城市扩张程度的影响因素分析——以武汉市为例

城市斑块层面扩张程度异质性深切影响着城市物理形态演变。研究城市扩张程度的影响因素及其机制,有助于深化理解城市扩张过程,管控城市空间扩张,促进城市紧凑发展。以中国湖北省武汉市为例,采用邻近度扩张指数度量城市扩张程度,并分析了城市扩张程度的空间特征,采用相关性分析探讨城市扩张程度与速度之间的联系,构建空间回归模型探究自然、社会经济、交通、规划等因素对城市扩张程度的影响。研究发现:(1)城市扩张程度存在空间差异,由主城区向外逐渐增大;(2)城市扩张程度存在正向空间相关性;(3)城市扩张程度与扩张速度之间呈强正相关关系;(4)交通区位、规划控制、到城市中心的距离是城市扩张程度的主要影响因素,且影响程度依次降低。城市扩张程度的影响因素分析结果可为城市空间管控提供科学依据。     

GIS多准则的城市垃圾楼空间布局优化选址

针对城市公共设施的传统选址方法较少考虑空间维度因素的问题,该文提出了基于地理信息系统(GIS)多准则的城市公共设施选址模型,利用GIS多准则技术基于空间维度分析了城市自然环境和人文要素等综合影响下的公共设施选址方法,并以城市垃圾楼选址为例进行了实例应用。实验结果表明:该方法不仅充分考虑了城市复杂的地理环境,还深度探究了影响城市公共设施选址的综合性因素。     

签到数据的城市热点分布特征与成因

针对当前面向城市热点空间分布特征与成因的研究缺乏考虑时效性和全面性的问题,该文提出了一种基于签到数据的城市热点探测方法,精准、便捷地探测到8种业态类型的城市热点,并在此基础上运用点模式分析和地理探测器等方法,对各业态类型城市热点的空间分布特征及其影响因素展开研究。研究结果表明:各业态类型的城市热点具有不同空间范围下的集聚性特征,且在空间分布上表现出明显的差异性;业态类型、GDP、人口规模、土地价格以及交通通达性是影响城市热点空间分布的重要因素,且同类因素对不同业态类型的城市热点的影响力不同。     

基于ArcGIS的城市居住区空间宜居性评价

随着我国居民生活水平的日益提高,人们对城市居住区的选取提出了更高的要求。本文基于ArcGIS的空间分析功能,对不同的居住区进行分析和比较,在分析过程中分别选择一些具有代表性的影响因素,通过这些影响因素对城市居住区宜居性进行空间评价。     

重庆市主城区城市空间扩展遥感监测研究

以2000~2013年多源遥感影像为数据源,结合专题资料,根据一定的判别原则提取了重庆市主城区4个时相的城区范围。通过对主城区城市扩展强度、城市用地增长弹性系数的计算,分析了城市空间扩展的时空变化情况;通过对主城区城市扩展空间象限方位、分形维数、紧凑度、重心转移的计算,分析了城市外部形态特征的演变。结果表明,重庆市主城区外部城市空间形态保持健康稳定的发展趋势;城市空间形态变化受政策和制度因素引导和调控;城市空间扩展形态受多种影响因素综合作用。     

城市因素对COVID-19疫情的影响——以武汉市为例

新型冠状病毒肺炎(coronavirus disease 2019,COVID-19)疫情是一次重大的突发公共卫生事件,深入研究城市空间因素对疫情发展的影响对于未来城市安全具有重要意义。武汉市是遭受COVID-19疫情影响最为严重的城市,以武汉市三环内的中心城区作为研究区域,基于COVID-19疫情的新浪微博求助数据,揭示了COVID-19疫情在城市中的空间分布格局及其对不同区域的影响。在此基础上,根据疫情的主要传播途径及相关影响因素,选取社会人口、城市形态、城市设施、城市功能4项指标进行了验证。通过将研究区域进行网格化处理,运用地理加权回归模型对这些因素的效应、空间异质性以及影响范围进行分析,解释并反演出疫情在武汉城市空间中发生、传播、扩散的实际情况和作用机制。结果显示,三甲医院密度、商业密度、地铁站点密度、建设规模、老龄化、土地混合使用对疫情有显著影响。对城市空间因素的分析和验证有利于在未来的突发性公共安全危机中采取有效的城市规划和建筑设计应对,帮助城市决策者制定科学合理的防治策略,提前规避或减小对脆弱性区域和群体的冲击。     

多维空间下长沙市热环境影响因素的研究

以长沙为研究区域,综合多源遥感数据,引入不同维度空间内的多种参数,从生态景观格局、城市多维空间发展的角度探索了城市热环境的主要影响因素,并提出了相关的建议。结果表明,长沙城市扩张迅速,热环境在多维空间内受多种因素影响,与之相关性最高的是建筑物的相关参数。城市体积的增加、建筑物破碎化下降及裸土地破碎化上升是影响城市热环境的关键参数。因此,控制城市扩张速度,避免建筑大规模集中布局,适当增加绿地、水体斑块,是缓解长沙热环境的重要举措。本研究可为长沙市改善城市热环境提供决策参考,也可为其他自然禀赋相似的城市提供热环境影响分析的案例借鉴。     

地类扩散模型在徐州市火灾风险评价中的应用

城市区域火灾风险评价是城市消防资源优化配置的基础,本文在研究火灾风险影响因素与空间及地类关系的基础之上,将GIS技术引入城市火灾风险评价领域,构建了基于地理网格的地类火灾风险扩散模型。并以徐州市区为例,通过对各影响因素的空间地类阻隔扩散分析与叠加分析,得出任意分辨率网格的火灾风险等级,为未来城市的火灾预警与资源优化提供一种新的思路。     

地下建筑物三维测绘技术初探

针对城市中地下空间建筑物的广泛开发和利用,探讨了地下建筑物的三维测量和三维可视化技术,并基于AUTO CAD和Sketchup绘制了地下建筑物的三维地形图。三维表达的地下建筑物能给管理和规划人员直观的展示地下空间信息,且能进行各种量算和分析,可以帮助其进行分析决策。因此,地下建筑物的三维测绘技术及据此建立的地下空间信息系统可广泛应用于城市地下空间规划、城市地下空间管理以及城市地下建筑物内部管理。     

南京市住宅租金空间分异特征与影响因素分析

针对当前城市住宅租金空间分异研究的不足,尤其是从宏观层面揭示城市租金空间结构影响因素研究的欠缺,通过网络爬虫法抓取南京市租房信息及相关数据,运用趋势面分析、空间插值分析、空间自相关分析等方法研究南京城市住宅租金的空间分异特征,并分析其空间分异的影响因素。结果表明:南京住宅租金具有显著空间自相关性,租金总体趋势呈现出由中心向外的圈层式递减特征,H-H集聚区域分布在中心城区,L-L集聚分布在地铁线末端等区域。人口分布及就业中心、城市规划引导、轨道交通及公共服务设施水平是影响南京住宅租金水平空间分异的主要因素。本研究可为城市住宅租金管理与住房开发决策提供参考。     

Urban growth and transport infrastructure interaction in Jeddah between 1980 and 2007

This paper aims to use spatial statistical tools to explore the reciprocal spatial–temporal effects of transport infrastructure and urban growth of Jeddah city, a fast developing polycentric city in Saudi Arabia. Global spatial autocorrelation (Moran's I) and local indicators of spatial association (LISA) are first used to analyze the spatial–temporal clustering of urban growth and transport infrastructure from 1980 to 2007. Then, spatial regression analysis is conducted to investigate the mutual spatial–temporal effects of urban growth and transport infrastructure. Results indicate a significant positive global spatial autocorrelation of all defined variables between 1980 and 2007. LISA results also reveal a constant significant spatial association of transport infrastructure expansion and urban growth variables from 1980 to 2007. The results not only indicate a mutual spatial influence of transport infrastructure and urban growth but also reveal that spatial clustering of transport infrastructure seems to be influenced by other factors. This study shows that transport infrastructure is a constant and strong spatial influencing factor of urban growth in the polycentric urban structure that Jeddah has. Overall, this study demonstrates that exploratory spatial data analysis and spatial regression analysis are able to detect the spatial–temporal mutual effects of transport infrastructure and urban growth. Further studies on the reciprocal relationship between urban growth and transport infrastructure using the study approach for the case of monocentric urban structure cities are necessary and encouraged.     

甘肃省主要城市空间扩张协调性分析

在全国地级以上城市以及典型城市群空间格局监测项目中,根据城市用地增长弹性系数判断城市空间扩展的协调性。结果表明,2000-2016年全国大多数城市用地增长弹性系数大于最优值1.12。分析了城市用地增长弹性系数最优值的来源以及城市空间扩张与人口增长关系的相关研究成果,将人均城市用地面积与城市用地增长弹性系数结合起来,用于城市空间扩张协调性分析,并在甘肃省主要城市进行了空间扩张分析实验。     

城市空间增长与土地开发时空格局

从研究城市空间增长机制入手，分析了城市居住空间选择行为和房地产开发的空间行为，并在地理信息系统，空间分析和可视的空间表达技术支持下，建立用于城市空间增长调控的动态模拟系统，为合理调控城市空间增长和格局变化提供有力的支持并以北海为例，进行了系统的实验研究。     

Urbanization in China from the end of 1980s until 2010 – spatial dynamics and patterns of growth using EO-data

Urbanization in China has been experiencing a remarkable dynamism in the past 40 years. The most evident implication of urbanization is the physical growth of cities. We analyze urban land growth rates and changes in spatial urban forms from the end of the 1980s to 2010 based on the authoritative National Land Use/Cover Database of China. We present new spatial measures that describe ‘urban land growth types’ and ‘fluctuations in urban land growth’ within the monitoring time span with a temporal interval of five-year steps. We evaluate the correlations between urban land growth rates and socioeconomic data. Results show that (1) distinct characteristics exist on the spatiotemporal evolutions of urban land growth rates in terms of area and perimeter, e.g. coastal areas exhibit the most dramatic growth rates; (2) the spatial distribution characteristics of ‘urban land growth types’ and ‘fluctuations in urban land growth’ follow similar spatial patterns across China, e.g. significant differences exist between the eastern region and other regions; and (3) a moderate correlation exists between urban area growth rate and urban population growth rate at an R² of 0.37. By contrast, we determine no significant correlation between urban area growth rate and tertiary industry value growth rate.     

Spatial Analysis of Urban Growth Impacts on Vegetative Greenness with Landsat TM Data

Abstract

Landsat Thematic Mapper (TM) data have been used to monitor land cover types and to estimate biophysical parameters. However, studies examining the spatial relationships between land cover change and biophysical parameters are generally lacking. With the integration of remote sensing and Geographic Information Systems (GIS), these relationships can be better explored. The research reported in this paper applies this integrated approach for detecting urban growth and assessing its impact on vegetative greenness in the Zhujiang Delta, China. Multi‐temporal Landsat TM data were utilized to map urban growth and to extract and identify changes in vegetative greenness. GIS analyses were conducted to examine the changing spatial patterns of urban growth and greenness change. Statistical analyses were then used to examine the impact of urban growth on vegetative greenness. The results revealed that there was a notably uneven urban growth pattern in the delta, and urban development had reduced the scaled Normalized Difference Vegetation Index (NDVI) value by 30% in the urbanized area.     

An artificial neural network based approach for urban growth zonation in Dehradun city,India

For regulating urban growth, it is imperative to produce urban growth zonation maps, in which future urbanizable areas along with their urban growth potential are delineated. As, these maps provide a rational and scientific basis for taking future decisions regarding the growth of the city. The conventional approach for generating urban growth zonation maps is subjective in nature. To reduce this subjectivity, an artificial neural network (ANN) approach has been proposed for generating urban growth zonation maps. The database required for ANN-based urban growth zonation has been compiled from remote sensing data and other existing maps. GIS is used for handling of this spatial data. A comparison of the ANN- and conventional approach-derived zonation maps was also done. The study demonstrated the potential of ANN for urban growth zonation of an area, which may provide a valuable input to the urban planning authorities for regulating urban growth     

Modelling urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata

The present study demonstrates the applicability of the Operational Linescan System (OLS) sensor in modelling urban growth at regional level. The nighttime OLS data provides an easy, inexpensive way to map urban areas at a regional scale, requiring a very small volume of data. A cellular automata (CA) model was developed for simulating urban growth in the Indo-Gangetic plain; using OLS data derived maps as input. In the proposed CA model, urban growth was expressed in terms of causative factors like economy, topography, accessibility and urban infrastructure. The model was calibrated and validated based on OLS data of year 2003 and 2008 respectively using spatial metrics measures and subsequently the urban growth was predicted for the year 2020. The model predicted high urban growth in North Western part of the study area, in south eastern part growth would be concentrated around two cities, Kolkata and Howrah. While in the middle portion of the study area, i.e., Jharkhand, Bihar and Eastern Uttar Pradesh, urban growth has been predicted in form of clusters, mostly around the present big cities. These results will not only provide an input to urban planning but can also be utilized in hydrological and ecological modelling which require an estimate of future built up areas especially at regional level.     

Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai

The rapid growth of megacities requires special attention among urban planners worldwide, and particularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this will bring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use change models – and in particular non-spatial logistic regression models (LR) and auto-logistic regression models (ALR) – for the Mumbai region over the period 1973–2010, in order to determine the drivers behind spatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variations in driving-forces across space. The study comes to two main conclusions. First, both global models suggest similar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimated coefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporal and spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth processes, and so can assist context-related planning and policymaking activities when seeking to secure a sustainable urban future.     

利用排水区间计算城市暴雨积水

提出了给定淹没水量条件下基于流域排水区间分布的城市暴雨积水过程计算模型。该模型针对城市暴雨积水过程的形成机理和特点提出了两个基本假设,以各积水淹没源的空间分布特征规律为依据将城市流域划分为若干个排水区间,并将排水区间作为积水淹没计算的基本单元。为了保证计算的效率,根据流域地形起伏分布状况,该模型将各个排水区间内的空间区域范围简化为一个规则几何体,并结合体积法和多种漫流计算思想模拟暴雨积水在各排水区间内部和淹没源点之间的漫延过程。实验结果表明,该模型高效、可行,对流域资料要求较低,在信息不完备条件下的城市暴雨积水模拟中具有一定的应用价值。