R. McDonald, Katherine F. Weber, J. Padowski
et al.
A B S T R A C T Urban growth is increasing the demand for freshwater resources, yet surprisingly the water sources of the world’s large cities have never been globally assessed, hampering efforts to assess the distribution and causes of urban water stress. We conducted the first global survey of the large cities’ water sources, and show that previous global hydrologic models that ignored urban water infrastructure significantly overestimated urban water stress. Large cities obtain 78 � 3% of their water from surface sources, some of which are far away: cumulatively, large cities moved 504 billion liters a day (184 km 3 yr � 1 ) a distance of 27,000 � 3800 km, and the upstream contributing area of urban water sources is 41% of the global land surface. Despite this infrastructure, one in four cities, containing $4.8 � 0.7 trillion in economic activity, remain water stressed due to geographical and financial limitations. The strategic management of these cities’ water sources is therefore important for the future of the global economy.
Significance Urban density significantly impacts urban energy use and the quality of life of urban residents. Here, we provide a global-scale analysis of future urban densities and associated energy use in the built environment under different urbanization scenarios. The relative importance of urban density and energy-efficient technologies varies geographically. In developing regions, urban density tends to be the more critical factor in building energy use. Large-scale retrofitting of building stock later rather than sooner results in more energy savings by the middle of the century. Reducing building energy use, improving the local environment, and mitigating climate change can be achieved through systemic efforts that take potential co-benefits and trade-offs of both higher urban density and building energy efficiency into account. Although the scale of impending urbanization is well-acknowledged, we have a limited understanding of how urban forms will change and what their impact will be on building energy use. Using both top-down and bottom-up approaches and scenarios, we examine building energy use for heating and cooling. Globally, the energy use for heating and cooling by the middle of the century will be between 45 and 59 exajoules per year (corresponding to an increase of 7–40% since 2010). Most of this variability is due to the uncertainty in future urban densities of rapidly growing cities in Asia and particularly China. Dense urban development leads to less urban energy use overall. Waiting to retrofit the existing built environment until markets are ready in about 5 years to widely deploy the most advanced renovation technologies leads to more savings in building energy use. Potential for savings in energy use is greatest in China when coupled with efficiency gains. Advanced efficiency makes the least difference compared with the business-as-usual scenario in South Asia and Sub-Saharan Africa but significantly contributes to energy savings in North America and Europe. Systemic efforts that focus on both urban form, of which urban density is an indicator, and energy-efficient technologies, but that also account for potential co-benefits and trade-offs with human well-being can contribute to both local and global sustainability. Particularly in growing cities in the developing world, such efforts can improve the well-being of billions of urban residents and contribute to mitigating climate change by reducing energy use in urban areas.
Abstract This study analyzes the effect of urban network externalities on urban growth and compares it with that of agglomeration economies from the perspective of the externality theory. Traditional regional and urban economic theories emphasize the role of agglomeration economies in promoting regional growth. However, urban networks have gradually become the main form of regional economic systems. Urban network externalities are also becoming increasingly critical with the dramatic development of infrastructure and information technology. This study identifies the national urban network and analyzes its structure and characteristics using complex network methods based on the data of train frequency among 273 municipal districts in China. Then, an urban growth model is constructed with Spatial Durbin Model specifications to examine the impact of urban network externalities on economic growth and compare it with that of agglomeration economies. The results show that the urban network externality has a significant effect on promoting urban economic development; cities with higher in-closeness centrality tend to enjoy higher economic growth due to their central position in the network. Moreover, compared with agglomeration economies, urban network externalities do not depend on the geographical proximity of cities but on the connections in the network, and can generate cross-spatial spillover effects.
Abstract Crime and perception of safety are two intertwined concepts affecting the quality of life and the economic development of a society. However, few studies have quantitatively examined the difference between the two due to the lack of granular data documenting public perceptions in a given geographic context. Here, by applying a pre-trained scene understanding algorithm, we infer the perception of safety score of streetscapes for census block groups in the city of Houston using a large number of Google Street View images. Then, using this inferred perception of safety, we create “perception bias” categories for each census block group. These categories capture the level of mismatch between people’s visually perceived safety and the actual crime rates. This measure provides scalable guidance in deciphering the relationship between the built environment and crime. Finally, we construct a series of models to examine the “perception bias” with static and dynamic urban factors, including socioeconomic features (e.g., unemployment rate and ethnic compositions), urban diversity (e.g., number and diversity of Points of Interest), and urban livelihood (i.e., hourly count of visitors). Analytical and numerical results suggest that the association between characteristics of urban space and “perception bias” over crime could be paradoxical. On the one hand, neighborhoods with a higher volume of day-time visitors appear more likely to be safer than it looks (low crime rate and low safety score). On the other hand, those with a higher volume of night-time visitors are likely to be more dangerous than it looks (high crime rate). The findings add further knowledge to the long-recognized relationship between built environment and crime as well as highlight the perception of safety in cities, which in turn enhances our capacity to design urban management strategies that prevent the emergence of extreme “perception bias”.
Abstract Historically, change detection reviews have examined and categorized algorithms based on their techniques for the remote sensing community. Here, we synthesize urban land change algorithms by the types of information they provide to a diverse and growing set of user communities. Two goals of the paper are first to synthesize past and current change detection studies to examine urban land change to help users of remote sensing algorithms understand and navigate the vast variety of available methods, and second to identify gaps in knowledge for the urban remote sensing community. We analyzed 644 peer-reviewed research papers published in English-language journals and conducted a systematic review of urban land change algorithms. All papers included in our study focus on urban land change; studies that concentrate on single-date urban classification or mapping for one point in time without an explicit urban land change component are not included in this analysis. The review showed five key results and knowledge gaps. First, most urban change detection algorithms are being developed and applied for only a few regions, with 75% of studies focused on high or upper-middle-income countries and the majority of these on China or the United States. This suggests a major gap in geographic coverage as well as the need for more studies on cities in low and lower-middle-income countries. Second, the results show that 41% of the algorithms have been developed or applied for cities of over 5 million inhabitants. This focus on large cities is problematic given that only 11% of the world's urban population lives in cities with populations greater than 5 million and that most future urban growth will occur in small- and medium-sized towns and cities with populations of fewer than 1 million people. Third, our analysis shows that 62% of the studies use three or fewer time points to measure urban land change with an average study length of 17 years. Since rapidly growing urban areas are highly dynamic, this suggests that existing algorithms using only a few time points are likely missing urban transitions that can only be captured with high temporal frequency analysis. Fourth, we find that urban expansion is the most commonly monitored type of urban land change. Comparatively fewer studies characterized intra-urban change or three-dimensional structural change. Fifth and finally, the results show that an overwhelming majority—87%—of all studies identify only one urban class, highlighting a need for more studies that distinguish intra-urban variation and differentiate multiple urban classes. Our analysis shows that it is very difficult—nearly impossible—to compare across algorithms. Thus, for users of urban land change information, it is difficult to navigate the literature and know which algorithms are most appropriate for a particular use. Taken together, this points to the need for improved reproducibility, replicability, and comparability of studies in order to help harmonize urban land change information across regions and countries. This is especially important given the growing user communities of urban land change products and analysis, especially from policy and practice.
OSMnx is a Python package for downloading, modeling, analyzing, and visualizing urban networks and any other geospatial features from OpenStreetMap data. A large and growing body of literature uses it to conduct scientific studies across the disciplines of geography, urban planning, transport engineering, computer science, and others. The OSMnx project has recently developed and implemented many new features, modeling capabilities, and analytical methods. The package now encompasses substantially more functionality than was previously documented in the literature. This article introduces OSMnx's modern capabilities, usage, and design -- in addition to the scientific theory and logic underlying them. It shares lessons learned in geospatial software development and reflects on open science's implications for urban modeling and analysis.
The integration of digital technologies into urban planning has given rise to "smart cities," aiming to enhance quality of life and operational efficiency. However, the implementation of such technologies introduces ethical challenges, including data privacy, equity, inclusion, and transparency. This article employs the Beard and Longstaff framework to discuss these challenges through a combination of theoretical analysis and case studies. Focusing on principles of self-determination, fairness, accessibility, and purpose, the study examines governance models, stakeholder roles, and ethical dilemmas inherent in smart city initiatives. Recommendations include adopting regulatory sandboxes, fostering participatory governance, and bridging digital divides to ensure that smart cities align with societal values, promoting inclusivity and ethical urban development.
Urban scaling laws reveal how cities evolve as their populations grow, yet the role of street network accessibility in this process remains underexplored. We analyze over 5,000 Brazilian cities to establish a scaling law linking average closeness centrality $\langle c_C\rangle$ -- a measure of structural accessibility in street networks-to population size N . Our results demonstrate that $\langle c_C\rangle$ decays sublinearly as $N^{-σ}$ ($σ\approx 0.38$), indicating that larger cities redistribute accessibility from cores to peripheries while maintaining navigability through hierarchical shortcuts. This scaling arises from the fractal interplay between infrastructure and population, characterized by a network dimension $d \approx 2.17$, which exceeds that of a 2D grid. The slower decline in closeness centrality ($σ< 0.5$) reflects a trade-off: urban expansion reduces proximity but enhances connectivity through optimized path diversity, fostering economic dynamism. By integrating the Molinero & Thurner model with network centrality metrics, we provide a framework to reconcile infrastructure efficiency with equitable accessibility in growing cities.
German Gregório Monterrosa Ayala Filho, Peterson Roberto da Silva
Smart cities are a growing urban trend, yet their definition remains elusive, often shaped by the interests of their project designers. Although smart cities did not originate in the Global South, they became a current discussion in Brazil, presented as a necessity for cities to compete for investments. In 2018, a group of local associations along with the Florianópolis City Hall in southern Brazil launched the “Smart Floripa” reports. This research aims to provide a critique of the conceptualisation of “smartness” implied in the reports, informed by anarchist theory and critical geography, uncovering it as a rhetorical move in conflicts concerning urban policies and principles of sociopolitical organisation. As method, we chose a documentary analysis of two reports: “Smart Floripa 2030: transforming Florianópolis into a smart city” and “Smart City Florianópolis: the journey to creating the innovation path of a tourist island”. We begin the article by considering neoliberalism and its impacts on Brazilian municipalities. We then discuss smart cities and the Smart Floripa reports, which reinforce neoliberal urbanization policies, verified by critical literature as an urban development paradigm. We conclude with remarks on the meaning of neoliberal “smartness”.