Supersampling and network reconstruction of urban mobility

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that aim to draw policies from the activities of humans in space. Despite recent availability of large scale data sets related to human mobility such as GPS traces, mobile phone data, etc., it is still true that such data sets represent a subsample of the population of interest, and then might give an incomplete picture of the entire population in question. Notwithstanding the abundant usage of such inherently limited data sets, the impact of sampling biases on mobility patterns is unclear -- we do not have methods available to reliably infer mobility information from a limited data set. Here, we investigate the effects of sampling using a data set of millions of taxi movements in New York City. On the one hand, we show that mobility patterns are highly stable once an appropriate simple rescaling is applied to the data, implying negligible loss of information due to subsampling over long time scales. On the other hand, contrasting an appropriate null model on the weighted network of vehicle flows reveals distinctive features which need to be accounted for. Accordingly, we formulate a "supersampling" methodology which allows us to reliably extrapolate mobility data from a reduced sample and propose a number of network-based metrics to reliably assess its quality (and that of other human mobility models). Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is needed.Comment: 14 pages, 4 figure

Analysis of Educational Distribution in Europe: Educational Attainment and Inequality Within Regions

The aim of this paper is to visualise and describe the educational attainment and inequality distributions and to detect patterns of global and local spatial autocorrelation, using the European Community Household Panel dataset for 102 regions over the period 1995-2000. It investigates the space-time dynamics of the European educational distributions measured as education level completed and age when the highest education level was completed. This paper also highlights the importance of spatial interaction and geographical location in the human capital performance of the European regions. Without imposing any prior restrictive assumptions on distributions, the exploratory analysis shows that education is geographically autocorrelated due to knowledge and skill diffusion and to the guidelines for education systems and structures which are, as a general rule, set nationally. Thus not only geographical factors such as location, but also institutional ones matter for spatial dependence. The exploratory analysis of the European educational distribution also illustrates the systematic differences between urban and rural areas and between North and South regions. Economies within a cluster interact more with each other than with those outside. Educational attainment is higher in the North and in urban areas, while educational inequality is lower in these areas. Hence spatial dependence and spatial heterogeneity are indeed required features of the European educational analysis.DYNREG, educational attainment, educational inequality, Exploratory Spatial Data Analysis, regions, Europe, urbanisation, EU North-South divide.

Assessing the role of the spatial scale in the analysis of lagoon biodiversity. A case-study on the macrobenthic fauna of the Po River Delta

The analysis of benthic assemblages is a valuable tool to describe the ecological status of transitional water ecosystems, but species are extremely sensitive and respond to both microhabitat and seasonal differences. The identification of changes in the composition of the macrobenthic community in specific microhabitats can then be used as an “early warning” for environmental changes which may affect the economic and ecological importance of lagoons, through their provision of Ecosystem Services. From a conservational point of view, the appropriate definition of the spatial aggregation level of microhabitats or local communities is of crucial importance. The main objective of this work is to assess the role of the spatial scale in the analysis of lagoon biodiversity. First, we analyze the variation in the sample coverage for alternative aggregations of the monitoring stations in three lagoons of the Po River Delta. Then, we analyze the variation of a class of entropy indices by mixed effects models, properly accounting for the fixed effects of biotic and abiotic factors and random effects ruled by nested sources of variability corresponding to alternative definitions of local communities. Finally, we address biodiversity partitioning by a generalized diversity measure, namely the Tsallis entropy, and for alternative definitions of the local communities. The main results obtained by the proposed statistical protocol are presented, discussed and framed in the ecological context