3,255 research outputs found
Delineating Intra-Urban Spatial Connectivity Patterns by Travel-Activities: A Case Study of Beijing, China
Travel activities have been widely applied to quantify spatial interactions
between places, regions and nations. In this paper, we model the spatial
connectivities between 652 Traffic Analysis Zones (TAZs) in Beijing by a taxi
OD dataset. First, we unveil the gravitational structure of intra-urban spatial
connectivities of Beijing. On overall, the inter-TAZ interactions are well
governed by the Gravity Model , where
, are degrees of TAZ , and the distance between
them, with a goodness-of-fit around 0.8. Second, the network based analysis
well reveals the polycentric form of Beijing. Last, we detect the semantics of
inter-TAZ connectivities based on their spatiotemporal patterns. We further
find that inter-TAZ connections deviating from the Gravity Model can be well
explained by link semantics.Comment: 6 pages, 4 figure
A stochastic model of randomly accelerated walkers for human mobility
The recent availability of large databases allows to study macroscopic
properties of many complex systems. However, inferring a model from a fit of
empirical data without any knowledge of the dynamics might lead to erroneous
interpretations [6]. We illustrate this in the case of human mobility [1-3] and
foraging human patterns [4] where empirical long-tailed distributions of jump
sizes have been associated to scale-free super-diffusive random walks called
L\'evy flights [5]. Here, we introduce a new class of accelerated random walks
where the velocity changes due to acceleration kicks at random times, which
combined with a peaked distribution of travel times [7], displays a jump length
distribution that could easily be misinterpreted as a truncated power law, but
that is not governed by large fluctuations. This stochastic model allows us to
explain empirical observations about the movements of 780,000 private vehicles
in Italy, and more generally, to get a deeper quantitative understanding of
human mobility.Comment: 10 pages, 6 figures + Supplementary informatio
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
Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data
The unprecedented urbanization in China has dramatically changed the urban
spatial structure of cities. With the proliferation of individual-level
geospatial big data, previous studies have widely used the network abstraction
model to reveal the underlying urban spatial structure. However, the
construction of network abstraction models primarily focuses on the topology of
the road network without considering individual travel flows along with the
road networks. Individual travel flows reflect the urban dynamics, which can
further help understand the underlying spatial structure. This study therefore
aims to reveal the intra-urban spatial structure by integrating the road
network abstraction model and individual travel flows. To achieve this goal, we
1) quantify the spatial interaction relatedness of road segments based on the
Word2Vec model using large volumes of taxi trip data, then 2) characterize the
road abstraction network model according to the identified spatial interaction
relatedness, and 3) implement a community detection algorithm to reveal
sub-regions of a city. Our results reveal three levels of hierarchical spatial
structures in the Wuhan metropolitan area. This study provides a data-driven
approach to the investigation of urban spatial structure via identifying
traffic interaction patterns on the road network, offering insights to urban
planning practice and transportation management
- …