzhou,China

Urban construction land has relatively high human activity and high carbon emissions. Research on urban construction land prediction under carbon peak and neutrality goals (hereafter “dual carbon” goals) is important for territorial spatial planning. This study analyzed quantitative relationships between carbon emissions and urban construction land, and then modified the construction land demand prediction model. Thereafter, an integrated model for urban construction land demand prediction and spatial pattern simulation under “dual carbon” goals was developed, where urban construction land suitability was modified based on carbon source and sink capacity of different land-use types. Using Guangzhou as a case study, the integrated model was validated and applied to simulate the spatiotemporal dynamics of its urban construction land during 2030-2060 under baseline development and “dual carbon” goals scenarios. The simulation results showed that Guangzhou’s urban construction land expanded rapidly until 2030, with the spatial pattern not showing an intensive development trend. Guangzhou’s urban construction land expansion slowed during 2030-2060, with an average annual growth rate of 0.2%, and a centralized spatial pattern trend. Under the “dual carbon” goal scenario, Guangzhou’s urban construction land evolved into a polycentric development pattern in 2030. Compared with the baseline development scenario, urban construction land expansion in Guangzhou during 2030-2060 is slower, with an average annual growth rate of only 0.1%, and the polycentric development pattern of urban construction land was more prominent. Furthermore, land maintenance and growth, that is, a carbon sink, is more obvious under the “dual carbon” goals scenario, with the forest land area nearly 10.6% higher than that under the baseline development scenario. The study of urban construction land demand prediction and spatial pattern simulation under “dual carbon” goals provides a scientific decision-making support tool for territorial spatial planning, aiding in quantifying territorial spatial planning.     

中国城市群城市用地扩张时空动态特征

认识城市群城市用地扩张特征、扩张质量及景观生态效应等综合信息将有助于城市群空间高质量发展规划与决策。选取10个不同发育等级的城市群作为研究对象,综合运用城市用地扩张强度指数、城市用地扩张强度差异指数、景观扩张指数、景观格局指数,分析1990—2015年期间城市群城市用地扩张时空特征及景观生态效应,并构建人口、经济与城市用地协同发展指数,据此分析城市用地扩张中“人—地”关系协同程度。结果表明：① 1990—2015年,各个城市群城市用地面积持续增加,建成区的范围不断扩大,扩张强度呈现“上升—下降”的趋势;在2010年之前成熟型城市群的扩张强度最高,2010年之后培育型城市群成为扩张强度最高的类型。② 城市群城市用地扩张总体上呈现不均衡布局的趋势,高速扩张区域主要分布在沿海地区。从全国视角来看,东部高于中、西、东北部,整体城市群快速扩张的中心由东向西移动。③ 城市群城市用地空间扩张模式表现出边缘式和飞地式两种空间扩张模式并存,不同发育等级之间具有明显差异性,成熟型城市群的空间结构较为稳定,其他两种类型的城市群空间结构初具雏形或尚未形成。④ 城市群城市用地扩张“吞没”了郊区和农村地区,破碎化程度、形状的复杂程度较高,景观格局表现出不稳定。⑤ 城市群“人口—土地”协同程度逐步增强,“经济—土地”协同程度整体较高,整体上表现出城市用地扩张质量有所提升。新时期城市群城市用地应严格控制扩张规模,加快中小城市的发展,促进城市群城市用地扩张均衡发展,为城市群国土空间高质量发展提供重要参考。     

基于空间模型和CA的城市用地扩展模拟研究——以大庆市为例

基于ArcGIS空间分析建模工具,将城市发展适宜性模块、邻域影响模块、约束影响模块、随机影响模块进行集成,实现CA模型的构建,并以大庆市为例对城市建设用地未来发展进行模拟。研究认为:(1) 将GIS空间分析和CA模型集成,模型结构清晰,避免二次程序开发;(2) 根据大庆市特点,考虑资源开发因素对城市发展的影响和人口增长对随机因素的影响;(3) 大庆市2015、2020年城市面积将继续扩展,城市重心将明显向北移动,并具有向东移动的趋势。     

哈长城市群城镇用地扩展进程及其驱动因素研究

以Landsat TM/ETM+/OLI遥感影像为主要数据源,提取哈长城市群1990、2000、2010、2015 年4 个年份的建设用地信息,采用年均扩展指数、景观扩展指数、多距离空间聚类函数、空间分维度等对城镇用地扩展进程进行分析。结果表明,1990~2015年间,哈长城市群城镇用地增加57 321.47 hm²,增长率为21.05%,整体扩展强度呈上升趋势;空间扩展表现出以中心城市为核心的扩展模式,边缘式增长在各个时期均为主导增长方式;空间扩展聚集性总体表现出“先增强、后减弱”趋势,整体空间结构相对稳定。国家宏观政策、非农业GDP增量和第三产业GDP增量是影响哈长城市群城镇用地扩展的重要因素。GDP增量、非农业人口增量、第二产业GDP增量对城镇用地的扩展起阶段性作用;人均GDP增量与城市用地扩展相关性不显著。     

大都市区城市扩张过程及形态对比分析——以东京、纽约和上海为例

大都市区化是全球城市化进程中的普遍现象,研究大都市区域城市扩张进程对理解其未来增长趋势、区域可持续发展具有重要意义。大都市区土地扩张及区域间的对比分析,以及都市区发育过程中城市扩张的一般化空间模式还未被深入理解。论文选取东京、纽约和上海3个大都市区,获取1990—2015年间3期Landsat影像数据,使用梯度和格网分析法,采用城市土地密度函数和城市扩张指数,从建设用地数量变化及其时空分布、城市形态变化等方面,多角度对比分析各城市不同阶段建设用地增长的时空差异。结果表明：① 大都市区边界仍在不断扩展,建设用地在研究期内持续增长;20多年间东京、纽约和上海的建设用地面积分别增长了63%、53%和233%;上海一直处于高强度扩张状态,而纽约的城市扩张在后期几近停滞。② 大都市区建设用地密度从中心向外呈圈层递减式分布,城市核心区和边缘区半径的增长最明显;城市形态均趋向于紧凑。③ 新增建设用地时空异质性明显。东京的远郊新城不断建设,纽约一些尚有发展潜力的区域次中心仍在扩张但速率逐渐放缓,上海的核心区外围增长剧烈并逐渐向远城区推进。④ 大都市区域扩张先后经历高速期—缓慢期—停滞期,扩张进程表现为核心区膨胀—次中心及新城建设—逐步形成稳定的多中心结构。     

Modeling Spatial Processes of Urban Growth in African Cities: A Case Study of Nairobi City

Africa's urban population growth has been especially rapid, averaging about 5% per year over the past two decades. As a result, many urban areas have experienced dramatic growth that is seriously outstripping the capacity of most cities to provide adequate services for their residents. Although population growth and urbanization rates in Africa have slowed recently due to a number of factors including HIV/AIDS, urban growth is still expected to double by 2030, leading to dramatic sprawl with serious environmental and social consequences. Using Nairobi as an example of a rapidly urbanizing African city, we studied the dynamics of land use and land cover change using satellite data and addressed the need for models and urban management tools that can guide sustainable urban planning policies. Cellular Automata, which integrate biophysical factors with dynamic spatial modeling, are used in this study. The model was calibrated and tested using time series of urbanized areas derived from land use/cover maps, produced from remotely sensed imagery, with future urban growth projected to 2030. Model assessment results showed high levels of accuracy, indicating that simulation findings were realistic, thereby confirming the effectiveness of the model. Results further showed that the model is a useful and effective tool to foresee the spatial consequences of planning policies in the context of many African cities. The forecast for Nairobi showed unsustainable sprawl.     

基于约束性CA的大都市郊区城镇增长的情景模拟与管控——以武汉市江夏区为例

大都市郊区是快速城镇化进程中空间演变最为频繁、人地矛盾最为突出的区域,尤其在中国加快推进“就地就近”城镇化战略的背景下,把握大都市郊区小城镇土地利用时空变化过程及其演变机制,对制定科学合理的管控政策和优化都市空间结构具有重要的现实意义。约束性元胞自动机(constrained Cellular Automata, constrained CA)能够通过简单的规则模拟复杂的城市动态演化过程。本文将土地利用总体规划指标、城镇空间发展战略布局、土地利用开发适宜性等,嵌入约束性CA的转换规则中,采用Logistic逐步回归法分析土地利用空间影响因素,对严格约束下的武汉市江夏区2020年土地利用进行情景模拟分析,并提出城市增长管控手段。结果表明：①研究时段内,江夏区城镇用地呈低效外延式扩张,土地利用集约节约程度较低,其人口规模并未有较大增长,对主城区人口的分散作用尚未真正形成;②约束性CA在模拟大都市郊区演化方面具有较高的可靠性,能够真实反映近郊小城镇的未来空间布局与结构,模拟结果与土地利用规划和城市规划较为契合;③将规划目标导向与现实发展趋势下的模拟结果进行叠加分析,可确定城镇增长需求与规划指标调控间冲突的空间分布,从而划定土地督察的重点监测区域,为加强大都市近郊区的违法用地监查和土地利用管控提供先验的预警知识。     

基于CA模型的乌鲁木齐都市圈城市用地扩展模拟研究

借助CA模型技术,基于乌鲁木齐都市圈城市用地发展的特点,构建了模拟多个城市群体扩展的CA模型,重点对模型的转换规则进行了扩展,把用地适宜性约束、城市内部社会经济驱动、城市之间相互作用力、区域生态格局限制、城市发展规模有限这几个方面的作用机制融入到模型的规则制定中,通过宏观约束、中观调节、微观驱动3个层次逐步实现;为了使社会经济数据与CA模型中的空间数据相匹配,对社会经济数据进行了空间化表达;在历史数据的参照下,所构建的模型经过反复调试与修正,模拟结果达到了较为满意的效果;应用此模型模拟了经济优先、生态优先与规划优先3种不同情景下的城市用地扩展,对都市圈城市用地今后的发展起到重要的启示作用。     

规划跨江通道对滨江副城建设用地增长的影响研究——以南京市浦口区为例

以南京市浦口区为例,引入SLEUTH元胞自动机模型,采用五个时相的Landsat TM/ETM+遥感数据,模拟和预测了规划跨江通道对滨江副城城市发展建设用地增长的影响。研究结果表明:①SLEUTH模型经校准检验后能够较好地模拟研究区建设用地的增长数量与空间分布,适用于城市增长研究;②跨江通道建设对滨江副城增长的助推效应显著,近期(2020年)和远期(2030年)研究区建设用地分别新增48.05%和62.33%,其中规划跨江通道对新增建设用地的贡献率分别达到20.67%和17.50%,助推效应随时间递减。     

南京大都市区建设用地扩张特征与机理

作为全球城市化最快的地区之一,中国建设用地的扩张引起了广泛的关注。以长三角北翼中心城市南京为例,利用遥感、土地利用调查等数据研究大都市区建设用地扩张的过程、格局与机理。研究表明,1985-2007年南京市在工业化、城市化和国际化的推动下经济快速增长,引致了建设用地的扩张,年均增长率为3.14%。2001年后随着城市发展模式由单中心向多中心转换,建设用地呈加速扩展趋势。南京市建设用地扩展具有明显的沿长江和南北交通走廊轴向发展的特征。开发区和新城建设是南京市建设用地扩张的主要方式。区、县尺度的回归分析表明,人口增加、全球化是城镇工矿用地扩张的重要推手;经济的服务业化有利于土地集约利用,减少对用地的需求,这种影响在城区更大;在分权化竞争中,都市区政府具有更强的控制力。     

Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi,Central China

Spatial patterns of urban expansion mainly include infilling, edge expansion, and outlying growth patterns. The cellular automata (CA) model, is an important spatio-temporal dynamic model and effectively simulates infilling and edge-expansion urban expansion. but is evidently lacking in outlying scenarios. In addition, CA cannot explain the causes and processes of urban land expansion. Given these limitations, we proposed a novel urban expansion model called simulation model of different urban growth pattern (SMDUGP), which can work well in both adjacent (i.e., infilling and edge expansion) and outlying growth patterns. SMDUGP has two main components. First, we divided the non-urban region into two categories, namely, candidate region for adjacent pattern urban growth (CRFAP) and candidate region for outlying pattern urban growth (CRFOP). Second, different methods were utilized to simulate urban expansion in the different categories. In CRFAP, a CA model based on the potential of urban growth was proposed to simulate urban growth in relatively randomly selected urban growth regions based on the discrete selection model and Monte Carlo method as the expansion area was implemented in CRFOP. Huangpi, a suburban area in Wuhan, is utilized as the case study area to simulate the spatial and temporal dynamics of urban growth from 2004 to 2024. SMDUGP can effectively simulate outlying urban growth with a highly improved simulation precision compared with the traditional CA model and can explain the causes and processes of urban land expansion.     

广州市海珠区高密度城区扩展SLEUTH模型模拟

高密度城区是城市的核心区,也是城区扩展的源,对该区域的精确识别以及扩展模拟研究,具有重要的意义与价值。以广州市海珠区1979、1990、2000、2008 年4 期Landsat 影像为数据源,运用非渗透表面端元选取模型(V-I-S) 与归一化混合光谱分析模型(NSMA) 相结合的方法,辅以单窗算法反演地表温度数据(LST),高精度提取非渗透表面丰度,进而设置合适阈值表征研究区高密度城区范围。在此基础上基于SLEUTH模型设置4 种场景模拟和预测海珠区高密度城区扩展,并用景观指数分析方法对研究区1979-2050 年长达70 年的空间扩展状况进行分析。主要结论为：① SLEUTH模型同样适用于小尺度区域的扩展模拟。② SLEUTH模型模拟中基于自然、人文以及城市扩展内在动力机制等条件参数的设置,促使模拟结果精度更高。③ SLEUTH模型模拟结果表明,自1979 至今,海珠区高密度城区以较快扩展速率扩张,尤以1990-2004 年间变动增长最快;未来的20 年其增长速率减缓,并于2030 年前后趋于稳定。这种扩张格局与变化状况与研究区产业结构、经济政策、土地规划决策等因素密不可分。     

长沙市中心城区居住用地扩张特征及影响因素研究

为探讨长沙市中心城区居住用地扩张特征及影响因素,对长沙市居住用地的合理布局提供借鉴。以1979年、1990年、2003年及2010年城市总体规划为基础,借助不同年份的统计年鉴,采用居住用地扩张速度、居住用地扩张弹性系数及不同时期的居住用地的叠加,对长沙市中心城区居住用地的扩展特征与影响因素进行了系统分析。结果表明：（1）从1949年到2010年长沙市中心城区居住用地增加了7582．52×10^4m^2,年均增长速度为6．4％。（2）1979～1990年居住用地在旧城区的基础上往东发展,扩张半径较小;1990-2003年延续1979～1990年扩张方式之外,出现了一些类似于“飞地”性质的居住用地;2003—2010年居住用地扩张速度加大,湘江西部、城市南部及北部居住用地的面积明显加大。