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"Urban growth"
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City futures
The 'mega-cities' of the developing world are home to over 10 million people each and even smaller cities are experiencing unprecedented population surges. The problems surrounding this influx of people - slums, poverty, unemployment and lack of governance - have been well-documented. This book provides ways on how to deal with these challenges.
eBook
Compact, Dispersed, Fragmented, Extensive? A Comparison of Urban Growth in Twenty-five Global Cities using Remotely Sensed Data, Pattern Metrics and Census Information
Despite growing recognition of the important role of cities in economic, political and environmental systems across the world, comparative, global-scale research on cities is severely limited. This paper examines the similarities and differences in urban form and growth that have occurred across 25 mid-sized cities from different geographical settings and levels of economic development. The results reveal four city types: low-growth cities with modest rates of infilling; high-growth cities with rapid, fragmented development; expansive-growth cities with extensive dispersion at low population densities; and frantic-growth cities with extraordinary land conversion rates at high population densities. Although all 25 cities are expanding, the results suggest that cities outside the US do not exhibit the dispersed spatial forms characteristic of American urban sprawl.
Journal Article
Reconstructing modernity
Reconstructing modernity assesses the character of approaches to rebuilding British cities during the decades after the Second World War. It explores the strategies of spatial governance that sought to restructure society and looks at the cast of characters who shaped these processes. It challenges traditional views of urban modernism and sheds new light on the importance of the immediate post-war for the trajectory of planned urban renewal in twentieth century. It examines plans and policies designed to produce and govern lived spaces- shopping centers, housing estates, parks, schools and homes - and shows how and why they succeeded or failed. It demonstrates how the material space of the city and how people used and experienced it was crucial in understanding historical change in urban contexts. The book is aimed at those interested in urban modernism, the use of space in town planning, the urban histories of post-war Britain and of social housing.
eBook
Decoding urban expansion: a machine learning perspective on Lucknow's growth trajectory
The urban growth prediction is essential for sustainable urban planning in rapidly urbanizing cities like Lucknow, India. In this study, three machine learning models, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) are evaluated in predicting the urban growth patterns from 2021 to 2031 using geospatial data comprising environmental and socioeconomic variables. The models generated urban growth probability maps that classified the study area into five probability classes very low, low, medium, high and very high. The RF model showed the highest accuracy (87.69%) and precision (75.36% for urban areas), and therefore proved to be the most appropriate model for localized and stable urban growth prediction. While the SVM model was effective at detecting emerging urbanized areas, it had a strong recall (74.58%), but at the cost of precision. The ANN model had the highest recall (78.25%) and the best ability to identify dispersed growth patterns in peri urban zones. This work highlights the application of machine learning in urban growth modeling and provides scalable methods for other urbanizing regions. The results offer essential lessons for data driven decision making which help in achieving sustainable urban development, balancing growth with environment and social factors.
Journal Article
Land Cover Mapping Analysis and Urban Growth Modelling Using Remote Sensing Techniques in Greater Cairo Region—Egypt
This study modeled the urban growth in the Greater Cairo Region (GCR), one of the fastest growing mega cities in the world, using remote sensing data and ancillary data. Three land use land cover (LULC) maps (1984, 2003 and 2014) were produced from satellite images by using Support Vector Machines (SVM). Then, land cover changes were detected by applying a high level mapping technique that combines binary maps (change/no-change) and post classification comparison technique. The spatial and temporal urban growth patterns were analyzed using selected statistical metrics developed in the FRAGSTATS software. Major transitions to urban were modeled to predict the future scenarios for year 2025 using Land Change Modeler (LCM) embedded in the IDRISI software. The model results, after validation, indicated that 14% of the vegetation and 4% of the desert in 2014 will be urbanized in 2025. The urban areas within a 5-km buffer around: the Great Pyramids, Islamic Cairo and Al-Baron Palace were calculated, highlighting an intense urbanization especially around the Pyramids; 28% in 2014 up to 40% in 2025. Knowing the current and estimated urbanization situation in GCR will help decision makers to adjust and develop new plans to achieve a sustainable development of urban areas and to protect the historical locations.
Journal Article