Dhaka Megacity

This book contains a series of chapters that describe various aspects of the megacity of Dhaka, the capital of Bangladesh. Bangladesh is a low-lying maritime nation in South Asia historically part of Bengal and governed until 1947 as part of the British colonial Indian Empire. When the constituent provinces of the Indian Empire were granted independence in 1947, Bengal was partitioned, with the eastern part becoming part of the new Dominion of Pakistan. Partition of the province caused considerable upheaval as the Dominion of Pakistan was intended as a homeland for Muslims on the Indian subcontinent and in time became the Islamic Republic of Pakistan. This resulted in a great deal of internal migration within the former provinces. Initially, Pakistan comprised two provinces, East and West Pakistan, but in 1971 East Pakistan obtained its independence as the separate sovereign nation of Bangladesh. Although the population of Bangladesh is predominately Muslim, the country currently has a secular constitution.

The continued expansion of Dhaka means that within the next 20 years, it will become one of the most populated megacities in the world. Considering the importance, to its development, of understanding the nature of its pattern of growth and evolution, we examined that growth over a period of 400 years. The dynamics of Dhaka’s urbanisation along with its trajectory over this period are discussed in this chapter. The pace of urbanisation was found to be increasingly rapid after independence from Pakistan in 1971. This rapid pace has caused a number of multifaceted problems, including severe environmental degradation. Issues associated with the urban planning process in Dhaka have been reviewed along with the major plans developed over the past 65 years. The chapter concludes with a critical and comparative analysis of those plans.

Population studies worldwide have suggested that urban population densities generally follow an exponential decay pattern as one travels outwards from the central business district (CBD). Dhaka has experienced phenomenal population growth over the past two decades. This chapter uses econometric and GIS techniques to map and model recent population dynamics using census data for three successive census years (1991, 2001 and 2011) aggregated at the lowest level of census geography. Linear and non-linear regression models were tested to examine urban density form. The study found that a negative exponential function was best suited for the study area since it produced the highest coefficient of determination (R ²). Additionally, temporal trends of the population density gradient for the study area revealed gradual flattening. Further, it was found that the y-axis intercept (an indicator of CBD density) did not drop over time as general theories for cities experiencing economic growth would suggest. The visualisation of population change was conducted through standard deviational ellipses and simple spatial analysis. The results revealed that, with the exception of a few census tracts, the magnitude of population change is (are) still high in the area, and that a suburbanisation trend has set in over the period since the penultimate census.

This chapter discusses the long-term variability of temperature and rainfall in Dhaka for the years 1953–2008 using surface meteorological data provided by the Bangladesh Meteorological Department (BMD). Baseline climate values are defined as the mean values between 1972 and 2000, and deviations from these means are examined as annual anomalies. The annual surface temperature anomaly has a positive trend with statistical significance and a rate of increase of 1.9 ° C per 100 years. Positive statistically significant trends are also found for three out of four individual seasons. No significant trends were found for annual and seasonal variability in rainfall.

This chapter looks at the use of a Markov chain–cellular automata method to model and then predict land-use change in Dhaka. Initially land-use/land-cover maps for three separate time periods were derived from satellite images and evaluated against ground truth. The Markov chain method was then used to establish transition probability matrices between land-cover categories for the time periods represented. The use of cellular automata in this work enables neighbourhood interactions to be accounted for. After an initial calibration run, the combined method is then used to predict land use and land cover in 2022 and 2033.

This chapter demonstrates the use of remote sensing and spatially referenced population data to estimate and model urban sprawl, growth and urban structures. Using spatial analytical tools within a GIS, the typology of urban growth and Dhaka’s spatial structure from 2000 to 2011was quantified. The results revealed a 33 % expansion of urban areas during the study period. Analysis of urban growth types showed that the extension growth type being the dominant followed by leapfrogging development. The amount of low-density development is increasing with time, indicating sprawling development. Investigation of changes in the population per unit area of built-up surface indicated that overcrowding and lack of space in the urban core are compelling people to settle in peripheral areas, thereby exerting tremendous pressure on a limited resource base.

In a rapidly urbanising megacity such as Dhaka, identifying the driving factors that influence urban growth at different spatio-temporal scales is of considerable importance. In this study, based on literature survey and data availability, a selection of drivers is chosen and then tested through logistic regression. Using the CLUE-s land use modelling framework, the ability of these drivers to simulate urbanisation for the periods of 1988–1999 and 1999–2005 was examined against observed data. The results indicated that the role of these driving factors, as contributors to explaining change dynamics of urban land in Dhaka, changes with time. The overall performance of the model, when validated against observed data, is similar to that reported for other urban growth models.

This chapter applies multi-criteria evaluation (MCE) within a GIS to the problem of land suitability assessment in Dhaka. A total of eight factors extracted from a spatial database were used as predictors with the analytic hierarchy process (AHP) followed by weighted linear combination (WLC) being used to identify areas appropriate for future urban development. The results revealed that approximately 21 % of land of the study area is highly suitable for urban development, whereas 29 % of land is moderately suitable. About 26 % of land, comprised of existing urban areas and waterbodies, is shown to be either unsuitable or of very low suitability for urban development although some redevelopment or vertical expansion may be possible. The analysis shows that GIS-based urban development suitability has major implications for a rapidly growing city such as Dhaka which can help urban planners and decision-makers to make better land-use planning decisions.

Recent unplanned and rapid urbanisation of Dhaka has a possibility to induce serious urban flooding in the near future. To evaluate the impacts of land-use changes on flood propagation patterns, we conducted flood inundation simulations using a two-dimensional finite element method with simplified Saint Venant equations. We used as a study site the mid-eastern part of the city of Dhaka, popularly known as “mid-eastern Dhaka”. Two different land-cover datasets were prepared—one showing land use in 1990 and the other for 2011. In addition, complete land-cover change scenarios were also considered. As for the boundary conditions for flood simulations, we first estimated river discharge by constructing a kind of conceptual hydrologic model called a tank model, since we only have water-level data with daily time resolution at the Balu River mouth. Changes in inundation areas, related to these different land-cover patterns, were then evaluated. The study shows that although no significant difference was detected between the results for land use in 1990 and in 2011, under the complete land-use conversion scenarios, with all wetlands converted to other uses, both flood propagation time and flooding area will significantly change. Thus, the simulation results prove that the presence of wetland in land cover reduces flood risk, as compared with other land use. While further validations of flood simulation results are required, our results may provide data useful for proper flood management in achieving urban sustainability.

Dhaka megacity is home to more than 14 million inhabitants. However, the adequate supply of urban utilities, particularly electricity to city dwellers, has been a major challenge mainly due to high population pressure and increasing demand. An insufficient supply of electricity (1,000–1,200 MW) compared to its peak demand (around 2,000 MW) has had a severe negative impact on the city dwellers’ lifestyles. Given the shortfall of power supply in Dhaka, electricity generation through rooftop solar PV systems is widely discussed nowadays. Based on secondary data (sunshine duration, GHI values, etc.) and literature (identification of well-illuminated rooftops within the city and calculation of solar PV-based electricity generation potential by the authors), this chapter reveals that the driving forces (geophysical, economic and sociopolitical and environmental factors) are fully supportive of harnessing solar energy for electricity generation. The application of solar PV system on the city’s extensive well-illuminated rooftops (>10 km²) would be very effective and could possibly meet around half of the city’s power demand.

The objectives of this chapter are to quantify the impact of land use and land cover (LULC) changes on land surface temperature (LST) and to map the changes in the spatial and temporal distribution of LST. A transition matrix was used to determine LULC changes in a multi-temporal manner. LST was retrieved using Landsat TIR data from 1990 to 2011, and statistical analyses were carried out to determine the relationship between LSTs and biophysical parameters. The results showed that the expansion of urban built-up surface over natural land cover such as floodplain and agricultural land has become conspicuous, significantly affecting the spatial and temporal distribution of surface temperature. Annual mean land surface temperature estimation revealed that urban built-up surface consistently has the highest ambient radiant temperature during the study period. A decrease in vegetation cover and subsequent increase in urban land cover were found to be associated with increased LST, suggesting an amplification of the UHI effect with time.

The objective of this chapter is to develop a quality of life (QOL) index at community level in the Dhaka City Corporation (DCC) area, which could describe the spatial patterns of QOL. Using remote sensing, census and other spatial data, a factor analysis was carried out to develop the different dimensions of QOL. Three principal factors were extracted from the analysis: environmental, economic and demographic. These three factors were then combined in a Geographic Information Systems (GIS) environment to construct a synthetic QOL for the study area. The results were subsequently validated using regression analysis, which revealed a better prediction of QOL based on environmental and socioeconomic variables. Interestingly, only a small portion of the population (1.4 %) in the study area was shown to have good QOL. As higher urban growth driven by rapid rural-urban migration is expected in Dhaka in the coming years, this study will be of substantial help for urban planners and policymakers in formulating related policies to ensure a better living environment for its inhabitants.

The objective of this chapter is to analyse the spatiotemporal patterns of crime in the Dhaka Metropolitan Area (DMA). Crime data for the period of August 2011 to July 2012 were acquired from Dhaka Metropolitan Police (DMP). Apart from descriptive statistics, spatial pattern analysis was carried out through local indicators of spatial autocorrelation (LISA). Linear regression was conducted using the crime incidence rate as a dependent variable and ten socio-demographic factors as potential explanatory variables. Descriptive analysis showed distinct temporal variation with the pre-monsoon season having the highest crime occurrences. Furthermore, there are three single-centred criminal activity hotspots of crime. Multivariate regression analysis showed that the size of male population and poverty rate were the best predictors of crime incidence in DMA; however, simple regression suggested that total population, number of males and size of unemployment population in the police districts were also suitable predictors. To our knowledge this is the first study that employed spatial techniques to analyse crime in DMA, and the results of this work should be valuable for informed decision-making for the law enforcing agency as well as relevant authorities whose task is to prevent increasing criminal activities in Dhaka.

The city of Dhaka has been experiencing significant environmental problems due mostly to a massive increase in population and unplanned development in the last few decades. The city administrators have resorted to a number of measures to address these problems, including the enforcement of environmental impact assessment procedures, with limited success. Based on analyses of relevant documents and interviews with officials involved in urban environmental governance, this chapter identifies some of the major environmental problems facing the city dwellers. This study critically analyses the status of environmental impact assessment and reveals that the government has established a good legal and organisational framework of environmental management but that there are deficiencies in enforcement. This chapter makes recommendations for improvements in environmental governance.

The objective of this chapter was to explore the potential of Landsat TM data, calibrated by in situ measurements, to map the spatial distribution of water quality in the rivers and lakes of Dhaka. The relationship of satellite brightness values and ground measurement was established through correlation and regression analyses. The results showed that the ratio of TM1 and TM3 was highly correlated with Secchi disk transparency (SDT), a measure of water clarity, while total suspended sediment (TSS) was strongly correlated with brightness values in the near-infrared portion of the electromagnetic spectrum. Regression analysis indicated that TM1 and the ratio of TM1/TM3 was the best predictor for SDT, and TM3 and the ratio of TM1 and TM3 was suitable for the estimation of TSS in waters. Maps of SDT and TSS are presented that illustrate the spatial variation of water quality in the inland water systems of Dhaka.

Brick manufacturing is the fastest-growing industrial sector in Bangladesh and among the major contributors to the air pollution and health problems in Dhaka, along with vehicle exhaust, resuspended road dust, and domestic fuel burning. There are about 1,000 brick kilns in Dhaka region from the districts of Dhaka, Gazipur, Manikganj, and Narayanganj. Brick manufacturing is confined to the non-monsoonal months and produces 3.5 billion bricks per year, using energy-inefficient fixed-chimney Bull trench kiln technology fuelled with coal and agricultural waste. The total annual emissions are estimated as 23,300 tons of PM_2.5, 15,500 tons of SO₂, 302,000 tons of CO, 6,000 tons of black carbon, and 1.8 million tons of CO₂. The associated health impacts largely fall on the densely populated districts of Dhaka, Gazipur, and Narayanganj. Using the ATMoS dispersion model, the impact of brick kiln emissions was estimated over Dhaka region – ranging from 7 to 99 μg/m³ (5th and 95th percentile concentration per model grid) at an average of 38 μg/m³ – and provincial cluster contributions of 27 % originating from Narayanganj (to the south with the highest kiln density), 30 % from Gazipur (to the north with equally large cluster spread along the river and canals), and 23 % from Savar of Dhaka district. The modelling results were validated using evidence from receptor modelling studies conducted in Dhaka region. An introduction of emerging vertical shaft combustion technology and a possible relocation of the northern clusters to the southeast can provide faster benefits for public health and reduce climate precursor emissions.

The numbers of patients with cholera and Aeromonas-positive diarrhoea show similar bimodal seasonality in Dhaka, Bangladesh. Considering the association between these two pathogens, our previous finding that the number of cholera cases increased following the period of high rainfall led us to investigate the potential role of rainfall on the transmission of Aeromonas-positive diarrhoea. This study quantifies the impact of rainfall on the number of cases of Aeromonas-positive diarrhoea, to gain a deeper insight into the mechanisms of the seasonality of the disease. We examined a time series of the number of hospital visits due to Aeromonas-positive diarrhoea per week in relation to weekly rainfall from 1996 to 2000, using Poisson regression models and adjusting for seasonal and between-year variation, public holidays and temperature. The weekly number of cholera cases increased by 20.7 % (95 % confidence interval, 10.6–31.6) for a 10-mm increase in average rainfall over lags of 0–16 weeks. There was no clear relationship between the number of cases and river level or temperature.

The objectives of this study were to analyse spatiotemporal patterns of reported typhoid cases between 2005 and 2009 and to model the spatial relationship of demographic and socioeconomic factors associated with the occurrence of typhoid in Dhaka. The lowest level census units were used as the scale of analysis. Data in relation to typhoid was collected from 11 major hospitals by scanning individual medical reports, while demographic and socioeconomic variables were encoded in GIS from the population censuses of 2001 and 2011. Global (Moran’s I) and local models (G _i *) were used to test how census districts were dispersed or clustered over space. The spatial relationships were modelled through ordinary least square (OLS) and geographically weighted regression (GWR) techniques. Spatial pattern analysis as measured by Moran’s I demonstrated that the distribution of the affected communities with typhoid was spatially autocorrelated across the study period, 2005–2009. Hotspot analysis using local G _i * indicated large variation in the locations and sizes of clusters. The demographic model outperformed the socioeconomic and demographic + socioeconomic models in predicting the occurrence of typhoid in the study area. The results of this study are of great aid to identify spatial risk factors, essential to develop the control and prevention measures to specific areas.