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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.

Infrastructure systems have direct implications for how health and well-being evolve across urban–rural systems. Scientists, practitioners, and policy-makers use domain-specific methods and tools to characterize sectors of infrastructure, but these approaches do not capture the cascading effects across interrelated infrastructure and governance domains. We argue that the development and management of sustainable urban infrastructure must focus on interactions across urban and rural places to advance equitable health and well-being. We call for a research agenda that focuses on urban–rural infrastructure systems, addressing trade-offs and synergies, decision-making, institutional arrangements, and effective co-production of knowledge across the diverse places connected by infrastructure.

We test the existence of a neighborhood based peer effect around participation in an incentive based conservation program called `Water Smart Landscapes' (WSL) in the city of Las Vegas, Nevada. We use 15 years of geo-coded daily records of WSL program applications and approvals compiled by the Southern Nevada Water Authority and Clark County Tax Assessors rolls for home characteristics. We use this data to test whether a spatially mediated peer effect can be observed in WSL participation likelihood at the household level. We show that epidemic spreading models provide more flexibility in modeling assumptions, and also provide one mechanism for addressing problems associated with correlated unobservables than hazards models which can also be applied to address the same questions. We build networks of neighborhood based peers for 16 randomly selected neighborhoods in Las Vegas and test for the existence of a peer based influence on WSL participation by using a Susceptible-Exposed-Infected-Recovered epidemic spreading model (SEIR), in which a home can become infected via autoinfection or through contagion from its infected neighbors. We show that this type of epidemic model can be directly recast to an additive-multiplicative hazard model, but not to purely multiplicative one. Using both inference and prediction approaches we find evidence of peer effects in several Las Vegas neighborhoods.

The energy-water nexus, or the dependence of energy on water and water on energy, continues to receive attention as impacts on both energy and water supply and demand from growing populations and climate-related stresses are evaluated for future infrastructure planning. Changes in water and energy demand are related to changes in regional temperature, and precipitation extremes can affect water resources available for energy generation for those regional populations. Additionally, the vulnerabilities to the energy and water nexus are beyond the physical infrastructures themselves and extend into supporting and interdependent infrastructures. Evaluation of these vulnerabilities relies on the integration of the disparate and distributed data associated with each of the infrastructures, environments and populations served, and robust analytical methodologies of the data. A capability for the deployment of these methods on relevant data from multiple components on a single platform can provide actionable information for interested communities, not only for individual energy and water systems, but also for the system of systems that they comprise. Here, we survey the highest priority data needs and analytical methods for inclusion on such a platform.

Fast urbanization is a common feature of many developing human societies. In many cases, past and present, explosive population growth in cities outstrips the rate of provision of housing and urban services and leads to the formation of informal settlements or slums. Slums are extremely varied in terms of their histories, infrastructure, and rates of change, but they share certain common features: informal land use, lack of physical accesses, and nonexistent or poor quality urban services. Currently, about 1 billion people worldwide live in slums, a number that could triple by 2050 if no practical solutions are enacted to reverse this trend. Underlying most problems of slums is the issue of lack of physical accesses to places of work and residence. This prevents residents and businesses from having an address, obtaining basic services such as water and sanitation, and being helped in times of emergency. Here, we show how the physical layout of any neighborhood can be classified quantitatively in terms of its access topology in a way that is independent of its geometry. Topological indices capturing levels of access to structures within a city block can then be used to define a constrained optimization problem, whose solution generates an access network that makes each structure in the settlement accessible to services with minimal disruption and cost. We discuss the general applicability of these techniques to several informal settlements in developing cities and demonstrate various technical aspects of our solutions. Finally, we discuss how these techniques could be used on a large scale to speed up human development processes in cities throughout the world while respecting their local identity and history.

Increasing population and drought have lead to growing concerns about water scarcity across the US despite a long decline in per-capita consumption. To what extent is this decline the result of water policy vs. other exogenous changes? Many municipalities implement multiple water-focused policies simultaneously – while still subject to other exogenous drivers – so it is important to pair policy evaluations with approaches that examine multiple drivers of water use. The importance of water policy, infrastructure change, and broader technological and demographic trends in influencing water demand has not been measured. We demonstrate a novel method for decomposing multiple drivers of consumption using a dataset of neighborhood water consumption, home infrastructure characteristics, and vegetation in Las Vegas. The largest measureable factor driving conservation for Las Vegas as a whole is lower consumption from new homes, while in established neighborhoods it is declining vegetation area. However, factors we measure directly account for only half of the observed consumption decline. This provides indirect evidence that consumption declines coincident with a drought alert, increased water waste enforcement, and other policy responses also played an important role in conservation. An array of approaches directed at both infrastructure and behavior can effectively reduce consumption.

A greater understanding of human dynamics as they play out in both physical space and through interpersonal communication is vital for the design and development of intelligent and resilient cities. Physical context provides insight into the space-time distribution of population and their activity patterns, while interpersonal communication can now be measured at the population scale through digital interactions. In this work, we propose a novel method to discover these dynamics. We use a dataset of 72 million tweets to develop a spatially embedded network of communication, and then use community detection algorithms to explore regional and urban delineation in the United States. We compare these results to US census regions and economic and infrastructural networks. We find that the broad spatial delineation of communities and sub-communities is consistent with United States regions, states, and major metropolitan areas. We describe how these methods could be extended to generate a measure of social regions that can be consistently applied anywhere there is a sufficiently rich data source. A deeper understanding of urban social structure measured by spatially embedded communication networks can enable a better understanding of the interactions between urban social and physical contexts. This, in turn, may enable urban managers and policy makers to identify strategies for supporting urban resilience.

Understanding the effectiveness of alternative approaches to water conservation is crucially important for ensuring the security and reliability of water services for urban residents. We analyze data from one of the longest-running "cash for grass" policies - the Southern Nevada Water Authority's Water Smart Landscapes program, where homeowners are paid to replace grass with xeric landscaping. We use a twelve year long panel dataset of monthly water consumption records for 300,000 households in Las Vegas, Nevada. Utilizing a panel difference-in-differences approach, we estimate the average water savings per square meter of turf removed. We find that participation in this program reduced the average treated household's consumption by 18 percent. We find no evidence that water savings degrade as the landscape ages, or that water savings per unit area are influenced by the value of the rebate. Depending on the assumed time horizon of benefits from turf removal, we find that the WSL program cost the water authority about $1.62 per thousand gallons of water saved, which compares favorably to alternative means of water conservation or supply augmentation.

Describing the mathematical logic of cities shows a way to help slum residents make space for streets where they do not yet exist., The world is urbanizing quickly with nearly 4 billion people presently living in urban areas, about 1 billion of them in slums. Achieving sustainable development from rapid urbanization relies critically on creating cities without slums. We show that it is possible to diagnose systematically the central physical problem of slums—the lack of spatial accesses and related services—using a topological analysis of neighborhood maps and resolved by finding solutions to a sequence of constrained optimization problems. We set up the problem by showing that the built environment of any city can be decomposed into two types of networked spaces—accesses and places—and prove that these spaces display universal topological characteristics. We then show that while the neighborhoods of developed cities express the same common topology, urban slums fall into a different topological class. We demonstrate that it is always possible to find solutions that grow a street network in existing slums, providing universal accesses at minimal disruption and cost. We then show how elaborations of this procedure that include local preferences and reduce travel distances between places result from additional access construction. These methods are presently taking effect in neighborhoods in Cape Town (South Africa) and Mumbai (India), demonstrating their practical feasibility and emphasizing their role as a platform to enable communities and local governments to combine technical knowledge with local aspirations into contextually appropriate urban sustainable development solutions.

Rapid worldwide urbanization is at once the main cause and, potentially, the main solution to global sustainable development challenges. The growth of cities is typically associated with increases in socioeconomic productivity, but it also creates strong inequalities. Despite a growing body of evidence characterizing these heterogeneities in developed urban areas, not much is known systematically about their most extreme forms in developing cities and their consequences for sustainability. Here, we characterize the general patterns of income and access to services in a large number of developing cities, with an emphasis on an extensive, high-resolution analysis of the urban areas of Brazil and South Africa. We use detailed census data to construct sustainable development indices in hundreds of thousands of neighborhoods and show that their statistics are scale-dependent and point to the critical role of large cities in creating higher average incomes and greater access to services within their national context. We then quantify the general statistical trajectory toward universal basic service provision at different scales to show that it is characterized by varying levels of inequality, with initial increases in access being typically accompanied by growing disparities over characteristic spatial scales. These results demonstrate how extensions of these methods to other goals and data can be used over time and space to produce a simple but general quantitative assessment of progress toward internationally agreed sustainable development goals.

In desert cities, securing sufficient water supply to meet the needs of both existing population and future growth is a complex problem with few easy solutions. Grass lawns are a major driver of water consumption and accurate measurements of vegetation area are necessary to understand drivers of changes in household water consumption. Measuring vegetation change in a heterogeneous urban environment requires sub-pixel estimation of vegetation area. Mixture Tuned Match Filtering has been successfully applied to target detection for materials that only cover small portions of a satellite image pixel. There have been few successful applications of MTMF to fractional area estimation, despite theory that suggests feasibility. We use a ground truth dataset over ten times larger than that available for any previous MTMF application to estimate the bias between ground truth data and matched filter results. We find that the MTMF algorithm underestimates the fractional area of vegetation by 5-10%, and calculate that averaging over 20 to 30 pixels is necessary to correct this bias. We conclude that with a large ground truth dataset, using MTMF for fractional area estimation is possible when results can be estimated at a lower spatial resolution than the base image. When this method is applied to estimating vegetation area in Las Vegas, NV spatial and temporal trends are consistent with expectations from known population growth and policy goals.

In desert cities, accurate measurements of vegetation area within residential lots are necessary to understand drivers of change in water consumption. Most residential lots are smaller than an individual 30-m pixel from Landsat satellite images and have a mixture of vegetation and other land covers. Quantifying vegetation change in this environment requires estimating subpixel vegetation area. Mixture-tuned match filtering (MTMF) has been successfully used for subpixel target detection. There have been few successful applications of MTMF to subpixel abundance estimation because the relationship observed between MTMF estimates and ground measurements of abundance is noisy. We use a groundtruth dataset over 10times larger thanthat available for any previous MTMF application to estimate the bias between ground data and MTMF results. We find thatMTMFunderestimatesthe fractionalarea ofvegetationby5%to10% and show that averaging over multiple pixels is necessary to reduce noise in the dataset. We conclude that MTMF is a viable technique for fractional area estimation when a large dataset is available for calibration. When this method is applied to estimating vegetation area in Las Vegas, Nevada, spatial and temporal trends are consistent with expectations from known population growth and policy changes.©2014Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.8.083660)