地理科学进展  2019 , 38 (2): 271-282 https://doi.org/10.18306/dlkxjz.2019.02.010

研究论文

1990—2014年印度城市扩张时空特征对比分析

熊瑶1, 潘润秋12*, 许刚1, 焦利民12, 李凯3

1. 武汉大学资源与环境科学学院,武汉430079
2. 武汉大学地理信息系统教育部重点实验室,武汉430079
3. 中建三局集团有限公司,武汉430073

A comparison of spatial and temporal characteristics of urban expansion in India during 1990-2014

XIONG Yao1, PAN Runqiu12*, XU Gang1, JIAO Limin12, LI Kai3

1. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
2. Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China
3. The Third Bureau of China State Construction Engineering Corporation, Wuhan 430073, China

通讯作者:  *通信作者简介:潘润秋(1964— ),男,黑龙江友谊县人,副教授,主要从事土地规划与评价研究。E-mail: 478622375@qq.com

收稿日期: 2018-04-3

修回日期:  2018-10-11

网络出版日期:  2019-02-28

版权声明:  2019 地理科学进展 《地理科学进展》杂志 版权所有

基金资助:  国家自然科学基金项目(41571385)

作者简介:

第一作者简介:熊瑶(1991— ),男,江西丰城人,硕士生,主要从事土地资源评价与城市扩张研究。E-mail: xiongy0@whu.edu.cn

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摘要

印度是人口仅次于中国的发展中国家,也是“一带一路”倡议的重要节点。印度正处于城市化快速发展阶段,分析印度城市化特征具有重要意义,而中国国内关于印度城市化和城市扩张的研究和报道相对较少。论文选取10个人口超过100万的印度城市,获取1990、2000和2014年基于Landsat影像的土地利用数据和人口数据,采用圈层分析法将城市划分为等间距的同心圆圈层后统计各圈层内建设用地密度,选取增长率、密度、强度以及景观指数指标对印度城市扩张进行多维度对比分析。研究发现:①城市土地扩张快于人口增长,1990—2000、2000—2014年土地年均增长率分别是人口年均增长率的3.27和2.43倍。②建设用地密度随着与城市中心距离增加而衰减,且在一定距离内快速下降;同圈层内建设用地密度随时间逐渐增加;城市以分散的方式向外扩张,城市形态变得更加松散,特别是在第二阶段(2000—2014年)。③建设用地斑块破碎化程度与城市扩张强度的空间变化相吻合。城市扩张最活跃区域与景观破碎度最严重区域都随时间不断向外推进,城市扩张对景观格局产生显著影响。在全球城市扩张背景下,研究结果将为理解城市扩张时空特征而提供印度样本,也将为研究其他地区城市扩张提供分析方法和思路。

关键词: 城市扩张 ; 城市形态 ; 圈层分析 ; 景观破碎度 ; 印度

Abstract

India is a developing country with a population only smaller than China, and is an important location of the Belt and Road Initiative. India is in the process of rapid urbanization and studying the urbanization characteristics of India is of great significance for understanding urban expansion globally. But within China there are relatively limited studies and reports on urbanization and urban expansion in India. In this study, we selected 10 Indian cities with more than 1 million people, and obtained the land use and population data of these cities based on the Landsat images in 1990, 2000, and 2014. The built-up density in each ring was calculated after the cities were divided into equidistant concentric rings by using the concentric ring analysis method. A multi-dimensional comparative analysis of urban expansion was conducted for these India cities through growth rate, density, intensity, and landscape indices. The results show that: 1) Urban land expansion is faster than population growth. The average annual growth rate of urban land is 3.27 and 2.43 times of the average annual growth rate of the population. 2) The density of urban land is decreasing with the distance from the city center and it quickly decreases within the urban core area. Temporally, urban land density gradually increases in the same concentric ring over time. Cities have expanded outwardly in a dispersed manner, and urban forms have become scattered, especially in the second period (2000-2014). 3) The degree of fragmentation of urban land is consistent with the spatial variation of urban expansion intensity. Areas with the most active urban expansion and the most severe landscape fragmentation shifted outward over time, and urban expansion has a significant impact on the landscape pattern. In the context of global urban expansion, this study provides an Indian sample for understanding the spatial and temporal characteristics of urban expansion and also provides analytical methods and ideas for studying urban expansion in other regions.

Keywords: urban expansion ; urban form ; concentric analysis ; landscape fragmentation ; India

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熊瑶, 潘润秋, 许刚, 焦利民, 李凯. 1990—2014年印度城市扩张时空特征对比分析[J]. 地理科学进展, 2019, 38(2): 271-282 https://doi.org/10.18306/dlkxjz.2019.02.010

XIONG Yao, PAN Runqiu, XU Gang, JIAO Limin, LI Kai. A comparison of spatial and temporal characteristics of urban expansion in India during 1990-2014[J]. Progress in Geography, 2019, 38(2): 271-282 https://doi.org/10.18306/dlkxjz.2019.02.010

印度位于亚洲南部,是“一带一路”倡议沿线体量最大的国家;2016年印度GDP达2.26万亿美元,居世界第7位。随着社会经济的发展,印度城市人口迅速增长,2016年底,其城市化率达到33.14%(Word Bank, 2017);同时,城市土地不断扩张。但由于城市公共服务与基础设施极度缺乏导致印度城市“贫民窟”现象严重(Ezeh et al, 2017; Nagpure et al, 2018),快速发展的城镇化也使城市生态环境急剧恶化(Mohanraj et al, 2005; Narain, 2008; Sood, 2012)。印度是仅次于中国的人口大国,加之城镇化水平相对较低且处于快速城镇化发展阶段,未来将有更多的农村人口转移至城市。大量的新增城市人口势必会给有限的城市土地资源利用带来巨大压力,城市土地扩张无法避免。

如何测度城市扩张并分析其时空特征是城市地理学的重要研究内容。Galster等(2001)认为,城市扩张是个多维度现象,可以从密度、连续性、集中度、聚集度、中心性、核心度、混合度和邻近度这8个不同维度进行测度和刻画。Frenkel等(2008)将刻画城市扩张特征的指标分成5大类:增长率、密度、空间几何、通达性和对称性指标。城市扩张存在多种类型,由各种维度的不同组合反映(Galster et al, 2001; Torrens, 2008; Ewing et al, 2015)。如蒋芳等(2007)从扩展形态、扩展效率和外部影响等3个方面选取13个指标来测度和量化北京市城市扩张;张琳琳等(2014)利用城市土地扩张指标和人口密度指标对杭州市城市扩张进行时空动态变化分析。城市生态学领域的研究发现城市扩张过程伴随着城市景观格局的变化,因此也有不少学者运用景观格局指标反映城市扩张的时空演变(Irwin et al, 2007; 杨叶涛等, 2010; 杨振山等, 2010; Jiao et al, 2017)。城市扩张主要是人口、土地变化以及由此引起的景观格局改变,衡量城市扩张可选择反映这几方面变化的指标,具体有增长率、密度、强度以及景观指数等。

城市扩张作为一种普遍的城市地理现象,既有全球性的共同特征,也存在地区性差异。在空间增长模式上,研究发现中国城市呈现同心圆结构模式由城市中心向城市外围蔓延的特点;而美国城市主要以内部填充的模式向外扩张(Schneider et al, 2008;Kuang et al, 2014)。从城市形态上来看,美国城市具有典型的松散特征(Huang et al, 2007;Kuang et al, 2014),而欧洲城市则表现为更加紧凑的形态(Schwarz, 2010)。近年来已有研究关注了印度的城市扩张,例如对比分析不同数据来确定城市变化(Pandey et al, 2013; Pandey et al, 2015),结合变化率指标和社会经济指标研究城市扩张动态(史佳颖, 2013; Kantakumar et al, 2016; 李佳洺等, 2017),以及城市扩张过程中的景观破碎度特征(Ramachandra et al, 2012; Ramachandra et al, 2015)等。但现有关于印度城市扩张及其空间特征的研究多是基于单个或少量城市,研究的时段也局限于单个或少量时点(Dwarakish, 2012; Ramachandra et al, 2012; Ramachandra et al, 2015; Ahmad et al, 2016; Kantakumar et al, 2016),对多个样本城市的多个时点对比研究较少(Taubenböck et al, 2008; Taubenböck et al, 2009)。

本文获取印度10个人口超过100万的城市,采用圈层分析方法,将研究样本城市划分成若干同心圆环,以刻画城市内部土地利用变化特征。选用增长率、密度、强度以及景观指数等多个指标,对比并综合不同指标的结果来反映印度城市扩张时空变化。研究印度城市扩张的时空特征,有利于了解发现印度城市发展规律。本文对城市扩张模式及其空间结构演变规律的研究方法和思路也可为研究其他地区城市扩张提供借鉴。

1 研究区域与数据

本文基于美国纽约大学、联合国人居署、美国林肯土地政策研究所研究团队联合发布的全球城市扩张数据集(http://www.atlasofurbanexpansion.org),筛选印度2014年人口超过100万的10个样本城市,样本城市空间位置分布如图1所示。其中,孟买人口规模最大,2014年人口总数为1960万;科泽科德人口总数最少,只有将近117万。

图1   样本城市空间分布
(图中使用的2015年人口密度影像数据来源于WorldPop网站(https://www.worldpop.org/)。该数据只用于图1图2的空间分布展示,不作其他分析。)

Fig.1   Spatial distribution of the 10 sample cities in India

从上述城市扩张数据集获取1990、2000和2014年3个时点的土地利用数据和城市范围内人口数据。土地利用数据来源于Landsat影像(空间分辨率30 m),解译Landsat影像后,采用Google Earth高空间分辨率影像进行了分类精度验证,结果显示,91%的分类后土地利用类型与Google Earth影像一致(Angel et al, 2016)。城市范围内人口是依据城市统计区(人口调查统计的城市行政区域)内人口,利用内插或外推得到城市范围内人口总数。详细数据生产和处理流程见文献(Angel et al, 2016)。

本文首先将原土地利用数据重分类为3种类型:建设用地、植被及水域。该部分数据结合圈层分析方法来分析各个城市的城市土地密度变化、城市扩张强度变化、城市核心度值变化以及景观破碎度特征情况。印度各城市的建成区范围1990—2014年不断增加,图2展示了10个样本城市在3个时间点其建成区边界的空间变化情况。与中国同等人口规模城市相比,印度城市建成区规模相对较小。例如在2014年,建成区面积最大的城市加尔各答只有603 km2,其人口为1512万人,人口密度为25090人/km2。样本城市中有一半城市的建成区面积不到200 km2,其中最小只有47 km2

图2   研究城市在3个时点的空间范围变化
注:图中城市下方数据为各城市2014年建成区面积。

Fig.2   Urban extent at three temporal points in the sample cities

2 研究方法

2.1 圈层分析

圈层分析是指以城市中心为圆心,将城市划分成一定半径圆环。每个同心圆环作为城市扩张空间分异的基本单元,以此计算有关维度指标(李晓文等, 2003; 焦利民等, 2015)。城市中心的确定首先是依据数据集中公布的CBD位置,然后对比Google高分影像进行判读、验证和调整。在划分圆环时,一般采用1 km作为缓冲区半径,但由于对艾哈迈达巴德、坎普尔和维杰亚瓦达划分的圆环数相对较少,其缓冲区半径改为0.5 km,以增加圈层样本。各圈层城市土地密度等于各圈层内建设用地面积与总土地面积减去不可建设用地(水体)的比值(高向东等, 2005; 焦利民等, 2015)。

2.2 城市扩张指标

2.2.1 城市扩张强度指数

城市扩张强度指数(Urban Expansion Intensity, UEI)表示一个区域在2个时间点内建设用地的年平均增长率,该指标反映城市发展的强弱快慢(李晓文等, 2003; 凌赛广等, 2016),其计算公式为:

UEI=Ut+n-UtA×1n×100%(1)

式中:UtUt+n分别为前后2个时间点的建设用地面积;A为土地总面积;n为时段长度。UEI值越大,城市扩张强度越高。

2.2.2 城市扩张核心度指数

城市扩张核心度指数(Urban Concentricity Index, UCI)是城市核心区半径与外围区半径的比值(Schneider et al, 2008; 焦利民等, 2015; 孙斌栋等, 2017)。它衡量城市扩张的紧凑程度,即整个城市建成区向中心城区的集中程度。该比值越小,表明建成区越分散,趋向于蔓延式的低密度扩张。比值越大,城市结构越紧凑,城市为紧凑型的城市(燕月等, 2013; 匡文慧等, 2014)。其计算公式为:

UCI=Rcore/Rfringe(2)

式中:RcoreRfringe分别代表城市核心区(城市中心到建设用地密度≥50%圆环之间的区域)和外围区(核心区边界到建设用地密度≥10%的圆环)的半径。

2.3 景观格局指标

本文总结已有研究(邬建国, 2007; 洪冬晨, 2015)并结合实际情况选取了最能刻画景观破碎度的2个指标:斑块密度(Patch Density, PD)和边缘密度(Edge Density, ED)。斑块密度是指单位面积的斑块数;边缘密度是指单位面积内含有斑块边界的长度。斑块密度和边缘密度的计算公式分别为:

PD=NiA×106(3)

ED=EiA×106(4)

式中:Ni为景观类型i的斑块个数;A为景观总面积(m2);乘以106,单位转化成km2;Ei为景观类型i斑块边界总长度。PD取值范围大于0,无上限;ED取值范围也大于0,无上限。

在景观分析背景下,斑块密度作为整个景观格局中分析空间异质性的基本指标,可用于描述景观破碎度和优势度;指标值的大小与景观破碎度成正比,指标值越大,景观的破碎度越大。边缘密度作为分析景观形状的基本指标,可反映景观类型的复杂性,指标值大小与景观类型的复杂性成正比。这2个指标有利于不同规模景观之间的比较,从而从优势度和形状复杂性2个方面来对照分析城市扩张过程中景观破碎度。

3 城市土地空间结构及扩张强度对比

3.1 人口增长与土地扩张的数量分析

样本城市规模存在很大差异,包括人口规模和土地规模(叶尔肯·吾扎提等, 2014)。图3展示了样本城市人口数量和土地数量的变化情况。

图3   城市人口数量(a)和建成区面积(b)时间变化
注:图中百分数分别为T1至T2、T2至T3的年平均增长率。

Fig.3   Temporal variations of urban population (a) and built-up areas (b)

从数量上看,每个城市人口总数初始规模显著不同(约1990年),大小从20万~1179万。建成区面积也存在明显差异,从科泽科德的2.17 km2到孟买的278.06 km2。从T1(约1990年)到T2(约2000年)再到T3(约2014年),城市人口数量和土地面积呈现不同程度的增加,其中大部分城市(除维杰亚瓦达与斋浦尔之外)的城市土地面积增加数量在T2至T3时要比在T1至T2时大。

在变化率上,对于人口数量增长速度,其中增长较快的城市有普纳、科泽科德和哥印拜陀。对于城市土地面积增加速度,大部分城市增速比较大,且后一个时段较前一个时段有所下降,规模相对较小的城市更明显。对比两者,发现基本上所有城市在2个时段中城市人口数量的变化速度都要小于城市建成区面积的变化速度(除个别城市在个别时期不同)。尤其是规模较小的城市要相对更加明显,例如科泽科德在T1时段人口增长速度为11.73%,土地增长速度为66.82%,前者比后者小55.09%。且计算得到样本城市人口年均增长率前后2个阶段分别为5.31%和3.66%;而土地年均增长率分别为17.39%和8.92%。土地年均增长率分别是人口年均增长率的3.27和2.43倍。

3.2 城市土地密度

城市圈层内建设用地密度在各个时点随到城市中心距离的变化如图4所示。大部分城市土地密度曲线呈现类似反“S”曲线的形状,此结果与之前的相关学者的研究相同(Jiao, 2015)。从空间特征来看,每个城市随着离城市中心距离增加,城市土地密度逐渐降低,且在一定距离内快速下降。从T1至T2再到T3,城市各个圈层土地密度增加。该变化规律说明随着时间的推移,各圈层建设用地面积逐渐增加,内部有新开发土地填充。

图4   各圈层内城市土地密度时空变化

Fig.4   Temporal and spatial changes of urban land densities of concentric rings

3.3 城市扩张核心度指数

城市扩张核心度指数反映城市紧凑度的重要指标,由图5可知,各个城市的城市扩张核心度指数值各不相同,其中较为突出的是艾哈迈达巴德和科泽科德2个城市。艾哈迈达巴德在3个研究时点比其他城市的要大,特别在T1时值高达2.6,由此说明该城市相对其他城市来说,城市建成区向中心城区的集中程度要高;而科泽科德的城市扩张核心度值相对较小,且在T1时值为0(因其在该时点核心区半径为0),这反映该城市集中程度低。

图5   样本城市在3个时间点的城市扩张核心度指数变化

Fig.5   Urban expansion concentricity index at three temporal points in the sample cities

T1至T2时,样本城市中有4个城市的指数值降低,分别为艾哈迈达巴德、坎普尔、加尔各答和维杰亚瓦达,这4个城市在前一时段,城市发展表现出以蔓延式的形式向外扩张;哥印拜陀、斋浦尔、孟买和普纳的指数值增加,其中斋浦尔的增加幅度最大,说明这4个城市的建成区向中心城区集中,城市体现出以紧凑方式向外扩张。T2至T3时,基本所有城市的指数值都有所降低(除科泽科德不变外),城市扩张模式更加趋向于蔓延式的低密度发展模式。

3.4 城市扩张强度指数

城市扩张强度指数反映2个时点期间建设用地增加幅度情况,常作为城市扩张分析的重要指标之一,可描述空间结构演变发展进程。各个城市每个环内建设用地扩张强度如图6所示。

图6   样本城市在2个时间段的城市扩张强度指数空间变化

Fig.6   Spatial variation of urban expansion intensity index during two periods in the sample cities

每个城市的土地扩张强度随着离城市中心距离增大而变化,其扩张强度曲线均出现至少一个峰值。其中像孟买和加尔各答有多个峰值,城市扩张强度曲线的形状呈现明显的锯齿状,这表明这些城市可能除了市中心外还含有其他的扩张源出现。艾哈迈达巴德、哥印拜陀和普纳等城市的扩张强度曲线较为光滑,曲线峰值只有1~2个,这些城市扩张强度最大值处在2个时点的核心区边界范围之内,例如普纳在T1至T2时段,城市增长最大值的位置位于离城市中心的3 km处,其在T1时点城市核心区边界位于离城市中心的2 km处,T2时点位于5 km处。

比较不同时段,发现样本城市的曲线峰值普遍右移。此外,在距离城市中心近的区域内,后一时段的城市扩张强度要小于前一时段城市扩张强度;而距离城市中心越远,则呈现相反的情况。此特征规律明显反映城市扩张向外以分散的方式发展,城市空间范围向郊区化、边缘化发展。除维杰亚瓦达没有此规律,该城市各圈层内城市扩张强度在后一时段均小于前一时段。

4 城市扩张下的景观破碎度响应分析

4.1 斑块密度

建设用地斑块密度可以反映建设用地在该区域的破碎度大小以及建设用地的优势度。图7反映各个城市的每个圈层在时间和空间上的斑块密度变化情况。

图7   斑块密度时空变化

Fig.7   Patch density measured across space and time

整体上,样本城市在最内圆环的斑块数最少,但从城市中心向外移动斑块数快速增加。随着离城市中心的距离增加,斑块密度曲线开始出现第一个峰值,其位置大约在城市核心区边界远离城市中心的位置。在城市核心区边缘的建设用地周围混杂着农林用地、水域和其他非建设用地,其呈现出更多分散斑块的特点。在核心区边缘周围的峰值之后,每个圆环内斑块数开始降低,这是因为在这些圆环处没有大量的建设用地。离城市中心的距离进一步增加,大部分城市开始出现第2个或是2个以上的峰值,这与图6的城市扩张强度指数图有着类似规律。从这些峰值可以看出,城市扩张更多地趋向于宏观尺度上扩散而不是破碎本身所造成的。此外,值得注意的是,像艾哈迈达巴德、坎普尔等部分城市的斑块密度在城市中心周围的一定区域内出现下降特征,这是因为内环的建设用地斑块数相差不大甚至相等,而圆环的面积逐渐增加,所以靠外的圆环内斑块密度要小些。

从城市中心到少部分外围区的区域,各圈层内建设用地的斑块密度值依次为T1>T2>T3,这也说明城市在扩张过程中,靠近城市中心的一段区域内建设用地斑块数减少,建设用地的优势度逐渐增强。然而从外围区开始,离城市中心的距离逐渐增加,逐步出现T2的斑块密度值大于T1的斑块密度值,T3的大于T2的。这是由于随着时间的变化,该区域建设用地斑块数和斑块密度的增加,建设用地的景观格局呈现出越来越分散的景观特征,可能是因为在该区域,其他用地转化为建设用地,出现大量小的建设用地斑块,增加该区域的景观破碎度。

4.2 边缘密度

边缘密度变化如图8所示。在空间特征上,大部分城市从城市中心到核心区边界,边缘密度增加,城市核心区内建设用地斑块的形状复杂性增加。接着从城市核心区边界向外移动,边缘密度减少,该区域内建设用地斑块形状的复杂性降低。对比各个时点变化,发现从城市中心到核心区边界周围,各圈层内建设用地的边缘密度值依次为T1>T2>T3,建设用地复杂性依次降低。随着离城市中心距离的继续增加,边缘密度大小从T1、T2至T3依次出现反超,建设用地复杂性增加。

图8   边缘密度时空变化

Fig.8   Edge density measured across space and time

边缘密度反映斑块形状的复杂性,斑块密度呈现斑块类型的优势度。样本城市边缘密度曲线变化趋势与斑块密度相似,两者从不同角度上呈现各个城市在时间和空间上景观破碎度变化。两者的曲线峰值随着时间向右移动,都反映景观破碎度的最活跃点在城市核心区边界周围,且不断向外推移。其主要的不同点在于边缘密度曲线要相对平滑一些,且曲线上离城市中心最近的峰值一般处在核心区边界位置上或是较近的周围。而离城市中心最近斑块密度峰值一般在城市核心区范围外稍微远离城市中心的位置,也就是说城市在向外扩张中形状复杂性达到最大晚于斑块数量增加到最大值。

5 结论与讨论

本文以印度10个人口超过100万的城市为例,对比分析了印度城市扩张的时空演变规律。研究发现:

(1) 土地扩张速度快于人口增长速度,1990—2000、2000—2014年土地年均增长率分别是人口年均增长率的3.27和2.43倍。1990—2014年间,研究区10个城市土地和人口均显著增长,2个阶段(1990—2000、2000—2014年)研究区10个城市土地年均增长率分别为17.39%和8.92%,而人口年均增长率分别为5.31%和3.66%,土地扩张速度快于人口增长速度。

(2) 城市土地密度呈现随到城市中心距离增加而衰减的特征且城市形态变得更加松散。圈层分析表明城市建设用地密度随到城市中心距离增加而衰减,且在一定距离内快速下降;各圈层内建设用地密度随时间增加。各城市建设用地密度大于50%的核心区半径与建设用地密度介于50%~10%之间的城市外围区具有明显差异,反映了城市之间形态的差异;且核心区半径与外围区半径之比随时间呈下降趋势,说明城市形态更加松散,特别是在第2阶段(2000—2014年)。

(3) 建设用地斑块破碎化程度与城市扩张强度的空间变化相吻合。城市扩张强度随到城市中心距离增加呈现先增加后降低的趋势,城市扩张强度最高值区域(城市扩张最活跃区域)随时间向城市外围推移。同时,建设用地斑块破碎程度和形状复杂度从城市中心到外围也呈现先上升后降低的趋势,与建设用地扩张强度的空间变化相吻合,反映了城市扩张对景观格局的显著影响。

印度和中国都正在经历快速的经济发展和城市化,城市扩张特征存在一定的相似性。随着到城市中心距离增加,城市土地密度在一定距离内快速降低(Kuang et al, 2014),且土地增长速度快于人口增长速度,城市形态变得更加松散(秦志锋, 2008; 焦利民等, 2016)。美国城市具有典型的松散扩张特征,城市人口密度及城市紧凑度都比印度城市要低(Huang et al, 2007;Kuang et al, 2014)。把印度放在全球城市扩张的视野下,研究印度城市扩张,将为理解城市扩张机理提供印度样本。本文的研究方法和思路可为研究其他地区城市扩张模式及其空间结构演变规律提供参考。同时,对比分析不同地区城市扩张动态过程和城市形态特征有利于加强和提升对城市扩张普遍规律的认知。城市扩张是复杂的系统过程,是由多变量共同作用的结果,本文仅通过不同的维度来了解印度城市扩张的模式及其空间结构演变规律,今后还应开展深层次的研究,分析印度城市扩张背后的驱动力和效率等问题。

The authors have declared that no competing interests exist.


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Characterizing urban expansion of main metropolises in China based on built-up densities in concentric rings

. Resources and Environment in the Yangtze Basin, 24(10): 1721-1728. ]

https://doi.org/10.11870/cjlyzyyhj201510014      URL      Magsci      [本文引用: 3]      摘要

基于圈层建设用地密度分析,提出了城市扩张核心度指数;通过计算城市扩张核心度指数和城市扩张强度指数,分析了1990~2010年中国27个主要城市距城市中心不同距离区域的城市扩张的时空模式。研究发现:(1)1990~2010年,大部分城市核心度指数不断降低,城市空间结构变化与国家区域政策密不可分,呈现明显的区域特征。1990~2000年东部沿海地区城市核心度值减少幅度较大,2000~2010年则是东北部和中西部城市降低明显。总体上看,西部和东北部城市的核心度指数要高于东部和中部的城市,结构更加紧凑。(2)城市扩张最活跃的地方总是出现在核心区边界附近,并不断向外推移。(3)城市的形态与城市的发展阶段密切相关。1990~2000年,一线城市扩张的强度和范围远大于其他城市;2000~2010年,二三线城市的扩张强度和范围明显增加。经济比较发达的城市,建设用地的扩张逐渐由单中心扩展向多中心协同发展转变,而经济相对落后西部城市和东北部城市,城市结构比较紧凑,保持着单中心的城市形态。
[6] 匡文慧, 迟文峰, 史文娇. 2014.

中国与美国大都市区城市内部土地覆盖结构时空差异

[J]. 地理学报, 69(7): 883-895.

https://doi.org/10.11821/dlxb201407001      URL      [本文引用: 2]      摘要

城市内部土地覆盖结构对城市生态服务功能和人居环境质量产生重要影响。中国与美国不同发展阶段城市不透水地表和绿地时空分布格局存在显著差异。本文基于Landsat TM/MSS影像获取1978、1990、2000、2010年城市内部土地覆盖和不透水地表分类信息,监测并比较中国和美国六个特大城市扩展时空动态、土地覆盖结构差异及城市不同功能区特征。研究表明,在过去30多年以来,中国城市以相对紧凑形态发展,美国城市较为离散;美国三大城市植被所占的比例是中国的2.21倍;中国城市内部结构土地利用功能类型更加复杂,不透水地表密度更高,而美国城市CBD和居住区功能相对独立,特别居住区以镶嵌式低不透水地表和高绿地比例结构为主。

[Kuang W H, Chi W F, Shi W J.2014.

Spatio-temporal characteristics of intra-urban land cover in the cities of China and USA from 1978 to 2010

. Acta Geographica Sinica, 69(7): 883-895. ]

https://doi.org/10.11821/dlxb201407001      URL      [本文引用: 2]      摘要

城市内部土地覆盖结构对城市生态服务功能和人居环境质量产生重要影响。中国与美国不同发展阶段城市不透水地表和绿地时空分布格局存在显著差异。本文基于Landsat TM/MSS影像获取1978、1990、2000、2010年城市内部土地覆盖和不透水地表分类信息,监测并比较中国和美国六个特大城市扩展时空动态、土地覆盖结构差异及城市不同功能区特征。研究表明,在过去30多年以来,中国城市以相对紧凑形态发展,美国城市较为离散;美国三大城市植被所占的比例是中国的2.21倍;中国城市内部结构土地利用功能类型更加复杂,不透水地表密度更高,而美国城市CBD和居住区功能相对独立,特别居住区以镶嵌式低不透水地表和高绿地比例结构为主。
[7] 李佳洺, 杨宇, 樊杰, . 2017.

中印城镇化区域差异及城镇体系空间演化比较

[J]. 地理学报, 72(6): 986-1000.

https://doi.org/10.11821/dlxb201706004      URL      [本文引用: 1]      摘要

中国和印度作为两个正在崛起的大国,发展历程较为相似,但发展路径和模式差异较大。两个国家政治制度、经济体系、发展环境等的显著差异已经吸引了学者的广泛关注,本文将从地理学视角出发,重点关注两国城镇化及城镇体系的区域差异和空间演化过程。以人口普查和联合国城市人口数据为基础,采用空间分析、参数估计、非参数估计等多种方法,对中印两国城镇化和城镇体系的空间特征进行系统的比较分析,结果表明:① 20世纪90年代以来,中国城镇化的区域差异由南北差异转变为沿海—内部差异,而印度南北差异的格局则基本稳定;② 从省(邦)级空间尺度来看,中国和印度的人口密度和城镇化率都呈现正相关关系,当城镇化率超过50%后,两者的相关性更为显著,但是近年来中国人口密度与城镇化率的相关性不断增强,而印度则呈现降低的趋势;③ 现阶段中印两国以大中城市为主的城镇体系符合位序—规模分布的特征,但是经济改革对于两个国家城镇体系空间演化的影响差异明显,改革使得中国城镇发展的主要驱动力由地理历史因素向经济系统空间结构转变,而印度城镇发展的驱动力始终是地理历史因素,经济改革甚至降低了经济系统空间结构对城镇发展的影响。

[Li J M, Yang Y, Fan J, et al.2017.

Comparative research on regional differences in urbanization and spatial evolution of urban systems between China and India

. Acta Geographica Sinica, 72(6): 986-1000. ]

https://doi.org/10.11821/dlxb201706004      URL      [本文引用: 1]      摘要

中国和印度作为两个正在崛起的大国,发展历程较为相似,但发展路径和模式差异较大。两个国家政治制度、经济体系、发展环境等的显著差异已经吸引了学者的广泛关注,本文将从地理学视角出发,重点关注两国城镇化及城镇体系的区域差异和空间演化过程。以人口普查和联合国城市人口数据为基础,采用空间分析、参数估计、非参数估计等多种方法,对中印两国城镇化和城镇体系的空间特征进行系统的比较分析,结果表明:① 20世纪90年代以来,中国城镇化的区域差异由南北差异转变为沿海—内部差异,而印度南北差异的格局则基本稳定;② 从省(邦)级空间尺度来看,中国和印度的人口密度和城镇化率都呈现正相关关系,当城镇化率超过50%后,两者的相关性更为显著,但是近年来中国人口密度与城镇化率的相关性不断增强,而印度则呈现降低的趋势;③ 现阶段中印两国以大中城市为主的城镇体系符合位序—规模分布的特征,但是经济改革对于两个国家城镇体系空间演化的影响差异明显,改革使得中国城镇发展的主要驱动力由地理历史因素向经济系统空间结构转变,而印度城镇发展的驱动力始终是地理历史因素,经济改革甚至降低了经济系统空间结构对城镇发展的影响。
[8] 李晓文, 方精云, 朴世龙. 2003.

上海及周边主要城镇城市用地扩展空间特征及其比较

[J]. 地理研究, 22(6): 769-779.

https://doi.org/10.3321/j.issn:1000-0585.2003.06.012      URL      [本文引用: 2]      摘要

基于多时段TM遥感影像资料,运用缓冲分析法对上海市区及其周边主要城镇城市用地扩展的时空特征进行了分析和比较.研究结果表明:1)区域城市土地利用扩展过程主要受距中心市区(CBD)的距离的影响;2)城市用地扩展在距CBD 10km范围内主要表现为上海市区扩展的空间异向性,距CBD 10km范围之外城市扩展的异向性则源于不同时期、不同方向上周边城镇的异速扩展过程,并受河流、渠系等小尺度地貌格局差异的影响;3)上海主要郊区城镇扩展可分为标准型、被动扩展型、平缓扩展型和不规则扩展型.

[Li X W, Fang J Y, Piao S L.2003.

The comparison of spatial characteristics in urban land use growth among the central and sub-cities in Shanghai region

. Geographical Research, 22(6): 769-779. ]

https://doi.org/10.3321/j.issn:1000-0585.2003.06.012      URL      [本文引用: 2]      摘要

基于多时段TM遥感影像资料,运用缓冲分析法对上海市区及其周边主要城镇城市用地扩展的时空特征进行了分析和比较.研究结果表明:1)区域城市土地利用扩展过程主要受距中心市区(CBD)的距离的影响;2)城市用地扩展在距CBD 10km范围内主要表现为上海市区扩展的空间异向性,距CBD 10km范围之外城市扩展的异向性则源于不同时期、不同方向上周边城镇的异速扩展过程,并受河流、渠系等小尺度地貌格局差异的影响;3)上海主要郊区城镇扩展可分为标准型、被动扩展型、平缓扩展型和不规则扩展型.
[9] 凌赛广, 焦伟利, 龙腾飞, . 2016.

2000-2014年武汉市城市扩展时空特征分析

[J]. 长江流域资源与环境, 25(7): 1034-1042.

https://doi.org/10.11870/cjlyzyyhj201607004      URL      Magsci      [本文引用: 1]      摘要

基于不同时相的高分辨率遥感影像,采用面向对象的解译方法提取了武汉市2000、2005、2010和2014年的土地覆被信息,从城市扩展强度指数,城市中心坐标迁移和分形维数等方面分析了武汉市2000~2014年城市扩展时空特征。结果表明:2000~2014年间,武汉市城市扩展强度指数为1.41,各主城区城市扩展强度不一,洪山区建设用地的增加对主城区城市扩展的贡献最大;在扩展过程中,武汉市城市分形维数增加,城市空间形态变复杂;其扩展呈现核心-放射扩展模式,并逐渐转向圈层式;自然条件、经济、人口、交通、政策和城市规划是武汉市城市扩展的主要驱动力,但随着交通的发展,自然条件和经济对武汉市城市扩展的驱动作用正逐渐减弱。

[Ling S G, Jiao W L, Long T F, et al.2016.

Analysis of spatial and temporal characteristics of urban expansion about Wuhan City in the year of 2000-2014

. Resources and Environment in the Yangtze Basin, 25(7): 1034-1042. ]

https://doi.org/10.11870/cjlyzyyhj201607004      URL      Magsci      [本文引用: 1]      摘要

基于不同时相的高分辨率遥感影像,采用面向对象的解译方法提取了武汉市2000、2005、2010和2014年的土地覆被信息,从城市扩展强度指数,城市中心坐标迁移和分形维数等方面分析了武汉市2000~2014年城市扩展时空特征。结果表明:2000~2014年间,武汉市城市扩展强度指数为1.41,各主城区城市扩展强度不一,洪山区建设用地的增加对主城区城市扩展的贡献最大;在扩展过程中,武汉市城市分形维数增加,城市空间形态变复杂;其扩展呈现核心-放射扩展模式,并逐渐转向圈层式;自然条件、经济、人口、交通、政策和城市规划是武汉市城市扩展的主要驱动力,但随着交通的发展,自然条件和经济对武汉市城市扩展的驱动作用正逐渐减弱。
[10] 秦志锋. 2008.

中国城市蔓延现状与控制对策研究

[D]. 郑州: 河南大学.

[本文引用: 1]     

[Qin Z F.2008.

Research on Chinese urban sprawl and its control

. Zhengzhou, China: Henan University. ]

[本文引用: 1]     

[11] 史佳颖. 2013.

中国和印度的人口转变与经济增长机遇

[D]. 天津: 南开大学.

[本文引用: 1]     

[Shi J Y.2013.

The demographic transition and economic growth opportunities in China and India

. Tianjin, China: Nankai University. ]

[本文引用: 1]     

[12] 孙斌栋, 华杰媛, 李琬, . 2017.

中国城市群空间结构的演化与影响因素: 基于人口分布的形态单中心-多中心视角

[J]. 地理科学进展, 36(10): 1294-1303.

https://doi.org/10.18306/dlkxjz.2017.10.011      URL      [本文引用: 1]      摘要

城市群是未来中国城镇化的主要空间载体,研究城市群空间结构的演化特征和影响因素具有重要意义。已有文献较少从形态单中心—多中心视角研究中国城市群空间结构的演化和影响因素,而且通常采用户籍人口而非常住人口测度城市群空间结构,导致测量误差。为解决这个问题,本文采用基于人口普查的常住人口数据进行分析,并运用联合国数据对结果进行佐证。以已有文献采用客观标准界定的13个城市群为样本,使用规模-位序法则测度城市群的形态单中心—多中心程度,探究城市群的空间结构演化趋势和影响因素。结果发现,1980年代以来大多数城市群的空间结构呈现多中心化趋势;人均GDP水平的提高和人口规模的增加是导致城市群空间结构多中心化的主要原因。因此,政府对于空间结构偏多中心的城市群,应强化城市间的交通联系和政策一体化,获取更大的发展红利;对于个别处于单中心化阶段的城市群,政策导向则避免过早地多中心化。

[Sun B D, Hua J Y, Li W, et al.2017.

Spatial structure change and influencing factors of city clusters in China: From monocentric to polycentric based on population distribution

. Progress in Geography, 36(10): 1294-1303. ]

https://doi.org/10.18306/dlkxjz.2017.10.011      URL      [本文引用: 1]      摘要

城市群是未来中国城镇化的主要空间载体,研究城市群空间结构的演化特征和影响因素具有重要意义。已有文献较少从形态单中心—多中心视角研究中国城市群空间结构的演化和影响因素,而且通常采用户籍人口而非常住人口测度城市群空间结构,导致测量误差。为解决这个问题,本文采用基于人口普查的常住人口数据进行分析,并运用联合国数据对结果进行佐证。以已有文献采用客观标准界定的13个城市群为样本,使用规模-位序法则测度城市群的形态单中心—多中心程度,探究城市群的空间结构演化趋势和影响因素。结果发现,1980年代以来大多数城市群的空间结构呈现多中心化趋势;人均GDP水平的提高和人口规模的增加是导致城市群空间结构多中心化的主要原因。因此,政府对于空间结构偏多中心的城市群,应强化城市间的交通联系和政策一体化,获取更大的发展红利;对于个别处于单中心化阶段的城市群,政策导向则避免过早地多中心化。
[13] 邬建国. 2007. 景观生态学: 格局、过程、尺度与等级 [M]. 2版. 北京: 高等教育出版社.

[本文引用: 1]     

[Wu J G.2007.Landscape ecology: Pattern, process, scale and hierarchy. The 2nd Edition. Beijing, China: Higher Education Press. ]

[本文引用: 1]     

[14] 燕月, 陈爽, 李广宇, . 2013.

城市紧凑性测度指标研究及典型城市分析: 以南京、苏州建设用地紧凑度为例

[J]. 地理科学进展, 32(5): 733-742.

https://doi.org/10.11820/dlkxjz.2013.05.005      URL      Magsci      [本文引用: 1]      摘要

量化表征城市紧凑性是当前地理学界和规划领域共同关注的热点问题,明确紧凑性指标反映的实际意义与适用范围,成为构建测度体系的关键.当前已有众多研究提出数量相当可观的量化指标,本文结合国内外城市紧凑性定量研究进展,根据表征紧凑内涵的不同将其归纳为形状、规模、密度、结构、功能和过程6 类,分析了不同类型指标的适用范围,并以结构类指标为例,采用南京和苏州的实际建设用地数据,根据指标自身稳定特性识别的实用性和局限性,达到评判和筛选指标的目的.结果显示:指标能有效指示建设用地的实际空间结构特征;4 个指标受尺度变化的影响不强烈,具有良好的应用性能,其中<i>Gini</i> 系数和<i>Moran’s</i> I 在应用中,前提条件局限小,适用广泛;而连续度和向心度较直观,但在进行城市比较研究时限制条件较多.该研究结果能够为城市紧凑性量化研究提供一定的借鉴,并指导城市规划与管理政策制定.

[Yan Y, Chen S, Li G Y, et al.2013.

Urban compactness index and its application: Compactness of built-up areas in Nanjing and Suzhou

. Progress in Geography, 32(5): 733-742. ]

https://doi.org/10.11820/dlkxjz.2013.05.005      URL      Magsci      [本文引用: 1]      摘要

量化表征城市紧凑性是当前地理学界和规划领域共同关注的热点问题,明确紧凑性指标反映的实际意义与适用范围,成为构建测度体系的关键.当前已有众多研究提出数量相当可观的量化指标,本文结合国内外城市紧凑性定量研究进展,根据表征紧凑内涵的不同将其归纳为形状、规模、密度、结构、功能和过程6 类,分析了不同类型指标的适用范围,并以结构类指标为例,采用南京和苏州的实际建设用地数据,根据指标自身稳定特性识别的实用性和局限性,达到评判和筛选指标的目的.结果显示:指标能有效指示建设用地的实际空间结构特征;4 个指标受尺度变化的影响不强烈,具有良好的应用性能,其中<i>Gini</i> 系数和<i>Moran’s</i> I 在应用中,前提条件局限小,适用广泛;而连续度和向心度较直观,但在进行城市比较研究时限制条件较多.该研究结果能够为城市紧凑性量化研究提供一定的借鉴,并指导城市规划与管理政策制定.
[15] 杨叶涛, 龚建雅, 周启鸣, . 2010.

土地利用景观格局对城市扩张影响研究

[J]. 自然资源学报, 25(2): 320-329.

https://doi.org/10.3969/j.issn.1673-1328.2010.16.100      URL      Magsci      [本文引用: 1]      摘要

我国处于快速城市化阶段,城市化过程所带来的问题十分突出。针对城市扩张问题,涌现出大量关于城市扩张影响因素以及城市扩张建模的研究。目前对城市扩张行为进行研究选取的影响因素主要可以分为两类:①空间可达性以及空间约束,包括水源、道路、城市中心等可达性,地形、保护用地等约束;②社会经济因素,包括人口增长、经济增长、政策等。该研究突破传统城市扩张影响因素研究范围,选择从城市土地利用景观格局特征来研究城市扩张,以北京市为例分析了按网格划分提取的多种景观格局指标与建设用地增长之间的相关性,并探讨了时间尺度对探测这种相关性的影响。从结果当中可以得到以下3个结论:城市景观格局特征对于非建设用地向建设用地转化在位置与数量上的影响机制有稳定性;时间尺度对于正确地表达它们之间的关系至关重要;由于不同时期城市化特征的差异,其关系不断发生变化。

[Yang Y T, Gong J Y, Zhou Q M, et al.2010.

Impacts of landscape pattern on urban expansion: A case study of Beijing City

. Journal of Natural Resource, 25(2): 320-329. ]

https://doi.org/10.3969/j.issn.1673-1328.2010.16.100      URL      Magsci      [本文引用: 1]      摘要

我国处于快速城市化阶段,城市化过程所带来的问题十分突出。针对城市扩张问题,涌现出大量关于城市扩张影响因素以及城市扩张建模的研究。目前对城市扩张行为进行研究选取的影响因素主要可以分为两类:①空间可达性以及空间约束,包括水源、道路、城市中心等可达性,地形、保护用地等约束;②社会经济因素,包括人口增长、经济增长、政策等。该研究突破传统城市扩张影响因素研究范围,选择从城市土地利用景观格局特征来研究城市扩张,以北京市为例分析了按网格划分提取的多种景观格局指标与建设用地增长之间的相关性,并探讨了时间尺度对探测这种相关性的影响。从结果当中可以得到以下3个结论:城市景观格局特征对于非建设用地向建设用地转化在位置与数量上的影响机制有稳定性;时间尺度对于正确地表达它们之间的关系至关重要;由于不同时期城市化特征的差异,其关系不断发生变化。
[16] 杨振山, 蔡建明, 文辉. 2010.

郑州市2001—2007年城市扩张过程中城市用地景观特征分析

[J]. 地理科学, 30(4): 600-605.

URL      [本文引用: 1]      摘要

Rapid urbanization, especially the speed and scale of the current Chinese urbanization, requires detailed understanding of spatial characteristics of the changing of urban landscape. This has a significant implication for rational and effective urban land use. The purpose of this paper is to derive the information of the landscape change during the spatial expansion of Zhengzhou City during 2001-2007. We detected the change of land cover avail of the remote sensed imageries of Landsat7 ETM and Spot 4. Further, we calculated the landscape matrices of mean patch size, landscape shape index, landscape fragment index, and contiguity index to measure the size of land plots, compactness of the developing, shape and connectivity respectively. In order to show the heterogeneous character of the spatial expansion, the gradient analysis was conducted in the range of 20 km radius centered the place nearby the city center, Erqi Square. The change of detection of land cover illustrated that, during that period, urban land use increased 70.6 km at the average annual rate of 4%, and the east, northeast and southeast were the main urban land use directions of urban expansion. The speed of expanding of the east part was nearly as two times as that of west part. The landscape matrix analysis showed that the size of land parcels increased in general, yet with a great variation. Generally, the change of landscape and urban expansion are correlated. The areas with economic-spatial driver such as the Zhengdong Xinqu and Economic Zone become a lead in the urban expansion. In the process, the size of plots and connectivity increase. Within the 10 km radius to the center, the urban land of the city became more compact, especially in the rapid urbanizing directions. But beyond that distance, the developmental pattern could be compact or loose. By comparing, landscape is slightly changed along the corridor with less economic drivers. These results call for particular attentions of planners to optimizing urban spatial structure.

[Yang Z S, Cai J M, Wen H.2010.

Urban expansion and landscape characteristics of land use in Zhengzhou City during 2001-2007

. Scientia Geographica Sinica, 30(4): 600-605. ]

URL      [本文引用: 1]      摘要

Rapid urbanization, especially the speed and scale of the current Chinese urbanization, requires detailed understanding of spatial characteristics of the changing of urban landscape. This has a significant implication for rational and effective urban land use. The purpose of this paper is to derive the information of the landscape change during the spatial expansion of Zhengzhou City during 2001-2007. We detected the change of land cover avail of the remote sensed imageries of Landsat7 ETM and Spot 4. Further, we calculated the landscape matrices of mean patch size, landscape shape index, landscape fragment index, and contiguity index to measure the size of land plots, compactness of the developing, shape and connectivity respectively. In order to show the heterogeneous character of the spatial expansion, the gradient analysis was conducted in the range of 20 km radius centered the place nearby the city center, Erqi Square. The change of detection of land cover illustrated that, during that period, urban land use increased 70.6 km at the average annual rate of 4%, and the east, northeast and southeast were the main urban land use directions of urban expansion. The speed of expanding of the east part was nearly as two times as that of west part. The landscape matrix analysis showed that the size of land parcels increased in general, yet with a great variation. Generally, the change of landscape and urban expansion are correlated. The areas with economic-spatial driver such as the Zhengdong Xinqu and Economic Zone become a lead in the urban expansion. In the process, the size of plots and connectivity increase. Within the 10 km radius to the center, the urban land of the city became more compact, especially in the rapid urbanizing directions. But beyond that distance, the developmental pattern could be compact or loose. By comparing, landscape is slightly changed along the corridor with less economic drivers. These results call for particular attentions of planners to optimizing urban spatial structure.
[17] 叶尔肯·吾扎提, 刘慧, 刘卫东. 2014.

1992—2011年哈萨克斯坦城镇化过程及其影响因素

[J]. 地理科学进展, 33(2): 181-193.

https://doi.org/10.11820/dlkxjz.2014.02.004      URL      Magsci      [本文引用: 1]      摘要

哈萨克斯坦自1992年独立以来,经历了独特的发展历程。通过建立城镇化水平测度综合指标体系,运用熵值法,测算哈萨克斯坦城镇化水平,并从人口、经济、社会及土地等4个方面解析了1992-2011年哈萨克斯坦城镇化演变过程。结果发现,自1992年以来,哈萨克斯坦城镇化进程呈“U”字型,经历了4个阶段,即:城镇化水平快速下降阶段(1992-1996年)、城镇化水平缓慢下降阶段(1997-2000年)、城镇化水平缓慢上升阶段(2001-2004年)和城镇化水平快速上升阶段(2005-2011年),定量分析了4个子系统对城镇化综合水平变化的贡献度。其中,快速下降阶段主要表现为社会城镇化减退,缓慢下降阶段主要受人口城镇化的影响,2000年之后的城镇化主要表现为经济、社会及土地城镇化的恢复和加快。最后,从政治突变、政治移民、国家政策、石油工业以及全球化与贸易等方面,对城镇化演变过程的影响因素进行了分析。

[Yeerken W, Liu H, Liu W D.2014.

Evaluation of Kazakhstan's urbanization during 1992-2011 and its influencing factors

. Progress in Geography, 33(2): 181-193. ]

https://doi.org/10.11820/dlkxjz.2014.02.004      URL      Magsci      [本文引用: 1]      摘要

哈萨克斯坦自1992年独立以来,经历了独特的发展历程。通过建立城镇化水平测度综合指标体系,运用熵值法,测算哈萨克斯坦城镇化水平,并从人口、经济、社会及土地等4个方面解析了1992-2011年哈萨克斯坦城镇化演变过程。结果发现,自1992年以来,哈萨克斯坦城镇化进程呈“U”字型,经历了4个阶段,即:城镇化水平快速下降阶段(1992-1996年)、城镇化水平缓慢下降阶段(1997-2000年)、城镇化水平缓慢上升阶段(2001-2004年)和城镇化水平快速上升阶段(2005-2011年),定量分析了4个子系统对城镇化综合水平变化的贡献度。其中,快速下降阶段主要表现为社会城镇化减退,缓慢下降阶段主要受人口城镇化的影响,2000年之后的城镇化主要表现为经济、社会及土地城镇化的恢复和加快。最后,从政治突变、政治移民、国家政策、石油工业以及全球化与贸易等方面,对城镇化演变过程的影响因素进行了分析。
[18] 张琳琳, 岳文泽, 范蓓蕾. 2014.

中国大城市蔓延的测度研究: 以杭州市为例

[J]. 地理科学, 34(4): 394-400.

URL      Magsci      [本文引用: 1]      摘要

<p>从土地不连续利用和低人口密度这两个城市蔓延的核心特征出发,提出了城市土地扩张指标与人口密度指标相结合的蔓延测度方法,利用杭州市1978~2010 年的遥感影像,实证测度中国大城市蔓延的时空动态变化。测度结果表明,城市土地扩张与人口密度指标相结合的方法测度效果良好,与实际情况吻合度高。杭州城市蔓延十分典型,主城区蛙跳式开发以及大部分的边缘增长发生在人口低密度区或较低密度区,城市发展不紧凑,蔓延迅速;阶段特性显著,在空间上表现为由单一中心向多核心发展的态势。将城市土地扩张与人口密度指标结合来测度城市蔓延是十分有益的尝试,为中国大城市蔓延的测度研究提供了一种简单、易于推广使用的可选方法。</p>

[Zhang L L, Yue W Z, Fan B L.2014.

Measuring urban sprawl in large Chinese cities: A case study of Hangzhou

. Scientia Geographica Sinica, 34(4): 394-400. ]

URL      Magsci      [本文引用: 1]      摘要

<p>从土地不连续利用和低人口密度这两个城市蔓延的核心特征出发,提出了城市土地扩张指标与人口密度指标相结合的蔓延测度方法,利用杭州市1978~2010 年的遥感影像,实证测度中国大城市蔓延的时空动态变化。测度结果表明,城市土地扩张与人口密度指标相结合的方法测度效果良好,与实际情况吻合度高。杭州城市蔓延十分典型,主城区蛙跳式开发以及大部分的边缘增长发生在人口低密度区或较低密度区,城市发展不紧凑,蔓延迅速;阶段特性显著,在空间上表现为由单一中心向多核心发展的态势。将城市土地扩张与人口密度指标结合来测度城市蔓延是十分有益的尝试,为中国大城市蔓延的测度研究提供了一种简单、易于推广使用的可选方法。</p>
[19] Angel S, Blei M A, Parent J, et al.2016.

Atlas of urban expansion, volume 1: Areas and densities

[M]. 2016 Edition. New York: New York University, Nairobi: UN-Habitat, and Cambridge, MA: Lincoln Institute of Land Policy.

[本文引用: 2]     

[20] Ahmad S, Avtar R, Sethi M, et al.2016.

Delhi's land cover change in post transit era

[J]. Cities, 50: 111-118.

https://doi.org/10.1016/j.cities.2015.09.003      URL      [本文引用: 1]      摘要

61This study maps Delhi's land cover change between 2001 and 2011.61About 11% agriculture area has decreased, while 13% built-up area has increased.61Built-up area is largely added in peripheral areas rather than along transits.61Transit oriented development and integrated planning in peripheral areas are sought.61Robust implementation strategies are required for sustainable interventions.
[21] Dwarakish G S.2012.

Land use/land cover change and urban expansion during 1983-2008 in the coastal area of Dakshina Kannada District, South India

[J]. Journal of Applied Remote Sensing, 6(10): 3576. doi: 10.1117/1.jrs.6.063576.

URL      [本文引用: 1]      摘要

Urban settlements in developing countries are, at present, growing five times as fast as those in developed countries. This paper presents the urban expansion and land use/land cover changes in the fast urbanizing coastal area of the Dakshina Kannada district in Karnataka state, South India, during the years 1983-2008 as a case study. Six Indian Remote Sensing (IRS) satellite images were used in the present work. Supervised classification was carried out using maximum likelihood algorithm. The overall accuracy of the classification varied from 79% to 86.6%, and the kappa statistics varied from 0.761 to 0.850. The results indicate that the urban/built-up area in the study area has almost tripled during the study period. During the same time, the population has increased by 215%. The major driving forces for the urbanization were the enhanced economic activity due to the port and industrialization in the area. The urban/built-up area is projected to increase to 381 kmand the population in the study area is expected to reach 2.68 million by the year 2028. Urban growth prediction helps urban planners and policymakers provide better infrastructure services to a huge number of new urban residents.
[22] Ewing R, Pendall R, Chen D.2015.

Measuring sprawl and its impact

[J]. Journal of Planning Education & Research, 57(1): 320-326.

https://doi.org/10.1177/0739456X14565247      URL      [本文引用: 1]      摘要

This study discusses urban sprawl and its impacts. It defines sprawl and describes the authors' four factor index, which helps to measure sprawl. It uses the index to compare and evaluate metropolitan regions, discusses the impact of sprawl on the quality of life in these regions, and presents policy recommendations to combat the effects of sprawl.
[23] Ezeh A, Oyebode O, Satterthwaite D, et al.2017.

The history, geography, and sociology of slums and the health problems of people who live in slums

[J]. The Lancet, 389: 547-558. doi: 10.1016/S0140-6736(16)31650-6.

URL      PMID: 27760703      [本文引用: 1]      摘要

Massive slums have become major features of cities in many low-income and middle-income countries. Here, in the first in a Series of two papers, we discuss why slums are unhealthy places with especially high risks of infection and injury. We show that children are especially vulnerable, and that the combination of malnutrition and recurrent diarrhoea leads to stunted growth and longer-term effects on cognitive development. We find that the scientific literature on slum health is underdeveloped in comparison to urban health, and poverty and health. This shortcoming is important because health is affected by factors arising from the shared physical and social environment, which have effects beyond those of poverty alone. In the second paper we will consider what can be done to improve health and make recommendations for the development of slum health as a field of study.
[24] Frenkel A, Ashkenazi M.2008.

Measuring urban sprawl: How can we deal with it?

[J]. Environment and Planning B: Planning and Design, 35(1): 56-79.

https://doi.org/10.1068/b32155      URL      [本文引用: 1]     

[25] Galster G, Hanson R, Ratcliffe M R, et al.2001.

Wrestling sprawl to the ground: Defining and measuring an elusive concept

[J]. Housing Policy Debate, 12(4): 681-717.

https://doi.org/10.1080/10511482.2001.9521426      URL      [本文引用: 2]      摘要

The literature on urban sprawl confuses causes, consequences, and conditions. This article presents a conceptual definition of sprawl based on eight distinct dimensions of land use patterns: density, continuity, concentration, clustering, centrality, nuclearity mixed uses, and proximity. Sprawl is defined as a condition of land use that is represented by low values on one or more of these dimensions. Each dimension is operationally defined and tested in 13 urbanized areas. Results for six dimensions are reported for each area, and an initial comparison of the extent of sprawl in the 13 areas is provided. The test confirms the utility of the approach and suggests that a clearer conceptual and operational definition can facilitate research on the causes and consequences of sprawl.
[26] Huang J N, Lu X X, Sellers J M.2007.

A global comparative analysis of urban form: Applying spatial metrics and remote sensing

[J]. Landscape and Urban Planning, 82(4): 184-197.

https://doi.org/10.1016/j.landurbplan.2007.02.010      URL      [本文引用: 2]      摘要

Currently, debates over urban form have generally focused on the contrast between the “sprawl” often seen as typical of the United States and “compact” urban forms found in parts of Europe. Although these debates are presumed to have implications for developing worlds as well, systematic comparison of urban forms between developed and developing countries has been lacking. This paper utilized satellite images of 77 metropolitan areas in Asia, US, Europe, Latin America and Australia to calculate seven spatial metrics that capture five distinct dimensions of urban form. Comparison of the spatial metrics was firstly made between developed and developing countries, and then among world regions. A cluster analysis classifies the cities into groups in terms of these spatial metrics. The paper also explored the origins of differences in urban form through comparison with socio-economic developmental indicators and historical trajectories in urban development. The result clearly demonstrates that urban agglomerations of developing world are more compact and dense than their counterparts in either Europe or North America. Moreover, there are also striking differences in urban form across regions.
[27] Irwin E G, Bockstael N E.2007.

The evolution of urban sprawl: Evidence of spatial heterogeneity and increasing land fragmentation

[J]. PNAS, 104(52): 20672-20677.

https://doi.org/10.1073/pnas.0705527105      URL      PMID: 2410062      [本文引用: 1]      摘要

We investigate the dynamics and spatial distribution of land use fragmentation in a rapidly urbanizing region of the United States to test key propositions regarding the evolution of sprawl. Using selected pattern metrics and data from 1973 and 2000 for the state of Maryland, we find significant increases in developed and undeveloped land fragmentation but substantial spatial heterogeneity as well. Estimated fragmentation gradients that describe mean fragmentation as a function of distance from urban centers confirm the hypotheses that fragmentation rises and falls with distance and that the point of maximum fragmentation shifted outward over time. However, rather than outward increases in sprawl balanced by development infill, we find substantial and significant increases in mean fragmentation values along the entire urban-rural gradient. These findings are in contrast to the results of Burchfield et al. [Burchfield M, Overman HG, Puga D, Turner MA (2006) Q J Econ 121:587-633], who conclude that the extent of sprawl remained roughly unchanged in the Unites States between 1976 and 1992. As demonstrated here, both the data and pattern measure used in their study are systematically biased against recording low-density residential development, the very land use that we find is most strongly associated with fragmentation. Other results demonstrate the association between exurban growth and increasing fragmentation and the systematic variation of fragmentation with nonurban factors. In particular, proximity to the Chesapeake Bay is negatively associated with fragmentation, suggesting that an attraction effect associated with this natural amenity has concentrated development.
[28] Jiao L M.2015.

Urban land density function: A new method to characterize urban expansion

[J]. Landscape and Urban Planning, 139: 26-39.

https://doi.org/10.1016/j.landurbplan.2015.02.017      URL      [本文引用: 1]      摘要

The main objective of this paper is to compare European medium-sized cities in terms of compactness. Both existing and new metrics are used. The metrics are based on the fusion of recently available European-wide datasets with common standards for all countries. The fused data used in specific are Urban Atlas and Urban Audit. One source of inspiration of new metrics is landscape ecology but... [Show full abstract]
[29] Jiao L M, Xu G, Xiao F T, et al.

2017. Analyzing the impacts of urban expansion on green fragmentation using constraint gradient analysis

[J]. Professional Geographer, 2017: 1-14.

https://doi.org/10.1080/00330124.2016.1266947      URL      [本文引用: 1]      摘要

(2017). Analyzing the Impacts of Urban Expansion on Green Fragmentation Using Constraint Gradient Analysis. The Professional Geographer. Ahead of Print. doi: 10.1080/00330124.2016.1266947
[30] Kantakumar L N, Kumar S, Schneider K.2016.

Spatiotemporal urban expansion in Pune metropolis, India using remote sensing

[J]. Habitat International, 51: 11-22.

https://doi.org/10.1016/j.habitatint.2015.10.007      URL      [本文引用: 2]      摘要

Indian cities are expanding at an unprecedented rate. The speed of development poses a challenge for urban planners, as the expansion of cities frequently outpaces the planning process. This leads to further challenges for urban planners, namely i) the database for the planning is often outdated and ii) processes and patterns of unplanned urban growth are not accounted for appropriately. This paper presents an approach to address these challenges by utilizing generally available and inexpensive remote sensing data to study i) the land use and land cover change and ii) by analyzing the extent of urban areas to study the patterns and processes of urban growth. We assesses land-use/land-cover for three years (1992, 2001, 2013) using multi-temporal Landsat datasets. A detailed spatiotemporal analysis of urban expansion and typologies of urban growth at the scale of individual administrative units. The dynamics of urban growth was quantified using different metrics of urban expansion. Three types of urban expansion patterns were identified in the Pune metropolis, i) coalescence phase of urbanization in the main city areas, ii) diffusion phase in the suburbs and iii) marginal growth in the cantonments. The overall process of urban expansion in the Pune metropolis can thus be referred to as a diffusion oalescence pattern. Furthermore, our results show that the speed of the urban expansion in the Pune metropolis area has doubled from 2001 to 2013 as compared to 1992 2001. Urban land has increased at the cost of grasslands, barren and agricultural lands. The percentage of change is high in the suburbs under semi-urban and village council jurisdictions, whereas in terms of total growth, areas under the municipal corporation jurisdictions are among the highest contributors to urban expansion. Administrative units governed by cantonment boards have shown marginal growth as compared to the civil administrative units in the study area.
[31] Kuang W H, Chi W F, Lu D S, et al.2014.

A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces

[J]. Landscape and Urban Planning, 132: 121-135.

https://doi.org/10.1016/j.landurbplan.2014.08.015      URL      [本文引用: 3]      摘要

Research on physical characteristics and land-cover dynamic changes of megacities over time provides valuable insights for effectively regulating urban planning and management. This study conducts a comparative analysis of 30-year urban expansion patterns and rates among three metropolises in China (Beijing, Shanghai, and Guangzhou) and another three in the USA (New York, Los Angeles, and Chicago) based on time-series impervious surface area (ISA) data extracted from multitemporal Landsat images using the linear spectral mixture analysis approach. This research indicates significantly different urbanization patterns and rates between the Chinese and American megacities. The ISA expansion area in Chinese megacities was five times higher than that in American megacities during the past three decades. The Chinese megacities expand outward from the urban core to the periphery in a concentric ring structure, whereas the American megacities increase ISA mainly within the inner cities with patch-filling patterns. The Chinese megacities are in the development stage where population and economic conditions significantly influence urban expansion patterns and rates, but the American megacities are in the developed stage where population and economic conditions are not important forces driving the ISA expansion. The ISA intensity in the American megacities decreases constantly and smoothly, but ISA intensity in Chinese megacities decays abruptly within certain distances, depending on different cities and years. The most obvious urban expansions were between 8 and 20km in Beijing in the 1980s, between 14 and 50km in Shanghai in the 2000s, and between 8 and 18km in Guangzhou in the 1990s.
[32] Mohanraj R, Azeez P A.2005.

Urban development and particulate air pollution in Coimbatore city, India

[J]. International Journal of Environmental Studies, 62(1): 69-78.

https://doi.org/10.1080/0020723042000261713      URL      [本文引用: 1]      摘要

Haphazard urbanization and unprecedented vehicular growth that exacerbate air quality are prevalent features in India. Coimbatore, an important industrial city ranking 15th in terms of principal urban agglomerations of India, was classified as a moderately polluted area in National Ambient Air Quality Monitoring survey in 1997. The current study (March 1999–February 2001) was undertaken to assess suspended particulate matter (SPM) in urban and suburban Coimbatore. It was found that in the Coimbatore atmosphere SPM with a diameter of less than 100208m (respirable fraction, RSPM or PM10) and those with a diameter above 100208m, the non‐respirable (NRSPM) fraction, ranged between 30–1490208g/m3 and 24.4–4600208g/m3 respectively. The study infers that urban areas, especially those with frequent vehicular traffic and traffic congestion, had comparatively high RSPM exceeding the Indian prescribed standards (600208g/m3). Emission inventory estimated for current vehicle strength showed that about 840 00002kg of particulate matter was emitted during 2001. Wind speed negatively correlated with RSPM, while it was positively correlated with NRSPM. Temperature had a negative correlation with RSPM values.
[33] Nagpure A S, Reiner M, Ramaswami A.2018.

Resource requirements of inclusive urban development in India: Insights from 10 cities

[J]. Environmental Research Letters, 13(2). doi: 10.1088/1748-9326/aaa4fc.

[本文引用: 1]     

[34] Narain U.2008.

Urban air pollution in India

[J]. Review of Environmental Economics & Policy, 2(2): 276-291.

https://doi.org/10.1093/reep/ren010      URL      [本文引用: 1]      摘要

This article describes the main efforts undertaken to stem the growth of air pollution in Indian cities. We begin by examining trends in air quality across the country. This is followed by a description of the legal and institutional framework and policies for controlling air pollution in India. Next we report on efforts to improve air quality in Delhi. We conclude by describing recent actions to control air pollution in cities other than Delhi.
[35] Pandey B, Joshi P K, Seto K C.2013.

Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data

[J]. International Journal of Applied Earth Observation and Geoinformation, 23(1): 49-61.

https://doi.org/10.1016/j.jag.2012.11.005      URL      [本文引用: 1]      摘要

India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program - Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998-2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion.
[36] Pandey B, Seto K C.2015.

Urbanization and agricultural land loss in India: Comparing satellite estimates with census data

[J]. Journal of Environmental Management, 148: 53-66.

https://doi.org/10.1016/j.jenvman.2014.05.014      URL      PMID: 24958549      [本文引用: 1]      摘要

61We examined the urban conversion of agricultural lands (UCAL) in India from 2001 to 2010.61We used a hierarchical classification method and time-series analysis to identify location and timing of UCAL.61Results show that UCAL in India is greater in states where urbanization and economic growth are high.61Agricultural land loss in India is concentrated around smaller cities more than more bigger cities.61The total area under UCAL in India during the study period is relatively low but has been increasing since 2006.
[37] Ramachandra T V, Aithal B H, Sanna D D.2012.

Insights to urban dynamics through landscape spatial pattern analysis

[J]. International Journal of Applied Earth Observation and Geoinformation, 18: 329-343.

https://doi.org/10.1016/j.jag.2012.03.005      URL      [本文引用: 2]      摘要

Urbanisation is a dynamic complex phenomenon involving large scale changes in the land uses at local levels. Analyses of changes in land uses in urban environments provide a historical perspective of land use and give an opportunity to assess the spatial patterns, correlation, trends, rate and impacts of the change, which would help in better regional planning and good governance of the region. Main objective of this research is to quantify the urban dynamics using temporal remote sensing data with the help of well-established landscape metrics. Bangalore being one of the rapidly urbanising landscapes in India has been chosen for this investigation. Complex process of urban sprawl was modelled using spatio temporal analysis. Land use analyses show 584% growth in built-up area during the last four decades with the decline of vegetation by 66% and water bodies by 74%. Analyses of the temporal data reveals an increase in urban built up area of 342.83% (during 1973–1992), 129.56% (during 1992–1999), 106.7% (1999–2002), 114.51% (2002–2006) and 126.19% from 2006 to 2010. The Study area was divided into four zones and each zone is further divided into 17 concentric circles of 1km incrementing radius to understand the patterns and extent of the urbanisation at local levels. The urban density gradient illustrates radial pattern of urbanisation for the period 1973–2010. Bangalore grew radially from 1973 to 2010 indicating that the urbanisation is intensifying from the central core and has reached the periphery of the Greater Bangalore. Shannon's entropy, alpha and beta population densities were computed to understand the level of urbanisation at local levels. Shannon's entropy values of recent time confirms dispersed haphazard urban growth in the city, particularly in the outskirts of the city. This also illustrates the extent of influence of drivers of urbanisation in various directions. Landscape metrics provided in depth knowledge about the sprawl. Principal component analysis helped in prioritizing the metrics for detailed analyses. The results clearly indicates that whole landscape is aggregating to a large patch in 2010 as compared to earlier years which was dominated by several small patches. The large scale conversion of small patches to large single patch can be seen from 2006 to 2010. In the year 2010 patches are maximally aggregated indicating that the city is becoming more compact and more urbanised in recent years. Bangalore was the most sought after destination for its climatic condition and the availability of various facilities (land availability, economy, political factors) compared to other cities. The growth into a single urban patch can be attributed to rapid urbanisation coupled with the industrialisation. Monitoring of growth through landscape metrics helps to maintain and manage the natural resources.
[38] Ramachandra T V, Bharath A H, Sowmyashree M V.2015.

Monitoring urbanization and its implications in a mega city from space: Spatiotemporal patterns and its indicators

[J]. Journal of Environmental Management, 148: 67-81.

https://doi.org/10.1016/j.jenvman.2014.02.015      URL      PMID: 24768450      [本文引用: 2]      摘要

61Quantified land use dynamics using temporal remote sensing data with spatial metrics.61Delhi witnessed 835% growth in built-up during the last 4 decades.61Shannon entropy revealed the sprawl at periphery.61Density gradient reveals radial pattern of urban growth.61Spatial metrics aided in understanding urbanisation pattern and processes.
[39] Schneider A, Woodcock C E.2008.

Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information

[J]. Urban Studies, 45(3): 659-692. doi:10.1177/0042098007087340.

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[40] Schwarz N.2010.

Urban form revisited: Selecting indicators for characterising European cities

[J]. Landscape and Urban Planning, 96(1): 29-47.

https://doi.org/10.1016/j.landurbplan.2010.01.007      URL      [本文引用: 1]      摘要

Four out of five European citizens life in urban areas, and urban form - like the density or compactness of a city - influences daily life and is an important factor for both quality of life and environmental impact. Urban planning can influence urban form, but due to practicality needs to focus on a few indicators out of the numerous indicators which are available. The present study analyses urban form with respect to landscape metrics and population-related indicators for 231 European cities. Correlations and factor analysis identify the most relevant urban form indicators. Furthermore, a cluster analysis groups European cities according to their urban form. Significant differences between the clusters are presented. Results indicate that researchers. European administration and urban planners can select few indicators for analysing urban form due to strong relationships between single indicators. But they should be aware of differences in urban form when comparing European cities or working on planning policies for the whole of Europe. (C) 2010 Elsevier B.V. All rights reserved.
[41] Sood P R.2012.

Air pollution through vehicular emissions in urban India and preventive measures

[J]. International Proceedings of Chemical Biological & Environmenta, 33: 45-49.

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[42] Taubenböck H, Wegmann M, Berger C, et al.2008.

Spatiotemporal analysis of Indian mega cities

[C]// Proceedings of the International Archives of the Photogrammetry Remote Sensing & Spatial Information Sciences. Beijing,China: 75-82.

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[43] Taubenböck H, Wegmann M, Roth A, et al.2009.

Urbanization in India: Spatiotemporal analysis using remote sensing data

[J]. Computers, Environment and Urban Systems, 33(3): 179-188.

https://doi.org/10.1016/j.compenvurbsys.2008.09.003      URL      [本文引用: 1]      摘要

Urbanization is arguably the most dramatic form of irreversible land transformation. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. In this uncontrolled situation, city planners lack tools to measure, monitor, and understand urban sprawl processes. Multitemporal remote sensing has become an important data-gathering tool for analysing these changes. By using time-series of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban sprawl, redensification and urban development in the tremendously growing 12 largest Indian urban agglomerations. A multi-scale analysis aims to identify spatiotemporal urban types. At city level, the combination of absolute parameters (e.g. areal growth or built-up density) and landscape metrics (e.g. SHAPE index) quantitatively characterise the spatial pattern of the cities. Spider charts can display the spatial urban types at three time stages, showing temporal development and helping the reader compare all cities based on normalized scales. In addition, gradient analysis provides insight into location-based spatiotemporal patterns of urbanization. Therefore, we analyse zones defining the urban core versus the urban edges. The study aims to detect similarities and differences in spatial growth in the large Indian urban agglomerations. These cities in the same cultural area range from 2.5 million inhabitants to 20 million (in the metropolitan region of Mumbai). The results paint a characteristic picture of spatial pattern, gradients and landscape metrics, and thus illustrate spatial growth and future modelling of urban development in India.
[44] Torrens P M.2008.

A toolkit for measuring sprawl

[J]. Applied Spatial Analysis and Policy, 1(1): 5-36.

https://doi.org/10.1007/s12061-008-9000-x      URL      [本文引用: 1]      摘要

Debate regarding suburban sprawl in urban studies is contentious. It is fair to say that the phenomenon is not fully understood to satisfaction in the academic, policy, or planning communities and there are a host of reasons why this may be the case. Characterization of sprawl in the literature is often narrative and subjective. Measurement is piecemeal and largely data-driven. Existing studies yield contrary results for the same cities in many cases. The partial appreciation for the intricacies of sprawl is problematic. In practice, city planning agencies and citizen advocacy groups are scrambling to suggest and develop “smart growth” strategies to curb sprawl, without a strong empirical basis for measuring the phenomenon. Yet, sprawl is extremely popular with consumers. In this paper, we develop an innovative approach to diagnosing sprawl, looking across the full range of its characteristic attributes in a comprehensive fashion that is robust to some well-known challenges. This proves to be very useful in sweeping the parameter space of the phenomenon, enabling the visualization and valuation of sprawl surfaces across attributes, allowing us to check the pulse of a developing city. We apply the work to Austin, TX, a controversial exemplar of American sprawl, with the surprising result that sprawl and “smart growth” are found to co-exist and co-evolve. This raises questions about relationships between the two, with consequences for planning and public policy.
[45] World Bank. 2017.

World development indicators database

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