Despite the increasing importance of cities in human societies, our ability to understand them scientifically and manage them in practice has remained limited. The greatest difficulties to any scientific approach to cities have resulted from their many interdependent facets, as social, economic, infrastructural, and spatial complex systems that exist in similar but changing forms over a huge range of scales. Here, I show how all cities may evolve according to a small set of basic principles that operate locally. A theoretical framework was developed to predict the average social, spatial, and infrastructural properties of cities as a set of scaling relations that apply to all urban systems. Confirmation of these predictions was observed for thousands of cities worldwide, from many urban systems at different levels of development. Measures of urban efficiency, capturing the balance between socioeconomic outputs and infrastructural costs, were shown to be independent of city size and might be a useful means to evaluate urban planning strategies.

https://science.sciencemag.org/content/sci/340/6139/1438.full.pdf

The Origins of Scaling in Cities

To Dwell Among Friends: Personal Networks in Town and City.

: Scientific learning is an iterative process employing Criticism and Estimation. Correspondingly the formulated model factors into two complimentary parts - a predictive part allowing model criticism, and a Bayes posterior part allowing estimation. Implications for significance tests, the theory of precise measurement, and for ridge estimates are considered. Predictive checking functions for transformation, serial correlation, bad values, and their relation with Bayesian options are considered. Robustness is seen from a Bayesian viewpoint and examples are given. For the bad value problem a comparison with M estimators is made. (Author)

Sampling and Bayes' inference in scientific modelling and robustness

During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.

https://www.pnas.org/content/pnas/111/45/15888.full.pdf

Dynamic population mapping using mobile phone data

On generalise l'estimateur bien connu de Hill de l'indice d'une fonction de reparatition avec queue de variation reguliere a une estimation de l'indice d'une loi de valeurs extremes. On demontre la convergence et la normalite asymptotique. On utilise l'estimateur pour certaines estimations comme celle d'une quantile elevee et d'un point d'extremite

A moment estimator for the index of an extreme-value distribution

The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2013.0789

The scaling of human interactions with city size

In this article, we introduce a new estimator for the generalized Pareto distribution, which is based on the maximum likelihood estimation and the goodness of fit. The asymptotic normality of the new estimator is shown and a small simulation. From the simulation, the performance of the new estimator is roughly comparable with maximum likelihood for positive values of the shape parameter and often much better than maximum likelihood for negative values.

Estimation for the Generalized Pareto Distribution Using Maximum Likelihood and Goodness of Fit

The Pearson correlation coefficient is commonly used for quantifying the global level of degree-degree association in complex networks. Here, we use a probabilistic representation of the underlying network structure for assessing the applicability of different association measures to heavy-tailed degree distributions. Theoretical arguments together with our numerical study indicate that Pearson's coefficient often depends on the size of networks with equal association structure, impeding a systematic comparison of real-world networks. In contrast, Kendall-Gibbons' τ{b} is a considerably more robust measure of the degree-degree association.

Measuring degree-degree association in networks.

Within the framework of recent research projects, basic tools for GIS-based seismic risk assessment technologies were developed and applied to the building stock and regional particularities of German earthquake regions. Two study areas are investigated, being comparable by the level of seismic hazard and the hazard-consistent scenario events (related to mean return periods of 475, 2475 and 10000 years). Significant differences exist with respect to the number of inhabitants, the grade and extent of urbanisation, the quality and quantity of building inventory: the case study of Schmolln in Eastern Thuringia seems to be representative for the majority of smaller towns in Germany, the case study of Cologne (Koln) stands for larger cities. Due to the similarities of hazard and scenario intensities, the considerable differences do not only require proper decisions concerning the appropriate methods and acceptable efforts, they enable conclusions about future research strategies and needs for disaster reduction management. Not least important, results can sharpen the focus of public interest. Seismic risk maps are prepared for different scenario intensities recognising the scatter and uncertainties of site-dependent ground motion and also of the applied vulnerability functions. The paper illustrates the impact of model assumptions and the step-wise refinements of input variables like site conditions, building stock or vulnerability functions on the distribution of expected building damage within the study areas. Furthermore, and in contrast to common research strategies, results support the conclusion that in the case of stronger earthquakes the damage will be of higher concentration within smaller cities like Schmolln due to the site-amplification potential and/or the increased vulnerability of the building stock. The extent of damage will be pronounced by the large number of masonry buildings for which lower vulnerability classes have to be assigned. Due to the effect of deep sedimentary layers and the composition of building types, the urban centre of Cologne will be less affected by an earthquake of comparable intensity.

Mathias Raschke

Papers

The scaling of human interactions with city size

The scaling of human interactions with city size

Estimation for the Generalized Pareto Distribution Using Maximum Likelihood and Goodness of Fit

Measuring degree-degree association in networks.

Comparative Seismic Risk Studies for German Earthquake Regions on the Basis of the European Macroseismic Scale EMS-98