文化产业成长对城市空间形态演变的影响——以西安市核心区为例

基于扩展强度指数、等扇分析法、核密度分析及空间句法等方法,运用1998年、2008年、2018年3个时期的文化产业企业地理数据、夜间灯光数据及城市道路网轴线模型分析西安市核心区文化产业空间成长与城市空间形态演变的特征,探究二者空间关系与作用机制。结果表明：① 西安市核心区文化产业沿“东北-西南”走向扩展,其空间集聚格局逐渐由“单核”“一带两核”向“一轴一带两核”演变。② 核心区城市外部空间形态沿“东北-西南”走向扩展,内部空间形态由“团状单核”“条带状主次双核”向“团带状双核”演变。③ 核心区文化产业成长与城市空间形态演变呈显著的关联性,即不同时期二者空间扩展方向基本一致,空间形态格局演变高度耦合。④ 核心区文化产业先导型发展模式推动城市外部空间形态的扩展,而填充型发展模式,包括在城市建成区的空间扩展与向既有产业集聚区的持续集聚推动着城市内部空间形态的优化。研究对完善文化产业空间研究体系,提升产业空间支撑城市空间转型的协同度方面具有重要意义。     

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study

The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling.     

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.     

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.     

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.     

Delimitation of Urban Growth Boundary Based on the Coordination of Ecology and Residential Activity Spaces: A Case Study of Jinan,China

Delimitation of an urban growth boundary (UGB) can effectively curb disorderly urban expansion, optimize urban development space and protect the ecological environment. Eco-environmental sensitivity was evaluated and areas prohibiting construction expansion were extracted by establishing an index system. Point of interest (POI) and microblog data were utilized to analyze the expansion of residential activity space. Urban space expansion potential was calculated using a comprehensive evaluation model, and an urban growth boundary for Jinan in 2020 was delimited combining the predicted urban expansion scale. The results showed that: (1) An evaluation of eco-environmental sensitivity can effectively protect ecological land and provide an ecological basis for urban expansion. Regions with high eco-environmental sensitivities in Jinan are located along the banks of the Yellow River and Xiaoqing River and in southeast mountainous areas, but eco-environmental sensitivities in the central, north and southeast areas are relatively low; (2) The model to evaluate urban residential activity expansion can quantify the spatial distribution of urban residents' activities. Regions with high potential for residential activity space expansion in Jinan are mainly concentrated in the middle of Jinan and most are part of existing built-up areas and surrounding areas; (3) The method that delimits urban growth boundaries based on the coordination of ecology and residential activity space is reasonable. Spatial expansion in Jinan mainly extends towards the east and west wings, and the boundary conforms to the spatial strategy guiding Jinan’s development and is consistent with the overall layout in related plans. Considering both ecological protection and the internal forces driving urban expansion, the method of urban growth boundary delimitation used in this study can provide a reference and practical help for studies and management of urban development in the new era.     

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。     

基于空间扩展模型和地理加权回归模型的城市住房价格空间分异比较

鉴于已有研究主要集中探讨住房价格的空间依赖性,较少涉及空间异质性对住房特征价格的影响,也很少尝试构建不同计量模型来比较模型间刻画住房价格影响因素空间分异的准确性,以长沙市中心城区为研究区,采用空间扩展模型和地理加权回归模型比较分析城市住房价格影响因素的空间分异,结果表明：① 空间扩展模型和地理加权回归模型都表明,长沙市中心城区的住房属性边际价格随着区位的变化而变化,揭示住房价格影响因素具有显著的空间异质性;小区环境、交通条件、教育配套、生活设施等因素对住房价格的影响强度存在明显的空间分异。② 地理加权回归模型和空间扩展模型都能对传统特征价格模型进行改进,但地理加权回归模型在解释能力和精度方面都超过空间扩展模型;对属性系数估计空间模式的分析,地理加权回归模型形成的结果比采用坐标多义扩展的空间扩展模型更为复杂和直观。     

中国城市市辖区的空间结构及演化机制

本文提出了市辖区6种空间结构类型：圈层式、组合式、并排式、独立式、包围式及飞地式,并总结了6种类型的主要特征。通过对中国市辖区分布格局及演变分析,发现城市规模等级越高,市辖区数量越多、人口密度越大。6种类型城市分布情况为圈层式结构城市集中在东部地区,组合式结构城市在东北地区分布最密,独立式结构城市大多分布于中西部,包围式结构城市中部地区最多,并排式和飞地式结构城市在全国分布比较均衡。市辖区空间结构的一般演化路径为“独立式-并排式-组合式-圈层式”,圈层式结构会继续优化,通过“内城合并、外城扩张”向第三、四圈层发展。文章最后分析了增设、拆分与合并市辖区三种行政区划调整方式的影响因素及作用机制。     

Construction of an ecological resistance surface model and its application in urban expansion simulations

Urban expansion models are useful tools to understand urbanization process and have been given much attention. However, urban expansion is a complicated socio-economic phenomenon that is affected by complex and volatile factors involving in great uncertainties. Therefore, the accurate simulation of the urban expansion process remains challenging. In this paper, we make an attempt to solve such uncertainty through a reversal process and view urban expansion as a process wherein the urban landscape overcomes resistance from other landscapes. We developed an innovative approach derived from the minimum cumulative resistance (MCR) model that involved the introduction of a relative resistance factor for different source levels and the consideration of rigid constraints on urban expansion caused by ecological barriers. Using this approach, the urban expansion ecological resistance (UEER) model was created to describe ecological resistance surfaces suitable for simulating urban expansion and used to simulate urban expansion in Guangzhou. The study results demonstrate that the ecological resistance surface generated by the UEER model comprehensively reflects ecological resistance to urban expansion and indicates the spatial trends in urban expansion. The simulation results from the UEER-based model were more realistic and more accurately reflected ecological protection requirements than the conventional MCR-based model. These findings can enhance urban expansion simulation methods.     

Comparison of Volterra Model and Artificial Neural Networks for Rainfall–Runoff Simulation

This study evaluates the performances of two distinct linear and non-linear models for simulating non-linear rainfall–runoff processes and their applications to flood forecasting in the Navrood River basin, Iran. Due to the excellent capacity of the artificial neural networks [multilayer perceptron (MLP)] and Volterra model, these models were used to approximate arbitrary non-linear rainfall–runoff processes. The MLP model was trained using two different training algorithms. The Volterra model was applied as a linear model [the first-order Volterra (FOV) model] and solved using the traditional ordinary least-square (OLS) method. Storm events within the Navrood River basin were used to verify the suitability of the two models. The models’ performances were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge, and error of time for peak to arrive. Results indicated that the non-linear MLP models outperform the linear FOV model. The latter was ineffective because of the non-linearity of the rainfall–runoff process. Moreover, the OLS method is inefficient when the FOV model has many parameters that must be estimated.     

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.     

A new discovery of transition rules for cellular automata by using cuckoo search algorithm

This paper presents an intelligent approach to discover transition rules for cellular automata (CA) by using cuckoo search (CS) algorithm. CS algorithm is a novel evolutionary search algorithm for solving optimization problems by simulating breeding behavior of parasitic cuckoos. Each cuckoo searches the best upper and lower thresholds for each attribute as a zone. When the zones of all attributes are connected by the operator ‘And’ and linked with a cell status value, one CS-based transition rule is formed by using the explicit expression of ‘if-then’. With two distinct advantages of efficient random walk of Lévy flights and balanced mixing, CS algorithm performs well in both local search and guaranteed global convergence. Furthermore, the CA model with transition rules derived by CS algorithm (CS-CA) has been applied to simulate the urban expansion of Nanjing City, China. The simulation produces encouraging results, in terms of numeric accuracy and spatial distribution, in agreement with the actual patterns. Preliminary results suggest that this CS approach is well suitable for discovering reliable transition rules. The model validation and comparison show that the CS-CA model gets a higher accuracy than NULL, BCO-CA, PSO-CA, and ACO-CA models. Simulation results demonstrate the feasibility and practicability of applying CS algorithm to discover transition rules of CA for simulating geographical systems.     

基于景观生态安全格局构建的城镇空间扩展模式研究——以江苏沿海地区为例

基于景观生态学的“格局-过程”理论,对维护江苏沿海地区地形地貌、水源涵养、生物多样性、游憩景观等生态系统服务的单一安全格局进行构建;根据“最小-最大约束”准则,通过镶嵌运算进行叠加,构建综合生态安全格局。运用MCR模型,设置并比对“生态安全模式”“经济发展模式”和“生态与经济协调模式”等不同模式对城镇空间扩展的影响,识别需严格保护的低安全水平区和未来城镇空间重点开发的高安全水平区。研究得出：“生态保护与经济发展”协调型模式更好平衡“生态-社会-经济”效益,合理确定城镇建设和产业发展空间,是城镇健康有序发展的最优模式。     

中国建设用地与区域社会经济发展关系的空间计量研究

利用空间计量模型,对中国大陆地区的30个省、市、自治区2008年的建设用地面积与社会经济发展之间的关系进行了研究。研究表明:30个省、直辖市和自治区地区建设用地面积、GDP、总人口和城市化率都有显著的空间相关特征,一个区域社会经济的发展不仅会驱动自身区域建设用地的扩张,而且会带动邻近区域的建设用地的增长。传统上只从时间维度出发的研究思路,忽视空间维度的相关性和异质性,低估了区域社会经济发展对建设用地增长的作用,必须在普通面板线性回归模型中描述的基础上引入空间变量进行修正。计量模型检验表明,城市化水平对建设用地总规模的影响不甚显著。说明中国大部分省份的农村居民点用地的利用效率普遍偏低。因此,农村居民点用地的调整与优化已迫在眉睫,从长远看来,提高城市化水平,打破城乡二元化的土地制度,建立统一的土地市场,是缓解土地资源紧缺、提高土地利用效率的有效途径。     

南通市建设用地扩张情景模拟与景观生态效应

选择城镇化进程快速、区位条件特殊的南通市作为案例,集成CLUE-S模型与Auto-logistic回归模型构建适合不同情景的建设用地扩张的动态分布模型。通过1987年以来遥感影像获取的4个时段的南通市建设用地增长时空特征分析提取先验知识规则,模拟在基准、经济发展、生态保护等情景下建设用地增长与空间布局,通过景观学知识分析其空间扩张的生态效应。结果表明,1987年以来,南通市建设用地扩张剧烈,空间上围绕城区和乡镇中心快速蔓延,呈现“轴线增长-内部填充”交替的周期性波动规律。Kappa系数与模拟正确率（PCM）显示,构建的复合集成模型具有较高的可靠性,能够应用于对未来建设用地空间分布的模拟。模拟结果显示未来建设用地扩张集中在城镇周边,其中在经济发展情景下还有向东部沿海滩涂扩展的趋势。由于扩张剧烈,在建设用地集中区呈现出融合成更大面积分布区的现象。建设用地空间扩张的斑块密度和景观形状指数均较高,景观格局趋于不稳定,破碎化相对严重。     

Modelling spatial patterns of urban growth in Africa

The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.     

北京市住宿业空间结构时空演化及影响因素

将1997-2012 年划分为4 个时段,采用ArcGIS 与次序多元Logit 模型分析北京市住宿业空间结构时空演化特征与影响因素,探讨住宿业空间结构与城市空间结构的协同演化关系,反映城市空间结构重组特征。结果表明：① 北京住宿业时空演化的向心集聚与离心扩散趋势并存,属于相对扩散;扩张表现出集聚特征与方向上的差异性;同心圆式、局部扇面式、廊道式、飞地式、填充式等扩张形式并存;② 可达性、基本地价、空间集聚是住宿业时空演化的主要影响因素;③ 城市化进程影响住宿业的区位转移,住宿业是城市空间结构的塑造力量之一;④ 塑造城市结构的离心扩散作用增强,城市空间的异质性与复杂性增强。     

基于logistic回归模型的农牧交错区土地利用变化驱动力分析——以内蒙古翁牛特旗为例

本文以农牧交错带的典型区域——内蒙古翁牛特旗为例,考虑土地利用变化过程的空间变量,建立了不同土地利用变化过程的logistic回归模型。结果表明:模型中转为耕地的主要解释变量是到农村居民点的距离和农业气候区;转为草地的主要解释变量是到农村居民点的距离、土壤表层有机质含量和到乡镇中心的距离;转为林地的主要解释变量是到农村居民点的距离和海拔;空间异质性和土地利用变化过程的时间变量共同影响着使用logistic回归模型来解释土地利用变化驱动力的能力;通过对草地logistic回归模型的检验,得出空间统计模型能较好地揭示不同土地利用变化过程的主要驱动力及其作用机理。     

Spatiotemporal urbanization processes in the megacity of Mumbai,India: A Markov chains-cellular automata urban growth model

Several factors contribute to on-going challenges of spatial planning and urban policy in megacities, including rapid population shifts, less organized urban areas, and a lack of data with which to monitor urban growth and land use change. To support Mumbai's sustainable development, this research was conducted to examine past urban land use changes on the basis of remote sensing data collected between 1973 and 2010. An integrated Markov Chains–Cellular Automata (MC–CA) urban growth model was implemented to predict the city's expansion for the years 2020–2030. To consider the factors affecting urban growth, the MC–CA model was also connected to multi-criteria evaluation to generate transition probability maps. The results of the multi-temporal change detection show that the highest urban growth rates, 142% occurred between 1973 and 1990. In contrast, the growth rates decreased to 40% between 1990 and 2001 and decreased to 38% between 2001 and 2010. The areas most affected by this degradation were open land and croplands. The MC–CA model predicts that this trend will continue in the future. Compared to the reference year, 2010, increases in built-up areas of 26% by 2020 and 12% by 2030 are forecast. Strong evidence is provided for complex future urban growth, characterized by a mixture of growth patterns. The most pronounced of these is urban expansion toward the north along the main traffic infrastructure, linking the two currently non-affiliated main settlement ribbons. Additionally, urban infill developments are expected to emerge in the eastern areas, and these developments are expected to increase urban pressure.