The canopy layer urban heat island (UHI) effect, as manifested by elevated near-surface air temperatures in urban areas, exposes urban dwellers to additional heat stress in many cities, specially ...during heat waves. We simulate the urban climate of various generated cities under the same weather conditions. For mono-centric cities, we propose a linear combination of logarithmic city area and logarithmic gross building volume, which also captures the influence of building density. By studying various city shapes, we generalise and propose a reduced form to estimate UHI intensities based only on the structure of urban sites, as well as their relative distances. We conclude that in addition to the size, the UHI intensity of a city is directly related to the density and an amplifying effect that urban sites have on each other. Our approach can serve as a UHI rule of thumb for the comparison of urban development scenarios.
Urban climate is determined by a variety of factors, whose knowledge can help to attenuate heat stress in the context of ongoing urbanization and climate change. We study the influence of city size ...and urban form on the Urban Heat Island (UHI) phenomenon in Europe and find a complex interplay between UHI intensity and city size, fractality, and anisometry. Due to correlations among these urban factors, interactions in the multi-linear regression need to be taken into account. We find that among the largest 5,000 cities, the UHI intensity increases with the logarithm of the city size and with the fractal dimension, but decreases with the logarithm of the anisometry. Typically, the size has the strongest influence, followed by the compactness, and the smallest is the influence of the degree to which the cities stretch. Accordingly, from the point of view of UHI alleviation, small, disperse, and stretched cities are preferable. However, such recommendations need to be balanced against e.g. positive agglomeration effects of large cities. Therefore, trade-offs must be made regarding local and global aims.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The question of whether urbanization contributes to increasing carbon dioxide emissions has been mainly investigated via scaling relationships with population or population density. However, these ...approaches overlook the correlations between population and area, and ignore possible interactions between these quantities. Here, we propose a generalized framework that simultaneously considers the effects of population and area along with possible interactions between these urban metrics. Our results significantly improve the description of emissions and reveal the coupled role between population and density on emissions. These models show that variations in emissions associated with proportionate changes in population or density may not only depend on the magnitude of these changes but also on the initial values of these quantities. For US areas, the larger the city, the higher is the impact of changing its population or density on its emissions; but population changes always have a greater effect on emissions than population density.
Power-law city-size distributions are a statistical regularity researched in many countries and urban systems. In this history of science treatise we reconsider Felix Auerbach’s paper published in ...1913. We reviewed his analysis and found (i) that a constant absolute concentration, as introduced by him, is equivalent to a power-law distribution with exponent
≈
1
, (ii) that Auerbach describes this equivalence, and (iii) that Auerbach also pioneered the empirical analysis of city-size distributions across countries, regions, and time periods. We further investigate his legacy as reflected in citations and find that important follow-up work, e.g. by Lotka (Elements of physical biology. Williams & Wilkins Company, Baltimore, 1925) and Zipf (Human behavior and the principle of least effort: an introduction to human ecology, Martino Publishing, Manfield Centre, CT (2012), 1949), does give proper reference to his discovery—but others do not. For example, only approximately 20% of city-related works citing Zipf (1949) also cite Auerbach (Petermanns Geogr Mitteilungen 59(74):74–76, 1913). To our best knowledge, Lotka (1925) was the first to describe the power-law rank-size rule as it is analyzed today. Saibante (Metron Rivista Internazionale di Statistica 7(2):53–99, 1928), building on Auerbach and Lotka, investigated the power-law rank-size rule across countries, regions, and time periods. Zipf’s achievement was to embed these findings in his monumental 1949 book. We suggest that the use of “Auerbach–Lotka–Zipf law” (or “ALZ-law”) is more appropriate than “Zipf’s law for cities”, which also avoids confusion with Zipf’s law for word frequency. We end the treatise with biographical notes on Auerbach.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Sustainable development goals (SDGs) have set the 2030 agenda to transform our world by tackling multiple challenges humankind is facing to ensure well‐being, economic prosperity, and environmental ...protection. In contrast to conventional development agendas focusing on a restricted set of dimensions, the SDGs provide a holistic and multidimensional view on development. Hence, interactions among the SDGs may cause diverging results. To analyze the SDG interactions we systematize the identification of synergies and trade‐offs using official SDG indicator data for 227 countries. A significant positive correlation between a pair of SDG indicators is classified as a synergy while a significant negative correlation is classified as a trade‐off. We rank synergies and trade‐offs between SDGs pairs on global and country scales in order to identify the most frequent SDG interactions. For a given SDG, positive correlations between indicator pairs were found to outweigh the negative ones in most countries. Among SDGs the positive and negative correlations between indicator pairs allowed for the identification of particular global patterns. SDG 1 (No poverty) has synergetic relationship with most of the other goals, whereas SDG 12 (Responsible consumption and production) is the goal most commonly associated with trade‐offs. The attainment of the SDG agenda will greatly depend on whether the identified synergies among the goals can be leveraged. In addition, the highlighted trade‐offs, which constitute obstacles in achieving the SDGs, need to be negotiated and made structurally nonobstructive by deeper changes in the current strategies.
Key Points
Synergies, defined by positive correlations between indicator pairs, outweigh trade‐offs (negative correlations) for most sustainable development goals (SDGs) and countries
SDG 1 depicts synergies with most goals while SDG 12 shows trade‐offs; SDG 3 has synergies with other SDGs in most countries and populations
For attaining the SDGs, the synergies can be leveraged and the trade‐offs need to be overcome by deeper changes in the current strategies
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Urban scaling theory explains the increasing returns to scale of urban wealth indicators by the per capita increase of human interactions within cities. This explanation implicitly assumes urban ...areas as isolated entities and ignores their interactions. Here we investigate the effects of commuting networks on the gross domestic product (GDP) of urban areas in the US and Brazil. We describe the urban GDP as the output of a production process where population, incoming commuters, and interactions between these quantities are the input variables. This approach significantly refines the description of urban GDP and shows that incoming commuters contribute to wealth creation in urban areas. Our research indicates that changes in urban GDP related to proportionate changes in population and incoming commuters depend on the initial values of these quantities, such that increasing returns to scale are only possible when the product between population and incoming commuters exceeds a well-defined threshold.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
This collection provides a contemporary excerpt of "Cities as complex systems". The contributions have been submitted between April and October 2020. We briefly discuss example papers addressing the ...themes "urban scaling", "urban mobility", "flows in cities", "spatial analysis", "information technology and cities", and "cities in time". After motivating the intersection of cities and complexity, we provide an introduction and additional thoughts on urban scaling.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The distribution of city populations has attracted much attention, in part because it constrains models of local growth. However, there is no consensus on the distribution below the very upper tail, ...because available data need to rely on "legal" rather than "economic" definitions for medium and small cities. To remedy this difficulty, we construct cities "from the bottom up" by clustering populated areas obtained from high-resolution data. We find that Zipf 's law for population holds for cities as small as 5,000 inhabitants in Great Britain and 12,000 inhabitants in the US. We also find a Zipf 's law for areas. JEL: R11, R12, R23 PUBLICATION ABSTRACT
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BFBNIB, CEKLJ, INZLJ, IZUM, KILJ, NMLJ, NUK, ODKLJ, PILJ, PNG, SAZU, UL, UM, UPUK, ZRSKP
•Spatial clustering is applied to coordinates of the building stock in Germany.•We determine the percolation distance at which a country spanning cluster emerges.•The top five largest holes in that ...mesh are or were military training areas.•The building density decreases with the clustering threshold following a power-law with an exponent close to 0.75.•The overbuilding is a phenomenon that is beyond the dichotomy of sprawled and compact urban development.
In many countries and regions there is a strong interest in limiting urban sprawl and further land consumption. Thus, here we empirically assess to what extent the landscape is already overbuilt in the present day in Germany. Therefore, we analyze building locations as they provide the best possible information on built-up areas. Specifically, we apply the City Clustering Algorithm (CCA) and employ the percolation concept in order to quantify the connectivity of buildings. We find that at a critical distance of (830 ± 10) m the building stock in Germany undergoes a transition to a country-spanning building cluster. From this rather short critical distance we infer that the country is covered by a contiguous and dense mesh of buildings. Additionally, we find that the building density decreases as a power-law with an exponent close to 0.75. Complementarily, we identify the sites furthest away from any building. Ironically, the top five largest holes in that mesh are or were military training areas. The largest one corresponds to a radius of only 6.3 km. The results significantly contribute to a deeper understanding of landscape degradation. Settlement connectivity and poles of inaccessibility can serve for monitoring of settlement and landscape changes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Need for substantive linkages between health and urban sustainability sciences.•Urbanization, urbanicity, cities distinct concepts, each relevant for health.•Pervasive synergies and tradeoffs ...between determinants of health.•Research approaches must span scales, sectors, and disciplines.•Monitoring implementation, effective governance, equity-driven planning critical.
Goals and pathways to achieve sustainable urban development have multiple interlinkages with human health and wellbeing. However, these interlinkages have not been examined in depth in recent discussions on urban sustainability and global urban science. This paper fills that gap by elaborating in detail the multiple links between urban sustainability and human health and by mapping research gaps at the interface of health and urban sustainability sciences. As researchers from a broad range of disciplines, we aimed to: 1) define the process of urbanization, highlighting distinctions from related concepts to support improved conceptual rigour in health research; 2) review the evidence linking health with urbanization, urbanicity, and cities and identify cross-cutting issues; and 3) highlight new research approaches needed to study complex urban systems and their links with health. This novel, comprehensive knowledge synthesis addresses issue of interest across multiple disciplines. Our review of concepts of urban development should be of particular value to researchers and practitioners in the health sciences, while our review of the links between urban environments and health should be of particular interest to those outside of public health. We identify specific actions to promote health through sustainable urban development that leaves no one behind, including: integrated planning; evidence-informed policy-making; and monitoring the implementation of policies. We also highlight the critical role of effective governance and equity-driven planning in progress towards sustainable, healthy, and just urban development.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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