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基于神经网络方法和景观指标的孟加拉国库尔纳市城市增长边界划定应用

Application of urban growth boundary delineation based on a neural network approach and landscape metrics for Khulna City, Bangladesh.

作者信息

Bakshi Arpita, Esraz-Ul-Zannat Md

机构信息

Department of Urban and Regional Planning, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh.

出版信息

Heliyon. 2023 May 16;9(6):e16272. doi: 10.1016/j.heliyon.2023.e16272. eCollection 2023 Jun.

DOI:10.1016/j.heliyon.2023.e16272
PMID:37274635
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10238697/
Abstract

The rapid and unprecedented urban growth in Khulna, Bangladesh is making it difficult to implement measures to limit further expansion and define clear administrative boundaries, which is posing a significant threat to the environment and ecological sustainability. Using an Artificial Neural Network (ANN) based urban growth simulation model and landscape metrics, this study aims to evaluate the spatial extent and direction of urban growth and demarcate an Urban Growth Boundary (UGB) by examining the future contiguous expansion of the city for implementing effective land use provision. Utilizing data on biophysical, proximity, neighborhood, and market factors over the past twenty years, the neural network with Markov chain model allocates the land demand for buildup area by 2020 and 2030, concerning twelve explanatory variables. The simulated map of the urban area is further used by landscape metrics to quantify local-level urban patch information viz. landscape pattern, size, aggregation, etc. The compact patch characteristics are mostly found under the , while, fragmented and unstructured patches are prevailing between urban-rural interfaces. Finally, there has around 95 km gap between the existing service provided by KCC and the future demand of Khulna city, creating an imbalance between the supply and demand of urban services. Hence, restricted urban growth would make government investment in service facilities cost-effective and enable planners and decision-makers to intend a feasible trade-off between future land demand and the protection of natural resources.

摘要

孟加拉国库尔纳市迅速且前所未有的城市增长,使得限制进一步扩张及明确行政边界的措施难以实施,这对环境和生态可持续性构成了重大威胁。本研究使用基于人工神经网络(ANN)的城市增长模拟模型和景观指标,旨在评估城市增长的空间范围和方向,并通过研究城市未来的连续扩张来划定城市增长边界(UGB),以实施有效的土地利用规划。利用过去二十年的生物物理、邻近性、邻里和市场因素数据,带有马尔可夫链模型的神经网络根据十二个解释变量,对2020年和2030年建成区的土地需求进行分配。景观指标进一步利用城市区域的模拟地图来量化地方层面的城市斑块信息,即景观格局、规模、聚集度等。紧凑斑块特征大多出现在……之下,而城乡交界处则普遍存在破碎和无结构的斑块。最后,库尔纳市市政当局(KCC)提供的现有服务与未来需求之间存在约95公里的差距,造成城市服务供需失衡。因此,限制城市增长将使政府在服务设施方面的投资具有成本效益,并使规划者和决策者能够在未来土地需求与自然资源保护之间进行可行的权衡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/badeedd6e82d/gr18.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/4a34206ab420/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/f884996b735b/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/5bf2094ca8a1/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/56ecda857fee/gr15.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/badeedd6e82d/gr18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/f46db5f18dd9/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/014bdd2544a4/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/6e790732245f/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/1adbd0b20b9c/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/b0ac15ad7614/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/fbf1b8fc1831/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/59c6229d2086/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/d79fe85faa45/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/5921c318e831/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/8d15a47854da/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/dccab7bd49d1/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/4a34206ab420/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/f884996b735b/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/5bf2094ca8a1/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/56ecda857fee/gr15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/6975c99353f8/gr16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/6ff6b81ee652/gr17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fade/10238697/badeedd6e82d/gr18.jpg

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