Urban Neighborhood Green Index – A measure of green spaces in urban areas

度量(数据仓库) 索引(排版) 城市绿地 同种类的 质量(理念) 绿化 环境科学 数学 统计 地理 计算机科学 生态学 空格(标点符号) 物理 数据挖掘 组合数学 量子力学 生物 万维网 操作系统
作者
Kshama Gupta,Pramod Kumar,S. K. Pathan,S. Prasad
出处
期刊:Landscape and Urban Planning [Elsevier]
卷期号:105 (3): 325-335 被引量:378
标识
DOI:10.1016/j.landurbplan.2012.01.003
摘要

Urban green spaces (UGS) form an integral part of any urban area and quantity and quality of UGS is of prime concern for planners and city administrators. Objective measure of greenness using remote sensing images is percentage area of green, i.e., Green Index (GI), which is insensitive to spatial arrangement within the areal units. Measuring UGS at neighborhood level is important as neighborhood is the working level for application of greening strategies. Neighborhood (NH) is synonymous of nearness and can be defined as an area of homogeneous characteristics. The Urban Neighborhood Green Index (UNGI) aims to assess the greenness and can help in identifying the critical areas, which in turn can be used to identify action areas for improving the quality of green. For the development of UNGI, four parameters, i.e., GI, proximity to green, built up density and height of structures were used and weighted using Saaty's pair wise comparison method. Four different types of NH were compared and it was found that mean GI (0.44) is equal for high-rise low density and low-rise low density NH, i.e., both areas have same quality of urban green based on GI. But mean UNGI is higher for low-rise low-density NH (0.62), as compared to high-rise low-density NH (0.54), hence, area of highrise NH requires more amounts of good quality properly distributed green as compared to low-rise NH. The input for UNGI is easily derivable from RS images, besides the developed method is simple, and easily comprehendible by city administrators and planners.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
希望天下0贩的0应助小Ma采纳,获得10
1秒前
隐形曼青应助心想事成采纳,获得10
1秒前
沉默的倔驴应助Hy采纳,获得20
1秒前
1秒前
2秒前
2秒前
2秒前
Tici发布了新的文献求助10
3秒前
烂漫如松完成签到,获得积分10
3秒前
斧王发布了新的文献求助10
3秒前
3秒前
taotao完成签到,获得积分10
4秒前
4秒前
Keping发布了新的文献求助10
5秒前
Bo发布了新的文献求助10
5秒前
上官若男应助echo采纳,获得10
5秒前
六号线完成签到,获得积分10
5秒前
小蘑菇应助温婉的念文采纳,获得10
5秒前
NexusExplorer应助潮汐采纳,获得10
5秒前
xi发布了新的文献求助10
5秒前
6秒前
柚子完成签到,获得积分10
6秒前
是阿刁完成签到,获得积分10
6秒前
Lightdream__完成签到,获得积分10
6秒前
6秒前
小筱发布了新的文献求助10
7秒前
7秒前
ly完成签到,获得积分20
7秒前
空古悠浪发布了新的文献求助10
7秒前
无花果应助penguinli采纳,获得10
7秒前
sivan发布了新的文献求助10
7秒前
king完成签到,获得积分10
8秒前
TristanGuan发布了新的文献求助10
8秒前
8秒前
打打应助李端端采纳,获得10
8秒前
wanci应助铃儿响叮党采纳,获得10
9秒前
BowieHuang应助inininch采纳,获得50
10秒前
充电宝应助一口一个汤包采纳,获得10
10秒前
量子星尘发布了新的文献求助10
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
Superabsorbent Polymers 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5711035
求助须知:如何正确求助?哪些是违规求助? 5202070
关于积分的说明 15263091
捐赠科研通 4863454
什么是DOI,文献DOI怎么找? 2610771
邀请新用户注册赠送积分活动 1561017
关于科研通互助平台的介绍 1518534