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Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics

机译:使用元胞自动机和地理信息学对德里的时空城市增长进行监测和建模

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摘要

The study encompasses spatio-temporal land use/ land cover (LULC) monitoring (1989-2014) and urban growth modelling (1994-2024) of Delhi, India to deduce the past and future urban growth paradigm and its influence on varied LULC classes integrating geospatial techniques and Cellular Automata (CA). The study focused on scrutinising the reliability of the CA algorithm to function independently for urban growth modelling, provided with strong model calibration. For this purpose, satellite data of six stages of time at equal intervals along with the population density, distance to CBD and roads, and terrain slope are used. The satellite-based LULC during 1989-2014 exhibited 457 km(2) of net urban growth (275% change), cloned by the simulated LULC with net increase 448 km(2) (270% change). The spatial variation analysis using the principal component analysis (PCA) technique exhibit high similarity in classification ranging from 72% to 88%. The statistical accuracy between the satellite-based and simulated built-up extent of 2014 resulted in the overall accuracy 95.62% of the confusion matrix, and the area under the receiver operating characteristic (ROC) curve as 0.928-indication high model accuracy. The projected LULC exhibit that the urban area will increase to 708 km(2) and 787 km(2), primarily in western and eastern parts during 2019 and 2014 respectively. The rapid urban growth will replace and transform others LULC (net loss 138 km(2)) followed by vegetation cover (net loss 26 km(2)) during 2014-24. This rapid urban growth is detrimental to the habitat and may trigger critical risks to urban geo-environment and ecosystem in Delhi. Therefore, the study necessitates towards decentralization of urban functions and restoration of varied LULC in order to regulate the future urban growth patterns for sustainable development. The GDAL and NumPy libraries in Python 3.4 were efficient in spatial modelling and statistical calculations.
机译:该研究包括印度德里的时空土地利用/土地覆盖(LULC)监测(1989-2014)和城市增长模型(1994-2024),以推论过去和未来的城市增长范式及其对各种LULC类别整合的影响地理空间技术和细胞自动机(CA)。该研究的重点是仔细研究CA算法的可靠性,使其能够独立进行城市增长建模,并提供强大的模型校准功能。为此,使用了六个时间段的等间隔时间的卫星数据,以及人口密度,到CBD和道路的距离以及地形坡度。 1989年至2014年期间,基于卫星的LULC展示了457 km(2)的城市净增长(变化275%),由模拟LULC克隆,净增长448 km(2)(变化270%)。使用主成分分析(PCA)技术的空间变化分析在分类上显示出高度相似性,范围从72%到88%。 2014年基于卫星的建立程度与模拟的建成程度之间的统计准确性导致混淆矩阵的整体准确性达到95.62%,并且接收器工作特性(ROC)曲线下的面积为0.928表示高模型准确性。预计的土地利用,土地和土地利用变化显示,市区面积将分别在2019年和2014年增加到708公里(2)和787公里(2),主要分布在西部和东部。快速的城市增长将取代并改变其他土地利用,土地利用的变化(净损失138 km(2)),然后在2014-24年间进行植被覆盖(净损失26 km(2))。快速的城市增长不利于生境,并可能对德里的城市地理环境和生态系统造成重大风险。因此,为了规范未来城市可持续发展的增长模式,该研究有必要分散城市职能和恢复各种土地利用,土地利用的变化。 Python 3.4中的GDAL和NumPy库在空间建模和统计计算方面非常有效。

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  • 来源
    《Cities》 |2019年第7期|52-63|共12页
  • 作者

    Tripathy Pratyush; Kumar Amit;

  • 作者单位

    Cent Univ Jharkhand, Dept Land Resource Management, Ranchi 834205, Bihar, India|Indian Inst Human Settlements, Bengaluru 560080, India;

    Cent Univ Jharkhand, Dept Land Resource Management, Ranchi 834205, Bihar, India;

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