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Percolation transition of k-frequent destinations network for urban mobility
Authors:
Weiyu Zhang,
Furong Jia,
Jianying Wang,
Yu Liu,
Gezhi Xiu
Abstract:
Urban spatial interactions are a complex aggregation of routine visits and random explorations by individuals. The inherent uncertainty of these random visitations poses significant challenges to understanding urban structures and socioeconomic developments. To capture the core dynamics of urban interaction networks, we analyze the percolation structure of the $k$-most frequented destinations of i…
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Urban spatial interactions are a complex aggregation of routine visits and random explorations by individuals. The inherent uncertainty of these random visitations poses significant challenges to understanding urban structures and socioeconomic developments. To capture the core dynamics of urban interaction networks, we analyze the percolation structure of the $k$-most frequented destinations of intracity place-to-place flows from mobile phone data of eight major U.S. cities at a Census Block Group (CBG) level. Our study reveals a consistent percolation transition at $k^* = 130$, a critical threshold for the number of frequently visited destinations necessary to maintain a cohesive urban network. This percolation threshold proves remarkably consistent across diverse urban configurations, sizes, and geographical settings over a 48-month study period, and can largely be interpreted as the joint effect of the emergence of hubness and the level of mixing of residents. Furthermore, we examine the socioeconomic profiles of residents from different origin areas categorized by the fulfillment level of $k^*=130$ principal destinations, revealing a pronounced distinction in the origins' socioeconomic advantages. These insights offer a nuanced understanding of how urban spaces are interconnected and the determinants of travel behavior. Our findings contribute to a deeper comprehension of the structural dynamics that govern urban spatial interactions.
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Submitted 24 March, 2025; v1 submitted 21 June, 2024;
originally announced June 2024.
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Unraveling the Variations of the Society of England and Wales through Diffusion Maps Analysis on Census 2011
Authors:
Gezhi Xiu,
Huanfa Chen
Abstract:
We propose a new approach to identifying geographical clustering and hotspots of inequality from decadal census data. We use diffusion mapping to study the 181,408 Output Areas in England and Wales, which allows us to decompose the feature structures of countries in the census data space. Additionally, we develop a new localization metric inspired by statistical physics to uncover the importance o…
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We propose a new approach to identifying geographical clustering and hotspots of inequality from decadal census data. We use diffusion mapping to study the 181,408 Output Areas in England and Wales, which allows us to decompose the feature structures of countries in the census data space. Additionally, we develop a new localization metric inspired by statistical physics to uncover the importance of minority groups in London. The results of our study can be applied to other census-like data constructions that include spatial localization and differentiation from low degrees of freedom. This new approach can help us better understand the patterns of social deprivation and segregation across the country and aid in the development of policies to address these issues.
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Submitted 17 February, 2023;
originally announced February 2023.
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Mobility Census for monitoring rapid urban development
Authors:
Gezhi Xiu,
Jianying Wang,
Thilo Gross,
Mei-Po Kwan,
Xia Peng,
Yu Liu
Abstract:
Monitoring urban structure and development requires high-quality data at high spatiotemporal resolution. While traditional censuses have provided foundational insights into demographic and socioeconomic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analyzing alternative big-data source…
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Monitoring urban structure and development requires high-quality data at high spatiotemporal resolution. While traditional censuses have provided foundational insights into demographic and socioeconomic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analyzing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a byproduct of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high spatial resolution (here, 500m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.
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Submitted 19 March, 2024; v1 submitted 11 December, 2022;
originally announced December 2022.