Using the Sleuth Urban Growth Model to Simulate the Impacts of Future Policy Scenarios on Urban Land Use in the Baltimore-Washington Metropolitan Area

10.1068/b2983 ◽  
2004 ◽  
Vol 31 (2) ◽  
pp. 251-271 ◽  
Author(s):  
Claire A Jantz ◽  
Scott J Goetz ◽  
Mary K Shelley
Keyword(s):  
Land Use ◽  
Growth Model ◽  
Metropolitan Area ◽  
Urban Growth ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Growth Model ◽  
Future Policy ◽  
Policy Scenarios

scholarly journals Modeling Past, Present, and Future Urban Growth Impacts on Primary Agricultural Land in Greater Irbid Municipality, Jordan Using SLEUTH (1972–2050)

2021 ◽  
Vol 10 (4) ◽  
pp. 212
Author(s):  
Rana N. Jawarneh
Keyword(s):  
Land Use ◽  
Growth Model ◽  
Urban Growth ◽  
Agricultural Land ◽  
Urban Land Use ◽  
Agricultural Area ◽  
Grand Challenge ◽  
Negative Consequences ◽  
Urban Growth Model ◽  
Rate Of Increase

Urban expansion and loss of primarily agricultural land are two of the challenges facing Jordan. Located in the most productive agricultural area of Jordan, Greater Irbid Municipality (GIM) uncontrolled urban growth has posed a grand challenge in both sustaining its prime croplands and developing comprehensive planning strategies. This study investigated the loss of agricultural land for urban growth in GIM from 1972–2050 and denoted the negative consequences of the amalgamation process of 2001 on farmland loss. The aim is to unfold and track historical land use/cover changes and forecast these changes to the future using a modified SLEUTH-3r urban growth model. The accuracy of prediction results was assessed in three different sites between 2015 and 2020. In 43 years the built-up area increased from 29.2 km2 in 1972 to 71 km2 in 2015. By 2050, the built-up urban area would increase to 107 km2. The overall rate of increase, however, showed a decline across the study period, with the periods of 1990–2000 and 2000–2015 having the highest rate of built-up areas expansion at 68.6 and 41.4%, respectively. While the agricultural area increased from 178 km2 in 1972 to 207 km2 in 2000, it decreased to 195 km2 in 2015 and would continue to decrease to 188 km2 by 2050. The district-level analysis shows that from 2000–2015, the majority of districts exhibited an urban increase at twice the rate of 1990–2000. The results of the net change analysis of agriculture show that between 1990 and 2000, 9 districts exhibited a positive gain in agricultural land while the rest of the districts showed a negative loss of agricultural land. From 2000 to 2015, the four districts of Naser, Nozha, Rawdah, and Hashmyah completely lost their agricultural areas for urbanization. By 2050, Idoon and Boshra districts will likely lose more than half of their high-quality agricultural land. This study seeks to utilize a spatially explicit urban growth model to support sustainable planning policies for urban land use through forecasting. The implications from this study confirm the worldwide urbanization impacts on losing the most productive agricultural land in the outskirts and consequences on food production and food security. The study calls for urgent actions to adopt a compact growth policy with no new land added for development as what is available now exceeds what is needed by 2050 to accommodate urban growth in GIM.

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.

A Simulation Model on Urban Land Use in a Metropolitan Area – with Environmental Reflections

1977 ◽  
Vol 10 (8) ◽  
pp. 101-108
Author(s):  
A. Ando ◽  
K. Amano ◽  
M. Kashiwadani
Keyword(s):  
Land Use ◽  
Simulation Model ◽  
Metropolitan Area ◽  
Urban Land ◽  
Urban Land Use

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 Examining the Coupling Coordinated Relationship between Urban Industrial Co-Agglomeration and Intensive Land Use

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 499
Author(s):  
Qunxi Gong ◽  
Gengxuan Guo ◽  
Sipan Li ◽  
Xuedong Liang
Keyword(s):  
Land Use ◽  
Metropolitan Area ◽  
Structural Adjustment ◽  
Urban Land ◽  
Urban Land Use ◽  
Spatial Characteristics ◽  
Coordination Model ◽  
Dynamic Relationship ◽  
Increase Rate ◽  
Intensive Land Use

Exploring the dynamic relationship and coupling coordination between urban industrial co-agglomeration and intensive land use is vital to ensure high-quality urban development. Based on an industrial co-agglomeration model, a revised intensive land use model, and a coupling coordination model, this paper comprehensively measured the urban industrial co-agglomeration and intensive land use coupling coordination in eight cities in the Chengdu metropolitan area from 2004 to 2018. It was found that despite the structural adjustment of the secondary and tertiary industries during the study period, the industrial co-agglomeration in the Chengdu metropolitan area fluctuated, the intensive urban land use had spatial characteristics that were decreasing from the center to the periphery, and while the general coupling coordination level was increasing, the increase rate in each city was different. The coupling coordination between the urban industry co-agglomeration and the intensive land use was found to have “low-high-low” spatial characteristics from the southwest to the northeast of the Chengdu metropolitan area, which revealed the core position of Chengdu. Suggestions are provided to improve the industrial co-agglomeration and intensive urban land use coupling coordination.

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