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Register a new userA worldwide model for boundaries of urban settlements
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The shape of urban settlements plays a fundamental role in their sustainable planning. Properly defining the boundaries of cities is challenging and remains an open problem in the Science of Cities. Here, we propose a worldwide model to define urban settlements beyond their administrative boundaries through a bottom-up approach that takes into account geographical biases intrinsically associated with most societies around the world, and reflected in their different regional growing dynamics. The generality of the model allows to study the scaling laws of cities at all geographical levels: countries, continents, and the entire world. Our definition of cities is robust and holds to one of the most famous results in Social Sciences: Zipfs law. According to our results, the largest cities in the world are not in line with what was recently reported by the United Nations. For example, we find that the largest city in the world is an agglomeration of several small settlements close to each other, connecting three large settlements: Alexandria, Cairo, and Luxor. Our definition of cities opens the doors to the study of the economy of cities in a systematic way independently of arbitrary definitions that employ administrative boundaries.
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Defining an objective boundary for a city is a difficult problem, which remains to be solved by an effective method. Recent years, new methods for identifying urban boundary have been developed by means of spatial search techniques (e.g. CCA). However, the new algorithms are involved with another problem, that is, how to determine the radius of spatial search objectively. This paper proposes new approaches to looking for the most advisable spatial searching radius for determining urban boundary. The key is to find out the characteristic length of spatial search by certain functional relationships. A discovery is that the relationships between the spatial searching radius and the corresponding number of clusters take on an exponential function, in which the scale parameter just represents the characteristic length. Using the characteristic length, we can define the most objective urban boundary. Two sets of Chinese cities are employed to test this method, and the results lend support to judgment that the characteristic parameter can serve for the spatial searching radius. This study suggests a new way of determining urban boundary and determining city size in the right perspective.
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We model the spreading of a crisis by constructing a global economic network and applying the Susceptible-Infected-Recovered (SIR) epidemic model with a variable probability of infection. The probability of infection depends on the strength of economic relations between the pair of countries, and the strength of the target country. It is expected that a crisis which originates in a large country, such as the USA, has the potential to spread globally, like the recent crisis. Surprisingly we show that also countries with much lower GDP, such as Belgium, are able to initiate a global crisis. Using the {it k}-shell decomposition method to quantify the spreading power (of a node), we obtain a measure of ``centrality as a spreader of each country in the economic network. We thus rank the different countries according to the shell they belong to, and find the 12 most central countries. These countries are the most likely to spread a crisis globally. Of these 12 only six are large economies, while the other six are medium/small ones, a result that could not have been otherwise anticipated. Furthermore, we use our model to predict the crisis spreading potential of countries belonging to different shells according to the crisis magnitude.
Urban segregation of different communities, like blacks and whites in the USA, has been simulated by Ising-like models since Schelling 1971. This research was accompanied by a scientific segregation, with sociologists and physicists ignoring each other until 2000. We review recent progress and also present some new two-temperature multi-cultural simulations.
Human settlements on Earth are scattered in a multitude of shapes, sizes and spatial arrangements. These patterns are often not random but a result of complex geographical, cultural, economic and historical processes that have profound human and ecological impacts. However, little is known about the global distribution of these patterns and the spatial forces that creates them. This study analyses human settlements from high-resolution satellite imagery and provides a global classification of spatial patterns. We find two emerging classes, namely agglomeration and dispersion. In the former, settlements are fewer than expected based on the predictions of scaling theory, while an unexpectedly high number of settlements characterizes the latter. Our global classification of spatial patterns correlates with some urban outcomes, such as the amount of CO2 emitted for transportation, providing insights into the relationship between land use patterns and socio-economic and environmental indicators. To explain the observed spatial patterns, we also propose a model that combines two agglomeration forces and simulates human settlements historical growth. Our results show that our model accurately matches the observed global classification (F1: 0.73), helps to understand and estimate the growth of human settlements and, in turn, the distribution and physical dynamics of all human settlements on Earth, from small villages to cities.
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