1,664 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
Inferring land use from mobile phone activity
Understanding the spatiotemporal distribution of people within a city is
crucial to many planning applications. Obtaining data to create required
knowledge, currently involves costly survey methods. At the same time
ubiquitous mobile sensors from personal GPS devices to mobile phones are
collecting massive amounts of data on urban systems. The locations,
communications, and activities of millions of people are recorded and stored by
new information technologies. This work utilizes novel dynamic data, generated
by mobile phone users, to measure spatiotemporal changes in population. In the
process, we identify the relationship between land use and dynamic population
over the course of a typical week. A machine learning classification algorithm
is used to identify clusters of locations with similar zoned uses and mobile
phone activity patterns. It is shown that the mobile phone data is capable of
delivering useful information on actual land use that supplements zoning
regulations.Comment: To be presented at ACM UrbComp201
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