Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai

The rapid growth of megacities requires special attention among urban planners worldwide, and partic-ularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this willbring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use changemodels – and in particular non-spatial logistic regression models (LR) and auto-logistic regression mod-els (ALR) – for the Mumbai region over the period 1973–2010, in order to determine the drivers behindspatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variationsin driving-forces across space. The study comes to two main conclusions. First, both global models suggestsimilar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimatedcoefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporaland spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth pro-cesses, and so can assist context-related planning and policymaking activities when seeking to secure asustainable urban future

Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model

Several factors contribute to on-going challenges of spatial planning and urban policy in megacities, including rapid population shifts, less organized urban areas, and a lack of data with which to monitor urban growth and land use change. To support Mumbai's sustainable development, this research was conducted to examine past urban land use changes on the basis of remote sensing data collected between 1973 and 2010. An integrated Markov Chains–Cellular Automata (MC–CA) urban growth model was implemented to predict the city's expansion for the years 2020–2030. To consider the factors affecting urban growth, the MC–CA model was also connected to multi-criteria evaluation to generate transition probability maps. The results of the multi-temporal change detection show that the highest urban growth rates, 142% occurred between 1973 and 1990. In contrast, the growth rates decreased to 40% between 1990 and 2001 and decreased to 38% between 2001 and 2010. The areas most affected by this degradation were open land and croplands. The MC–CA model predicts that this trend will continue in the future. Compared to the reference year, 2010, increases in built-up areas of 26% by 2020 and 12% by 2030 are forecast. Strong evidence is provided for complex future urban growth, characterized by a mixture of growth patterns. The most pronounced of these is urban expansion toward the north along the main traffic infrastructure, linking the two currently non-affiliated main settlement ribbons. Additionally, urban infill developments are expected to emerge in the eastern areas, and these developments are expected to increase urban pressure

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: A case study.

The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling

USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India

MODELING URBAN DYNAMICS USING RANDOM FOREST: IMPLEMENTING ROC AND TOC FOR MODEL EVALUATION

The importance of spatial accuracy of land use/cover change maps necessitates the use of high performance models. To reach this goal, calibrating machine learning (ML) approaches to model land use/cover conversions have received increasing interest among the scholars. This originates from the strength of these techniques as they powerfully account for the complex relationships underlying urban dynamics. Compared to other ML techniques, random forest has rarely been used for modeling urban growth. This paper, drawing on information from the multi-temporal Landsat satellite images of 1985, 2000 and 2015, calibrates a random forest regression (RFR) model to quantify the variable importance and simulation of urban change spatial patterns. The results and performance of RFR model were evaluated using two complementary tools, relative operating characteristics (ROC) and total operating characteristics (TOC), by overlaying the map of observed change and the modeled suitability map for land use change (error map). The suitability map produced by RFR model showed 82.48% area under curve for the ROC model which indicates a very good performance and highlights its appropriateness for simulating urban growth

Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata

This study evaluates the effects of cellular automata (CA) with different neighborhood sizes on the predictive performance of the Land Transformation Model (LTM). Landsat images were used to extract urban footprints and the driving forces behind urban growth seen for the metropolitan areas of Tehran and Isfahan in Iran. LTM, which uses a back-propagation neural network, was applied to investigate the relationships between urban growth and the associated drivers, and to create the transition probability map. To simulate urban growth, the following two approaches were implemented: (a) the LTM using a top-down approach for cell allocation grounding on the highest values in the transition probability map and (b) a CA with varying spatial neighborhood sizes. The results show that using the LTM-CA approach increases the accuracy of the simulated land use maps when compared with the use of the LTM top-down approach. In particular, the LTM-CA with a 7 × 7 neighborhood size performed well and improved the accuracy. The level of agreement between simulated and actual urban growth increased from 58% to 61% for Tehran and from 39% to 43% for Isfahan. In conclusion, even though the LTM-CA outperforms the LTM with a top-down approach, more studies have to be carried out within other geographical settings to better evaluate the effect of CA on the allocation phase of the urban growth simulation

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: A case study.

The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling

Future urban rainfall projections considering the impacts of climate change and urbanization with a statistical-dynamical integrated approach

Impacts of global warming and local scale urbanization on precipitation are evident from observations; hence both must be considered in future projections of urban precipitation. Dynamic regional models at a fine spatial resolution can capture the signature of urbanization on precipitation, however simulations for multiple decades are computationally expensive. In contrast, statistical regional models are computationally inexpensive but incapable of assessing the impacts of urbanization due to the stationary relationship between predictors and predictand. This paper aims to develop a unique modelling framework with a demonstration for Mumbai, India, where future urbanization is projected using a Markov Chain Cellular Automata approach, long term projections with climate change impacts are performed using statistical downscaling and urban impacts are simulated with a dynamic regional model for limited number of years covering different precipitation characteristics. The evaluation of the statistical downscaling methodology over historical time period reveals large underestimation of the extreme rainfall, which is improved effectively by applying another regression model, for extreme days. The limited runs of dynamic downscaling models with different stages of urbanization for Mumbai, India, reveal spatially non uniform changes in precipitation, occurring primarily at the higher quantiles. The statistical and dynamical outputs are further integrated using quantile transformation for precipitation projection in Mumbai during 2050s. The projections show dominant impacts of urbanization compared to those from large scale changing patterns. The uniqueness of this computationally efficient framework lies in an integration of global and local factors for precipitation projections through a conjugal statistical–dynamical approach

Future urban rainfall projections considering the impacts of climate change and urbanization with a statistical-dynamical integrated approach

Impacts of global warming and local scale urbanization on precipitation are evident from observations; hence both must be considered in future projections of urban precipitation. Dynamic regional models at a fine spatial resolution can capture the signature of urbanization on precipitation, however simulations for multiple decades are computationally expensive. In contrast, statistical regional models are computationally inexpensive but incapable of assessing the impacts of urbanization due to the stationary relationship between predictors and predictand. This paper aims to develop a unique modelling framework with a demonstration for Mumbai, India, where future urbanization is projected using a Markov Chain Cellular Automata approach, long term projections with climate change impacts are performed using statistical downscaling and urban impacts are simulated with a dynamic regional model for limited number of years covering different precipitation characteristics. The evaluation of the statistical downscaling methodology over historical time period reveals large underestimation of the extreme rainfall, which is improved effectively by applying another regression model, for extreme days. The limited runs of dynamic downscaling models with different stages of urbanization for Mumbai, India, reveal spatially non uniform changes in precipitation, occurring primarily at the higher quantiles. The statistical and dynamical outputs are further integrated using quantile transformation for precipitation projection in Mumbai during 2050s. The projections show dominant impacts of urbanization compared to those from large scale changing patterns. The uniqueness of this computationally efficient framework lies in an integration of global and local factors for precipitation projections through a conjugal statistical–dynamical approach