Abstract
This book with data of land use/land cover of 47 Indian cities spanning over 1991–2007 period and covering different climatic zones and size classes of urban areas is timely. It gives city level land use classification of agriculture, built-up, urban green spaces, urban open spaces, forest, water bodies and rivers. The ‘land use’ phrase used is essentially land cover data as it does not get into the detail break-up of built-up spaces as the city master plans do. This is primarily because of the data source used, which is Landsat images (TM) of 30 meter resolution for the years 1990, 2000, 2010 and 2017. Some technical bit, the authors justify the use of Landsat images than the other satellite images because the former exhibit the same baseline parameters such as scale and resolution of the images across all the four years—as these provide continuous records used for the earth’s surface—and these are freely available for research and scientific purposes.
This book shows that there are new sets of data available that can be meticulously harvested for the purposes of urban planning. Not just this book, but there are now studies that apply the remote sensing data for multiple new issues that require attention in urban planning. For example, climate change is real and occuring (IPCC, 2018) that now requires monitoring, for which data are required. Satellite images, such as the Optical Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of Landsat 8, have been used over Mumbai city by researchers to measure land surface temperatures. A study in Mumbai found that the land surface temperature were, on average, 13 per cent higher (p. 168) for localities identified as urban slums than in the formal housing (Mehrotra, Bardhan, & Ramamritham, 2018).
Urban planning is expected to address multiple simultaneous challenges of urbanization, such as climate change, rising economic and social inequalities, large development deficits and violence. The global agendas, such as the New Urban Agenda (NUA) of Habitat III in 2016, Sustainable Development Goals (SDGs) of the United Nations General Assembly in 2015 1 and India’s Nationally Determined Commitments (NDCs) from the Paris Agreement on Climate Change in 2015, and India’s own submission at the Habitat III (Ministry of Housing and Urban Poverty Alleviation [MoHUPA], 2016) have laid emphasis on urban planning. But India’s urban planning has been criticized for its poor state, esoteric nature and non-practical (McKinsey Global Institute [MGI], 2010), one of the primary reasons for that being lack of appropriate data for planning.
The book does state in details the methodology used for computing the areas. Since the basemap area is the same, it is possible to observe the changes in the land uses on the whole cumulatively in the cities together. However, the nagging methodological issue is with regards to fixing the boundary of a basemap that can be compared over time. The authors state: ‘foremost challenge faced in base map creation was identification of the city boundaries’, which it seems they have fixed using secondary data, such as census, city development plans prepared for the Jawaharlal Nehru National Urban Renewal Mission (JNNURM) and some other online websources (not specified) (p. 21). It is not clear from the write-up as to which year’s boundary for this comparison has been taken. My guess is it would be that of the latest year, 2017. Given that the boundary demarcation of any city is arbitrary and done in spurts—boundary expanded after the peri-urban areas have been urbanized—such an exercise has unresolvable methodological flaw. This is important to interpret the results in the book.
For example, the overall built-up area has increased from 19.96 per cent in 1991 to 37.92 per cent in 2017, which is nearly doubled. Agriculture land has declined from 25.37 per cent to 14.61 per cent in this period, which is not halving of this land use. Which means that only agriculture land has not been converted to built-up area. Mangroves/swamp areas have declined from 13.32 per cent to 11.38 per cent; urban green has declined from 12.79 per cent to 9.41 per cent and urban open spaces from 10.51 per cent to 7.74 per cent. Thus, the areas of natural resources or what we would like to call urban commons have been sacrificed for the cities to create built-up space for increasing population. Interestingly, land use transformation patterns have changed over time; in 1990–2000 period, the change from agriculture to built-up, from urban green to built-up and from urban open to built-up had nearly the same proportion (Figure 3.12). But, in 2010–2017 period, the maximum transformation has occurred from agriculture to built-up indicating big time urban sprawl. This change had started in 2000–2010 period, but in this too, the transition from urban green to built-up was another prominent transformation while the shirinking of urban open for built-up had reduced than the previous decade. These data show that urban populations were being absorbed within the city by conversion of urban open and green spaces to built-up up to 2010, after which the sprawl activities accelerate.
The trajectories of increase in built-up spaces differ over different cities. For example, the built-up space in Chennai has increased from 74.51 per cent in 1990 to 84.75 per cent in 2017. In Nagpur, the same has incresed from 19.34 per cent to 56.04 per cent in 2017. The book also analyses the trends clubbed together for three different tiers of cities, tier-1 (8 largest metro cities), tier-2 (all other metro cities) and tier-3 (state capitals less than 1 million). The tier-1 cities had the largest proportion of built-up in total area, followed by tier-2 and then by tier-3.
This rich database, with the limitation of urban boundary delineation, does present a possibility of further interesting analysis, which this book falls short of. First, the book could have data annexures that would have allowed other researchers to undertake further analysis. For example, there could have been linkages established with individual city’s (or its district’s) GDP and its composition with built-up area expansion or its population growth and employment composition (at district level extracted from the National Sample Survey data sets) with changes in the built-up area and so on. An aggregate analysis of links with the GDP (at district level) and population is presented, which is not adequate. In short, an interesting set of database for spatial and environmental planners, but, falling short of further possibilities of linking the data with social and economic dynamics that drive urbanization.