28,365 research outputs found
Impact of the spatial context on human communication activity
Technology development produces terabytes of data generated by hu- man
activity in space and time. This enormous amount of data often called big data
becomes crucial for delivering new insights to decision makers. It contains
behavioral information on different types of human activity influenced by many
external factors such as geographic infor- mation and weather forecast. Early
recognition and prediction of those human behaviors are of great importance in
many societal applications like health-care, risk management and urban
planning, etc. In this pa- per, we investigate relevant geographical areas
based on their categories of human activities (i.e., working and shopping)
which identified from ge- ographic information (i.e., Openstreetmap). We use
spectral clustering followed by k-means clustering algorithm based on TF/IDF
cosine simi- larity metric. We evaluate the quality of those observed clusters
with the use of silhouette coefficients which are estimated based on the
similari- ties of the mobile communication activity temporal patterns. The area
clusters are further used to explain typical or exceptional communication
activities. We demonstrate the study using a real dataset containing 1 million
Call Detailed Records. This type of analysis and its application are important
for analyzing the dependency of human behaviors from the external factors and
hidden relationships and unknown correlations and other useful information that
can support decision-making.Comment: 12 pages, 11 figure
Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data
Despite the importance of predicting evacuation mobility dynamics after large
scale disasters for effective first response and disaster relief, our general
understanding of evacuation behavior remains limited because of the lack of
empirical evidence on the evacuation movement of individuals across multiple
disaster instances. Here we investigate the GPS trajectories of a total of more
than 1 million anonymized mobile phone users whose positions are tracked for a
period of 2 months before and after four of the major earthquakes that occurred
in Japan. Through a cross comparative analysis between the four disaster
instances, we find that in contrast with the assumed complexity of evacuation
decision making mechanisms in crisis situations, the individuals' evacuation
probability is strongly dependent on the seismic intensity that they
experience. In fact, we show that the evacuation probabilities in all
earthquakes collapse into a similar pattern, with a critical threshold at
around seismic intensity 5.5. This indicates that despite the diversity in the
earthquakes profiles and urban characteristics, evacuation behavior is
similarly dependent on seismic intensity. Moreover, we found that probability
density functions of the distances that individuals evacuate are not dependent
on seismic intensities that individuals experience. These insights from
empirical analysis on evacuation from multiple earthquake instances using large
scale mobility data contributes to a deeper understanding of how people react
to earthquakes, and can potentially assist decision makers to simulate and
predict the number of evacuees in urban areas with little computational time
and cost, by using population density information and seismic intensity which
can be observed instantaneously after the shock
Estimating Attendance From Cellular Network Data
We present a methodology to estimate the number of attendees to events
happening in the city from cellular network data. In this work we used
anonymized Call Detail Records (CDRs) comprising data on where and when users
access the cellular network. Our approach is based on two key ideas: (1) we
identify the network cells associated to the event location. (2) We verify the
attendance of each user, as a measure of whether (s)he generates CDRs during
the event, but not during other times. We evaluate our approach to estimate the
number of attendees to a number of events ranging from football matches in
stadiums to concerts and festivals in open squares. Comparing our results with
the best groundtruth data available, our estimates provide a median error of
less than 15% of the actual number of attendees
Exploring universal patterns in human home-work commuting from mobile phone data
Home-work commuting has always attracted significant research attention
because of its impact on human mobility. One of the key assumptions in this
domain of study is the universal uniformity of commute times. However, a true
comparison of commute patterns has often been hindered by the intrinsic
differences in data collection methods, which make observation from different
countries potentially biased and unreliable. In the present work, we approach
this problem through the use of mobile phone call detail records (CDRs), which
offers a consistent method for investigating mobility patterns in wholly
different parts of the world. We apply our analysis to a broad range of
datasets, at both the country and city scale. Additionally, we compare these
results with those obtained from vehicle GPS traces in Milan. While different
regions have some unique commute time characteristics, we show that the
home-work time distributions and average values within a single region are
indeed largely independent of commute distance or country (Portugal, Ivory
Coast, and Boston)--despite substantial spatial and infrastructural
differences. Furthermore, a comparative analysis demonstrates that such
distance-independence holds true only if we consider multimodal commute
behaviors--as consistent with previous studies. In car-only (Milan GPS traces)
and car-heavy (Saudi Arabia) commute datasets, we see that commute time is
indeed influenced by commute distance
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