A lunar city sounds possible until you calculate how fast it would use its water
A new study is challenging one of the most comforting assumptions in urban climate design: that adding vegetation to dense cities always makes hot weather safer. Research published in npj Urban Sustainability reports that compact, high-rise urban forms can amplify humid heat risks associated with vegetation transpiration—a process usually considered an essential cooling service. The finding does not suggest that trees and plants should be removed from cities. Instead, it reveals that the success of green infrastructure depends heavily on building geometry, airflow, humidity, and the way heat and moisture move through the urban atmosphere.
Vegetation transpiration occurs when plants release water vapor through microscopic openings in their leaves. This process consumes energy and can lower leaf and surrounding air temperatures, much like evaporation from human skin. In a well-ventilated environment, the added moisture may be dispersed efficiently, allowing evaporative cooling to provide a net thermal benefit. But in tightly packed high-rise districts, the same moisture can become trapped between buildings. The result is a more humid microclimate in which the body’s ability to cool itself through sweating is weakened.
That distinction matters because humid heat is more dangerous than air temperature alone suggests. The human body relies on evaporation to transfer heat away from the skin. When atmospheric moisture is already high, sweat evaporates more slowly, forcing the body to work harder to maintain a stable internal temperature. Heat-stress indicators such as wet-bulb temperature and the Wet-Bulb Globe Temperature account for this interaction between heat and humidity. A neighborhood with slightly lower temperatures but substantially higher humidity can therefore feel more oppressive—and pose greater health risks—than a drier location with hotter air.
The study focuses on the urban “canyon” effect created by tall buildings arranged close together. These structures can obstruct wind, reduce the exchange of air with the atmosphere above, and alter the amount of sunlight reaching streets and façades. When vegetation in such spaces transpires, the released water vapor may accumulate rather than disperse. The compact geometry can also create complex circulation patterns, with warm and moist air repeatedly moving through pedestrian-level spaces. In this setting, greenery may cool surfaces while simultaneously increasing the moisture burden experienced by people outdoors.
This is a crucial refinement of the popular idea that urban greening is universally beneficial. Plants perform several valuable functions: they provide shade, intercept solar radiation, reduce surface temperatures, store carbon, and improve the visual and psychological quality of public space. Yet their cooling performance is not fixed. It changes with species, soil moisture, canopy density, irrigation, wind conditions, building height, street width, and background humidity. The new research emphasizes that these factors must be evaluated together rather than treating vegetation as an isolated solution to urban heat.
The implications are particularly significant for rapidly growing cities where vertical development is being used to accommodate rising populations. High-rise construction can reduce land consumption, but dense towers may produce localized atmospheric conditions that differ dramatically from those in surrounding neighborhoods. A cooling strategy designed for an open suburban street may behave differently in a narrow urban canyon surrounded by tall façades. Planners could therefore face a difficult balancing act: maximizing shade and ecological benefits without creating stagnant pockets where moisture and heat combine into dangerous conditions.
The findings also raise questions about how cities measure the success of climate adaptation projects. A street may record a lower surface temperature after trees are planted, yet still expose pedestrians to greater physiological stress if humidity increases and air movement declines. This means future assessments should include multiple variables, including air temperature, relative humidity, wind speed, radiant heat, and human thermal response. Satellite observations and conventional weather stations may not fully capture conditions at street level, where building walls, tree canopies, pavement, and human activity interact within a few meters.
The research is likely to intensify debate over “smart” urban greening rather than green infrastructure in general. Possible responses include selecting vegetation according to local humidity and wind conditions, designing wider or better-connected ventilation corridors, combining trees with reflective or permeable surfaces, and ensuring that shaded areas do not become poorly ventilated enclosures. Building orientation and spacing may be as important as the number of trees planted. Irrigation practices could also matter, since additional water supplied to vegetation may increase transpiration during precisely the periods when atmospheric moisture is already high.
For residents, the message is both cautionary and practical. A leafy street can still be healthier than an exposed one, particularly when shade reduces direct solar radiation. But during extreme heat, people should not assume that every green space offers the same level of protection. Dense, humid, windless areas may require additional measures such as cooling centers, drinking-water access, timed outdoor activities, and heat alerts that account for humidity. The study’s central contribution is to show that urban climate risk is not determined by temperature alone—or by the presence of greenery alone—but by the interaction between city form, plant physiology, and human vulnerability.
As climate change increases the frequency and intensity of heat extremes, these interactions could become more consequential. The research presents cities with a deceptively simple lesson: cooling the urban environment is not just a matter of adding more plants. It requires understanding where the water released by vegetation goes, how buildings control its movement, and how the resulting air feels to a human body. In the race to make cities greener, the next generation of design may need to be equally focused on keeping them breathable.
Subject of Research: The relationship between compact high-rise urban form, vegetation transpiration, humidity, and humid heat risk.
Article Title: Urban high-rise compact form amplifies humid heat risk from vegetation transpiration.
Article References: Zhang, Y., Yang, K., Zhu, Y. et al. “Urban high-rise compact form amplifies humid heat risk from vegetation transpiration.” npj Urban Sustainability (2026). https://doi.org/10.1038/s42949-026-00459-1
Image Credits: AI Generated
DOI: 10.1038/s42949-026-00459-1
The long-running series in which readers answer other readers’ questions considers a hot topic within renewable energy
This week’s replies: Are there places on Earth where humans haven’t been?
I would like to know why we build solar farms over green space, when we could just put them over massive car parks as a popular current internet meme suggests. Chris, Middlesbrough
Post your answers (and new questions) below or send them to nq@theguardian.com. A selection will be published next Sunday.
Continue reading...
© Photograph: Justin Paget/Getty Images
© Photograph: Justin Paget/Getty Images
© Photograph: Justin Paget/Getty Images
The escalating threat posed by rising sea levels has become a defining environmental challenge of the 21st century, with profound implications for densely populated coastal regions worldwide. Recent research sheds new light on this crisis by examining the spatial and temporal evolution of sea level rise under the ongoing forces of climate change. Particularly vulnerable to these changes are Shanghai and other parts of China’s coastal waters, where natural and anthropogenic factors synergize to amplify the risks.
This pioneering study deploys advanced climate modeling combined with detailed regional analysis, revealing that sea level rise in China’s coastal zones exceeds average global projections. The unique convergence of local land subsidence, riverine water discharge variability, and dynamic marine currents exacerbates the vulnerability of these regions. Such localized processes interact with global sea level trends, creating hotspots where the risk is acutely intensified.
Land subsidence, the sinking or settling of the ground surface, has emerged as a critical factor accelerating local sea level rise in coastal China. Driven largely by groundwater extraction and urban development, subsidence lowers the land elevation relative to the rising ocean, effectively increasing flood risk and saltwater intrusion. This phenomenon, when combined with the gradual thermal expansion of seawater and melting ice sheets, underlines the compound nature of sea-level hazards facing mega-cities like Shanghai.
Moreover, the study brings to the forefront the role of river water discharge, an often overlooked element in sea level dynamics. Changes in the volume and timing of freshwater flow from major river systems alter sediment supply and coastal circulation patterns, which have direct impacts on shoreline stability and ecological health. In the Yangtze River estuary, fluctuations in discharge affect sediment deposition that could either reinforce or undermine coastal defenses against the rising tides.
Marine currents along China’s coastline further complicate the picture by influencing regional sea level changes. Currents can redistribute water masses, leading to regional variations in sea level rise that diverge significantly from global averages. Variability in monsoon strength and ocean-atmosphere interactions introduce additional temporal complexity, affecting sea level trends on seasonal to decadal scales.
The implications of these findings are far-reaching. Beyond the increased risk of flooding and coastal erosion, rising seas threaten the socio-economic fabric of communities reliant on coastal infrastructure and ecosystems. Shanghai, a global financial hub with millions residing in low-lying areas, confronts immense challenges in urban planning, disaster preparedness, and climate resilience efforts.
This comprehensive research integrates satellite data, ground observations, and climate projections to afford a high-resolution understanding of these localized sea level dynamics. By unraveling the intricate interplay between climatic and human-induced factors, the analysis offers crucial insights for policymakers and urban planners striving to mitigate and adapt to future sea level scenarios.
Furthermore, the study underscores the necessity of cross-disciplinary collaboration, merging geoscience, hydrology, oceanography, and urban studies. It challenges simplistic global sea level rise metrics by demonstrating that effective mitigation and adaptation require granular regional assessments that account for local geophysical and hydrodynamic conditions.
In the context of climate change mitigation strategies, this research elevates the urgency of addressing greenhouse gas emissions while concurrently managing human activities that exacerbate coastal vulnerability, such as groundwater over-extraction. It suggests that adaptation measures, including coastal defenses, managed retreat, and ecological restoration, must be tailored to localized conditions revealed through detailed spatial-temporal analysis.
Moreover, the insight gained concerning the temporal evolution of sea level rise offers valuable foresight into critical tipping points and thresholds. Understanding when and where acceleration of sea level increase may occur enables more precise targeting of resources and emergency preparedness efforts.
In conclusion, this comprehensive examination of sea level rise in China’s coastal waters highlights a complex mosaic of contributing factors that extend beyond traditional global climate models. The findings serve as a clarion call for integrative and localized climate action to safeguard vulnerable coastal megacities against the rising tides of the future.
Subject of Research: Spatial and Temporal Evolution of Sea Level Rise Under Climate Change
Article Title: Rising tides: Unveiling the spatial and temporal evolution of sea level rise under climate change
News Publication Date: 17-Jun-2026
Web References: http://dx.doi.org/10.1371/journal.pone.0347855
Image Credits: Bing Liang, CC-BY 4.0
Urban flooding has emerged as one of the most pressing environmental challenges faced by modern cities worldwide. As urbanization accelerates and impermeable surfaces replace natural landscapes, the capacity of drainage systems and natural waterways to manage stormwater dramatically decreases. This phenomenon exacerbates flood risks, leading to significant economic damage, disruption of daily life, and threats to public safety. In this context, the innovative research conducted by Dehdehi, Soleimani-Motlagh, and Haghizadeh offers a critical evaluation and in-depth analysis of the factors influencing urban flooding using advanced hydrological modeling techniques. Their work employs the Soil Conservation Service – Technical Release 20 (SCS-TR20) model and the Sacramento Soil Moisture Accounting Unit Hydrograph (SBUH) model to unravel the complexities behind flood dynamics in urban environments.
The study begins by contextualizing the incidence of urban flooding within rapidly expanding metropolitan areas. Urban landscapes are characterized by an extensive network of roads, buildings, and other paved surfaces, significantly altering the natural water cycle by accelerating runoff and reducing infiltration. The authors argue that understanding these changes requires precise hydrological models capable of simulating surface runoff, infiltration, and subsurface flow processes under varying climatic and land use scenarios. Both SCS-TR20 and SBUH models boast comprehensive frameworks that may be tailored to urban catchments, enabling the dissection of hydrological responses to precipitation events with high spatial and temporal resolution.
Fundamental to their research is the exploration of how different physical and environmental factors affect urban flooding. These include soil properties, topography, storm characteristics, land use patterns, vegetation cover, and urban infrastructure design. The SCS-TR20 model, originally developed to estimate direct runoff from small watersheds, is applied here to quantify the runoff generation process by integrating rainfall data with land surface conditions. It incorporates parameters like curve number (CN), which reflects soil infiltration capacity and land cover, to predict peak discharge during storm events. This allows the researchers to examine how changes in impervious surface area or soil moisture status may intensify or mitigate runoff production in urban catchments.
Simultaneously, the study leverages the SBUH hydrological model, a unit hydrograph-based method that simulates watershed response to rainfall by accounting for soil moisture dynamics. The SBUH model provides a mechanistic representation of subsurface flow and its contribution to streamflow, which is often overlooked in simpler runoff generation models. By embedding soil moisture accounting within the urban context, the authors can capture delayed flow components and baseflow behavior, enriching the flood prediction process. This is particularly important in urban areas where drainage infrastructure can alter natural groundwater recharge pathways.
A critical component of their methodology involves calibrating and validating these models against observed hydrological data from urban watersheds experiencing recurrent flooding. The researchers emphasize rigorous parameter optimization to enhance predictive accuracy. They adopt techniques such as sensitivity analysis to identify which parameters most significantly influence model outputs, thereby guiding priorities for data collection and urban planning interventions. The models’ outputs are then cross-verified through statistical measures, including Nash-Sutcliffe efficiency and root mean square error, confirming their robustness in simulating runoff processes in real-world urban systems.
Their findings highlight that urban flooding is not solely dependent on rainfall intensity and volume but is profoundly affected by intricate interactions between soil characteristics, land cover changes, and urban drainage infrastructure. For instance, increasing impervious surfaces significantly raises the curve number, leading to more immediate and higher runoff peaks. Moreover, poor maintenance or design flaws in stormwater systems exacerbate flooding by reducing conveyance capacity. The study also reveals that antecedent soil moisture conditions before rainfall events play a pivotal role; saturated soils contribute to quicker runoff generation and potential flash floods, emphasizing the need for integrated urban water management that considers soil moisture dynamics.
The implications of this research extend to urban planners, environmental managers, and policymakers. By elucidating how specific landscape and infrastructure features influence flood risks, the models can inform the design of resilient urban drainage networks. Incorporating green infrastructure, such as permeable pavements, rain gardens, and bio-retention basins, can modulate runoff by enhancing infiltration and delaying peak flows, a strategy validated by simulation scenarios using their models. Additionally, the work supports the advancement of early warning systems that integrate hydrological forecasts with real-time rainfall data, offering communities proactive flood risk mitigation.
Significantly, the study advances the hydrological modeling field by adapting traditional models—initially designed for rural or agricultural catchments—to the complex hydrodynamics of urban settings. This adaptation includes customizing parameter sets to account for heterogeneity in urban land use, addressing issues such as localized flooding resulting from blocked drains or topographical depressions. The dual-model approach combining SCS-TR20 and SBUH affords a more comprehensive understanding than reliance on a single modeling framework, as it captures both surface and subsurface hydrological processes with greater fidelity.
Furthermore, the research contributes to global climate change discussions by considering how altered precipitation patterns may interact with urban factors to exacerbate flooding. The authors simulate future storm scenarios based on climate projections, revealing potential increases in flood frequency and severity if urban areas do not enhance their stormwater resilience. This evidence reinforces the urgency of integrating climate adaptation measures into urban development policies, underscoring the indispensable role of hydrological models in forecasting and managing emerging flood risks.
An additional innovative aspect of this study is the use of high-resolution spatial data and advanced computational techniques to implement the hydrological models. Remote sensing technologies and geographic information systems (GIS) are applied to delineate watershed boundaries, map land cover, and analyze slope and soil properties with great precision. The incorporation of these technologies allows for dynamic model updating and supports scenario testing for various urban growth and land use change possibilities. By utilizing such data-driven methods, the authors ensure that their flood impact analyses are both spatially relevant and temporally sensitive.
The multi-faceted modeling framework also evaluates the effectiveness of different flood mitigation strategies using scenario-based simulations. For example, the researchers model the impact of increasing urban green spaces or enhancing stormwater storage capacity on reducing flood peak discharge. These simulations provide actionable insights for urban engineers and policymakers by demonstrating which interventions yield the greatest reduction in flood risks under diverse weather conditions. Consequently, their work serves as a decision-support tool to prioritize resource allocation and infrastructure investments in flood-prone urban regions.
In summary, the comprehensive evaluation and analysis conducted by Dehdehi, Soleimani-Motlagh, and Haghizadeh signify a breakthrough in understanding urban flood dynamics through integrated hydrological modeling. By leveraging the strengths of SCS-TR20 and SBUH models, their study provides a nuanced depiction of how natural, built, and climatic factors coalesce to influence flooding in cities. These insights pave the way for smarter urban water management, resilience building against extreme weather events, and sustainable urban growth.
Looking forward, the authors advocate for further refinement of hydrological models through enhanced data assimilation and coupling with hydraulic simulations to address flooding at the street and neighborhood scales. They also suggest expanding their approach to encompass socio-economic dimensions, thereby assessing the human and economic impacts of flood events alongside hydrological predictions. Such interdisciplinary integration will be vital as urban flood challenges intensify in the face of urban expansion and climate variability.
Ultimately, this pioneering research not only enriches the theoretical hydrology landscape but also offers practical tools for urban flood mitigation worldwide. As cities grapple with the escalating frequency of intense storm events, such scientifically rigorous and technologically sophisticated studies provide a beacon for safeguarding populations, infrastructure, and ecosystems from the devastating consequences of urban flooding.
Subject of Research: Evaluation and analysis of factors affecting urban flooding through hydrological modeling.
Article Title: Evaluation and analysis of the impact of factors affecting urban flooding using SCS-TR20 and SBUH hydrological models.
Article References:
Dehdehi, N., Soleimani‑Motlagh, M. & Haghizadeh, A. Evaluation and analysis of the impact of factors affecting urban flooding using SCS-TR20 and SBUH hydrological models. Sci Rep (2026). https://doi.org/10.1038/s41598-026-58279-3
Image Credits: AI Generated
Residential segregation remains one of the most pressing and persistent challenges facing American cities today. It fractures economic opportunity, stifles social cohesion, and underpins many of the inequities visible across urban landscapes. Despite decades of research into the patterns and consequences of segregation, the measurement of these patterns has long grappled with a thorny methodological problem: how the artificial boundaries used to aggregate population data influence estimates of segregation. This problem, often called the modifiable areal unit problem (MAUP), arises because segregation indices depend critically on how spatial units—like Census tracts—are defined and aggregated. Until now, a comprehensive assessment of this source of variability has remained out of reach.
In a groundbreaking study published in Nature Cities, researchers have employed an innovative approach that leverages redistricting algorithms to simulate millions of alternative spatial boundary configurations. These alternative maps adhere to official Census criteria but shuffle tract boundaries within those parameters, creating an unprecedented way to probe how sensitive traditional segregation indices are to the arbitrary boundaries that political and statistical geographies impose. The results challenge established assumptions and ultimately affirm the practicality—and peculiar robustness—of Census tracts as tools for racial segregation measurement across U.S. cities.
Traditionally, segregation research relies on fixed geographic units, most commonly Census tracts, to evaluate how populations of different racial and ethnic groups are distributed across cities. But these tracts, while designed to be relatively homogeneous and stable, are shaped by administrative decisions that do not directly correspond to natural or social boundaries. Since the exact delineation of these tracts can stray substantially, estimates of segregation derived from them have always carried a hidden risk of aggregation bias. Sensitivity analyses that explore alternate aggregations have been limited in scope, relying on hypothetical adjustments or crude spatial aggregations, rather than systematically exploring the vast universe of plausible boundary alternatives.
The new study breaks this impasse by borrowing tools from political redistricting theory—where map-drawing is a highly contested and technically advanced field. The researchers developed a computational framework using redistricting software capable of generating millions of alternate Census tract configurations within each city. These alternative configurations satisfy Census Bureau guidelines on population equality and contiguity, approximating what feasible boundary revisions might realistically look like. By calculating segregation indices for each configuration, the team was able to characterize the full probabilistic distribution of segregation estimates that arise purely from boundary variability.
Their analysis covered a wide range of U.S. cities, from smaller metropolitan areas with relatively few tracts to sprawling urban agglomerations comprising thousands of tracts. The findings reveal a striking pattern: in smaller cities, the estimated levels of racial segregation can fluctuate substantially when alternative boundary maps are considered. This variability highlights the dangers of over-reliance on fixed census geographies in contexts where granularity is limited and aggregation artifacts become significant. In contrast, as city size grows and spatial units multiply, the observed segregation estimates converge; the impact of redrawing boundaries diminishes, and estimates become more stable and trustworthy.
Perhaps more surprisingly, the study found no evidence that existing Census tract-based estimates are systematically biased in either direction. The official tract boundary estimates tended to fall near the mean of the simulated distribution of alternative boundaries. This outcome challenges the often-expressed concern that Census tracts might drastically overstate or understate segregation. Instead, while individual estimates do carry inherent spatial aggregation uncertainty, the conventional measures are, on average, representative. This nuance adds an important layer of confidence to decades of segregation research relying on these geographic units.
Technically, the probabilistic distributions generated by the redistricting simulations provide a novel way to frame segregation statistics in an uncertainty framework. Instead of presenting segregation as a point estimate, urban researchers and policymakers can now appreciate a spectrum of plausible segregation values conditioned on boundary choices. This opens avenues for more transparent reporting that reflects the inherent spatial ambiguity in aggregate data analysis, empowering better-informed decisions on interventions aimed at mitigating segregation and its effects.
The methodology itself is noteworthy for blending urban demographics with advanced computational geography. By applying constraints like population equivalence and spatial contiguity, the simulations closely mimic the federal redistricting process and ensure that the alternative boundaries are grounded in realistic criteria rather than purely theoretical constructs. This rigor elevates the findings beyond mere sensitivity checks, framing boundary uncertainty as a quantifiable dimension of segregation research rather than a theoretical limitation.
These insights carry significant implications not only for researchers but also for policymakers and activists engaged in urban planning, housing, and civil rights advocacy. Recognizing that reported segregation metrics come with an intrinsic range of variance caused by how boundaries are drawn can lead to greater caution in interpreting city rankings or trends over time. More fundamentally, it signals the need for complementary approaches that integrate non-aggregate data sources or spatially continuous methods to further refine segregation measurement.
The study also presents a generalizable framework adaptable to other domains where spatial aggregation introduces bias and uncertainty. Fields such as public health, environmental justice, and economic geography often rely on aggregated spatial data vulnerable to the MAUP. The redistricting simulation approach offers a powerful template for diagnosing and statistically correcting aggregation error in these varied contexts, paving the way for more robust spatial analyses across disciplines.
One of the most profound contributions of this research is its nuanced vindication of the Census tract as a unit of analysis. While researchers have long critiqued the arbitrariness inherent in fixed administrative boundaries, the study’s results demonstrate that in many urban contexts at scale, these units provide a sufficiently stable foundation for measuring segregation. This balancing of skepticism with empirical validation is rare in social science and signals a promising path toward methodological refinement rather than wholesale rejection of conventional metrics.
The findings also underscore the importance of city size and spatial scale in segregation measurement. Smaller cities, where the number of spatial units is limited, experience much greater volatility in segregation estimates. This volatility arises because each tract, or aggregation unit, represents a larger proportional share of the city’s population, amplifying boundary effects. Larger cities benefit from statistical smoothing through numerous smaller units, increasing estimate robustness. This insight could deeply inform urban comparative studies and the design of equitable zoning or integration policies tailored to city scale.
Going forward, the authors advocate for broad adoption and further development of their redistricting simulation platform. Enhancing the tool’s accessibility and integrating it with standard segregation calculations could democratize this approach, allowing urban researchers everywhere to rigorously address aggregation bias. Moreover, expanding simulations to explore additional boundary constraints or to incorporate social network data could refine understanding of how human geography and administrative geography intersect to shape segregation patterns.
The study arrives at a critical moment, as U.S. cities grapple with evolving demographic realities amid ongoing debates over race, equality, and urban policy. Precise and reliable measurement tools are foundational to both academic inquiry and effective governance. By making segregation measurement more transparent and statistically nuanced, this research provides a critical step towards evidence-based urban reform that acknowledges complexity without being paralyzed by it.
In conclusion, this pioneering study reframes the measurement of racial segregation by exposing the variability introduced through spatial aggregation while reassuring researchers and policymakers of the general reliability of established Census tract definitions. By harnessing computational redistricting techniques, it both deepens and democratizes understanding of urban racial geography, offering a blueprint for more rigorous and nuanced measurement in this socially consequential domain. The framework not only enriches segregation studies but sets a new standard for spatial data analysis in the social sciences, inviting an era where measurement uncertainty is openly acknowledged and expertly managed.
Subject of Research:
City-scale racial segregation measurement and spatial aggregation bias in U.S. metropolitan areas.
Article Title:
City racial segregation statistics are robust to aggregation bias.
Article References:
Brown, J.R., Kenny, C.T. & Simko, T. City racial segregation statistics are robust to aggregation bias. Nat Cities (2026). https://doi.org/10.1038/s44284-026-00459-3
Image Credits:
AI Generated
People often speak metaphorically of the heartbeat or pulse of a city, but according to the authors of a new paper published in the Proceedings of the National Academy of Sciences, cities do indeed have an "urban pulse"—an indication of urban "metabolic activity" that can be measured to suss out telltale patterns. And those patterns could help inform future public policy around urban planning.
The precise definition of urbanization has shifted over the centuries. Zhe Zhu of the University of Connecticut and his fellow authors adopted a broad version for their study. It features fundamental "processes of concurrent change in at least six dimensions, including demography, economy, infrastructure, environment, governance and culture," they wrote. "Together they give rise to outcomes, measurable results of the process, such as population growth, urban land expansion, GDP growth, and innovation." Their chosen metrics reflect this dynamic view: Cities are not static grids but "living, adaptive ecosystems."
“For decades, we had just been capturing the outcome of urbanization—a house that’s been built, or a road expansion,” said Zhu. “But you don’t really see the dynamics within an urban area. This is going to be a very impactful tool influencing not only top-down policy decisions from governments but also bottom-up decisions from everyday people navigating their cities.” One day we may be able to check a neighborhood's "urban pulse" while house-hunting, for instance, or while scouting potential locations for a new business.
© Zhe Zhu/GERS Lab
New York expands street sensors to map crossings, near-misses and traffic patterns.
Long before modern cities built upward, Rome learned how to stack human lives. 
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