A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics
https://orcid.org/0000-0002-9278-8995
https://orcid.org/0000-0002-4582-5677
Graphical abstract Display Omitted
Abstract 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 km2 of net urban growth (275% change), cloned by the simulated LULC with net increase 448 km2 (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 km2 and 787 km2, primarily in western and eastern parts during 2019 and 2014 respectively. The rapid urban growth will replace and transform others LULC (net loss 138 km2) followed by vegetation cover (net loss 26 km2) 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.
Highlights Satellite-based LULC exhibit net urban growth of 457 km2 during 1989–2014, cloned by CA based simulated LULC (448 km2). CA model exhibited high overall accuracy (95.62%) and the area under ROC (0.928). CA-based projected built-up area is expected to increase to 708 km2 in 2019 and 787 km2 in 2024 in Delhi. Study exhibited an increasing contribution of population density and decreasing contribution of proximity to CBD in urbanization process. Python 3.4 was found efficient in spatial modelling and the script was shared.
Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics
https://orcid.org/0000-0002-9278-8995
https://orcid.org/0000-0002-4582-5677
Graphical abstract Display Omitted
Abstract 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 km2 of net urban growth (275% change), cloned by the simulated LULC with net increase 448 km2 (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 km2 and 787 km2, primarily in western and eastern parts during 2019 and 2014 respectively. The rapid urban growth will replace and transform others LULC (net loss 138 km2) followed by vegetation cover (net loss 26 km2) 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.
Highlights Satellite-based LULC exhibit net urban growth of 457 km2 during 1989–2014, cloned by CA based simulated LULC (448 km2). CA model exhibited high overall accuracy (95.62%) and the area under ROC (0.928). CA-based projected built-up area is expected to increase to 708 km2 in 2019 and 787 km2 in 2024 in Delhi. Study exhibited an increasing contribution of population density and decreasing contribution of proximity to CBD in urbanization process. Python 3.4 was found efficient in spatial modelling and the script was shared.
Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics
https://orcid.org/0000-0002-9278-8995
https://orcid.org/0000-0002-4582-5677
Graphical abstract Display Omitted
Abstract 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 km2 of net urban growth (275% change), cloned by the simulated LULC with net increase 448 km2 (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 km2 and 787 km2, primarily in western and eastern parts during 2019 and 2014 respectively. The rapid urban growth will replace and transform others LULC (net loss 138 km2) followed by vegetation cover (net loss 26 km2) 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.
Highlights Satellite-based LULC exhibit net urban growth of 457 km2 during 1989–2014, cloned by CA based simulated LULC (448 km2). CA model exhibited high overall accuracy (95.62%) and the area under ROC (0.928). CA-based projected built-up area is expected to increase to 708 km2 in 2019 and 787 km2 in 2024 in Delhi. Study exhibited an increasing contribution of population density and decreasing contribution of proximity to CBD in urbanization process. Python 3.4 was found efficient in spatial modelling and the script was shared.
Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics
Tripathy, Pratyush (author) / Kumar, Amit (author)
Cities ; 90 ; 52-63
2019-01-16
12 pages
Article (Journal)
Electronic Resource
English
Understanding spatial and temporal processes of urban growth: cellular automata modelling
| British Library Online Contents | 2004
Understanding spatial and temporal processes of urban growth: Cellular automata modelling
| Online Contents | 2004
Assessment of spatio-temporal dynamics of soil erosional severity through geoinformatics
| Online Contents | 2012