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 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.

scholarly journals Comprehensive environmental evaluation of an urbanized territory (using the example of Zavodoukovsk city, Russia)

2019 ◽  
Vol 110 ◽  
pp. 02114
Author(s):  
Marina Podkovyrova ◽  
Olga Volobueva ◽  
Larisa Gilyova
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Development ◽  
Comprehensive Evaluation ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Resources ◽  
Environmental Evaluation ◽  
Urbanized Territory ◽  
The City

The article presents the technique and the result of a comprehensive evaluation of urban land use, ensuring the receipt of complete and reliable information about the urban development, socio-economic and environmental conditions of urban land resources that allows forming the maximum possible sustainable development of the city for the future.

scholarly journals SUSTAINABLE LAND USE EVALUATION BASED ON PRESERVATIVE APPROACH

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 653-660
Author(s):  
S. Khademi ◽  
M. Norouzi ◽  
M. Hashemi
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Planning ◽  
Land Use Planning ◽  
Urban Land ◽  
Sustainability Indicators ◽  
Urban Land Use ◽  
Sustainable Land Use ◽  
Ancient Monuments ◽  
The City

<p><strong>Abstract.</strong> Determining the manner of land-use and the spatial structure of cities on the one hand, and the economic value of each piece of land on the other hand, land-use planning is always considered as the main part of urban planning. In this regard, emphasizing the efficient use of land, the sustainable development approach has presented a new perspective on urban planning and consequently on its most important pillar, i.e. land-use planning. In order to evaluate urban land-use, it has been attempted in this paper to select the most significant indicators affecting urban land-use and matching sustainable development indicators. Due to the significance of preserving ancient monuments and the surroundings as one of the main pillars of achieving sustainability, in this research, sustainability indicators have been selected emphasizing the preservation of ancient monuments and historical observance of the city of Susa as one of the historical cities of Iran. It has also been attempted to integrate these criteria with other land-use sustainability indicators. For this purpose, Kernel Density Estimation (KDE) and the AHP model have been used for providing maps displaying spatial density and combining layers as well as providing final maps respectively. Moreover, the rating of sustainability will be studied in different districts of the city of Shush so as to evaluate the status of land sustainability in different parts of the city. The results of the study show that different neighborhoods of Shush do not have the same sustainability in land-use such that neighborhoods located in the eastern half of the city, i.e. the new neighborhoods, have a higher sustainability than those of the western half. It seems that the allocation of a high percentage of these areas to arid lands and historical areas is one of the main reasons for their sustainability.</p>

scholarly journals A regional spatial-retrofitting approach to geovisualise regional urban growth: An application to the Golden Horseshoe in Canada

2021 ◽  
Author(s):  
Eric Vaz ◽  
Amy Buckland ◽  
Kevin Worthington
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Urban Growth ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Region ◽  
Web Environment ◽  
Urban Land Use Change ◽  
Spatio Temporal ◽  
Share Information

Understanding urban change in particular for larger regions has been a great demur in both regional planning and geography. One of the main challenges has been linked to the potential of modelling urban change. The absence of spatial data and size of areas of study limit the traditional urban monitoring approaches, which also do not take into account visualization techniques that share information with the community. This is the case of the Golden Horseshoe in southern Ontario in Canada, one of the fastest growing regions in North America. An unprecedented change on the urban environment has been witnessed, leading to an increased importance of awareness for future planning in the region. With a population greater than 8 million, the Golden Horseshoe is steadily showing symptoms of becoming a mega-urban region, joining surrounding cities into a single and diversified urban landscape. However, little effort has been done to understand these changes, nor to share information with policy makers, stakeholders and investors. These players are in need of the most diverse information on urban land use, which is seldom available from a single source. The spatio-temporal effect of the growth of this urban region could very well be the birth of yet another North American megacity. Therefore, from a spatial perspective there is demand for joint collaboration and adoption of a regional science perspective including land use and spatio-temporal configurations. This calls forth a novel technique that allows for assessment of urban and regional change, and supports decision-making without having the usual concerns of locational data availability. It is this sense, that we present a spatial-retrofitting model, with the objective of (i) retrofitting spatial land use based on current land use and land cover, and assessing proportional change in the past, leading to four spatial timestamps of the Golden Horseshoe’s land use, while (ii) integrating this in a multi-user open source web environment to facilitate synergies for decision-making. This combined approach is referred to as a regional-spatial-retrofitting approach (RSRA), where the conclusions permit accurate assessment of land use in past time frames based on Landsat imagery. The RSRA also allows for a collective vision of regional urban growth supporting local governance through a decision-making process adhering to Volunteered Geographic Information Systems. Urban land use change can be refined by means of contribution from end-users through a web environment, leading to a constant understanding and monitoring of urban land use and urban land use change.

Spatial Dynamic Modeling of Ecological and Agricultural Land Preservation Strategies for Sustainable Urban Development in Nanjing, China

2020 ◽  
Author(s):  
Zipan Cai ◽  
Si Chen ◽  
Vladimir Cvetkovic
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Ecosystem Services ◽  
Environmental Impact ◽  
Dynamic Modeling ◽  
Agricultural Land ◽  
Urban Land ◽  
Urban Land Use ◽  
Ecological Environment ◽  
Spatial Dynamic

&lt;p&gt;In the context of accelerated urbanization, ecological and agricultural lands are continuously sacrificed for urban construction, which may severely affect the urban ecological environment and the health of citizens in cities in the long-term. To explore the sustainable development of cities, it is of considerable significance to study the complex and non-linear coupling relationship between urban expansion and the ecological environment. Different from static quantitative analysis, this paper will establish a spatial dynamic modeling approach couples the urban land-use change and ecosystem services. The spatial dynamic modeling approach combines a network-based analysis method with accurate environmental assessments, which includes a causal change mechanism that simplifies the complex interaction between the urban system and the surrounding environment. Because the model can use a pre-determined cell transformation rules to simulate the conversion probability of land cells at a specific point in time, it provides the opportunity to test the impact of changes in different policy scenarios. In the phase of the environmental impact assessment, the change probability will be converted into an environmental impact based on the calculation of the ecosystem services values under different development scenarios. Taking Nanjing, a rapidly developing city in China as an example, this paper will set up a variety of sustainable development policy scenarios based on the feedback relationship of local land use driving factors. We will test and evaluate the &amp;#8220;what-if&amp;#8221; consequences through a comparative study to help design the optimal environmental regulation scheme. Planning and decision support will be made to further guide the rational allocation of land use parcel and land development intensity towards a sustainable development future. As a result, this study can support policy decision makings on urban land-use planning and achieve ecological and agricultural land preservation strategies.&lt;/p&gt;

Evaluation of urban land-use structures with a view to sustainable development

2002 ◽  
Vol 22 (5) ◽  
pp. 475-492 ◽  
Author(s):  
Nguyen Xuan Thinh ◽  
Günter Arlt ◽  
Bernd Heber ◽  
Jörg Hennersdorf ◽  
Iris Lehmann
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Land ◽  
Urban Land Use
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