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In West Africa, malaria is a leading cause of disease-induced fatalities. While it is generally thought that urbanization reduces malaria incidence, the relationship between urbanicity, i.e., the impact of living in urban areas, and transmission remains unclear. This study aims to explore this association in Ghana, using eight district-level datasets comprising epidemiological, health, hygiene, and education data over four years (2015–2018) aggregated by sex and age groups. Applying a series of aspatial and spatial quantitative analysis methods, we showed significant heterogeneity in malaria incidences across space and socio-demographic groups. We find that the determinants of malaria incidence vary depending on the degree of urbanization. Moreover, there is a seasonal signature of malaria temporal dynamic that is not perceptible in big urban regions. A denser population in urban areas is associated with an increased malaria incidence. In peri-urban areas, an increase in malaria incidence is associated with a denser vegetation cover. In rural areas, built areas are associated with an increase in malaria incidence. The findings suggest that understanding and addressing malaria incidences vis-à-vis urbanization will require a focus on these contextual factors.

Urbanization can challenge sustainable development if it produces unequal outcomes. Infrastructure is an important urbanization dimension, providing services to support diverse urban activities. However, it can lock in unequal outcomes due to its durable nature. This paper studies inequalities in infrastructure distributions to derive insights into the structure and characteristics of unequal outcomes associated with urbanization. We analyzed infrastructure inequalities in two emerging economies in the Global South: India and South Africa. We developed and applied an inequality measure to understand the structure of inequality in infrastructure provisioning (based on census data) and infrastructure availability (based on satellite nighttime lights [NTLs] data). Consistent with differences in economic inequality, results show greater inequalities in South Africa than in India and greater urban inequalities than rural inequalities. Nevertheless, inequalities in urban infrastructure provisioning and infrastructure availability increase from finer to coarser spatial scales. NTL-based inequality measurements additionally show that inequalities are more concentrated at coarse spatial scales in India than in South Africa. Finally, results show that urban inequalities in infrastructure provisioning covary with urbanization levels conceptualized as a multidimensional phenomenon, including demographic, economic, and infrastructural dimensions. Similarly, inequalities in urban infrastructure availability increase monotonically with infrastructure development levels and urban population size. Together, these findings underscore infrastructure inequalities as a feature of urbanization and suggest that understanding urban inequalities requires applying an inequality lens to urbanization.

The shift towards urban living is changing food demand. Past studies on India show significant urban–rural differences in food consumption. However, a scientific understanding of the underlying relationships between urbanization and food consumption is limited. This study provides the first detailed analysis of how urbanization influences both quantity and diversity of food consumption in India by harnessing the strength of multiple datasets, including consumer expenditure surveys, satellite imagery, and census data. Our statistical analysis shows three main findings. First, in contrast to existing studies, we find that much of the variation in food consumption quantity is due to income and not urbanization. After controlling for income and state-level differences, our results show that average consumption is higher in urban than rural areas for fewer than 10% of all commodities. That is, there is nearly no difference in average consumption between urban and rural residents. Second, we find the influence of urbanization as a population share on food consumption diversity to be statistically insignificant (p-value > 0.1). Instead, the results show that infrastructure, market access, percentage working women in urban areas, and norms and institutions have a statistically significant influence. Third, all covariates of food consumption diversity we tested were found to be associated with urbanization. This suggests that urbanization influences on food consumption are both indirect and multidimensional. These results show that increases in the urban population size alone do not explain changes in food consumption in India. If we are to understand how food consumption may change in the future due to urbanization, the study points to the need for a more complex and multidimensional understanding of the urbanization process that goes beyond demographic shifts.

Urban land use change is one of the most impactful land transitions on the biosphere, resulting in land conversion, habitat loss, and changes in biogeochemical cycling, climate, and hydrology. Thus...

Urban land use has impacts spread across time and place. How urban areas are built and configured in the coming decades will have long-term implications on the environment and the lives of billions of people. A systems approach that emphasizes the importance of science and the relevance of policy to understanding urban land use is vital to building a sustainable future.

With over two decades of historical observations, DMSP/OLS nighttime lights (NTL) are an invaluable asset for monitoring, characterizing, and understanding human activity. Due to the lack of on-board calibration, there are systematic biases in NTL data. Consequently, a key deterrent to the use of the entire NTL archive is the difficulty in generating a consistent NTL time series. Currently, there are a number of methods to calibrate NTL data in order to generate a consistent time series. However, there is no systematic evaluation of the performances of these calibration efforts. The purpose of this paper is to compare and evaluate existing calibration methods and to provide guidance to NTL end users on their relative strengths and weaknesses. We apply two widely adopted criteria to assess the performance of the methods: a) systematic bias minimization, and b) relation with ancillary socio-economic data on economic activity and electricity consumption. Our results show that global-scale calibration methods outperform regionally-based calibration methods. Furthermore, inconsistencies, in the form of differences in pixel values from two satellites of the same region and year, continue to exist in NTL data even after the calibration methods are applied, both across regions and across scales. We find that dimly lit regions are more difficult to calibrate compared to brightly lit regions and that inconsistencies persist in higher northern and lower southern latitudes. We conclude that significant improvements in calibration results will require a shift either towards absolute correction methods using in-situ data or relative calibration methods using NTL from VIIRS.

The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time.

Urban consumption of processed and fast foods is a challenge to nutrition security. Observed differences in urban versus rural consumption are commonly attributed to higher income levels in urban areas. Yet, there is still no clear understanding of why and how urban dwellers consume differently. Using India as a case study, we analyze expenditures on processed foods and consumption of food away from home (FAFH) of urban, metropolitan, and rural populations using OLS regression models. We show that urban households spend more on processed foods and consume more FAFH than rural households. Most of this difference can be attributed to differing socio-economic and demographic factors, such as higher income, or smaller urban household size. However, even after controlling for these factors, we find differences not only between rural and urban areas but also between different urban areas: households in large metropolitan areas consume more than households in smaller non-metropolitan urban areas. These inter-urban variations suggest that the dichotomy of urban versus rural consumption does not adequately capture the full spectrum of food consumption complexities. Our findings indicate that urbanization is affecting how people consume food beyond shaping their socio-economic and demographic status. We also highlight the need to account for the role of urbanization—beyond an urban-rural dichotomy—when addressing the challenges associated with changing food consumption patterns.

Explaining urban growth patterns is a fundamental need to understand the recent rapid urbanization globally. This study identifies geographic features explaining the spatial patterns of urban land expansion (ULE) in the rapidly urbanizing Pune metropolitan region (India). ULE maps were derived from Landsat Thematic Mapper and Operational Land Imager images using support vector machine (SVM) classification. Relation between geographic features and spatial patterns of ULE was analyzed using statistical modelling including ordinary least squares (OLS) regression, spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). SEM specification best modeled ULE patterns. High density of existing urban areas is identified to negatively affect ULE, suggesting dominant dispersed urban growth. In addition, proximity to special economic zones and transportation infrastructure explains multicentric growth in the region. GWR model was identified inappropriate due to the presence of high local collinearity. Models accounting for spatial dependencies are recommended while studying ULE patterns.

NASA's Black Marble nighttime lights product suite (VNP46) is available at 500 m resolution since January 2012 with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF effects; (3) geometric-optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Initial validation results are presented together with example case studies illustrating the scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full range of retrieval conditions.

While environmental and social threats to society changes faster than in recent centuries, there is more of a need for faster, globally scalable and locally relevant risk information from developing Banks and the countries they serve. Big Data can range from gigabytes (call details records), to terabytes (satellite data), to petabytes (web traffic), with each magnitude requiring unique algorithms to extract the signal from the noise. This chapter explores how one type of sensor data—satellite imagery—can be made more useful through the development of an application that leverages Cloud Computing—Google Earth Engine—to turn data into insight for decision-makers on the ground.

We examine the impacts of urbanization on agricultural land loss in India from 2001 to 2010. We combined a hierarchical classification approach with econometric time series analysis to reconstruct land-cover change histories using time series MODIS 250 m VI images composited at 16-day intervals and night time lights (NTL) data. We compared estimates of agricultural land loss using satellite data with agricultural census data. Our analysis highlights six key results. First, agricultural land loss is occurring around smaller cities more than around bigger cities. Second, from 2001 to 2010, each state lost less than 1% of its total geographical area due to agriculture to urban expansion. Third, the northeastern states experienced the least amount of agricultural land loss. Fourth, agricultural land loss is largely in states and districts which have a larger number of operational or approved SEZs. Fifth, urban conversion of agricultural land is concentrated in a few districts and states with high rates of economic growth. Sixth, agricultural land loss is predominantly in states with higher agricultural land suitability compared to other states. Although the total area of agricultural land lost to urban expansion has been relatively low, our results show that since 2006, the amount of agricultural land converted has been increasing steadily. Given that the preponderance of India's urban population growth has yet to occur, the results suggest an increase in the conversion of agricultural land going into the future.

India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion.

The study evaluates and compares Digital Elevation Model (DEM) data of various grid spacing derived using high resolution Cartosat 1 stereo data for hydrologic applications. DEM is essential in modeling different environmental processes which depend on surface elevation. The accuracy of derived DEM varies with grid spacing and source. The CartoDEM is the photogrammetric DEM derived from stereo pairs. Damanganga basin lying in the Western Ghats was analysed using 11 Carto stereo pairs. The process of triangulation resulted in RMSE of 0.42. DEM was extracted at 10 m, 20 m, 30 m, 40 m, 50 m and 90 m grid spacing and compared with ASTER GDEM (30 m) and SRTM DEM (90 m). DEM accuracy was checked with Root Mean Square Error (RMSE) statistic for random points generated in different elevation zones. Extracted stream networks were compared based on Correctness Index and Figure of Merit index, calculated for all the Digital Elevation Models at varying cell sizes. In order to further evaluate the DEM’s, a simple flood simulation with no water movement and no consideration of real time precipitation data was carried out and relationship between heights of flood stage and inundation area for each Digital Elevation Model was also established.

This study explores application of multi agent system (MAS) to simulate spatial patterns of urban growth in Chandigarh and its surrounding region (India). A numerical simulation model is developed with MAS considering the dynamics of urban and rural population as the principal driver of urban growth. The model utilizes static and dynamic environment variables initialized using a logistic regression model. The logistic regression model uses pixel wise change/no-change information derived using Landsat TM data (1989–1999) as dependent variable and proximity, density, elevation and slope as independent variables. The optimum resolution of 90 m for modelling is decided using fractal analysis of series of transition probability surfaces generated using logistic regression from 30 to 240 m spatial resolution at 30 m interval. The model was finally calibrated using sensitivity analysis and behaviours space experiments with multiple simulation runs. A change to built-up area of 32.55 km2 is observed during 1989–1999 and 113.51 km2 in 1999–2009. The modelling shows a total 14.42 % disagreement between predicted map and reference map for the year 2009. The results were validated using ROC statistics and accuracy estimates with satellite data. The model was further used to predict urban growth for the year 2019. Diversity index was used to determine the potential of the model to capture overall spatial patterns of urban growth.