A neural network and landscape metrics to propose a flexible urban growth boundary: A case study
A neural network and landscape metrics to propose a flexible urban growth boundary: A case study
复制标题
DOI：
10.1016/j.ecolind.2018.05.036
复制
发表时间：
2018-10-01
影响因子：
6.9
通讯作者：
Feng, Yongjiu
中科院分区：
文献类型：
--
作者：
Chakraborti, Suman;Das, Dipendra Nath;Feng, Yongjiu
城市扩张是世界范围内精确划定行政边界的主要障碍。在印度，中小城镇的发展至今超出了规划设想，导致了跨越式、无序的增长。本文试图模拟城市扩张和边界划分的空间范围，以实现有效的城市规划和土地资源管理。使用人工神经网络 (ANN) 模型和一组景观指标来描绘城市增长边界 (UGB) 并描述西里古里市政公司（印度 SMC）未来的增长模式。特别是，模拟了两个城市边界，即城市硬边界（UHB）和城市软边界（USB）。结果表明，面积为 123 km(2) 的 USB 用于解决基本服务提供问题，而面积为 211.88 km(2) 的 UHB 用于管理生态碎片化。
Urban sprawl is a major barrier for the precise demarcation of administrative boundary in the world. In India, medium and small towns have so far developed outside the envisaged planning, resulting in a leapfrog and haphazard growth. This paper has attempted to simulate the spatial extent of urban expansion and boundary demarcation for the purpose of efficient urban planning and land resource management. An Artificial Neural Network (ANN) model and a set of landscape metrics were used to delineate the Urban Growth Boundary (UGB) and characterize the future patterns of growth in Siliguri Municipal Corporation (SMC, India). In particular, two urban boundaries - namely, Urban Hard Boundary (UHB) and Urban Soft Boundary (USB) - were simulated. The results suggest a USB with the area of 123 km(2) to address the basic service delivery and a UHB with the area of 211.88 km(2) to manage the ecological fragmentation.