213 research outputs found
Spatio-Temporal Sentiment Hotspot Detection Using Geotagged Photos
We perform spatio-temporal analysis of public sentiment using geotagged photo
collections. We develop a deep learning-based classifier that predicts the
emotion conveyed by an image. This allows us to associate sentiment with place.
We perform spatial hotspot detection and show that different emotions have
distinct spatial distributions that match expectations. We also perform
temporal analysis using the capture time of the photos. Our spatio-temporal
hotspot detection correctly identifies emerging concentrations of specific
emotions and year-by-year analyses of select locations show there are strong
temporal correlations between the predicted emotions and known events.Comment: To appear in ACM SIGSPATIAL 201
Uncovering Urban Temporal Patterns from Geo-Tagged Photography
We live in a world where digital trails of different forms of human activities compose big urban data, allowing us to detect many aspects of how people experience the city in which they live or come to visit. In this study we propose to enhance urban planning by taking into a consideration individual preferences using information from an unconventional big data source: dataset of geo-tagged photographs that people take in cities which we then use as a measure of urban attractiveness. We discover and compare a temporal behavior of residents and visitors in ten most photographed cities in the world. Looking at the periodicity in urban attractiveness, the results show that the strongest periodic patterns for visitors are usually weekly or monthly. Moreover, by dividing cities into two groups based on which continent they belong to (i.e., North America or Europe), it can be concluded that unlike European cities, behavior of visitors in the US cities in general is similar to the behavior of their residents. Finally, we apply two indices, called “dilatation attractiveness index” and “dilatation index”, to our dataset which tell us the spatial and temporal attractiveness pulsations in the city. The proposed methodology is not only important for urban planning, but also does support various business and public stakeholder decision processes, concentrated for example around the question how to attract more visitors to the city or estimate the impact of special events organized there.Singapore-MIT Alliance for Research and Technology (SMART)Center for Complex Engineering Systems (CCES) at KACST and MITCoca-Cola CompanyAccenture (Firm
Large-Scale Mapping of Human Activity using Geo-Tagged Videos
This paper is the first work to perform spatio-temporal mapping of human
activity using the visual content of geo-tagged videos. We utilize a recent
deep-learning based video analysis framework, termed hidden two-stream
networks, to recognize a range of activities in YouTube videos. This framework
is efficient and can run in real time or faster which is important for
recognizing events as they occur in streaming video or for reducing latency in
analyzing already captured video. This is, in turn, important for using video
in smart-city applications. We perform a series of experiments to show our
approach is able to accurately map activities both spatially and temporally. We
also demonstrate the advantages of using the visual content over the
tags/titles.Comment: Accepted at ACM SIGSPATIAL 201
Scaling of city attractiveness for foreign visitors through big data of human economical and social media activity
Scientific studies investigating laws and regularities of human behavior are
nowadays increasingly relying on the wealth of widely available digital
information produced by human social activity. In this paper we leverage big
data created by three different aspects of human activity (i.e., bank card
transactions, geotagged photographs and tweets) in Spain for quantifying city
attractiveness for the foreign visitors. An important finding of this papers is
a strong superlinear scaling of city attractiveness with its population size.
The observed scaling exponent stays nearly the same for different ways of
defining cities and for different data sources, emphasizing the robustness of
our finding. Temporal variation of the scaling exponent is also considered in
order to reveal seasonal patterns in the attractivenessComment: 8 pages, 3 figures, 1 tabl
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
Using Flickr to identify and connect tourism Points of Interest: The case of Lisbon, Porto and Faro
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsUnderstanding the movement of tourists helps not only the management of cities but also to enhance the most attractive places. The growth of people in social media allows us to have greater access to information about user preferences, reviews, and shared moments. Information can be used to study tourist activity.
Here, it is used geo-tagged photographs from the social media platform Flickr, to identify the locations of tourists’ Points of Interest in Lisbon, Porto and Faro and quantify their relationship from the user’s co-occurrence in the identified points.
The results show that, using standard clustering methods, it is possible to identify likely candidate Points of Interest. The association of the Points of Interest from users’ social media activity (i.e., posting of photos) results in a non-trivial network that breaks geographical proximity. It was found that, in all the cities under study, historical places (such as churches and cathedrals), viewpoints and beaches are captured
The network structure of visited locations according to geotagged social media photos
Businesses, tourism attractions, public transportation hubs and other points
of interest are not isolated but part of a collaborative system. Making such
collaborative network surface is not always an easy task. The existence of
data-rich environments can assist in the reconstruction of collaborative
networks. They shed light into how their members operate and reveal a potential
for value creation via collaborative approaches. Social media data are an
example of a means to accomplish this task. In this paper, we reconstruct a
network of tourist locations using fine-grained data from Flickr, an online
community for photo sharing. We have used a publicly available set of Flickr
data provided by Yahoo! Labs. To analyse the complex structure of tourism
systems, we have reconstructed a network of visited locations in Europe,
resulting in around 180,000 vertices and over 32 million edges. An analysis of
the resulting network properties reveals its complex structure.Comment: 8 pages, 3 figure
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