A quantitative framework for analysing long term spatial clustering and vegetation fragmentation in an urban landscape using multi-temporal landsat data

Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994–2017 as shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p < 0.05) of vegetation patches. Conversely, more dispersed vegetation patches were found in the highly and densely urbanized western, eastern and southern parts of the city. This suggest that with increasing vegetation fragmentation, small and isolated vegetation patches do not spatially cluster but are dispersed geographically. The findings of the study underline the potential of LISA measures as a valuable spatially explicit method for the assessment of spatial clustering and fragmentation of urban vegetation patterns.     

基于ESDA-GIS的新疆县域经济时空差异研究

区域经济差异历来是国内外学者关注的热点问题。从时空角度出发,本文利用新疆1978-2004年县域人均GDP数据,采用变差系数、ESDA全局和局部空间自相关分析方法与GIS技术相结合,对新疆县域经济总体发展差异和局部空间异质性的演变特征进行了实证研究。变差系数分析显示新疆县际经济差异并不沿着"倒U字"型的轨迹变化,1978-2004年县域间的经济差距先减少,而后缓慢扩大。通过新疆各县人均GDP的空间自相关系数(Moran’sI)的计算,我们发现改革开放以来新疆经济发展的空间集聚效应增强,集聚区域间的经济差距不断拉大。对不同年度新疆各县人均GDP的局域空间自相关分析进一步揭示出北疆中、西部、南疆西、北部逐渐形成"HH"和"LL"两种类型空间集聚。实践证明空间分析方法是传统经济差异量度方法的一种有益补充,使我们更加深入理解区域经济的空间格局及其变化规律。     

Investigating the land use characteristics of urban integration based on remote sensing data: experience from Guangzhou and Foshan

Abstract

Urban integration is an increasingly popular phenomenon. It is important to characterize the dynamic processes of urban integration from the perspective of geography. However, previous studies mainly focussed on the overall characteristics of a single entity and failed to consider inter-city influences. Therefore, this study aims to systematically investigate this phenomenon. Firstly, spatial clustering technique and landscape metric were used to analyze the temporal change of spatial layout. Secondly, we built multiple ring buffers of city boundary to reveal the directional differences in urban expansion. Lastly, we compared the observed land use data with the results simulated by cellular automata model. These experiments have shown that the urban expansion characteristics during the urban integration process are rather different from those of a single city, and the above methodology can effectively characterize the urban integration phenomenon. The successful example of Guangzhou–Foshan integration could also provide practical experience for other similar attempts.     

Testing spatial patterns and growth spillover effects in clusters of cities

 This paper analyzes the spatial patterns of households' distribution in clusters of cities and the effects on regional growth using spatial exploratory techniques and a model of growth that incorporates spatial location. Our empirical analysis shows that, over the 1980–1990 period, in Southern New England, patterns of spatial clustering of households did create heterogeneous growth rates in the region. Also, there is evidence that clusters of cities and isolated cities created spillover growth effects in bordering towns. Received: 5 September 2001 / Accepted: 2 August 2002     

Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area,China

Urban sprawl has led to environmental problems and large losses of arable land in China. In this study, we monitor and model urban sprawl by means of a combination of remote sensing, geographical information system and spatial statistics. We use time-series data to explore the potential socio-economic driving forces behind urban sprawl, and spatial models in different scenarios to explore the spatio-temporal interactions. The methodology is applied to the city of Wuhan, China, for the period from 1990 to 2013. The results reveal that the built-up land has expanded and has dispersed in urban clusters. Population growth, and economic and transportation development are still the main causes of urban sprawl; however, when they have developed to certain levels, the area affected by construction in urban areas (Jian Cheng Qu (JCQ)) and the area of cultivated land (ACL) tend to be stable. Spatial regression models are shown to be superior to the traditional models. The interaction among districts with the same administrative status is stronger than if one of those neighbors is in the city center and the other in the suburban area. The expansion of urban built-up land is driven by the socio-economic development at the same period, and greatly influenced by its spatio-temporal neighbors. We conclude that the integration of remote sensing, a geographical information system, and spatial statistics offers an excellent opportunity to explore the spatio-temporal variation and interactions among the districts in the sprawling metropolitan areas. Relevant regulations to control the urban sprawl process are suggested accordingly.     

Modelling of urban growth boundary using geoinformatics

Abstract

Urban growth boundary (UGB) is a regulatory measure of local government for delineating limits of urban growth over a period of time. Land within the UGB allows urban development, while the land outside of this boundary remains primarily non-urban. The increasing popularity of UGB demands an easy and effective method to design this boundary. This article introduces a new concept, Ideal Urban Radial Proximity (IURP), to designate a spatial UGB using geoinformatics in the digital environment. The Kolkata urban agglomeration was considered to demonstrate this model. Remotely sensed imageries of three temporal instants (years 1975, 1990 and 2005) were considered to determine the information on urban extent and growth of the city. These data were then used as inputs to model the UGB for the years 2020 and 2035. The proposed model discourages scattered development and increase in urban growth rate. It preserves urban vegetation, water bodies and any other important non-urban areas within the inner city space. The IURP concept will also be useful to make the cities circular and polycentric urban blobs into a monocentric tract. Apart from the proposed model and derived results, this research also proves the potential of geoinformatics in modelling a UGB.     

Nighttime light data-based analysis of spatiotemporal patterns evolution and driving forces of economic development in China at prefecture level (1992–2020)

Intercity lighting data are an important resource for studying spatial and temporal patterns in regional urban development as an indicator of the intensity of urban social and economic activity. Understanding the evolutionary characteristics of the spatial pattern of regional economic development can support decision-making in regional economic coordination and sustainable development strategies. Based on a long time series of nighttime lighting data from 1992 to 2020, this study used the Theil index, Markoff transfer matrix, spatial autocorrelation, and spatial regression to analyze spatiotemporal evolutionary characteristics and drivers of urban economic development in China. The study found that from 1992 to 2020, China's economic development hot spots have been concentrated in highly developed urban agglomerations namely the Beijing–Tianjin–Hebei region, Shandong Peninsula, Yangtze River Delta, and Pearl River Delta. Cold spots were mainly concentrated in the central-west and southwest of the country. The economic growth rate shows an opposite spatial pattern, which demonstrates the effectiveness of the national coordinated development strategy for regions. The Theil index for urban economic development in China shows an overall downward trend, and the overall economic disparity is mainly due to the relatively low economic development of Tibet, Xinjiang, Gansu, and other western provinces. Therefore, regional economic development remains significantly uneven. In China, the economic type of cities is relatively stable, and the probability of leapfrogging types is low; however, the level of cities with high resource dependence or a single economic structure easily downgrades. The level of economic development and the related socioeconomic factors of neighboring cities influence an obvious spatial spillover effect in the development of urban economies in China. The pattern of China's urban economic development is mainly affected by innovation capacity, financial support, capital investment, transportation infrastructure, and industrial structure.     

基于空间连续数据的小流域景观格局破碎化研究

基于空间连续数据，采用局部空间关联指标（LISA）——局部Moran指数（Local Moran Index, LMI），通过探测小流域内景观均质性和异质性的变化情况来反映景观格局破碎化的变化过程。作为一种空间明确的景观格局研究方法，LMI能够发现流域景观格局变化过程中的热点地区，并分析其与流域土地利用变化之间的联系，明确了土地利用变化是引起小流域景观格局变化的最主要的驱动因素。研究表明，基于空间连续数据的局部空间关联指标方法可以作为传统景观格局变化研究方法的有益补充。     

Modeling Urban Growth Using GIS and Remote Sensing

Based on remote sensing and GIS, this study models the spatial variations of urban growth patterns with a logistic geographically weighted regression (GWR) technique. Through a case study of Springfield, Missouri, the research employs both global and local logistic regression to model the probability of urban land expansion against a set of spatial and socioeconomic variables. The logistic GWR model significantly improves the global logistic regression model in three ways: (1) the local model has higher PCP (percentage correctly predicted) than the global model; (2) the local model has a smaller residual than the global model; and (3) residuals of the local model have less spatial dependence. More importantly, the local estimates of parameters enable us to investigate spatial variations in the influences of driving factors on urban growth. Based on parameter estimates of logistic GWR and using the inverse distance weighted (IDW) interpolation method, we generate a set of parameter surfaces to reveal the spatial variations of urban land expansion. The geographically weighted local analysis correctly reveals that urban growth in Springfield, Missouri is more a result of infrastructure construction, and an urban sprawl trend is observed from 1992 to 2005.     

New urban settlements in a perspective of public and private interests. Case study: a Swedish municipality within the hinterland of the Stockholm city

The changes of land use patterns and urban structures could be seen as the dynamic result of the trade off between public and private interests. Thereby the land use change is to some extent unpredictable. The focus in the current study is to measure the importance of spatial location factors regarding new residential and commercial buildings in relation to existing urban amenities and political guidelines. The relative importance of the location factors was studied by multinomial regression analysis. Results from this study reveal that the location profiles of new urban object types attained here indicate strong correspondence with local political land use guidelines and to clustering. The spatial distribution of new urban settlements does not in general correspond to the monocentric urban scheme where firms and residents locate in spatial proximity to urban centres.      

昆明市2000-2010年乡镇人口空间自相关及其时空演变分析

昆明作为云南省的省会城市,其人口的空间分布特征对该地区的城市规划和经济发展具有重要意义.本文以昆明市112个乡镇的矢量数据和2000、2010年的人口数据为基础,运用Moran I指数和Moran I散点图对昆明市2000和2010年的乡镇人口进行了空间自相关及其时空演变分析.结论如下:①2000和2010年,昆明市乡镇人口分布呈现显著的正向空间自相关特征,且人口分布出现以昆明市市中心和滇池为中心的"人口高地带"和北部山区的"人口低地带",这与昆明市海拔"北高南低"和地势"北部起伏大、南部和缓"趋于一致;②地形要素是昆明市乡镇人口2000和2010年空间自相关的基础,而随着交通条件的改善和城市化的发展,2000―2010年10年间昆明市乡镇人口空间自相关显著增强,但人口密度出现较大的空间差异.     

我国地理信息系统建设及进展(2)

着重叙述了目前城市GIS的建设与应用和国家空间数据基础设施的建设与应用，指出建设数字城市和UGIS的框架体系结构，在GIS建设中的重要的作用。同时指出国家空间数据基础设施(NSDI)为各领域GIS建设提供统一、规范的地理空间基础平台和基础地理数据，对于最大限度地实现各领域的信息共享和加速推进我国GIS建设等，都具有重要的理论和实际意义。     

出租车轨迹数据的南京人群出行模式挖掘

针对现有出租车轨迹数据挖掘中时间序列邻近度量方法存在的问题,提出一种基于DBSCAN算法和改进的DTW距离的时间序列聚类算法提取具有相似性出行特征的时空模式,进而研究城市人群出行行为的时空差异。以南京市为例,结合电子地图对出行模式的空间分布特征进行分析,证明了本文所提出的方法的有效性。实验结果表明:在空间分布上,工作日出租车出行模式按照平均出行频次由高到低排序,从城市中心向四周扩散,呈中心环状分布,出行模式区域界限较为明显,同类出行模式分布区域对应相似的功能。提出了一种基于DBSCAN算法和改进的DTW距离的时间序列聚类算法提取具有相似性出行特征的时空模式,有效地分析城市人群出行行为的时空差异。     

新疆克拉玛依市商业地价空间分布规律研究

城市地价是一个具有时空性质的多维概念,在空间分布上具有较强的关联性和特殊性。但目前对城市地价空间分布规律方面的研究相对较少,还没有形成系统性的理论与方法依据,本文以克拉玛依市2007年土地调查商业用地数据和地价动态监测数据为依据,以ARCGIS软件为平台,采用地统计学的方法,对克拉玛依市商业用地价格的空间分布进行了研究。研究表明:地统计学的理论与方法对城市地价空间分布进行研究具有独特的优越性;城市商业用地地价的分布在空间上既有连续性,也存在变异性,受区域整体趋势的影响明显高于小范围的变异性,属于强空间自相关;商业用地地价分布整体呈现圈层结构,交通道路对地价的空间分布具有重要影响。     

Entropy Approach for Assessment of Urban Growth: A Case Study of Jaipur,INDIA

In India the last 30 years had witnessed a radical transformation of urban scene. In particular, during this period, the one lakh cities and million plus cities began to grow rapidly. In the present study, urban growth of Jaipur city in the last 31 years (1975–2006) was assessed. Jaipur ranks 11th in India with a total population of 2.3 million and has shown a consistent increase in the past 50 years. It is one of the fastest growing mega cities of the country with an annual average growth rate of 4.5% which is quite high from the national urban growth rate. Remote sensing and GIS have been used to extract the information related to urban growth-built up area and its spatial and temporal variation. The Shannon’s entropy at two levels (city as a whole and ward wise) is computed in order to quantify the form and patterns of urban growth using built up area as spatial phenomena. Further, multivariate statistical techniques have been used to establish the relationship between the urban growth and its causative and determining factors. Results of this study reveal that the growth rate of built up in Jaipur has outstripped the rate of population growth. Shannon’s entropy quantifies as dispersed form of urban growth till 2000 and after it, there is relative compactness in 2006.     

广东省县域经济实力差异空间自相关分析

以2005年广东省87个县（市、区）级区域为研究对象,选取总人口、国民生产总值等8个指标作为评价因子,通过因子分析和主成分分析法获得各县级单元经济实力综合得分。对综合得分为变量进行空间自相关分析,计算得出全局相关Moran＇s值和LISA值。结果显示：广东省的县域经济具有明显的空间集聚特征,86.21%的县市经济表现为空间正相关,但仍有少部分县市的经济发展存在异质性。     

基于RS与GIS的宁波市鄞州区土地利用变化分析

利用1990年土地详查变更调查数据和2006年SPOT遥感影像数据,借助GIS空间分析能力,通过计算土地利用类型动态度、转移矩阵、扩展强度指数分析了鄞州区的城镇土地利用变化的时空特征。研究结果表明:1)鄞州区城市、建制镇、交通运输用地、工矿用地扩展的大部分来源于耕地;2)区各街道城镇建设用地扩展强度指数大于建制镇;3)建设用地扩展以新城区及宁波城区周边的街道镇为主。     

基于遥感与GIS的城镇用地扩展研究

利用1990年土地详查变更调查数据和2006年的SPOT遥感影像,借助GIS空间分析能力,通过计算土地利用类型动态度、转移矩阵、扩展强度指数分析了慈溪市的城镇用地利用变化的时空特征.研究结果表明:1)慈溪市城市、建制镇、交通运输用地扩展的大部分来源于耕地;2)浒山街道城镇,交通建设用地扩展强度指数大于其他镇;3)建设用...     

Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows

Since before the inception of work by Okabe, the intermingling of spatial autocorrelation (i.e., local distance and configuration) and distance decay (i.e., global distance) effects has been suspected in spatial interaction data. This convolution was first treated conceptually because technology and methodology did not exist at the time to easily or fully address spatial autocorrelation effects within spatial interaction model specifications. Today, however, sufficient computer power coupled with eigenfunction-based spatial filtering offers a means for accommodating spatial autocorrelation effects within a spatial interaction model for modest-sized problems. In keeping with Okabe’s more recent efforts to dissemination spatial analysis tools, this paper summarizes how to implement the methodology utilized to analyze a particular empirical flows dataset.