57 research outputs found
Creating Full Individual-level Location Timelines from Sparse Social Media Data
In many domain applications, a continuous timeline of human locations is
critical; for example for understanding possible locations where a disease may
spread, or the flow of traffic. While data sources such as GPS trackers or Call
Data Records are temporally-rich, they are expensive, often not publicly
available or garnered only in select locations, restricting their wide use.
Conversely, geo-located social media data are publicly and freely available,
but present challenges especially for full timeline inference due to their
sparse nature. We propose a stochastic framework, Intermediate Location
Computing (ILC) which uses prior knowledge about human mobility patterns to
predict every missing location from an individual's social media timeline. We
compare ILC with a state-of-the-art RNN baseline as well as methods that are
optimized for next-location prediction only. For three major cities, ILC
predicts the top 1 location for all missing locations in a timeline, at 1 and
2-hour resolution, with up to 77.2% accuracy (up to 6% better accuracy than all
compared methods). Specifically, ILC also outperforms the RNN in settings of
low data; both cases of very small number of users (under 50), as well as
settings with more users, but with sparser timelines. In general, the RNN model
needs a higher number of users to achieve the same performance as ILC. Overall,
this work illustrates the tradeoff between prior knowledge of heuristics and
more data, for an important societal problem of filling in entire timelines
using freely available, but sparse social media data.Comment: 10 pages, 8 figures, 2 table
Inferring Nighttime Satellite Imagery from Human Mobility
Nighttime lights satellite imagery has been used for decades as a uniform,
global source of data for studying a wide range of socioeconomic factors.
Recently, another more terrestrial source is producing data with similarly
uniform global coverage: anonymous and aggregated smart phone location. This
data, which measures the movement patterns of people and populations rather
than the light they produce, could prove just as valuable in decades to come.
In fact, since human mobility is far more directly related to the socioeconomic
variables being predicted, it has an even greater potential. Additionally,
since cell phone locations can be aggregated in real time while preserving
individual user privacy, it will be possible to conduct studies that would
previously have been impossible because they require data from the present. Of
course, it will take quite some time to establish the new techniques necessary
to apply human mobility data to problems traditionally studied with satellite
imagery and to conceptualize and develop new real time applications. In this
study we demonstrate that it is possible to accelerate this process by
inferring artificial nighttime satellite imagery from human mobility data,
while maintaining a strong differential privacy guarantee. We also show that
these artificial maps can be used to infer socioeconomic variables, often with
greater accuracy than using actual satellite imagery. Along the way, we find
that the relationship between mobility and light emissions is both nonlinear
and varies considerably around the globe. Finally, we show that models based on
human mobility can significantly improve our understanding of society at a
global scale.Comment: 9 pages, 3 figures, presented at the 34th AAAI conference on
Artificial Intelligenc
Uncovering the socioeconomic facets of human mobility
Given the rapid recent trend of urbanization, a better understanding of how
urban infrastructure mediates socioeconomic interactions and economic systems
is of vital importance. While the accessibility of location-enabled devices as
well as large-scale datasets of human activities, has fueled significant
advances in our understanding, there is little agreement on the linkage between
socioeconomic status and its influence on movement patterns, in particular, the
role of inequality. Here, we analyze a heavily aggregated and anonymized
summary of global mobility and investigate the relationships between
socioeconomic status and mobility across a hundred cities in the US and Brazil.
We uncover two types of relationships, finding either a clear connection or
little-to-no interdependencies. The former tend to be characterized by low
levels of public transportation usage, inequitable access to basic amenities
and services, and segregated clusters of communities in terms of income, with
the latter class showing the opposite trends. Our findings provide useful
lessons in designing urban habitats that serve the larger interests of all
inhabitants irrespective of their economic status.Comment: main manuscript 25 page, 7 Figures. Supplementary material 11 pages,
8 figure
Impact of Urban Structure on Infectious Disease Spreading
Trabajo presentado en la Conference on Complex Systems (CCS), celebrada en Lyon del 25 al 29 de octubre de 2021.The ongoing SARS-CoV-2 pandemic evidences that promptly detecting which areas and regions are most susceptible to reinfections is critical to implement adequate containment policies and prevent new waves by potentially deadlier strains. Mobility has always been key to viral spreading, but the relatively slow pace of vaccination in the majority of countries, together with the appearance new aggressive variants, have forced mitigation measures to rely primarily on non-pharmaceutical interventions, such as lockdowns. This puts urban centers in the focus of epidemic surveillance and intervention. Here we show that the organization of urban flows has a tremendous impact on disease spreading and on the amenability of different mitigation strategies. By studying anonymous and aggregated intra-urban flows in a variety of cities in the United States and other countries, and a combination of empirical analysis and analytical methods, we demonstrate that the response of cities to epidemic spreading can be roughly classified in two major types, according to the overall organization of those flows. Centralized cities, where flows are concentrated primarily between mobility hotspots, are particularly vulnerable to the rapid spread of epidemics. Nevertheless, mobility restrictions in such types of cities are very effective in mitigating the spread of a virus. Conversely, in sprawled cities which present many centers of activity, the spread of an epidemic is much slower, but the response to mobility restrictions is much weaker and less effective
Reputation Agent: Prompting Fair Reviews in Gig Markets
Our study presents a new tool, Reputation Agent, to promote fairer reviews
from requesters (employers or customers) on gig markets. Unfair reviews,
created when requesters consider factors outside of a worker's control, are
known to plague gig workers and can result in lost job opportunities and even
termination from the marketplace. Our tool leverages machine learning to
implement an intelligent interface that: (1) uses deep learning to
automatically detect when an individual has included unfair factors into her
review (factors outside the worker's control per the policies of the market);
and (2) prompts the individual to reconsider her review if she has incorporated
unfair factors. To study the effectiveness of Reputation Agent, we conducted a
controlled experiment over different gig markets. Our experiment illustrates
that across markets, Reputation Agent, in contrast with traditional approaches,
motivates requesters to review gig workers' performance more fairly. We discuss
how tools that bring more transparency to employers about the policies of a gig
market can help build empathy thus resulting in reasoned discussions around
potential injustices towards workers generated by these interfaces. Our vision
is that with tools that promote truth and transparency we can bring fairer
treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202
Context-specific emergence and growth of the SARS-CoV-2 Delta variant
The SARS-CoV-2 Delta (Pango lineage B.1.617.2) variant of concern spread globally, causing resurgences of COVID-19 worldwide1,2. The emergence of the Delta variant in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions. Here we analyse 52,992 SARS-CoV-2 genomes from England together with 93,649 genomes from the rest of the world to reconstruct the emergence of Delta and quantify its introduction to and regional dissemination across England in the context of changing travel and social restrictions. Using analysis of human movement, contact tracing and virus genomic data, we find that the geographic focus of the expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced more than 1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers reduced onward transmission from importations; however, the transmission chains that later dominated the Delta wave in England were seeded before travel restrictions were introduced. Increasing inter-regional travel within England drove the nationwide dissemination of Delta, with some cities receiving more than 2,000 observable lineage introductions from elsewhere. Subsequently, increased levels of local population mixing-and not the number of importations-were associated with the faster relative spread of Delta. The invasion dynamics of Delta depended on spatial heterogeneity in contact patterns, and our findings will inform optimal spatial interventions to reduce the transmission of current and future variants of concern, such as Omicron (Pango lineage B.1.1.529)
Far Out: Predicting Long-Term Human Mobility
Much work has been done on predicting where is one going to be in the immediate future, typically within the next hour. By contrast, we address the open problem of predicting human mobility far into the future, a scale of months and years. We propose an efficient nonparametric method that extracts significant and robust patterns in location data, learns their associations with contextual features (such as day of week), and subsequently leverages this information to predict the most likely location at any given time in the future. The entire process is formulated in a principled way as an eigendecomposition problem. Evaluation on a massive dataset with more than 32,000 days worth of GPS data across 703 diverse subjects shows that our model predicts the correct location with high accuracy, even years into the future. This result opens a number of interesting avenues for future research and applications
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