24,391 research outputs found
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
A Hierarchical Approach for Investigating Social Features of a City from Mobile Phone Call Detail Records
Cellphone service-providers continuously collect Call Detail Records (CDR) as
a usage log containing spatio-temporal traces of phone users. We proposed a
multi-layered hierarchical analytical model for large spatio-temporal datasets
and applied that for the progressive exploration of social features of a city,
e.g., social activities, relationships, and groups, from CDR. This approach
utilizes CDR as the preliminary input for the initial layer, and analytical
results from consecutive layers are added to the knowledge-base to be used in
the subsequent layers to explore more detailed social features. Each subsequent
layer uses the results from previous layers, facilitating the discovery of more
in-depth social features not predictable in a single-layered approach using
only raw CDR. This model starts with exploring aggregated overviews of the
social features and gradually focuses on comprehensive details of social
relationships and groups, which facilitates a novel approach for investigating
CDR datasets for the progressive exploration of social features in a
densely-populated city
Sequences of purchases in credit card data reveal life styles in urban populations
Zipf-like distributions characterize a wide set of phenomena in physics,
biology, economics and social sciences. In human activities, Zipf-laws describe
for example the frequency of words appearance in a text or the purchases types
in shopping patterns. In the latter, the uneven distribution of transaction
types is bound with the temporal sequences of purchases of individual choices.
In this work, we define a framework using a text compression technique on the
sequences of credit card purchases to detect ubiquitous patterns of collective
behavior. Clustering the consumers by their similarity in purchases sequences,
we detect five consumer groups. Remarkably, post checking, individuals in each
group are also similar in their age, total expenditure, gender, and the
diversity of their social and mobility networks extracted by their mobile phone
records. By properly deconstructing transaction data with Zipf-like
distributions, this method uncovers sets of significant sequences that reveal
insights on collective human behavior.Comment: 30 pages, 26 figure
Cell Towers as Urban Sensors: Understanding the Strengths and Limitations of Mobile Phone Location Data
Understanding urban dynamics and human mobility patterns not only benefits a wide range of real-world applications (e.g., business site selection, public transit planning), but also helps address many urgent issues caused by the rapid urbanization processes (e.g., population explosion, congestion, pollution). In the past few years, given the pervasive usage of mobile devices, call detail records collected by mobile network operators has been widely used in urban dynamics and human mobility studies. However, the derived knowledge might be strongly biased due to the uneven distribution of people’s phone communication activities in space and time.
This dissertation research applies different analytical methods to better understand human activity and urban environment, as well as their interactions, mainly based on a new type of data source: actively tracked mobile phone location data. In particular, this dissertation research achieves three main research objectives. First, this research develops visualization and analysis approaches to uncover hidden urban dynamics patterns from actively tracked mobile phone location data. Second, this research designs quantitative methods to evaluate the representativeness issue of call detail record data. Third, this research develops an appropriate approach to evaluate the performance of different types of tracking data in urban dynamics research.
The major contributions of this dissertation research include: 1) uncovering the dynamics of stay/move activities and distance decay effects, and the changing human mobility patterns based on several mobility indicators derived from actively tracked mobile phone location data; 2) taking the first step to evaluate the representativeness and effectiveness of call detail record and revealing its bias in human mobility research; and 3) extracting and comparing urban-level population movement patterns derived from three different types of tracking data as well as their pros and cons in urban population movement analysis
MOBILITY AND ACTIVITY SPACE: UNDERSTANDING HUMAN DYNAMICS FROM MOBILE PHONE LOCATION DATA
Studying human mobility patterns and people’s use of space has been a major focus in geographic research for ages. Recent advancements of location-aware technologies have produced large collections of individual tracking datasets. Mobile phone location data, as one of the many emerging data sources, provide new opportunities to understand how people move around at a relatively low cost and unprecedented scale. However, the increasing data volume, issue of data sparsity, and lack of supplementary information introduce additional challenges when such data are used for human behavioral research. Effective analytical methods are needed to meet the challenges to gain an improved understanding of individual mobility and collective behavioral patterns.
This dissertation proposes several approaches for analyzing two types of mobile phone location data (Call Detail Records and Actively Tracked Mobile Phone Location Data) to uncover important characteristics of human mobility patterns and activity spaces. First, it introduces a home-based approach to understanding the spatial extent of individual activity space and the geographic patterns of aggregate activity space characteristics. Second, this study proposes an analytical framework which is capable of examining multiple determinants of individual activity space simultaneously. Third, the study introduces an anchor-point based trajectory segmentation method to uncover potential demand of bicycle trips in a city.
The major contributions of this dissertation include: (1) introducing an activity space measure that can be used to evaluate how individuals use urban space around where they live; (2) proposing an analytical framework with three individual mobility indicators that can be used to summarize and compare human activity spaces systematically across different population groups or geographic regions; (3) developing analytical methods for uncovering the spatiotemporal dynamics of travel demand that can be potentially served by bicycles in a city, and providing suggestions for the locations and daily operation of bike sharing stations
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