Resources
Displaying 11 - 15 of 2258Developing an Agent-Based Model to Mitigate Famine Risk in North Korea: Insights from the “Artificial North Korean Collective Farm” Model
North Korea experienced a catastrophic famine in the mid-1990s that resulted in millions of deaths. This study aims to build an agent-based model to understand the risk of land degradation and famine in North Korea and explore potential solutions to mitigate this risk. The model concept reflects the general information of collective farms in North Korea, which was set in 1960, and the abstract of the social–ecological system of North Korean agriculture. The model comprises the agent, environment, and external factors.
Collaborative Optimal Allocation of Urban Land Guide by Land Ecological Suitability: A Case Study of Guangdong–Hong Kong–Macao Greater Bay Area
Urban land optimization in urban agglomerations plays an important role in promoting territorial spatial planning to achieve high-quality development, land ecological suitability (LES) is one of the important variables influencing its urbanization and needs to be considered in urban growth simulation and modeling. This research proposed a multi-objective urban land optimization (MULO) model based on the non-dominated sorting genetic algorithm II (NSGA-II) which integrates the LES assessment.
Large-Scale Land Acquisition and Household Farm Investment in Northern Ghana
Many studies have investigated the effects of large-scale land acquisition (LSLA) on livelihood, while the effects of LSLA by different actors on investment decisions and levels of investment have largely gone without academic scrutiny. Consequently, information concerning the implications of LSLA by actors on investment is scarce in the literature pertaining to policy.
Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges
In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals with smart designs, data mining and management for complex problem-solving based on experimental data on urban applications (land use and cover, configurations of the built environment and architectural design, etc.), but with few explorations and relevant studies.
Trade-Offs, Adaptation and Adaptive Governance of Urban Regeneration in Guangzhou, China (2009–2019)
This paper explores the specific “authoritarian” type of adaptive governance of urban regeneration using the example of Guangzhou city as the frontier of China’s reforms. As opposed to the “democratic” type of adaptive governance with its bottom-up policy initiations, community autonomy, polycentric power, participation in decision making, and self-organized policy actors, adaptive governance in Guangzhou is based on top-down decision making and implementation of public authorities’ solutions with the high role of political considerations.