Spatial Patterns Of Urban Expansion Research Articles

Spatial patterns of urban expansion and cropland loss during 2017–2022 in Guangdong, China

Urban expansion often occurs at the expense of cropland loss, posing challenges to sustainable urban growth and food security. However, detailed investigations into urban expansion and cropland loss remain limited, particularly in regions with varying levels of urbanization. Here, we take Guangdong Province, China, as a case study to exemplify how urban expansion affects cropland using remotely sensed land use products. We adopted geospatial analysis, correlation indicators, and landscape metrics to uncover their spatial relationships at 10-m spatial resolutions. Results showed that urban areas increased by 6335 km2 while cropland decreased by 3780 km2 from 2017 to 2022. Notably, 41 % of newly expanded urban areas were from croplands, and 45 % of lost croplands were converted to urban areas. Western Guangdong experienced the largest extent of urban expansion and cropland loss, emerging as a hotspot region in recent years. Additionally, our analysis observed the increasing compactness of urban areas and the growing fragmentation of cropland landscapes over time. These findings shed light on the intricate dynamics between urban expansion and cropland loss in rapidly urbanizing regions, which provide valuable insights for sustainable urban development, agricultural practice, and land management in the future.

Comparative framework for spatially explicit urban growth modeling for monitoring urban land-use efficiency and sustainable urban development (SDG 11.3.1): a study on Kolkata metropolitan area, India

The present study attempts to model the spatiotemporal urban growth of the Kolkata metropolitan area (KMA), India, in a comparative modeling framework using three (remote sensing and geographic information system) RS-GIS integrated techniques, namely stochastic-choice Markov-chain (STCHOICE), cellular automata-Markov (CA-MARKOV), and multi-layer perceptron neural network (MLPNN) coupled with Markov-chain approaches intending to monitor land-use efficiency defined by United Nations for sustainable urban development in KMA. In order to find out the best modeling approach, each of the three techniques is engaged in modeling KMA’s growth, and the model thus obtained is employed to predict future growth. The MLPNN (Kappa = 0.9025) appears to be a considerably better approach as compared to the other two approaches: CA-MARKOV (Kappa = 0.6941) and STCHOICE (Kappa = 0.5392). The MLPNN simulated urban growth for 2036 reveals that the urban built-up cover is expected to be about 55% from that of 31% in 2016 due to the significant conversion of other land covers. The study reveals that urban and peri-urban areas do not have a similar pattern of land consumption and land-use efficiency in KMA. The central KMA reflects better land-use efficiency due to the compact built-up growth compared to the periphery reflecting leapfrog growth as a result of rapid urban sprawling. HIGHLIGHTS Predicted future urban growth in Kolkata Metropolitan Area (KMA) using comparative modeling framework Monitored urban land use efficiency (SDG 11.3.1) for sustainable urban development in KMA The study reveals that urban and peri-urban land consumption and land use efficiency are different Identified most influential driving factors from the selected factors responsible for the spatial pattern of urban expansion in KMA

Spatial Features of Urban Expansion in Vietnam Based on Long-Term Nighttime Lights Data

As a developing country, Vietnam has experienced rapid economic development since the 21st century. It is therefore becoming increasingly important to understand its spatial pattern of urban expansion. However, the key challenge in this endeavor lies in the lack of national accounting data for the sub-administrative divisions of Vietnam at the national level, especially longitudinal data over a long time series. The nighttime lights (NTL) data can objectively reflect the scope and intensity of human development and construction in urban built-up areas, which can effectively support the empirical analysis of the urban expansion process in Vietnam. This paper uses the intercalibration model to correct and fit the long time series of DMSP/OLS and VIIRS/NPP NTL data. Based on this, the data for the urban built areas of Vietnam from 2000 to 2018 are further extracted. The results are as follows. (1) The main urban expansion in Vietnam is concentrated in the southern Mekong Delta and the northern Red River Delta, represented by Ho Chi Minh City and Hanoi City, respectively. (2) Vietnam’s urban NTL has significant high–high clustering characteristics in the north-south delta regions. The main urban expansion hotspots were concentrated around Ho Chi Minh City before 2012, the northern region represented by Hanoi City was gradually transformed into a critical area that gathering urban expansion hotspots after 2012. (3) The cities with significant influence and high coupling degree of industrialization and globalization on the urbanization of Vietnam are concentrated in Ho Chi Minh City, Hanoi, and some northern delta provinces, showing that the impact of industrialization and globalization on urbanization in Vietnam is still limited to some regions. In addition, the results show that the size of the population and the level of industrialization are the main drivers of urban expansion in Vietnam, while the level of foreign investment shows little significance. The results are helpful for promoting the application of long time series NTL data in urban expansion and for further analyzing the urban pattern changes in Vietnam and the influencing factors behind them.

Expansion and Evolution of a Typical Resource-Based Mining City in Transition Using the Google Earth Engine: A Case Study of Datong, China

China’s resource-based cities have made tremendous contributions to national and local economic growth and urban development over the last seven decades. Recently, such cities have been in transition from resource-centered development towards human-oriented urbanization to meet the requirements of long-term sustainability for the natural environment and human society. A good understanding of urban expansion and evolution as a consequence of urbanization has important implications for future urban and regional planning. Using a series of remote sensing (RS) images and geographical information system (GIS)-based spatial analyses, this research explores how a typical resource-based mining city, Datong, has expanded and evolved over the last two decades (2000–2018), with a reflection on the role of urban planning and development policies in driving the spatial transformation of Datong. The RS images were provided and processed by the Google Earth Engine (GEE) platform. Spatial cluster analysis approaches were employed to examine the spatial patterns of urban expansion. The results indicate that the area of urban construction land increased by 132.6% during the study period, mainly along with the Chengqu District, the Mining Area, and in the southeast of the Nanjiao District, where most new towns are located. Reflection on the factors that influence urban expansion shows that terrain, urban planning policies, and social economy are driving Datong’s urban development.

Are there differences in the forces driving the conversion of different non-urban lands into urban use? A case study of Beijing.

Examining the heterogeneous factors behind the conversion of various types of non-urban land into urban use is of great significance for controlling urban land expansion and formulating reasonable land use policies. Taking Beijing as an example, this study identified the spatial patterns of urban expansion in China's large cities and then explored the different driving factors behind its various sources. The results showed that, from 2001 to 2010, Beijing's urban land presented a compound expansion mode in which multiple spatial modes coexisted. Urban encroachment contributed differently to the loss of different non-urban lands. Cultivated land and ecological land were the main sources of newly developed urban land, of which the conversion was driven jointly by topography, location, transportation, socioeconomic development, and spatial planning. Moreover, the main factors behind the conversion of various land types varied: closing to existing built-up area and infrastructures increases the conversion probability of most land types; socioeconomic development has common but differentiated effects; governments at different levels have their influences on the conversion of different types of non-urban land. Based on the results, this study suggested the importance of considering varied approaches in managing non-urban lands to better controlling their conversion into urban use and the different roles that could be played by governments at various levels.

A spatial multinomial logit model for analysing urban expansion

ABSTRACT The paper proposes a Bayesian multinomial logit model to analyse spatial patterns of urban expansion. The specification assumes that the log-odds of each class follow a spatial autoregressive process. Using recent advances in Bayesian computing, our model allows for a computationally efficient treatment of the spatial multinomial logit model. This allows us to assess spillovers between regions and across land-use classes. In a series of Monte Carlo studies, we benchmark our model against other competing specifications. The paper also showcases the performance of the proposed specification using European regional data. Our results indicate that spatial dependence plays a key role in the land-sealing process of cropland and grassland. Moreover, we uncover land-sealing spillovers across multiple classes of arable land.

Quantification of Urban Sprawl for Past-To-Future in Abha City, Saudi Arabia

Given that many cities in Saudi Arabia have been observing rapid urbanization since the 1990s, scarce studies on the spatial pattern of urban expansion in Saudi Arabia have been conducted. Therefore, the present study invest... | Find, read and cite all the research you need on Tech Science Press

Spatiotemporal Patterns and Driving Forces of Urban Expansion in Coastal Areas: A Study on Urban Agglomeration in the Pearl River Delta, China

Since the beginning of the 21st century, the spatial pattern of urban expansion and the mechanism of urbanization in coastal areas have undergone significant changes. This study aims to reveal the spatiotemporal patterns of urban land expansion and analyze the dynamic driving forces of urban agglomeration in the Pearl River Delta of China from 2000 to 2015. The urban-land-expansion intensity index, expansion difference index, and fractal dimension were used to study how the urban land in this area was developed, and the geographical detector was applied to explore the relative importance, expansion intensity, and interactions of physical and socioeconomic factors. The results revealed that the urban-land-expansion intensity of the Pearl-River-Delta urban agglomerations exhibit a downward trend, while cities exhibited a trend of developing more coordinately from 2000 to 2015. Physical factors determined the direction and scale of urban development, and the urban land expansion in the Pearl-River-Delta urban agglomeration is mainly distributed in plain areas that have an elevation below 120 m and a slope less than 5°. Socioeconomic factors have a greater influence on the expansion of urban land, and their effects have changed over time. Population growth and economic development has played a significant role in the expansion of urban land before 2005. Subsequently, the factor of GDP and distance to the core cities of Guangzhou and Shenzhen controlled the expansion to the greatest extent. The impacts of various factors tended to become balanced during 2010–2015. The majority of the factors enhanced each other via their interactions, and the distance to the rivers always exhibited a greater enhancement when there was interaction with other factors. The spatial and temporal analysis of the urban expansion and the mechanism of the Pearl River Delta urban agglomeration could provide useful information for coastal urban planning. This study also offers new knowledge regarding the interactions between different drivers of urban land expansion.

Spatial Patterns of Urban Expansion in Chinese Cities

Abstract. China has experienced high rate urbanization in recent years, with urban population increased from 460 to 750 million and the built-up area of metropolis expanded by almost 60% since 2000. Urban landscape has been dramatically changed by intensified inner-city development and urban sprawl. In recent years, the changes in urban form has transformed from expansion to restructuring. A good understanding of urban expansion and spatial restructuring as a consequence of urbanization has important policy implications, enhancing the knowledge of spatial variations in urban growth in transitional Chinese cities and assisting with sustainable urban and regional planning.Using the Landsat satellite images from 1995 to 2015, this research explores urban expansion and its spatial patterns in second-tier Chinese cities, taking Tianjin, Hangzhou and Chengdu as examples, which are the leaders in the urbanization in Northern, Eastern and Western China, respectively. The study area includes the city proper in the three case study cities. Figure 1 shows the expansion of urban area in the three cities over the study time period. By visual inspection, it can be observed that the spatial pattern of urban land use growth varies across the three cities. For example, Tianjin has been largely expanding towards the east, particularly the Tianjin Binhai New Area, which has been rapidly developed into a new city core. The city of Chengdu seems having been expanding toward all directions of the surrounding area, integrated with the adjacent towns into a larger urban agglomeration. Further work will focus on quantitative analysis of the spatial patterns of urban expansion using geographical information system (GIS)-based spatial analytics, as well as the association between urban expansion and socioeconomic changes, with a reflection on the role of national/local policies.

Examining Spatial Patterns of Urban Distribution and Impacts of Physical Conditions on Urbanization in Coastal and Inland Metropoles

Urban expansion has long been a research hotspot and is often based on individual cities, but rarely has research conducted a comprehensive comparison between coastal and inland metropoles for understanding different spatial patterns of urban expansions and driving forces. We selected coastal metropoles (Shanghai and Shenzhen in China, and Ho Chi Minh City in Vietnam) and inland metropoles (Ulaanbaatar in Mongolia, Lanzhou in China, and Vientiane in Laos) with various developing stages and physical conditions for examining the spatiotemporal patterns of urban expansions in the past 25 years (1990–2015). Multitemporal Landsat images with 30 m spatial resolution were used to develop urban impervious surface area (ISA) distributions and examine their dynamic changes. The impacts of elevation, slope, and rivers on spatial patterns of urban expansion were examined. This research indicates that ISA is an important variable for examining urban expansion. Coastal metropoles had much faster urbanization rates than inland metropoles. The spatial patterns of urban ISA distribution and expansion are greatly influenced by physical conditions; that is, ISA is mainly distributed in the areas with slopes of less than 10 degrees. Rivers are important geographical factors constraining urban expansion, especially in developing stages, while bridges across the rivers promote urban expansion patterns and rates. The relationships of spatial patterns of urban ISA distribution and dynamics with physical conditions provide scientific data for urban planning, management, and sustainability.

Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method

Urban growth boundaries (UGBs) have been commonly regarded as a useful tool for controlling urban sprawl. There is a need to create models that can establish plausible UGBs for fast growing regions. Previous methods have merely focused on establishing a single UGB scenario over different time intervals, but rarely considered the influences of macro policy (e.g., future urban demand) and spatial policy (e.g., master plan) for regional planning. However, the spatial patterns of urban expansion are significantly affected by regional planning. In this paper, a CA-based method called the future land use simulation (FLUS) is applied to the delineation of UGBs. We argue that the delineation needs to integrate the top-down approach with CA for projecting complex land use changes under designed scenarios. The system dynamics model (SD) and cellular automaton model (CA) were interactively coupled in the FLUS model during the projection period. The top-down SD is used to project scenarios that relate to macro policy and socioeconomic status, and the bottom-up CA accounts for urban growth simulations under the influence of different driving factors and spatial planning policies. A morphological technology based on erosion and dilation is further proposed to generate the UGBs from the FLUS model’s simulated urban forms. The proposed UGB-FLUS model was applied to the establishment of UGBs in the Pearl River Delta region (PRD) from 2020 to 2050. The results demonstrate that the method can support urban planning by generating feasible patterns for UGBs under different planning scenarios.

Spatial pattern of urban change in two Chinese megaregions: Contrasting responses to national policy and economic mode

Spatial pattern of urban change reflects the urbanization process. Previously, studies on the change of city more focused on the large cities and neglected the urban megaregion growth processes that may include changes in smaller cities. In this study, we investigated the spatial pattern of urban change in the megaregion development process. In addition, we discussed the megaregion responses to the national policy and economic growth mode. We chose the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) urban megaregions as our study areas and comparatively quantified the urban expansion and change based on developed land at both the regional and city scales during 1984–2010. We found: 1) both the BTH and YRD experienced rapid urbanization, the magnitudes, rates, and spatial patterns of urban expansion, however, were very different between the two megaregions. In BTH, the magnitudes and rates of urban expansion were greater in larger cities than in medium- and small-sized cities through the entire period of 1984–2010. In contrast, the magnitudes and rates of urban expansion in YRD were greater in some medium- and small-sized cities than in larger cities from 1984 to 2000, and became similar from 2000 to 2010. 2) The BTH consistently followed a double-nucleated expansion pattern, while the spatial pattern of the YRD changed from single nucleated pattern to multi- nucleated pattern. A further analysis showed that while national policies on urbanization have to some extent guided the urbanization development in the two megaregions, economic growth modes seemed to play a more important role in affecting the urban growth patterns.

Proximity Expansion Index: An improved approach to characterize evolution process of urban expansion

The spatial relationship of newly grown urban patches to existing urban areas lies at the core of understanding the properties of urban expansion dynamics. Some existing landscape metrics have been used to identify patch expansion types, i.e., infilling, edge-expansion and outlying, capturing the evolution process of urban expansion patterns based on quantifying spatial relationship. However, these existing metrics cannot comprehensively describe the spatial distributions of all new patches relative to old built-up areas, especially for outlying patches, which are the significant elements affecting the urban expansion pattern. We propose a new landscape metric, the Proximity Expansion Index (PEI), to address this problem by incorporating two factors of proximity - distance and boundary sharing rate to old patches. The value of PEI is continuous and has the clear physical meaning for depicting the gradient of the spatial relationship. The landscape expansion types are then redefined by PEI, while the sprawl level of outlying patches is clearly reflected. The variants of PEI are designed as global indices to capture information of the dynamic process of urban expansion from a bottom-up view. We selected Wuhan, a metropolis in central China, as a case area to evaluate PEI based on four periods of remote sensing images (1995, 2000, 2005 and 2010). The results show that the spatial pattern of urban expansion becomes increasingly dispersed, demonstrating that PEI is capable of capturing information of urban expansion evolution. PEI can depict the spatial relationship between new and old patches in a more detailed way by comparing PEI and previous metrics. Using PEI, we can also discover regions of great significance, called outlying seed regions, which have a profound impact on the coalescence of urban morphology.

京津冀城市群空间扩张及其经济溢出效应

PDF HTML阅读 XML下载 导出引用 引用提醒 京津冀城市群空间扩张及其经济溢出效应 DOI: 10.5846/stxb201801310276 作者: 作者单位: 中国科学院生态环境研究中心，城市与区域生态国家重点实验室,中国科学院生态环境研究中心，城市与区域生态国家重点实验室,中国科学院生态环境研究中心，城市与区域生态国家重点实验室,中国科学院生态环境研究中心，城市与区域生态国家重点实验室,成都理工大学地球科学学院,成都理工大学地球科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金重大项目（41590843） Urban expansion and its spillover effects on economic growth in the Beijing-Tianjin-Hebei urban agglomeration Author: Affiliation: State Key Laboratory of Urban and Regional Ecology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,State Key Laboratory of Urban and Regional Ecology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,State Key Laboratory of Urban and Regional Ecology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,State Key Laboratory of Urban and Regional Ecology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:随着我国城市化的快速发展，区域城市之间的联系逐渐加强，不同的扩张模式对城市间经济增长溢出效应会产生不同的影响。基于Capello模型，计算了京津冀城市群地区13个城市扩张带来的经济增长溢出效应，通过比较扇状扩张城市和轴向扩张城市得到和给出的城市增长溢出效应，研究了不同扩张类型城市溢出效应的差异，以及京津冀地区经济增长溢出效应的空间分布特征。结果表明，京津冀城市群地区城市间经济增长溢出效应明显，北京和天津给出的溢出效应远高于其他城市，扇状扩张城市比轴向扩张城市更有利于城市之间的增长效益溢出。京津冀地区已经形成了以北京、天津、廊坊和唐山为中心的核心区，石家庄、保定为核心的次级核心区，位于城市群边缘的城市与核心城市联系较弱，在未来的城市群发展中，可通过政策和规划优化城市形态，使城市空间形态更有利于城市经济发展。 Abstract:There are different types of expansion during urban development, and the relationship between regional and urban economies has gradually strengthened with rapid urbanization in China. Different spatial patterns of urban expansion have different effects on growth spillovers of the urban economy; however, there is limited information on the relationships between urban expansion and growth spillovers. In this study, we evaluated the growth spillover effects of the urban economy at an urban scale using the Capello model in the Beijing-Tianjin-Hebei urban agglomeration from 1984 to 2015. There are three types of spatial pattern of urban expansion: ring-, belt-, and fan-shaped, to analyze urban growth spillovers. The results revealed that the spillover effects of economic growth are obvious among 13 cities in the Beijing-Tianjin-Hebei urban agglomeration and displayed the differences of urban growth spillovers between belt- and fan-shaped cities in this area. From 1984 to 2015, there were remarkable economic growth spillover effects in the Beijing-Tianjin-Hebei urban agglomeration; however, the effects of growth spillovers were much greater in Beijing and Tianjin than in other cities, and were unbalanced in other areas; the effects of growth spillovers were higher for fan- than for belt-shaped cities. Based on the results, Beijing, Tianjin, Tangshan, and Langfang were the core areas in the Beijing-Tianjin-Hebei urban agglomeration, followed by Shijiazhuang and Baoding as sub-core areas, and cities on the edge of urban agglomerations showed weak relationships with the core cities. In the future, in the Beijing-Tianjin-Hebei urban agglomeration, the urban spatial form could be optimized through urban policies and planning, so it would be more conductive to urban economic development. 参考文献 相似文献 引证文献

Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

Urban expansion has significant effects on forest loss and fragmentation. Previous studies mostly focused on how the amount of developed land affected forest loss and fragmentation, but neglected the impacts of its spatial pattern. This paper examines the effects of both the amount and spatial pattern of urban expansion on forest loss and fragmentation. We conducted a comparison study in the six largest urban megaregions in China—Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Wuhan (WH), Chengdu-Chongqing (CY), and Changsha-Zhuzhou-Xiangtan (CZT) urban megaregions. We first quantified both the magnitude and speed of urban expansion, and forest loss and fragmentation from 2000 to 2010. We then examined the relationships between urban expansion and forest loss and fragmentation by Pearson correlation and partial correlation analysis using the prefecture city as the analytical unit. We found: (1) urban expansion was a major driver of forest loss in the CZT, PRD, and CY megaregions, with 34.05%, 22.58%, and 19.65% of newly-developed land converted from forests. (2) Both the proportional cover of developed land and its spatial pattern (e.g., patch density) had significant impacts on forest fragmentation at the city level. (3) Proportional cover of developed land was the major factor for forest fragmentation at the city level for the PRD and YRD megaregions, but the impact of the spatial pattern of developed land was more important for the BTH and WH megaregions.

The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: the case of Mekelle City of northern Ethiopia

ABSTRACTInformation on the rate and pattern of urban expansion is required by urban planners to devise proper urban planning and management policy directions. This study evaluated the dynamics and spatial pattern of Mekelle City’s expansion in the past three decades (1984–2014). Multi-temporal Landsat images and Maximum Likelihood Classifier were used to produce decadal land use/land cover (LULC) maps. Changes in LULC and spatial pattern of urban expansion were analysed by post-classification change detection and spatial metrics, respectively. The results showed that in the periods 1984–1994, 1994–2004, and 2004–2014, the built-up area increased annually by 10%, 9%, and 8%, respectively; with an average annual increment of 19% (100 ha year−1), from 531 ha in 1984 to 3524 ha in 2014. Between 1984 and 2014, about 88% of the gain in built-up area was from conversion of agricultural lands, which decreased by 39%. Extension of existing urban areas was the dominant growth type, which accounted for 54%, 75%, and 81% of the total new development during 1984–1994, 1994–2004, and 2004–2014, respectively. The spatial metrics analyses revealed urban sprawl, with increased heterogeneity and gradual dispersion in the outskirts of the city. The per capita land consumption rate (ha per person) increased from 0.009 in 1984 to 0.014 in 2014, indicating low density urban growth. Based on the prediction result, the current (2014) built-up area will double by 2035, and this is likely to have multiple socioeconomic and environmental consequences unless sustainable urban planning and development policies are devised.

Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi, Central China

Spatial patterns of urban expansion mainly include infilling, edge expansion, and outlying growth patterns. The cellular automata (CA) model, is an important spatio-temporal dynamic model and effectively simulates infilling and edge-expansion urban expansion. but is evidently lacking in outlying scenarios. In addition, CA cannot explain the causes and processes of urban land expansion. Given these limitations, we proposed a novel urban expansion model called simulation model of different urban growth pattern (SMDUGP), which can work well in both adjacent (i.e., infilling and edge expansion) and outlying growth patterns. SMDUGP has two main components. First, we divided the non-urban region into two categories, namely, candidate region for adjacent pattern urban growth (CRFAP) and candidate region for outlying pattern urban growth (CRFOP). Second, different methods were utilized to simulate urban expansion in the different categories. In CRFAP, a CA model based on the potential of urban growth was proposed to simulate urban growth in relatively randomly selected urban growth regions based on the discrete selection model and Monte Carlo method as the expansion area was implemented in CRFOP. Huangpi, a suburban area in Wuhan, is utilized as the case study area to simulate the spatial and temporal dynamics of urban growth from 2004 to 2024. SMDUGP can effectively simulate outlying urban growth with a highly improved simulation precision compared with the traditional CA model and can explain the causes and processes of urban land expansion.

A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS

Urban development is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. To understand urban expansion, it is important to establish models that can simulate urbanization process and its deriving factors behaviours, monitor deriving forces interactions and predict spatio-temporally probable future urban growth patterns explicitly. In this research, therefore, we presented a hybrid model that integrates the chi-squared automatic integration detection decision tree (CHAID-DT), Markov chain (MC) and cellular automata (CA) models to analyse, simulate and predict future urban expansions in Tripoli, Libya in 2020 and 2025. First, CHAID-DT model was applied to investigate the contributions of urban factors to the expansion process, to explore their interactions and to provide future urban probability map; second, MC model was employed to estimate the future demand of urban land; third, CA model was used to allocate estimated urban land quantity on the probability map to present future projected land use map. Three satellite images of the study area were obtained from the periods of 1984, 2002 and 2010 to extract land use maps and urban expansion data. We validated the model with two methods, namely, receiver operating characteristic and the kappa statistic index of agreement. Results confirmed that the proposed hybrid model could be employed in urban expansion modelling. The applied hybrid model overcame the individual shortcomings of each model and explicitly described urban expansion dynamics, as well as the spatio-temporal patterns involved.

Use of Geospatial Techniques in Monitoring Urban Expansion and Land Use Change Analysis: A Case of Lahore, Pakistan

Rapid urban expansion and resultant temporal land use changes have a profound effect on the city’s environment and its surroundings. Due to its significance, it is essential to evaluate the urban expansion patterns and land use change analysis of mega cities of the world. For land use change detection, multi-source & multi-temporal satellite images along with GIS & remote sensing (RS) techniques are significant aspects in analyzing urban expansion all over the world. In present study, two image data sets of the Landsat system in 7/ETM+ and 8/OLI modes, along with ground truthing data were utilized to examine the spatio-temporal dynamics of land use changes and assess the spatial patterns of urban expansion in Lahore, Pakistan from the year 2000 & 2014. Supervised classification using maximum likelihood algorithm has been carried out for land use classification andPost classification change detection technique was used to produce change detection map of the study area. The output land use and change detection map revealed that the areal expansion has been attributed due to loss of agricultural land and urban sprawl while major change in land use has taken place in built-up and agricultural areas. The results indicated that 40.81% of built-up area increased, while agricultural land has decline by -12.98% during the study period (2000-2014). Due to this the observed expansion of the city has been toward the South-east, South and South-west along with major roads. The results infer can provide better understanding and information about the past and current spatial dynamics of land use change in Lahore, Pakistan.

Urban Land Expansion and Spatial Dynamics in Globalizing Shanghai

Urban land expansion in China has attracted considerable scholarly attention. However, more work is needed to apply spatial modeling to understanding the mechanisms of urban growth from both institutional and physical perspectives. This paper analyzes urban expansion in Shanghai and its development zones (DZs). We find that, as nodes of global-local interface, the DZs are the most significant components of urban growth in Shanghai, and major spatial patterns of urban expansion in Shanghai are infilling and edge expansion. We apply logistic regression, geographically weighted logistic regression (GWLR) and spatial regime regression to investigate the determinants of urban land expansion including physical conditions, state policy and land development. Regressions reveal that, though the market has been an important driving force in urban growth, the state has played a predominant role through the implementation of urban planning and the establishment of DZs to fully capitalize on globalization. We also find that differences in urban growth dynamics exist between the areas inside and outside of the DZs. Finally, this paper discusses policies to promote sustainable development in Shanghai.