arXiv subjects

Minjin Lee

Publications and source records attributed to Minjin Lee.

6 recordsLinked to original sources

arXiv2025

We Are What We Buy: Extracting urban lifestyles using large-scale delivery records

Lifestyle has been used as a lens to characterize a society and its people within, which includes their social status, consumption habits, values, and cultural interests. Recently, the increasing availability of large-scale purchasing records, such as credit card transaction data, has enabled data-driven studies to capture lifestyles through consumption behavior. However, the lack of detailed information on individual purchases prevents researchers from constructing a precise representation of lifestyle structures through the consumption pattern. Here, we extract urban lifestyle patterns as a composition of fine-grained product categories that are significantly consumed together. Leveraging 103,342,186 package delivery records from 2018 to 2022 in Seoul, Republic of Korea, we construct a co-consumption network of detailed product categories and systematically identify lifestyles as clusters in the network. Our results reveal five lifestyle clusters: 'Beauty lovers', 'Fashion lovers', 'Work and life', 'Homemakers', and 'Baby and hobbyists', which represent distinctive lifestyles while also being connected to each other. Moreover, the geospatial distribution of lifestyle clusters aligns with regional characteristics (business vs. residential areas) and is associated with multiple demographic characteristics of residents, such as income, birth rate, and age. Temporal analysis further demonstrates that lifestyle patterns evolve in response to external disruptions, such as COVID-19. As urban societies become more multifaceted, our framework provides a powerful tool for researchers, policymakers, and businesses to understand the shifting dynamics of contemporary lifestyles.

physics.soc-ph↗

arXiv2024

A Computational Analysis of Traffic Cluster Dynamics Using a Percolation-Based Approach in Urban Road Networks

Understanding the dynamics of traffic clusters is crucial for enhancing urban transportation systems, particularly in managing congestion and free-flow states. This study applies computational percolation theory to analyze the formation and growth of traffic clusters within urban road networks, using high-resolution taxi data from Chengdu, China. Presenting the road network as a time-dependent, weighted, directed graph, we identify distinct behaviors in traffic jam and free-flow clusters through the growth patterns of giant connected components (GCCs). A persistent gap between GCC size curves, especially during rush hours, highlights disparities driven by spatial traffic correlations. These are quantified through long-range weight-weight correlations, offering a novel computational metric for traffic dynamics. Our approach demonstrates the influence of network topology and temporal variations on cluster formation, providing a robust framework for modeling complex traffic systems. The findings have practical implications for traffic management, including dynamic signal optimization, infrastructure prioritization, and strategies to mitigate congestion. By integrating graph theory, percolation analysis, and traffic modeling, this study advances computational methods in urban traffic analysis and offers a foundation for optimizing large-scale transportation systems.

physics.soc-ph↗

arXiv2023

Exploring the relationship between the spatial distribution of roads and universal pattern of travel-route efficiency in urban road networks

Urban road networks are well known to have universal characteristics and scale-invariant patterns, despite the different geographical and historical environments of cities. Previous studies on universal characteristics of the urban road networks mostly have paid attention to their network properties but often ignored the spatial networked structures. To fill the research gap, we explore the underlying spatial patterns of road networks. In doing so, we inspect the travel-route efficiency in a given road network across 70 global cities which provides information on the usage pattern and functionality of the road structure. The efficiency is quantified by the detour patterns of the travel routes, estimated by the detour index (DI). The DI is a long-standing popular measure, but its spatiality has been barely considered so far. In this study, we probe the behavior of DI with respect to spatial variables by scanning the network radially from a city center. Through empirical analysis, we first discover universal properties in DI throughout most cities, which are summarized as a constant behavior of DI regardless of the radial position from a city center and clear collapse into a single curve for DIs for various radii with respect to the angular distance. Especially, the latter enables us to know the scaling factor in the length scale. We also reveal that the core-periphery spatial structure of the roads induces the universal pattern, which is supported by an artificial road network model. Furthermore, we visualize the spatial DI pattern on the city map to figure out the city-specific characteristics. The most and least efficient connections of several representative cities show the potential for practical implications in analyzing individual cities.

physics.soc-ph↗

arXiv2018

Imbalance of pairwise efficiency in urban street network

We investigate how efficient each area of urban street network is connected. Using the detour index, geographic features of street network and pairwise efficiency is studied. To do so, the detour index of 1,832,118 travel route pairs for 85 global cities are explored. We show that the detour index in urban street network is strongly dependent on the angular separation of a pair respective to the city center and that it is a unique property introduced by the intrinsic core-periphery structure of urban street networks. Lastly, a new way of mapping the street network to visualize the overview of efficiency level is proposed.

physics.soc-ph↗

arXiv2017

Morphology of travel routes and the organization of cities

The city is a complex system that evolves through its inherent social and economic interactions. Mediating the movements of people and resources, urban street networks offer a spatial footprint of these activities; consequently their structural characteristics have been of great interest in the literature. In comparison, relatively limited attention has been devoted to the interplay between street structure and its functional usage, i.e., the movement patterns of people and resources. To address this, we study the shape of 472,040 spatiotemporally optimized travel routes in the 92 most populated cities in the world. The routes are sampled in a geographically unbiased way such that their properties can be mapped on to each city, with their summary statistics capturing mesoscale connectivity patterns representing the complete space of possible movement in cities. The collective morphology of routes exhibits a directional bias that could be described as influenced by the attractive (or repulsive) forces resulting from congestion, accessibility and travel demand that relate to various socioeconomic factors. To capture this feature, we propose a simple metric, inness, that maps this force field. An analysis of the morphological patterns of individual cities reveals structural and socioeconomic commonalities among cities with similar inness patterns, in particular that they cluster into groups that are correlated with their size and putative stage of urban development as measured by a series of socioeconomic and infrastructural indicators. Our results lend weight to the insight that levels of urban socioeconomic development are intrinsically tied to increasing physical connectivity and diversity of road hierarchies.

physics.soc-ph↗

arXiv2015

Relating land use and human intra-city mobility

Understanding human mobility patterns -- how people move in their everyday lives -- is an interdisciplinary research field. It is a question with roots back to the 19th century that has been dramatically revitalized with the recent increase in data availability. Models of human mobility often take the population distribution as a starting point. Another, sometimes more accurate, data source is land-use maps. In this paper, we discuss how the intra-city movement patterns, and consequently population distribution, can be predicted from such data sources. As a link between land use and mobility, we show that the purposes of people's trips are strongly correlated with the land use of the trip's origin and destination. We calibrate, validate and discuss our model using survey data.

physics.soc-ph↗