What drives urban growth in Pune? A logistic regression and relative importance analysis perspective

Proactive planning and management of rapidly urbanizing cities using up-to-date spatially explicit datasets is an urgent need. This requires a good understanding of the driving factors responsible for urban growth. Using Pune metropolis as test site, this paper presents an approach to assess the relative importance of urban growth driving factors from inexpensive geospatial datasets with respect to (i) urbanization process, (ii) urban planning (iii) urban growth modelling by utilizing relative importance analysis (RIA) as a supplement to logistic regression. Furthermore, this research proposes a new approach to reduce the parameterization and data requirement of urban growth models. Our research shows, that proximity to essential infrastructure has the highest predictive power in explaining urban growth of Pune. The importance of policy factors increase with time. Our results reveal that RIA is a suitable method, which can assist planners in deeper understanding of the urbanization process and to devise sustainable urban development strategies, utilizing a limited amount of data, which can be easily updated from geospatial datasets. The proposed break point method based on RIA to reduce parameterization of urban models performed at par with the model results achieved with the traditional AIC approach using less than half of the total number of driving factors

SUSM: a scenario-based urban growth simulation model using remote sensing data

The introduction of the Foreign Direct Investment (FDI) policy in 1991 made India one of the fastest growing economies in the world. This has had a profound effect on India’s urbanization. The rapid urbanization of Indian cities poses a threat to natural and social environments, as expansion of the cities often outpaces the urban planning process. Thus, smart and strategic planning processes that use current and easily available datasets in combination with future urbanization scenarios are needed. To this end, we developed the scenario-based urban growth simulation model (SUSM), which can be used for impact analysis of different planning measures in both spatial and temporal contexts. SUSM uses remote sensing derived inputs, such as land use maps, slope, roads and centres of urban areas along with urban development scenarios. It uses logistic regression for calibration and a constrained stochastic cellular automaton for simulation of urban growth. SUSM is tested in one of the fastest growing urban agglomerations of India: The Pune metropolis, which covers an area of 1642 km2. SUSM is calibrated using urban growth maps derived from LANDSAT satellite images from 1992 to 2001. Subsequently, SUSM was used to simulate urban growth of Pune for 2013. A comparison of the SUSM simulation result with the actually measured urban growth derived from a LANDSAT 8 scene from 2013 is used to validate SUSM and to assess the effect of urban plans upon the growth of Pune. Our results show that: (i) SUSM is capable of predicting the location of future urbanization with an accuracy of 79% and a fuzzy kappa index of agreement 0.81; (ii) inclusion of official urban development plans as input for SUSM did not provide a better agreement with the observed growth; (iii) SUSM, parameterized with remote sensing data, can be used effectively to understand urban growth and assess the effects of alternative urban development plans in terms of the spatial expansion of cities

Spatiotemporal urban expansion in Pune metropolis, India using remote sensing

Indian cities are expanding at an unprecedented rate. The speed of development poses a challenge for urban planners, as the expansion of cities frequently outpaces the planning process. This leads to further challenges for urban planners, namely i) the database for the planning is often outdated and ii) processes and patterns of unplanned urban growth are not accounted for appropriately. This paper presents an approach to address these challenges by utilizing generally available and inexpensive remote sensing data to study i) the land use and land cover change and ii) by analyzing the extent of urban areas to study the patterns and processes of urban growth. We assesses land-use/land-cover for three years (1992, 2001, 2013) using multi-temporal Landsat datasets. A detailed spatiotemporal analysis of urban expansion and typologies of urban growth at the scale of individual administrative units. The dynamics of urban growth was quantified using different metrics of urban expansion. Three types of urban expansion patterns were identified in the Pune metropolis, i) coalescence phase of urbanization in the main city areas, ii) diffusion phase in the suburbs and iii) marginal growth in the cantonments. The overall process of urban expansion in the Pune metropolis can thus be referred to as a diffusion coalescence pattern. Furthermore, our results show that the speed of the urban expansion in the Pune metropolis area has doubled from 2001 to 2013 as compared to 1992-2001. Urban land has increased at the cost of grasslands, barren and agricultural lands. The percentage of change is high in the suburbs under semi-urban and village council jurisdictions, whereas in terms of total growth, areas under the municipal corporation jurisdictions are among the highest contributors to urban expansion. Administrative units governed by cantonment boards have shown marginal growth as compared to the civil administrative units in the study area. (C) 2015 Elsevier Ltd. All rights reserved

Acute Intestinal Obstruction: A Rare Aetiology

Internal herniation of small intestine is a very rare entity, and it poses a real diagnostic challenge clinically. Recurrent entrapment of the bowel may lead to partial to complete intestinal obstruction and eventually strangulation of the small bowel. Of this rare clinical entity, left paraduodenal hernia is more common. High index of suspicion with prompt management may prevent bowel strangulation and gangrene. We present a case of acute intestinal obstruction due to left paraduodenal hernia with malrotation of midgut in a 55-year-old male patient

Development of a new downscaling method for hydrologic assessment of climate change impacts in data scarce regions and its application in the Western Ghats, India

Climate change affects local and regional water resources. Especially in regions with water scarcity, high climate sensitivity, and dynamic socioeconomic development, an adaptation of water management strategies is needed. Our study aims at (i) testing a new downscaling approach to utilize climate model results in a meso-scale hydrologic model and at (ii) analyzing the impact of climate change on the water balance components in the Mula and Mutha Rivers catchment upstream of the city of Pune, India. The new downscaling approach relies on the inherent consistency of both, the climate model and the measured data. It allows to derive a representation of a future climate scenario (2009-2099) by rearranging past measurements (1988-2008). We found a good agreement of the monthly statistics of the rearranged and the original measured data in the baseline period. However, the downscaling method is limited by the range of measured values provided in the baseline period, which results in an underestimation of temperatures in the last 20 years of the scenario period. The downscaled weather data for IPCC emission scenario A1B were used in a hydrologic impact assessment with SWAT. The scenario resulted in higher evapotranspiration, particularly in the first months of the dry season and in repeated low water storages in the reservoirs at the end of rainy season. Consequently, local and downstream water users as well as rain-fed agriculture and semi-natural vegetation in the Western Ghats increasingly suffer from water stress