154,386 research outputs found
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data
Existing urban boundaries are usually defined by government agencies for
administrative, economic, and political purposes. Defining urban boundaries
that consider socio-economic relationships and citizen commute patterns is
important for many aspects of urban and regional planning. In this paper, we
describe a method to delineate urban boundaries based upon human interactions
with physical space inferred from social media. Specifically, we depicted the
urban boundaries of Great Britain using a mobility network of Twitter user
spatial interactions, which was inferred from over 69 million geo-located
tweets. We define the non-administrative anthropographic boundaries in a
hierarchical fashion based on different physical movement ranges of users
derived from the collective mobility patterns of Twitter users in Great
Britain. The results of strongly connected urban regions in the form of
communities in the network space yield geographically cohesive, non-overlapping
urban areas, which provide a clear delineation of the non-administrative
anthropographic urban boundaries of Great Britain. The method was applied to
both national (Great Britain) and municipal scales (the London metropolis).
While our results corresponded well with the administrative boundaries, many
unexpected and interesting boundaries were identified. Importantly, as the
depicted urban boundaries exhibited a strong instance of spatial proximity, we
employed a gravity model to understand the distance decay effects in shaping
the delineated urban boundaries. The model explains how geographical distances
found in the mobility patterns affect the interaction intensity among different
non-administrative anthropographic urban areas, which provides new insights
into human spatial interactions with urban space.Comment: 32 pages, 7 figures, International Journal of Geographic Information
Scienc
Navigating MazeMap: indoor human mobility, spatio-logical ties and future potential
Global navigation systems and location-based services have found their way
into our daily lives. Recently, indoor positioning techniques have also been
proposed, and there are several live or trial systems already operating. In
this paper, we present insights from MazeMap, the first live indoor/outdoor
positioning and navigation system deployed at a large university campus in
Norway. Our main contribution is a measurement case study; we show the spatial
and temporal distribution of MazeMap geo-location and wayfinding requests,
construct the aggregated human mobility map of the campus and find strong
logical ties between different locations. On one hand, our findings are
specific to the venue; on the other hand, the nature of available data and
insights coupled with our discussion on potential usage scenarios for indoor
positioning and location-based services predict a successful future for these
systems and applications.Comment: 6 pages, accepted at PerMoby Workshop at IEEE PerCom 201
- …