黄河流域城市群扩张的时空格局演化及情景模拟——以呼包鄂榆城市群为例

黄河流域城镇扩张对区域景观格局影响显著,城市群人口聚集与增长引发了流域“人-地”矛盾和“空间冲突”等一系列生态环境问题。基于土地利用数据和FLUS模型对2025年和2035年呼包鄂榆城市群城镇化与土地利用时空演化特征进行多情景模拟预测。结果表明：① 1990—2018年呼包鄂榆城市群整体发展水平较低,建设用地面积经历了“平稳增加-缓慢增加-急剧增加”的变化过程,区域总体以草地为主,其占土地总面积的50%以上,其次是未利用土地和耕地,林地和建设用地次之。② 城市群扩张最剧烈地区在空间上主要发生在呼和浩特市、包头市等城市主城区,且扩张模式以外延式扩张为主,扩张来源主要是耕地、草地等生态用地。③ 三种情景模拟发现,2025年和2035年区域土地利用变化的空间结构和特征差异明显。自然发展情景下,城市扩张不受约束,高速增长占据了大量生态用地;加入生态约束条件很好的控制了对草地和林地的占用;经济发展情景下,城市扩张将进一步占据更多的未利用土地和耕地。本研究通过城市群扩张时空格局演化及情景模拟分析,尝试为区域规划、城市空间规划和区域生态空间保护提供多角度、多情景和可选择的政策决策参考。     

中原经济区城市间相互作用时空格局演变研究

将中原经济区30个省辖市市区作为研究对象,按照由“线”→“点”→“面”的分析思路,运用引力模型、潜能模型与潜能得分模型、经济隶属度模型等各种模型,定量分析1990~2010年间中原经济区城市间相互作用时空格局的演变过程与特征。结果表明：城市间相互作用强度的时空差异明显。“线”层次上,城市间的引力和城市联结线数目的增多,逐渐形成辐射网络,引力和联结线数目的变化存在时空差异性。“点”层次上,通过“线-点”叠加分析,城市最大联结线数目增多和城市潜能等级提升的时空差异性明显。而且1990~2010年间各个城市潜能等级跃迁的时空差异明显。“面”层次上,近20多年来,区域中心城市没有变化,但4个中心城市的腹地变化明显,核心组团——郑州组团1990~2000年北扩,2000~2010年东扩;1990~2010年,潜能高值区域的空间收缩也表现出阶段性特征。     

中国东部沿海城市旅游发展的时空演变

城市旅游时空演变是旅游地理学研究的热点领域。以中国东部沿海城市为研究区,分析城市旅游发展的时空演变格局。首先分析沿海城市旅游发展强度的时空演变,进而采用DEA数据包络分析法计算沿海城市旅游发展效率的时空演变。研究表明：① 在旅游发展强度方面,东部沿海城市之间的旅游发展强度差异较大,长三角、珠三角以及环渤海的辽东半岛和山东半岛地区的旅游发展强度较强,海峡西岸经济区和环北部湾地区的旅游发展强度较弱。② 10年间在旅游发展综合效率方面基本维持不变,而旅游发展纯技术效率显著增强,旅游发展的规模效率则显著减弱。说明沿海城市的旅游发展已经逐渐由规模效率向技术效率转变。③ 综合旅游发展强度和旅游发展效率两个方面看,将东部沿海城市旅游发展类型分为“高—有效型”、“低—有效型”、“高—无效型”和“低—无效型”四种类型。其中“高—有效型”城市旅游发展较为成熟,“低—有效型”和“高—无效型”城市旅游发展一般,“低—无效”城市旅游发展相对较差。整体来看,珠三角和海峡西岸地区旅游发展强度和旅游发展效率均出现一定程度下滑,长三角、环渤海和北部湾地区的旅游发展强度和旅游发展效率均呈现不同程度增加。     

1948—2018年国际河流跨境水冲突的时空演化规律

随着全球气候变化和人类社会经济高速发展,水资源供需矛盾日益突出,国际河流流域跨境水冲突不断加剧,其时空演化规律成为当前跨学科研究的热点和难点。为此,从水冲突“事件—关系”视角,对1948—2018年间全球跨境水冲突的时空演化规律进行系统分析发现：① 1948—2018年全球跨境水冲突规模总体呈非单调波动增长态势,在1987年前后发生显著突变,表现出兼具趋势性、突变性和波动性等多维特征的非单调动力过程;② 全球跨境水冲突事件以围绕水文干预和资源权属争夺的低强度事件为主,大坝等水利工程建设引发的冲突显著增加,南亚、西亚和东非地区成为主导全球跨境水冲突趋势演化的“序参量”;③ 全球跨境水冲突呈现由单中心向多中心格局转变的空间扩散趋势,但“北多南少、东多西少、相对集中”的总体不均衡格局基本锁定,形成以30°N为中心、横跨南亚—中亚—西亚—东非高水资源压力区的水冲突密集带;④ 国家间跨境水冲突关系趋于普遍化、复杂化和网络化,发育多个流域性网络社团,但水冲突关系的“马太效应”和极化分布特征持续显著,产生“路径锁定”效应;水冲突“规模—强度”关系存在一定的空间错配。     

长江中游城市群“文—旅”产业融合发展的空间效应及驱动机制研究

“文—旅”产业融合发展为产业结构高质量转型、空间统筹优化和区域经济韧性提升赋能。在由“宏观—中观—微观”三维尺度复合而成的城市群“文—旅”产业融合发展理论基础上,论文构建了城市群“文—旅”产业融合发展评价指标体系,以长江中游城市群为研究对象,借助耦合协调模型和极化指数模型对2010—2019年区域“文—旅”产业融合发展水平进行测算并分析其空间效应,并探究该区域“文—旅”产业融合发展的驱动机制。结果显示：① 长江中游城市群“文—旅”产业融合发展的各子系统间具有较强的互动关系,但多数城市暂未达到良性发展状态;② 研究期内,长江中游城市群“文—旅”产业融合发展的空间效应表现出“极化效应”显著、“回程效应”与“扩散效应”微弱的特征;③ 长江中游城市群“文—旅”产业融合发展在空间上呈现以“武汉—长沙—南昌”为核心的“核心—边缘”结构;④ 长江中游城市群“文—旅”产业融合发展是在资源禀赋基础力、居民消费需求力、基础设施保障力、规模效益催动力、人力资源潜在力、经济发展拉动力“六力”共同驱动下形成的。最后,根据长江中游城市群“文—旅”产业融合发展现状,提出强化区域联动、瞄准核心驱动力等针对性建议,推动长江中游城市群区域一体化建设和地方经济高质量发展。     

城市收缩：内涵·中国化·研究框架

城市收缩是中国当前快速城镇化进程中不断涌现的一个新现象,给传统基于“增长情景模拟”的区域—城市规划范式和发展认知论带来极大的冲击和挑战;如何构建城市收缩“中国化”理论分析框架也由此成为学术界和规划管理部门关注的焦点话题。据此,论文围绕如下几个问题展开分析：① 系统梳理城市收缩概念的由来、城市收缩与城市增长、城镇化,以及与本土化、国际化的关系认知,指出城市收缩与城市增长是城镇化发展过程中2个方向互异的过程产物,也是一个嵌入全球化而根植于本土化的结果产物,研究中应避免“就收缩城市论收缩”;② 辨析与界定城市收缩与收缩城市的概念内涵,指出当前收缩城市识别标准的不统一,源于城市收缩与收缩城市概念混淆、概念重现象描述而忽视要素—效应的发展关联;③ 从研究范围、研究时段、界定标准和研究背景与区情差异性等方面对城市收缩内涵的“中国化”进行了初探,提出以城市建成区为研究范围、以10年为考察期、以综合取代单一人口指标构建收缩城市识别标准与方法体系,立足区域关联剖析、预测与响应收缩城市等内容与形式来落实“中国化”城市收缩内涵; ④ 基于产权区域、场势理论、区域关联与区域差异性构建中国语境收缩城市研究框架与实践路径。研究结果以期抛砖引玉唤起国内广大地理学者对城市收缩研究的“中国化”进行深入探讨,为世界城市收缩研究提供“中国经验”。     

特大城市“三线冲突”空间格局及影响因素

划定“三线”(生态红线、城市开发边界、基本农田保护红线)是构建城市生产、生态、生活空间格局和实现“多规合一”的关键。但在实际工作中,“三线”由不同部门依据不同准则划定,在空间上往往存在冲突。本文以武汉都市发展区为例,从图斑层面探讨“三线冲突”的类型与表征,并分别采用空间格局分析和相关分析法研究“三线冲突”的空间格局特征和区域条件对其影响。研究结果表明：①武汉都市发展区内“三线冲突”以生态—耕地冲突和生态—城市冲突为主,城市—耕地冲突面积较小;②生态—耕地、生态—城市和城市—耕地冲突热点分别集中于都市发展区边缘、都市发展区内部和城乡过渡带,呈圈层分异的特点;③区域条件的6个指标对3类“三线冲突”子类型冲突强度的影响因素各不相同。研究结论为：一是“三线冲突”强度可以用“三线冲突”强度指数(TLCI)表征;二是空间自相关分析、梯度分析、景观格局分析法可以很好地刻画“三线冲突”空间特征;三是对“三线冲突”的自然—人文驱动机制的研究有待进一步完善和深化。本文有助于从城市空间视角加深对“多规冲突”问题的理解,识别“三线冲突”的自然—人文驱动因子,为实现“多规合一”提供理论支撑。     

基于生态适应性循环三维框架的城市景观生态风险评价

本研究以城市社会—生态系统为风险评价对象,引入生态适应性循环三维框架,将景观生态风险评价指标从单一的景观指数层面扩展至“潜力—连通度—恢复力”三维准则,并以深圳市为研究区,基于有序加权平均（OWA）算法对评价结果进行情景设置。研究结果显示,评价中干扰指标主要影响风险评价结果属性值域,而风险空间格局则受暴露指标制约;深圳全市景观生态风险整体呈现“西高东低”的分布格局,城市新建成区风险最高,大鹏半岛风险最低,羊台山与笔架山公园则是城区内部的相对风险低值区;基于OWA方法设置情景偏好,绘制“忽视”、“正常”及“重视”三种风险情景下的城市景观生态风险图。本研究基于生态适应性循环理念集成社会—生态系统时空动态干扰与暴露指标表征城市景观生态风险,并通过OWA方法变换主观偏好、降低评价不确定性,可以满足不同发展思路下的城市开发布局需求,从而为城市景观发展空间权衡提供决策支持。     

消费升级视角下城市生活服务空间的演变及影响因素研究——以武汉菜市场为例

消费升级时代孕育了生活性服务新业态,改变了生活服务业的城市空间格局。论文以菜市场作为生活服务空间的代表,选取武汉市这一中国城市消费升级样本及城市疫后复苏“风向标”作为典型案例区,结合GIS空间计量和STATA统计分析,探讨菜市场综合体系的形成过程、类型差异和影响因素。研究发现：① 武汉市菜市场经历了传统农贸市场主导—零售新业态扩张—社区尺度回落的历程,形成以大卖场生鲜部、农贸市场、生鲜超市及社区菜场为主的复合型生活服务空间,并在疫后展现出一定韧性;② 武汉市菜市场整体上由双核心向多核心转变,由城市中心向外围延伸,且呈现出农贸市场“团簇状”、生鲜超市“条型”、大卖场“点状”、社区菜场“蝶形”的分类型差异化格局;③ 全业态菜市场布局受社会、经济、区位和发展基础影响显著,传统业态受市场和区位要素影响显著,零售新业态受经济要素和前期菜市场集聚程度的影响显著,且消费升级态势下新建菜市场选址更加偏好购物中心代表的体验式消费场所。研究结果对于丰富现代菜市场体系的类型研究,拓展城市生活服务空间研究的学科视域具有一定理论意义,同时对于优化城市生活服务空间结构以满足居民日益增长的美好生活需要和服务品质追求具有一定现实价值。     

地表温度“源—汇”景观贡献度的影响因素分析

在中国快速城市化进程中,城市热岛是严重的环境问题之一。探究城市热岛的影响因素,建立与自然因子、社会经济因子、景观形态因子的关联,对解决中国“城市病”,实现可持续发展具有重要的意义。本文首先在全国尺度上基于地表温度“源—汇”景观指数识别地表温度的“源”景观/“汇”景观,在此基础上计算“源—汇”景观贡献度及其效应,分析其时空演变规律;其次,基于空间杜宾模型分析自然因子、社会经济因子和景观形态因子对“源—汇”景观贡献度的影响,结果发现：① 2005—2015年间,可缓解热岛效应的省份冬季较夏季多,冬季“强”和“较弱”景观贡献度比值区域集中分布在中国北部和南部;夏季“强”的“汇”与“源”景观贡献度比值区域集中分布在中国北部地区。② 对于本地因子,景观形态对“源”或“汇”景观贡献度的影响较大,缓解热岛效应将附近小“汇”斑块结合来降低地表温度“汇”景观的离散度;其次,夏季适当在“源”景观里增加相对湿度、并增加其植被覆盖率,严格控制“源”景观和“汇”景观的建筑密度及建筑高度,可达到最佳降温效果。③ 邻域因子中相对湿度和建筑密度的影响更大,景观形态中增加“源”景观的离散度及其边缘密度,或增加“汇”景观集聚度,简化其形状均有利于缓解邻域的热岛效应。     

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.     

Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata

Traditional urban cellular automata (CA) model can effectively simulate infilling and edge-expansion growth patterns. However, most of these models are incapable of simulating the outlying growth. This paper proposed a novel model called LEI-CA which incorporates landscape expansion index (LEI) with CA to simulate urban growth. Urban growth type is identified by calculating the LEI index of each cell. Case-based reasoning technique is used to discover different transition rules for the adjacent growth type and the outlying growth type, respectively. We applied the LEI-CA model to the simulation of urban growth in Dongguan in southern China. The comparison between logistic-based CA and LEI-CA indicates that the latter can yield a better performance. The LEI-CA model can improve urban simulation accuracy over logistic-based CA by 13.8%, 10.8% and 6.9% in 1993, 1999 and 2005, respectively. Moreover, the outlying growth type hardly exists in the simulation by logistic-based CA, while the proposed LEI-CA model performs well in simulating different urban growth patterns. Our experiments illustrate that the LEI-CA model not only overcomes the deficiencies of traditional CA but might also better understand urban evolution process.     

基于智能体和人工神经网络的元胞自动机建模及城市扩展模拟

城市扩展模拟可为城市可持续发展与国土空间规划提供参考。智能体模型(ABM)与元胞自动机(CA)结合可兼顾城市空间增长的自组织性和不同决策主体的决策过程,人工神经网络(ANN)可描述智能体与城市扩展之间复杂的非线性关系。该文基于ANN-ABM-CA耦合模型,在构建CA转换规则时基于ABM刻画人类决策行为的影响,并采用ANN挖掘不同类型的智能体在城市扩展过程中的偏好差异,同时考虑宏观和微观层面的智能体决策行为,结合城市扩展的10个驱动因素,模拟武汉市主城区2005-2015年的扩展情况,结果表明:1)相比传统的ANN-CA模型,ANN-ABM-CA模型模拟性能更优,从宏观与微观相结合的角度更好地解释了城市扩展的驱动机制,OA值为97.46%,Kappa系数为0.9176,FoM值为0.4375,结果可靠且合理;2)不同收入层级的居民智能体对城市扩展的决策偏好不同;3)武汉主城区城市扩展模式主要为边缘型扩展,洪山区西南部有少部分填充型扩展、东南部出现飞地型扩展,与实际扩展情况相符。     

Simulating urban dynamics by coupling top-down and bottom-up strategies

ABSTRACT

Cellular automata (CA) are effective tools for simulating urban dynamics. Coupling top-down and bottom-up CA models are often used to address macro-scale demand and micro-scale allocation in the simulation of urban dynamics. However, those models typically ignore spatial differences in terms of the coupling process between macro-scale demand and micro-scale allocation. Herein, a novel approach for combining top-down and bottom-up strategies based on simulating urban dynamics is proposed. An optimizing strategy was used to predict the parameter of the inverse S-shaped function of future urban land use pattern and further deduce urban land increment within each concentric ring. The maximum probability transformation rule was incorporated into the CA model to address the micro-scale allocation. Wuhan was selected to test the performance of the proposed approach, and the conventional and the proposed approaches were compared. The results demonstrated that the proposed approach can not only retain the model’s accuracies but also better simulate the macro morphology of urban development dynamics and generate more realistic urban dynamic pattern in the urban sub-center and fringe regions. The proposed coupling approach can also be used to generate different development scenarios. The approach is expected to provide new perspectives for coupling top-down and bottom-up CA models in modeling urban expansion.     

一种新的城市景观扩张过程测度方法：多阶邻接度指数

提出多阶邻接度指数（Multi-order Adjacency Index, MAI）,应用多阶缓冲区完善对新旧斑块间空间关系的识别,从而更为详细地揭示城市扩张过程特征。MAI的延伸公式可供自下而上从宏观分析城市整体的扩张程度及空间变化过程。选取武汉市作为研究区,基于4期遥感影像（1995、2000、2005和2010年）划分3个时段应用MAI分析武汉市城市景观格局的演变特征。结果表明,MAI能够详细地反映出新增城市斑块在空间上有更加离散的分布趋势,新增斑块的扩张程度逐渐加深,建成区的边界不断向外延展。比较MAI与LEI（Landscape Expansion Index, LEI）的特征差异,指出MAI能够更加详尽地反映新旧斑块间的空间位置关系。     

A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion

A new metaheuristic approach is presented to discover transition rules for a cellular automaton (CA) model using a novel bat movement algorithm (BA). CA is capable of simulating the evolution of complex geographical phenomena, and transition rules lie at the core of these models. An intelligence algorithm based on the echolocation behavior of bats is used to discover explicit transition rules for use in simulating urban expansion. CA transition rules are formed by links between attribute constraint items and classification items. The transition rules are derived using the BA to optimize the lower and upper threshold values for each attribute. The BA-CA model is then constructed for the simulation of urban expansion observed for Nanjing City, China. The total accuracy of newly formulated BA-CA model for this application is 86.9%, and the kappa coefficient is 0.736, which strongly suggest that the interactions of bats are effective in capturing the relationships between spatial variables and urban dynamics. It is further demonstrated that this bat-inspired BA-CA model performs better than the null model, the particle swarm optimization-based CA model (PSO-CA), and the ant colony optimization-based CA model (ACO-CA) using the same dataset. The model validation and comparison illustrate the novel capability of BA for discovering transition rules of CA during the simulation of urban expansion and potentially for other geographic phenomena.     

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.     

一种顾及驱动因子重要性的双约束RF-Patch-CA城市扩张模拟方法

城市扩张模拟为实现土地资源合理分配与制定城市发展规划政策提供依据。该文针对传统元胞自动机(CA)在城市扩张模拟中存在城市元胞密集区域团簇现象,耦合随机森林(RF)与基于斑块(Patch)扩张的CA模型,在顾及驱动因子重要性基础上构建基于斑块最大面积和城市扩张总量的双约束RF-Patch-CA模型,并利用该模型模拟重庆主城都市区2010-2017年城市扩张。结果显示:该模型总体精度达97.62%,相比传统的RF-CA、ANN-CA和Logistic-CA模型,Kappa系数分别提高了0.0222、0.0231和0.0245,FoM分别提高了0.0376、0.0391和0.0414;在景观相似度上,该模型相比以上3种模型分别提高了40.92%、41.16%和32.33%,最接近真实情况,而且避免了模拟结果产生城市元胞团簇现象,有效提高了城市扩张模拟精度。     

基于随机森林的元胞自动机城市扩展模拟——以佛山市为例

本文提出一种基于随机森林的元胞自动机城市扩展(RF-CA)模型。通过在多个决策树的生成过程中分别对训练样本集和分裂节点的候选空间变量引入随机因素,提取城市扩展元胞自动机的转换规则。该模型便于并行构建,能在运算量没有显著增加的前提下提高预测的精度,对城市扩展中存在的随机因素有较强的容忍度。RF-CA模型可进行袋外误差估计,以快速获取模型参数;也可度量空间变量重要性,解释各空间变量在城市扩展中的作用。将该模型应用于佛山市1988-2012年的城市扩展模拟中,结果表明,与常用的逻辑回归模型相比,RF-CA模型进行模拟和预测分别能够提高1.7%和2.6%的精度,非常适用于复杂非线性特征的城市系统演变模型与扩展研究;通过对影响佛山市城市扩展的空间变量进行重要性度量,发现对佛山城市扩张模拟研究而言,距国道的距离与距城市中心的距离具有最重要的作用。     

A spatial zoning approach to calibrate and validate urban growth models

Calibration and validation of models predicting urban growth have been largely developed using internal variables. Further investigation is required to improve model’s calibration and validation mixing internal and external variables. To reach this objective, a spatial zoning approach simulating long-term expansion of Mashhad, the second largest city of Iran, was presented in this study. Spatial zoning approaches distinguish local-scale urban dynamics in districts with different socioeconomic characteristics. Thiessen polygons were used to identify districts with different morphology and functional attributes. Urban growth was subsequently simulated for each district using a Multi-Layer Perceptron (MLP) neural network and Markov chains (MC) analysis. MLP and MC algorithms were respectively used to derive transition maps from non-urban to urban use of land and to determine spatial evolution of built-up areas at the metropolitan scale. Results of simulations based on spatial zoning were compared with outcomes of traditional urban growth models. Spatial zoning improved significantly model’s accuracy in respect to more traditional simulation modes. The approach proposed here is appropriate when simulating land-use changes under discontinuous urban expansion.