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We show here that population growth, resolved at the county level, is spatially heterogeneous both among and within the U.S. metropolitan statistical areas. Our analysis of data for over 3,100 U.S. counties reveals that annual population flows, resulting from domestic migration during the 2015 - 2019 period, are much larger than natural demographic growth, and are primarily responsible for this heterogeneous growth. More precisely, we show that intra-city flows are generally along a negative population density gradient, while inter-city flows are concentrated in high-density core areas. Intra-city flows are anisotropic and generally directed towards external counties of cities, driving asymmetrical urban sprawl. Such domestic migration dynamics are also responsible for tempering local population shocks by redistributing inflows within a given city. This "spill-over" effect leads to a smoother population dynamics at the county level, in contrast to that observed at the city level. Understanding the spatial structure of domestic migration flows is a key ingredient for analyzing their drivers and consequences, thus representing a crucial knowledge for urban policy makers and planners.

Rapid urbanization and climate change trends are intertwined with complex interactions of various social, economic, and political factors. The increased trends of disaster risks have recently caused numerous events, ranging from unprecedented category 5 hurricanes in the Atlantic Ocean to the COVID-19 pandemic. While regions around the world face urgent demands to prepare for, respond to, and to recover from such disasters, large-scale location data collected from mobile phone devices have opened up novel approaches to tackle these challenges. Mobile phone location data have enabled us to observe, estimate, and model human mobility dynamics at an unprecedented spatio-temporal granularity and scale. The COVID-19 pandemic has spurred the use of mobile phone location data for pandemic and disaster response. However, there is a lack of a comprehensive review that synthesizes the last decade of work leveraging mobile phone location data and case studies of natural hazards and epidemics. We address this gap by summarizing the existing work, and pointing promising areas and future challenges for using data to support disaster response and recovery.

Cities are complex systems comprised of socioeconomic systems relying on critical services delivered by multiple physical infrastructure networks. Due to interdependencies between social and physical systems, disruptions caused by natural hazards may cascade across systems, amplifying the impact of disasters. Despite the increasing threat posed by climate change and rapid urban growth, how to design interdependencies between social and physical systems to achieve resilient cities have been largely unexplored. Here, we study the socio-physical interdependencies in urban systems and their effects on disaster recovery and resilience, using large-scale mobility data collected from Puerto Rico during Hurricane Maria. We find that as cities grow in scale and expand their centralized infrastructure systems, the recovery efficiency of critical services improves, however, curtails the self-reliance of socio-economic systems during crises. Results show that maintaining self-reliance among social systems could be key in developing resilient urban socio-physical systems for cities facing rapid urban growth.

Cities are the drivers of socio-economic innovation, and are also forced to address the accelerating risk of failure in providing essential services such as water supply today and in the future. Here, we investigate the resilience of urban water supply security, which is defined in terms of the services that citizens receive. The resilience of services is determined by the availability and robustness of critical system elements, or 'capitals' (water resources, infrastructure, finances, management efficacy and community adaptation). We translate quantitative information about this portfolio of capitals from seven contrasting cities on four continents into parameters of a coupled systems dynamics model. Water services are disrupted by recurring stochastic shocks, and we simulate the dynamics of impact and recovery cycles. Resilience emerges under various constraints, expressed in terms of each city's capital portfolio. Systematic assessment of the parameter space produces the urban water resilience landscape, and we determine the position of each city along a continuous gradient from water insecure and non-resilient to secure and resilient systems. In several cities stochastic disturbance regimes challenge steady-state conditions and drive system collapse. While water insecure and non-resilient cities risk being pushed into a poverty trap, cities which have developed excess capitals risk being trapped in rigidity and crossing a tipping point from high to low services and collapse. Where public services are insufficient, community adaptation improves water security and resilience to varying degrees. Our results highlight the need for resilience thinking in the governance of urban water systems under global change pressures., Comment: 32 pages + Supplementary Material, 4 figures, article under review

Just as natural river networks are known to be globally self-similar, recent research has shown that human-built urban networks, such as road networks, are also functionally self-similar, and have fractal topology with power-law node-degree distributions (p(k) = a k). Here we show, for the first time, that other urban infrastructure networks (sanitary and storm-water sewers), which sustain flows of critical services for urban citizens, also show scale-free functional topologies. For roads and drainage networks, we compared functional topological metrics, derived from high-resolution data (70,000 nodes) for a large US city providing services to about 900,000 citizens over an area of about 1,000 km2. For the whole city and for different sized subnets, we also examined these networks in terms of geospatial co-location (roads and sewers). Our analyses reveal functional topological homogeneity among all the subnets within the city, in spite of differences in several urban attributes. The functional topologies of all subnets of both infrastructure types resemble power-law distributions, with tails becoming increasingly power-law as the subnet area increases. Our findings hold implications for assessing the vulnerability of these critical infrastructure networks to cascading shocks based on spatial interdependency, and for improved design and maintenance of urban infrastructure networks.

We investigated the scaling and topology of engineered urban drainage networks (UDNs) in two cities, and further examined UDN evolution over decades. UDN scaling was analyzed using two power-law characteristics widely employed for river networks: (1) Hack's law of length ($L$)-area ($A$) scaling [$L \propto A^{h}$], and (2) exceedance probability distribution of upstream contributing area $(\delta)$ [$P(A\geq \delta) \sim a \delta^{-\epsilon}$]. For the smallest UDNs ($<2 \>\text{km}^2$), length-area scales linearly ($h\sim 1$), but power-law scaling emerges as the UDNs grow. While $P(A\geq \delta)$ plots for river networks are abruptly truncated, those for UDNs display exponential tempering [$P(A\geq \delta) \>\text{=}\> a \delta^{-\epsilon}\exp(-c\delta)$]. The tempering parameter $c$ decreases as the UDNs grow, implying that the distribution evolves in time to resemble those for river networks. However, the power-law exponent $\epsilon$ for large UDNs tends to be slightly larger than the range reported for river networks. Differences in generative processes and engineering design constraints contribute to observed differences in the evolution of UDNs and river networks, including subnet heterogeneity and non-random branching., Comment: 14 pages, 4 figures, 2 tables