arXiv subjects

Keith A. Burghardt

Publications and source records attributed to Keith A. Burghardt.

5 recordsLinked to original sources

arXiv2025

CHRONEX-US: City-level historical road network expansion dataset for the conterminous United States

Geospatial datasets on the long-term evolution of road networks are scarce, hampering our quantitative understanding of how the contemporary road network has evolved over the course of the 20th century. However, such information is crucial to better understand the dynamics of road network growth and expansion, and to shed light on the consequences of (sub-) urbanization processes, such as increasing mobility, traffic congestion, land take and transportation inequality. Herein, we describe CHRONEX-US ('City-level historical road network expansion dataset for the conterminous United States'), a geospatial vector dataset reporting estimates of the construction year for each road segment in densely and semi-densely built-up spaces within 693 core-based statistical areas (i.e., Metropolitan and Micropolitan statistical areas) in the conterminous US. CHRONEX-US is based on the USGS National Transportation Dataset (NTD), integrated with the historical settlement compilation for the US (HISDAC-US). CHRONEX-US reports model-based construction epoch estimates for local residential road network segments within urban and peri-urban areas, using different model-based scenarios. The vector data inherit topological integrity from the NTD data allowing for routing and other connectivity-based analyses within temporal strata of urban road networks. CHRONEX-US vector geometries are attributed with the US Census Bureau's MAF/TIGER Feature Class Code (MTFCC), enabling stratification of the data by road category. CHRONEX-US is available at https://doi.org/10.6084/m9.figshare.28644674.

physics.soc-ph↗

arXiv2021

The Emergence of Heterogeneous Scaling in Research Institutions

Research institutions provide the infrastructure for scientific discovery, yet their role in the production of knowledge is not well characterized. To address this gap, we analyze interactions of researchers within and between institutions from millions of scientific papers. Our analysis reveals that the number of collaborations scales superlinearly with institution size, though at different rates (heterogeneous densification). We also find that the number of institutions scales with the number of researchers as a power law (Heaps' law) and institution sizes approximate Zipf's law. These patterns can be reproduced by a simple model with three mechanisms: (i) researchers collaborate with friends-of-friends, (ii) new institutions trigger more potential institutions, and (iii) researchers are preferentially hired by large institutions. This model reveals an economy of scale in research: larger institutions grow faster and amplify collaborations. Our work provides a new understanding of emergent behavior in research institutions and how they facilitate innovation.

physics.soc-ph↗

arXiv2021

A Model of Densifying Collaboration Networks

Research collaborations provide the foundation for scientific advances, but we have only recently begun to understand how they form and grow on a global scale. Here we analyze a model of the growth of research collaboration networks to explain the empirical observations that the number of collaborations scales superlinearly with institution size, though at different rates (heterogeneous densification), the number of institutions grows as a power of the number of researchers (Heaps' law) and institution sizes approximate Zipf's law. This model has three mechanisms: (i) researchers are preferentially hired by large institutions, (ii) new institutions trigger more potential institutions, and (iii) researchers collaborate with friends-of-friends. We show agreement between these assumptions and empirical data, through analysis of co-authorship networks spanning two centuries. We then develop a theoretical understanding of this model, which reveals emergent heterogeneous scaling such that the number of collaborations between institutions scale with an institution's size.

physics.soc-ph↗

arXiv2017

On Quitting: Performance and Practice in Online Game Play

We study the relationship between performance and practice by analyzing the activity of many players of a casual online game. We find significant heterogeneity in the improvement of player performance, given by score, and address this by dividing players into similar skill levels and segmenting each player's activity into sessions, i.e., sequence of game rounds without an extended break. After disaggregating data, we find that performance improves with practice across all skill levels. More interestingly, players are more likely to end their session after an especially large improvement, leading to a peak score in their very last game of a session. In addition, success is strongly correlated with a lower quitting rate when the score drops, and only weakly correlated with skill, in line with psychological findings about the value of persistence and "grit": successful players are those who persist in their practice despite lower scores. Finally, we train an epsilon-machine, a type of hidden Markov model, and find a plausible mechanism of game play that can predict player performance and quitting the game. Our work raises the possibility of real-time assessment and behavior prediction that can be used to optimize human performance.

cs.CY↗

arXiv2016

Competing opinions and stubbornness: connecting models to data

We introduce a general contagion-like model for competing opinions that includes dynamic resistance to alternative opinions. We show that this model can describe candidate vote distributions, spatial vote correlations, and a slow approach to opinion consensus with sensible parameter values. These empirical properties of large group dynamics, previously understood using distinct models, may be different aspects of human behavior that can be captured by a more unified model, such as the one introduced in this paper.

physics.soc-ph↗