scholarly journals Endless Urban Growth? On the Mismatch of Population, Household and Urban Land Area Growth and Its Effects on the Urban Debate

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e66531 ◽  
Author(s):  
Dagmar Haase ◽  
Nadja Kabisch ◽  
Annegret Haase
Keyword(s):  
Urban Growth ◽  
Urban Land ◽  
Land Area ◽  
Area Growth ◽  
Urban Debate

scholarly journals Optimization of a Novel Urban Growth Simulation Model Integrating an Artificial Fish Swarm Algorithm and Cellular Automata for a Smart City

2021 ◽  
Vol 13 (4) ◽  
pp. 2338
Author(s):  
Xinxin Huang ◽  
Gang Xu ◽  
Fengtao Xiao
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Growth ◽  
Smart City ◽  
Urban Land ◽  
Urban Land Use ◽  
Growth Simulation ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Ca Model

As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.

scholarly journals Urban Growth: From pixel to reality

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Shyamantha Subasinghe
Keyword(s):  
Pattern Recognition ◽  
Urban Growth ◽  
Growth Pattern ◽  
Open Space ◽  
Spatial Configuration ◽  
Urban Land ◽  
Growth Patterns ◽  
Zone Model ◽  
Spatial Pattern Analysis ◽  
Ancillary Data

<p><strong>Abstract.</strong> Urban growth is a complex process created through the interaction of human and environmental conditions. The spatial configuration and dynamic process of urban growth is an important topic in contemporary geographical studies (Thapa and Murayama, 2010). However, urban growth pattern recognition is a challengeable task and it has become one of the major fields in Cartography. Since classical era of cartography, several methods have been employed in modelling and urban growth pattern recognition. It shows that there is no agreement among cartographer or any other spatial scientists on how to map the diverse patterns of urban growth.</p><p>Typical urban theories such as von Thünen’s (1826) bid-rent theory, Burgess’s (1925) concentric zone model, Christaller’s (1933) central place theory, and Hoyt’s (1939) sector model explain the urban structure in different manner. Most of them do not contribute to visualize the urban growth pattern spatiotemporally. Recently, by addressing this limitations, several sophisticated methods are used in urban growth visualization. Among them, morphological spatial pattern analysis (MSPA) is one of emerging raster data analysis methods which allows us to integrate neighbourhood interaction rules in urban growth pattern recognition and visualization. Angel et al. (2010) developed urban land classification (urban, suburban, rural, fringe open space, exterior open space, and rural open space) based on built and non-built land categories and detected three major types of urban growth (infill, extension, and leapfrog). However, developing urban land classifications using binary land use type and recognising only three types of urban growth pattern may be insufficient due to the existence of a higher complexity of urban growth. In such context, the present study introduce a geovisualization approach to map spatial patterns of urban growth using multiple land categories and develops three sub-levels of urban growth pattern for each major urban growth pattern.</p><p>The entire process of urban growth pattern recognition developed in this study can be summarized into three steps (Figure 1): (1) urban land mapping &amp;ndash; Landsat imageries representing two time points (2001 and 2017) were classified into two land categories (built and non-built) and developed into multiple classes using ancillary data, (2) recognizing three major patterns of urban growth (infill, extension, and leapfrog) &amp;ndash; the raster overlay method based on neighbourhood interaction rules, (3) development of sublevels of urban growth &amp;ndash; major three patterns were further developed and visualized nine urban growth patterns, namely low infill (LI), moderate infill (MI), high infill (HI), low extension (LE), moderate extension (ME), high extension (HE), low leapfrog (LL), moderate leapfrog (ML), and high leapfrog (HL). The developed procedure of this study in urban growth pattern recognition was tested using a case study of Colombo metropolitan area, Sri Lanka.</p>

scholarly journals Impact of Rapid Urbanization on Vulnerability of Land System from Complex Networks View: A Methodological Approach

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Ying Wang ◽  
Xiangmei Li ◽  
Jiangfeng Li ◽  
Zhengdong Huang ◽  
Renbin Xiao
Keyword(s):  
Land Use ◽  
Complex Networks ◽  
Methodological Approach ◽  
Urban Land ◽  
Land Area ◽  
Wuhan City ◽  
Rapid Urbanization ◽  
Land System ◽  
Land Use Types ◽  
Land Systems

Rapid urbanization is responsible for the increased vulnerability of land systems and the loss of many crucial ecosystem services. Land systems are typical complex systems comprised of different land use types which interact with each other and respond to external environment processes (such as urbanization), resulting in dynamics in land systems. This work develops a methodology approach by integrating complex networks and disruptive scenarios and applies it to a case study area (Wuhan City in China) to explore the effects of urbanization on land system structural vulnerability. The land system network topologies of Wuhan City during five time periods from 1990 to 2015 are extracted. Our results reveal that (1) the urban land expands at a higher speed than the urban population in Wuhan City; (2) the period of 2005–2010 has witnessed more land area conversions from ecological lands to urban land than other periods; (3) the land system is more vulnerable to intentional attacks on nodes with higher integrated node centrality and larger land area, such as paddy, dryland, and lake; and (4) the network efficiency of the land system would decline sharply if the area shrinkage of paddy, dryland, and lake is larger than 30%, 50%, and 20%, respectively. The results provide some insights into building a resilient urban land system, such as increasing the efficiency of existing urban land and controlling the shrinkage rate of important land use types. This study contributes to existing literature on complex networks by expanding its application in land systems, which highlight the potential of complex networks to capture the complexity, dynamics, heterogeneity, and emergent phenomena in land systems.

scholarly journals Machine Learning Algorithms for Urban Land Use Planning: A Review

Urban Science ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Vineet Chaturvedi ◽  
Walter T. de Vries
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Use Change ◽  
Urban Growth ◽  
Land Use Planning ◽  
Pattern Analysis ◽  
Urban Land ◽  
Earth Observation ◽  
Urban Land Use

Urbanization is persistent globally and has increasingly significant spatial and environmental consequences. It is especially challenging in developing countries due to the increasing pressure on the limited resources, and damage to the bio-physical environment. Traditional analytical methods of studying the urban land use dynamics associated with urbanization are static and tend to rely on top-down approaches, such as linear and mathematical modeling. These traditional approaches do not capture the nonlinear properties of land use change. New technologies, such as artificial intelligence (AI) and machine learning (ML) have made it possible to model and predict the nonlinear aspects of urban land dynamics. AI and ML are programmed to recognize patterns and carry out predictions, decision making and perform operations with speed and accuracy. Classification, analysis and modeling using earth observation-based data forms the basis for the geospatial support for land use planning. In the process of achieving higher accuracies in the classification of spatial data, ML algorithms are being developed and being improved to enhance the decision-making process. The purpose of the research is to bring out the various ML algorithms and statistical models that have been applied to study aspects of land use planning using earth observation-based data (EO). It intends to review their performance, functional requirements, interoperability requirements and for which research problems can they be applied best. The literature review revealed that random forest (RF), deep learning like convolutional neural network (CNN) and support vector machine (SVM) algorithms are best suited for classification and pattern analysis of earth observation-based data. GANs (generative adversarial networks) have been used to simulate urban patterns. Algorithms like cellular automata, spatial logistic regression and agent-based modeling have been used for studying urban growth, land use change and settlement pattern analysis. Most of the papers reviewed applied ML algorithms for classification of EO data and to study urban growth and land use change. It is observed that hybrid approaches have better performance in terms of accuracies, efficiency and computational cost.

The urban land area change in China from 1820 to 1999

2002 ◽  
Vol 12 (4) ◽  
pp. 427-434
Author(s):  
He Fanneng ◽  
Ge Quansheng ◽  
Zheng Jingyun
Keyword(s):  
Urban Land ◽  
Land Area ◽  
Area Change

Back to Von Thunen: a Southern European perspective on mono-centric urban growth, economic structure and non-urban land decline

2016 ◽  
Vol 22 (3) ◽  
pp. 173-188 ◽  
Author(s):  
Andrea Colantoni ◽  
Efstathios Grigoriadis ◽  
Adele Sateriano ◽  
Efthymia Sarantakou ◽  
Luca Salvati
Keyword(s):  
Urban Growth ◽  
Economic Structure ◽  
Urban Land ◽  
European Perspective ◽  
Growth Economic

scholarly journals Analyzing Urban Growth Pattern and Driving Factors Using Remote Sensing and GIS: A Case Study of Banepa Municipality, Nepal

2020 ◽  
Vol 20 (1) ◽  
pp. 9-18
Author(s):  
Rabina Twayana ◽  
Sijan Bhandari ◽  
Reshma Shrestha
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Growth ◽  
Growth Pattern ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Urban Land ◽  
Driving Factors ◽  
Rapid Urbanization ◽  
The Impact

Nepal is considered one of the rapidly urbanizing countries in south Asia. Most of the urbanization is dominated in large and medium cities i.e., metropolitan, sub-metropolitan, and municipalities. Remote Sensing and Geographic Information System (GIS) technologies in the sector of urban land governance are growing day by day due to their capability of mapping, analyzing, detecting changes, etc. The main aim of this paper is to analyze the urban growth pattern in Banepa Municipality during three decades (1992-2020) using freely available Landsat imageries and explore driving factors for change in the urban landscape using the AHP model. The Banepa municipality is taken as a study area as it is one of the growing urban municipalities in the context of Nepal. The supervised image classification was applied to classify the acquired satellite image data. The generated results from this study illustrate that urbanization is gradually increasing from 1992 to 2012 while, majority of the urban expansion happened during 2012-2020, and it is still growing rapidly along the major roads in a concentric pattern. This study also demonstrates the responsible driving factors for continuous urban growth during the study period. Analytical Hierarchy Process (AHP) was adopted to analyze the impact of drivers which reveals that, Internal migration (57%) is major drivers for change in urban dynamics whereas, commercialization (25%), population density (16%), and real estate business (5%) are other respective drivers for alteration of urban land inside the municipality. To prevent rapid urbanization in this municipality, the concerned authorities must take initiative for proper land use planning and its implementation on time. Recently, Nepal Government has endorsed Land Use Act 2019 for preventing the conversion of agricultural land into haphazard urban growth.

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