Scenario based urban growth allocation in Kathmandu Valley, Nepal
Scenario based urban growth allocation in Kathmandu Valley, Nepal
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DOI：
10.1016/j.landurbplan.2011.12.007
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发表时间：
2012-03
影响因子：
9.1
通讯作者：
R. Thapa;Y. Murayama
中科院分区：
文献类型：
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作者：
R. Thapa;Y. Murayama
在世界范围内，城市地区正在以非常快的速度扩张。管理城市增长的任务在范围和复杂性上都有所增加，已成为21世纪最重要的挑战之一。本文旨在研究尼泊尔加德满都谷地的城市发展，并基于情景分析优化未来城市增长配置的空间格局，为城市环境管理提供依据。建立了基于人工神经网络的谷地城市增长配置模型。该预测模型优化了自发型、环保型和资源节约型三种情景下未来城市增长配置的空间格局。预测的空间格局反映了到2050年山谷城市发展的方向和方式，并为未来的土地开发项目提供了重要的土地可用性信息。结果表明，当前的城市发展进程正处于关键阶段，到未来10年，城市和城郊地区将面临前所未有的土地资源压力，河流和森林生态系统以及其他环境敏感地区将遭受损失。农村地区将面临城市增长分配的区域不平衡。然而，在环境保护和资源节约型情景下，通过严格执行城市增长政策，改变当前的增长趋势，仍有可能改善河谷地区的城市环境和管理潜在的土地需求。
Urban areas are expanding at a very fast rate worldwide. The task of managing urban growth has increased in both scope and complexity and has become one of the most important challenges in the 21st century. This article aims to examine the urban development and optimize spatial patterns of future urban growth allocation based on scenarios to provide a basis for urban environmental management in the Kathmandu Valley, Nepal. We developed an artificial neural network based urban growth allocation model for the valley. This predictive model optimizes spatial patterns of future urban growth allocation under the three scenarios, i.e., spontaneous, environment-protecting, and resources-saving. The predicted spatial patterns mirrors where and how the urban development in the valley is likely heading by 2050 and provides a crucial information on land availability for future land development projects. The results suggest that the current urban development process is in critical stage where urban and peri-urban areas will face unprecedented stress on land resources costing river and forest ecosystems and other environmentally sensitive areas by the next decade. Rural vicinities will face regional imbalance on urban growth allocations. However, there are still possibilities to improve urban environment and manage the potential land demands in the valley by enforcing the urban growth policies strictly and altering the current growth trend as revealed in the environment-protecting and resources-saving scenarios.