46,803 research outputs found
Predicting human mobility through the assimilation of social media traces into mobility models
Predicting human mobility flows at different spatial scales is challenged by
the heterogeneity of individual trajectories and the multi-scale nature of
transportation networks. As vast amounts of digital traces of human behaviour
become available, an opportunity arises to improve mobility models by
integrating into them proxy data on mobility collected by a variety of digital
platforms and location-aware services. Here we propose a hybrid model of human
mobility that integrates a large-scale publicly available dataset from a
popular photo-sharing system with the classical gravity model, under a stacked
regression procedure. We validate the performance and generalizability of our
approach using two ground-truth datasets on air travel and daily commuting in
the United States: using two different cross-validation schemes we show that
the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure
On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks
We report on a data-driven investigation aimed at understanding the dynamics
of message spreading in a real-world dynamical network of human proximity. We
use data collected by means of a proximity-sensing network of wearable sensors
that we deployed at three different social gatherings, simultaneously involving
several hundred individuals. We simulate a message spreading process over the
recorded proximity network, focusing on both the topological and the temporal
properties. We show that by using an appropriate technique to deal with the
temporal heterogeneity of proximity events, a universal statistical pattern
emerges for the delivery times of messages, robust across all the data sets.
Our results are useful to set constraints for generic processes of data
dissemination, as well as to validate established models of human mobility and
proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data
diffusion in ad-hoc networks, Ad Hoc Netw. (2011
Electrophoretic Analysis of Blood Serum Proteins in Three Species of Water Snakes (Genus Nerodia)
Serum from three species of water snakes (Nerodia rhombifera, N. erythrogaster and N. fasciata) from one geographic region was analyzed electrophoretically on cellulose acetate, and anodic mobility and relative concentration of the fractions were determined by a recording densitometer with an automatic integrator. Classification of fractions was based on mobility (Rf, values), and for identification purposes, bands were labeled in order of decreasing mobility (albumin and alpha₁, alpha₂, alpha₃, beta₁, beta₂, gamma₁, and gamma₂ globulins). Seven fractions were identified in each species with alpha₃ being absent from N. rhombifera and N. erythrogaster, and only one gamma fraction was observed in N. fasciata. In the three species, gamma globulin was the predominant protein (42-46%), and albumin levels were characteristically low ;however, a distinct difference was observed in albumin concentration (N. fasciata, 7%; N. rhombifera and N. erythrogaster, 16-18%). The Rf values and relative concentrations of other globulins showed heterogeneity in the three species, with the protein pattern of N. fasciata being distinct from the other two species
Self-Employment of Immigrants: A Cross-National Study of 17 Western Societies
This study examines the role of immigrants’ country of origin, country of destination and combinations thereof (settings or communities) in the likelihood of immigrants being selfemployed. I pooled census data from three classic immigrant countries (Australia, Canada and the United States) and labor-force surveys from 14 countries in the European Union for a cross-national data set. Using multilevel techniques, I find that (1) immigrants from non-Christian countries of origin have higher odds of self-employment, (2) higher levels of unemployment among natives increase the odds of self-employment, and (3) selfemployment is more frequent among immigrant communities that are small, highly educated and have a longer settlement history.
Gravity model explained by the radiation model on a population landscape
Understanding the mechanisms behind human mobility patterns is crucial to
improve our ability to optimize and predict traffic flows. Two representative
mobility models, i.e., radiation and gravity models, have been extensively
compared to each other against various empirical data sets, while their
fundamental relation is far from being fully understood. In order to study such
a relation, we first model the heterogeneous population landscape by generating
a fractal geometry of sites and then by assigning to each site a population
independently drawn from a power-law distribution. Then the radiation model on
this population landscape, which we call the radiation-on-landscape (RoL)
model, is compared to the gravity model to derive the distance exponent in the
gravity model in terms of the properties of the population landscape, which is
confirmed by the numerical simulations. Consequently, we provide a possible
explanation for the origin of the distance exponent in terms of the properties
of the heterogeneous population landscape, enabling us to better understand
mobility patterns constrained by the travel distance.Comment: 14 pages, 4 figure
Causal Inference in Disease Spread across a Heterogeneous Social System
Diffusion processes are governed by external triggers and internal dynamics
in complex systems. Timely and cost-effective control of infectious disease
spread critically relies on uncovering the underlying diffusion mechanisms,
which is challenging due to invisible causality between events and their
time-evolving intensity. We infer causal relationships between infections and
quantify the reflexivity of a meta-population, the level of feedback on event
occurrences by its internal dynamics (likelihood of a regional outbreak
triggered by previous cases). These are enabled by our new proposed model, the
Latent Influence Point Process (LIPP) which models disease spread by
incorporating macro-level internal dynamics of meta-populations based on human
mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
causal inference, outbreaks are more likely driven by statewide global
diffusion over time, leading to complex behavior of disease spread. In terms of
reflexivity, precursory growth and symmetric decline in populous regions is
attributed to slow but persistent feedback on preceding outbreaks via
inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
model reveals probabilistic causal relationships between discrete events based
on intra- and inter-group dynamics and also covers direct and indirect
diffusion processes (contact-based and vector-borne disease transmissions).Comment: arXiv admin note: substantial text overlap with arXiv:1711.0635
Multi-scale Population and Mobility Estimation with Geo-tagged Tweets
Recent outbreaks of Ebola and Dengue viruses have again elevated the
significance of the capability to quickly predict disease spread in an emergent
situation. However, existing approaches usually rely heavily on the
time-consuming census processes, or the privacy-sensitive call logs, leading to
their unresponsive nature when facing the abruptly changing dynamics in the
event of an outbreak. In this paper we study the feasibility of using
large-scale Twitter data as a proxy of human mobility to model and predict
disease spread. We report that for Australia, Twitter users' distribution
correlates well the census-based population distribution, and that the Twitter
users' travel patterns appear to loosely follow the gravity law at multiple
scales of geographic distances, i.e. national level, state level and
metropolitan level. The radiation model is also evaluated on this dataset
though it has shown inferior fitness as a result of Australia's sparse
population and large landmass. The outcomes of the study form the cornerstones
for future work towards a model-based, responsive prediction method from
Twitter data for disease spread.Comment: 1st International Workshop on Big Data Analytics for Biosecurity
(BioBAD2015), 4 page
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