40 research outputs found
Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries
Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions.National Science Foundation (U.S.)Singapore-MIT Alliance for Research and Technolog
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
Geo-located Twitter as the proxy for global mobility patterns
In the advent of a pervasive presence of location sharing services
researchers gained an unprecedented access to the direct records of human
activity in space and time. This paper analyses geo-located Twitter messages in
order to uncover global patterns of human mobility. Based on a dataset of
almost a billion tweets recorded in 2012 we estimate volumes of international
travelers in respect to their country of residence. We examine mobility
profiles of different nations looking at the characteristics such as mobility
rate, radius of gyration, diversity of destinations and a balance of the
inflows and outflows. The temporal patterns disclose the universal seasons of
increased international mobility and the peculiar national nature of overseen
travels. Our analysis of the community structure of the Twitter mobility
network, obtained with the iterative network partitioning, reveals spatially
cohesive regions that follow the regional division of the world. Finally, we
validate our result with the global tourism statistics and mobility models
provided by other authors, and argue that Twitter is a viable source to
understand and quantify global mobility patterns.Comment: 17 pages, 13 figure
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