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"Cheng, Siwei"
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Human mobility patterns are associated with experienced partisan segregation in US metropolitan areas
Partisan sorting in residential environments is an enduring feature of contemporary American politics, but little research has examined partisan segregation individuals experience in activity spaces through their daily activities. Relying on advances in spatial computation and global positioning system data on everyday mobility flows collected from smartphones, we measure experienced partisan segregation in two ways:
place-level partisan segregation
based on the partisan composition of its daily visitors and
community-level experienced partisan segregation
based on the segregation level of places visited by its residents. We find that partisan segregation experienced in places varies across different geographic areas, location types, and time periods. Moreover, partisan segregation is distinct from experienced segregation by race and income. We also find that partisan segregation individuals experience is relatively lower when they visit places beyond their residential areas, but partisan segregation in residential space and activity space is strongly correlated. Residents living in predominantly black, liberal, low-income, non-immigrant, more public transit-dependent, and central city communities tend to experience a higher level of partisan segregation.
Journal Article
Short-Term Load Forecasting Based on Similar Day Theory and BWO-VMD
Short-term power load forecasting at the regional level is essential for maintaining grid stability and optimizing power generation, consumption, and maintenance scheduling. Considering the temporal, periodic, and nonlinear characteristics of power load, a novel short-term load forecasting method is proposed in this paper. First, Random Forest importance ranking is applied to select similar days and a weighted eigenspace coordinate system is established to measure similarity. The daily load sequence is then decomposed into high-, medium-, and low-frequency components using Variational Mode Decomposition (VMD). The high-frequency component is predicted using the similar day averaging method, while neural networks are employed for the medium and low-frequency components, leveraging historical and similar-day data, respectively. This multi-faceted approach enhances the accuracy and granularity of load pattern analysis. The final forecast is obtained by summing the predictions of these components. The case study demonstrates that the proposed model outperforms LSTM, GRU, CNN, TCN and Transformer, with an RMSE of 660.54 MW and a MAPE of 7.81%, while also exhibiting fast computational speed and low CPU usage.
Journal Article
Assortative mating without assortative preference
Significance Assortative mating, the tendency of men and women who marry to have similar social characteristics, is a commonly observed phenomenon in human societies. This study shows that assortative mating could result from structural causes independent of human agents’ preference, because unmarried persons who newly enter marriage are systematically different from those who married earlier. Thus, assortative mating could result from selection, not by rational choice, but by the dynamics of social structures.
Assortative mating—marriage of a man and a woman with similar social characteristics—is a commonly observed phenomenon. In the existing literature in both sociology and economics, this phenomenon has mainly been attributed to individuals’ conscious preferences for assortative mating. In this paper, we show that patterns of assortative mating may arise from another structural source even if individuals do not have assortative preferences or possess complementary attributes: dynamic processes of marriages in a closed system. For a given cohort of youth in a finite population, as the percentage of married persons increases, unmarried persons who newly enter marriage are systematically different from those who married earlier, giving rise to the phenomenon of assortative mating. We use microsimulation methods to illustrate this dynamic process, using first the conventional deterministic Gale–Shapley model, then a probabilistic Gale–Shapley model, and then two versions of the encounter mating model.
Journal Article
Hybrid Model for Medium-Term Load Forecasting in Urban Power Grids
In urban power planning, it is typically necessary to predict future monthly, quarterly, and annual electricity consumption to conduct advance planning and ensure the stable operation of the power grid. Therefore, accurate medium-term load forecasting is of critical importance for urban power grid planning and operation. However, current research primarily focuses on short-term forecasting, which is largely limited to a single timescale. To address this issue, this paper proposes a combined model for medium-term load forecasting, enabling predictions of loads over multiple timescales within the next year. This can help optimize power supply planning. First, by improving the 3σ criterion and incorporating holiday corrections, the original data are processed. Combining the advantages of the Prophet algorithm in capturing linear relationships and future trends with the Random Forest algorithm in capturing nonlinear relationships, a Prophet–Random Forest combined forecasting model is constructed. This model is then applied to predict the electricity consumption of a city in southern China. The results demonstrate that the proposed model achieves high accuracy in medium-term forecasting and can predict loads across multiple timescales. Specifically, for annual, quarterly, and monthly predictions, the average prediction errors are 1.02%, 2.66%, and 3.92%, respectively, showcasing strong forecasting performance.
Journal Article
Economic Inequality and the Geography of Activity Space Segregation: Combining Mobile Device Data and Census Data
This article combines daily mobility data collected via mobile device and the American Community Survey to create comprehensive measures of activity space segregation across geographic areas in the United States. We extend conventional measures of spatial segregation to incorporate exposure in individuals’ routine activities, weighted by the flows of individuals between census block groups. Our analysis reveals three key findings. First, metropolitan areas vary significantly in the degree of activity space segregation. Second, individuals exhibit a lesser degree of income and racial segregation in their activity space than in their residential space. Third, income inequality at the metropolitan statistical area level is associated with greater isolation for both lowest and highest income groups; economic inequality exerts a more substantial influence on activity space isolation than residential segregation.
Journal Article
Family Structure and Cohort Trends in Childhood Family Income Volatility
The authors examine cohort trends in childhood income volatility among U.S. children born between 1970 and 1990. In contrast to previous studies that focused mainly on period trends, the authors adopt a cohort-life-course perspective and measure children’s exposure to income volatility from birth to age 17, which provides a more adequate account of the economic environment during early life stages. Using data from the Panel Study of Income Dynamics, the authors investigate (1) how income volatility among U.S. children has changed across cohorts, (2) how cohort trends in income volatility differ by family structure, and (3) the extent to which the increasing prevalence of single-headed families and the growth of income volatility among single-headed families contribute to the overall trend in childhood income volatility. The results show that (1) income volatility in childhood has increased over time; (2) children who lived in single-headed families experienced greater increase in childhood income volatility than those from two-headed families; and (3) both the increasing prevalence of single-headed families (“composition effects”) and the fact that single-headed families’ incomes have become less stable (“volatility effects”) in the past decades account for a significant proportion of the increasing income volatility for U.S. children, yet their relative contributions differ between White and Black families.
Journal Article
Structural effect of size on interracial friendship
Social contexts exert structural effects on individuals' social relationships, including interracial friendships. In this study, we posit that, net of group composition, total context size has a distinct effect on interracial friendship. Under the assumptions of (i) maximization of preference in choosing a friend, (ii) multidimensionality of preference, and (iii) preference for same-race friends, we conducted analyses using microsimulation that yielded three main findings. First, increased context size decreases the likelihood of forming an interracial friendship. Second, the size effect increases with the number of preference dimensions. Third, the size effect is diluted by noise, i.e., the random component affecting friendship formation. Analysis of actual friendship data among 4,745 American high school students yielded results consistent with the main conclusion that increased context size promotes racial segregation and discourages interracial friendship.
Journal Article
A Life Course Trajectory Framework for Understanding the Intracohort Pattern of Wage Inequality
Much research has been devoted to cross-sectional and intercohort patterns of wage inequality, but relatively little is known about the mechanisms for the intracohort pattern of wage inequality. To fill this intellectual gap, this article establishes a life course trajectory (LCT) framework for understanding the intracohort pattern of wage inequality. First, the author proposes and conceptualizes three essential properties of the LCT framework (random variability, trajectory heterogeneity, and cumulative advantage) that are used to establish a mathematical formalization of the LCT framework. Both the conceptualization and the formalization imply that intracohort wage inequality will increase over the life course due to random variability, trajectory heterogeneity, and cumulative advantage. Finally, the author combines the LCT framework with the multilevel growth curve model, then applies the model to data from the NLSY79, and finds support for the significance of random variability, trajectory heterogeneity, and between-group cumulative advantage properties but not the within-group cumulative advantage property. Adapted from the source document.
Journal Article