Urban Sprawl Analysis of Tripoli Metropolitan City (Libya) Using Remote Sensing Data and Multivariate Logistic Regression Model

The main objective of this paper is to analyze urban sprawl in the metropolitan city of Tripoli, Libya. Logistic regression model is used in modeling urban expansion patterns, and in investigating the relationship between urban sprawl and various driving forces. The 11 factors that influence urban sprawl occurrence used in this research are the distances to main active economic centers, to a central business district, to the nearest urbanized area, to educational area, to roads, and to urbanized areas; easting and northing coordinates; slope; restricted area; and population density. These factors were extracted from various existing maps and remotely sensed data. Subsequently, logistic regression coefficient of each factor is computed in the calibration phase using data from 1984 to 2002. Additionally, data from 2002 to 2010 were used in the validation. The validation of the logistic regression model was conducted using the relative operating characteristic (ROC) method. The validation result indicated 0.86 accuracy rate. Finally, the urban sprawl probability map was generated to estimate six scenarios of urban patterns for 2020 and 2025. The results indicated that the logistic regression model is effective in explaining urban expansion driving factors, their behaviors, and urban pattern formation. The logistic regression model has limitations in temporal dynamic analysis used in urban analysis studies. Thus, an integration of the logistic regression model with estimation and allocation techniques can be used to estimate and to locate urban land demands for a deeper understanding of future urban patterns.     

Exploring land use policy scenarios with the use of a cellular automata-based model: urban sprawl containment and sustainable development in Thessaloniki

This study addresses the issue of urban sprawl through the application of a cellular automata (CA)-based model in the area of Thessaloniki, Greece. The model integrates a multiple regression model at the regional level with a CA model at the local level. New urban land is allocated in a disaggregated field of land units (cells) taking into account a wide range of data. Particular emphasis is placed on the way zoning regulations and land availability data are inserted into the model, so that alternative land use policy scenarios could be examined. Thessaloniki, a typical Mediterranean city, is used as a case study. The model is used to compare two scenarios of urban growth up to year 2030; the first one assuming a continuation of existing trends, whereas the second one assuming the enactment of various land use zoning regulations in order to contain urban sprawl.     

Wavelet Based Post Classification Change Detection Technique for Urban Growth Monitoring

Urban areas are the most dynamic region on earth. Their size has been constantly increased during the past and this process will go on in the future. Since there is no standard policy and guidelines for construction of buildings and urban planning, cities tend to have irregular growth. Many cities in the world face the problem of urban sprawl in its suburbs. So issues of urban sprawl need to be settled with the help of technologies such as satellite remote sensing and automated change detection. This paper presents a wavelet based post classification change detection technique that is applied to 1996 and 2004 MSS images of Madurai City, South India to determine the urban growth. The classification stage of the technique uses coilflet wavelet filter to correlate with the MSS land cover images of Madurai city to derive texture feature vector and this feature vector is inputted to a fuzzy-c means classifier, an unsupervised classification procedure. The post classification change detection technique is employed for identifying the newly developed urban fringe of the study area. The error matrix analysis is used to assess the accuracy of the change map. The performance of the presented technique is found superior than that of classical change detection methods such as image differencing, change vector analysis and principal component analysis.     

Manifestation of Remote Sensing Data in Modeling Urban Sprawl Using the SLEUTH Model and Brute Force Calibration: A Case Study of Sana’a City, Yemen

Sana’a the metropolitan capital of Yemen, has experienced rapid spatial growth and uncontrolled development for decades. In the absence of a means to forecast and predict urban growth trends, planning and urban policy decisions have been found wanting. In this study the SLEUTH (Slope, landuse, exclusion, urban extent, transportation and hillshade) model which has been widely and successfully applied in developed countries, has been applied to predict the spatial urban sprawl pattern from 2004–2020 in Sana’a. This was to provide the necessary forecast for better planning and decision making. The model performed well as per the calibration coefficient values. The results showed that there will a 29 % increase in spatial urban sprawl growth during the modeling period. Growth of the sprawl will be mainly at the edges of the urban boundary, there will also be a wide area of scattered urban clusters. Factors that will have major influence on spatial expansion of the city will be diffusion, natural and internal growth, slope (that will hinder spread) and transportation (along which most of the urban sprawl will occur). The study also provides an insight into how the SLEUTH model performs in a poorly planned urban environment as compared to the planned and controlled environment where it has been applied.     

Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network

In recent decades, the world has experienced unprecedented urban growth which endangers the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area of Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001–2011, namely simple extrapolation, Markov Chain (MC), and system dynamic (SD) modelling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands.     

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.     

Spatio-temporal analysis for monitoring urban growth – a case study of Indore City

Urban sprawl is characterized by haphazard patchwork of development, which leads to an improper development in any city. To prevent this kind of sprawl in future, it is necessary to monitor the growth of the city. Hence, an attempt has been made in the present study to monitor the urban growth over a period of time by employing Remote Sensing and Geographic Information System techniques in conjunction with Shannon entropy. Shannon entropy is a measure to determine the compactness or dispersion of built-up land growth in the urban areas. The growth patterns of urban built-up land have been studied initially by dividing the area into four zones. The observations have been made with respect to each zone. Then, the study area has been divided into concentric circles of 1 km buffers and the growth patterns have been studied based on urban built-up density with respect to each circular buffer in all four zones. These observations have been integrated with road network to check the influence of infrastructure on haphazard urban growth. It has been found from the study that Shannon entropy is a good measure to determine the spatial concentration or dispersion of built-up land in the city. The study also proved the potential of RS and GIS techniques in the spatio-temporal analysis of urban growth trends and their consequences in the lands adjoining to urban areas.     

基于时间序列相似度的城市功能区识别研究

随着城市化的快速发展,城市空间结构愈发复杂,城市功能区的快速有效识别对资源的有效配置和城市规划具有重要意义.传统的功能区识别缺乏对居民这一城市空间活动主体的动态表征,而长时间序列的出租车数据能动态表征居民出行行为,进而反映城市空间结构.动态时间扭曲(DTW)距离比传统的欧氏距离更能有效挖掘高维数据,泛化后的LB_Keo...     

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.     

Examining urban sprawl in Europe using spatial metrics

Urbanisation is a global phenomenon with an important impact on the quality of human life. Europe has been widely affected by urbanisation. One of the main characteristics of urban growth is sprawl, a negative form of urban expansion, which affects large cities and most types of urban landscapes. Spatial indicators are applied to CORINE Urban Morphological Zones (UMZ) changes in order to measure urban sprawl between 1990–2000 and 2000–2006 in 24 European countries. The indicators calculate urban morphological properties such as shape, aggregation, compactness and dispersion. The results revealed that the urban areas (UMZ) increased by 146% during 1990–2006 and the urbanisation becomes more circle-shaped and less complex where mostly sprawl occurs. Moreover, urban form becomes less clumped or aggregated. Therefore, due to accelerating rates of urban sprawl, European urban planning should intensify appropriate initiatives to avoid negative impacts on human life.     

Geosimulation of urban growth and demographic decline in the Ruhr: a case study for 2025 using the artificial intelligence of cells and agents

The Ruhr is an “old acquaintance” in the discourse of urban decline in old industrialized cities. The agglomeration has to struggle with archetypical problems of former monofunctional manufacturing cities. Surprisingly, the image of a shrinking city has to be refuted if you shift the focus from socioeconomic wealth to its morphological extension. Thus, it is the objective of this study to meet the challenge of modeling urban sprawl and demographic decline by combining two artificial intelligent solutions: The popular urban cellular automaton SLEUTH simulates urban growth using four simple but effective growth rules. In order to improve its performance, SLEUTH has been modified among others by combining it with a robust probability map based on support vector machines. Additionally, a complex multi-agent system is developed to simulate residential mobility in a shrinking city agglomeration: residential mobility and the housing market of shrinking city systems focuses on the dynamic of interregional housing markets implying the development of potential dwelling areas. The multi-agent system comprises the simulation of population patterns, housing prices, and housing demand in shrinking city agglomerations. Both models are calibrated and validated regarding their localization and quantification performance. Subsequently, the urban landscape configuration and composition of the Ruhr 2025 are simulated. A simple spatial join is used to combine the results serving as valuable inputs for future regional planning in the context of multifarious demographic change and preceding urban growth.     

基于分形维度法的都市区城市蔓延分析——以广佛为例

根据1957与1982年两个时期的地形冈并结合1995与2008年的遥感卫星影像提取城镇建设用地专题信息。利用ArcGIS建立广佛都市区城镇建设用地数据库．采用分形维度的计锋方法,对广佛都市区城市蔓延的情况进行测度和分析。结果表明广佛都市区三个发展阶段城镇建设用地面积增长速度并不均匀,具有加速发展的特点。四个测度年份的分形维数分别为1．6454,1．6285,1．5586和1．5270,均在1-2之间．分形维数呈递降趋势,总体下降了0．1184,城市发展模式由紧凑型逐渐向松散型演变,存在城市蔓延情况,而且城市发展模式以年均0．14％的速度从紧凑型向松散型演变。广佛都市区城市蔓延的地域分异较为明硅,具有较强的空间集聚性和中心向心件,广州和佛山毗邻地区以及中心城区是城市蔓延的活跃区和集中区,1982—1995年问研究区内蔓延速度比1995．2008年间要快。通过深入分析发现,经济快速发展、城市人口的持续增长、交通道路网络的建设是广佛都市区快速扩张和城市蔓延的基本动力因素之一,开发区土地扩张,进一步加剧了无序扩张和城市蔓延。     

Capturing the heterogeneity of urban growth in South Korea using a latent class regression model

This study aims to analyze the spatial patterns of urban growth in South Korea between 2000 and 2010. Fourteen suspected causative independent variables were selected and latent class regression (LCR) was used to analyze the relationship between dependent (urban growth) and independent (causative) variables. The goodness‐of‐fit was assessed by comparison to logistic regression (LR) analysis. The LR analysis produced consistent coefficients for each independent variable across the study area. In contrast, an LCR analysis, with a three‐class assumption, resulted in a different magnitude and directional effects of the coefficients for each class. The LCR analysis enabled the identification of both spatially homogeneous and heterogeneous areas. In addition, the LCR analysis performed better than the LR analysis with a lower Akaike information criterion and Bayesian information criterion value, and a higher receiver operating characteristic value. We conclude that LCR analysis should be used to establish causative “driving” factors for efficient urban growth planning and urban spatial policy.     

A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data

Urban sprawl has been identified as one of the most negative effects of global population growth on the environment and biodiversity. Frequent monitoring of urban sprawl is needed to limit the impact of this ongoing phenomenon. This paper proposes precise monitoring of building construction using an object-based classification methodology applied to Spot 5 images with a 2.5 m resolution. An application at a regional scale on Reunion Island in the Indian Ocean shows that this building extraction methodology has limitations in the production of reference urban maps because of difficulties in defining the shape and the number of buildings compared to classical photo-interpretation of aerial photography. However, these results are of great value for planning in urban sprawl areas where up-to-date information is lacking because of the rapid pace of house construction and residential development.     

2000-2016年中国地级以上城市高精度城区边界遥感提取及时空扩展分析

城区边界和城区面积是城镇化的重要表征和扩展分析的基础。然而城区边界存在概念和提取标准不统一、精度较低、可比性较差等问题。为此,提出了基于高分辨率影像和地理信息资料辅助的城区半自动化提取方法,充分利用高分辨率影像上的城市景观特征、先验地理信息知识和一系列标准规则,以得到精度高、一致性强的数据。以中国337个地级以上城市为研究区,采用该方法得到了2000年、2005年、2010年、2016年4期城区边界成果,并开展了城区时空扩展及用地效率等相关分析。结果表明:①16年间城区扩展迅速,城区主要集中分布在东部和中部,东西部地区差异大;②城市用地效率与城镇化发展水平显著相关,城区扩展以外延型为主;③大多城市城区扩展超前于人口增长,少量城市城区扩展滞后于人口增长;④城区扩展以占用耕地为主。     

Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio

The creation of an accurate simulation of future urban growth is considered one of the most important challenges in urban studies that involve spatial modeling. The purpose of this study is to improve the simulation capability of an integrated CA-Markov Chain (CA-MC) model using CA-MC based on the Analytical Hierarchy Process (AHP) and CA-MC based on Frequency Ratio (FR), both applied in Seremban, Malaysia, as well as to compare the performance and accuracy between the traditional and hybrid models. Various physical, socio-economic, utilities, and environmental criteria were used as predictors, including elevation, slope, soil texture, population density, distance to commercial area, distance to educational area, distance to residential area, distance to industrial area, distance to roads, distance to highway, distance to railway, distance to power line, distance to stream, and land cover. For calibration, three models were applied to simulate urban growth trends in 2010; the actual data of 2010 were used for model validation utilizing the Relative Operating Characteristic (ROC) and Kappa coefficient methods Consequently, future urban growth maps of 2020 and 2030 were created. The validation findings confirm that the integration of the CA-MC model with the FR model and employing the significant driving force of urban growth in the simulation process have resulted in the improved simulation capability of the CA-MC model. This study has provided a novel approach for improving the CA-MC model based on FR, which will provide powerful support to planners and decision-makers in the development of future sustainable urban planning.     

基于多时相卫星遥感数据的上虞城市用地动态扩展分析

采用地形图和MSS/TM,SPOT5卫星遥感数据,提取城市建成区范围,计算多种城市扩展指数,研究分析了1976~2010年上虞城市用地扩展情况。研究发现,20世纪70年代至21世纪10年代,上虞城市用地扩展面积和强度在最初爆发性增长后,保持高位稳定。城市紧凑度指数持续降低,城市分形维度在大幅下降后保持稳定,城市发展日益复杂化,边缘日益曲折、破碎化。今后市政规划建设应着力发展建设城市用地边缘离散曲折之处,提高城市紧凑度,降低分形维度,从而提高城市区域市政服务职能效率。     

A comparative study on four major cities in Northeastern Thailand using urban land density function

Urban land density is an important factor to understand how cities expand. An “Inverse S-shape Rule” was implemented for the first time to analyze urban land density in Northeastern Thailand using the four cities Khon Kaen, Udon Thani, Nakhon Phanom, and Nong Khai as study sites. Land density function was tested using different data classification techniques from previous studies. Each city was investigated over two different time periods between 2002 and 2015. Declining pattern characteristics of metropolitan area density outward from city centers can be quantified by fitting the parameters to urban land density functions. An inverse S-shape function was identified as the best data fit. The four selected cities showed conventional density variation for decline in urban land area from city centers to outlying areas. Overall trend indicated that cities became more compact over time since the density differences between the urban core and urban fringe were greater with increasing infilling growth within the urban boundary. All four cities increased in size over time; however, the increasing amount of built-up land in the surrounding rural areas did not follow the same trend in each case. Some functional parameters required careful interpretation because of the linear shape of the city as in the case of Nakhon Phanom. Using highly detailed urban data resulted in lower densities of urban areas compared to the conventional pixel-based classification, and this affected the overall shape of the inverse S-shape function. The fitted parameters and their changing trends indicated that the urban land density function was useful for understanding urban form and urban sprawl in Thailand. Results can be used to develop a specific framework for other cities with similar attributes in the future.     

Urban growth analysis using spatial and temporal data

Urban growth identification, quantification, knowledge of rate and the trends of growth would help in regional planning for better infrastructure provision in environmentally sound way. This requires analysis of spatial and temporal data, which help in quantifying the trends of growth on spatial scale. Emerging technologies such as Remote Sensing, Geographic Information System (GIS) along with Global Positioning System (GPS) help in this regard. Remote sensing aids in the collection of temporal data and GIS helps in spatial analysis. This paper focuses on the analysis of urban growth pattern in the form of either radial or linear sprawl along the Bangalore — Mysore highway. Various GIS base layers such as built-up areas along the highway, road network, village boundary etc. were generated using collateral data such as the Survey of India toposheet, etc. Further, this analysis was complemented with the computation of Shannon’s entropy, which helped in identifying prevalent sprawl zone, rate of growth and in delineating potential sprawl locations. The computation Shannon’s entropy helped in delineating regions with dispersed and compact growth. This study reveals that the Bangalore North and South taluks contributed mainly to the sprawl with 559% increase in built-up area over a period of 28 years and high degree of dispersion. The Mysore and Srirangapatna region showed 128% change in built-up area and a high potential for sprawl with slightly high dispersion. The degree of sprawl was found to be directly proportional to the distances from the cities.     

面向城市建成区扩展模拟的CA-Markov模型改进

对城市建成区扩展的预测是防止城市蔓延的重要管理依据。目前,元胞自动机-马尔可夫链模型,已成为城市建成区扩展预测的重要方法。该模型对指标权重的赋值方法较为敏感,以往的单一指标赋值法,影响了城市建成区扩展预测的精度和可信度。为此,本研究提出整合传统权重赋值法的AHP和逻辑回归模型改进CA-Markov模型。研究选择云南省大理市为案例,对2020、2030年的城市建成区扩展进行模拟和预测,最后进行精度验证。研究结果表明：①Kappa指数可达到96.8%,预测结果有较好的一致性。②大理市的城市建成区扩展均表现为继续向外扩展,以东南、西北方向和两片建成区之间为主要扩展方向。研究提供了组合权重赋值法改进CA-Markov模型,这将为规划者在未来规划中提供强有力的支持。