EPJ Data Science最新文献_第8页

Human-network regions as effective geographic units for disease mitigation 将人类网络区域作为有效缓解疾病的地理单元

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-18 DOI: 10.1140/epjds/s13688-023-00426-1

Clio Andris, Caglar Koylu, Mason A. Porter

{"title":"Human-network regions as effective geographic units for disease mitigation","authors":"Clio Andris, Caglar Koylu, Mason A. Porter","doi":"10.1140/epjds/s13688-023-00426-1","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00426-1","url":null,"abstract":"Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"36 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138717253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UTDRM: unsupervised method for training debunked-narrative retrieval models UTDRM：训练揭穿性叙事检索模型的无监督方法

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-13 DOI: 10.1140/epjds/s13688-023-00437-y

Iknoor Singh, Carolina Scarton, Kalina Bontcheva

引用次数: 0

Multiple gravity laws for human mobility within cities 城市内人类流动的多重重力定律

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-11 DOI: 10.1140/epjds/s13688-023-00438-x

Oh-Hyun Kwon, Inho Hong, Woo-Sung Jung, Hang-Hyun Jo

引用次数: 0

Linking physical violence to women’s mobility in Chile 将身体暴力与智利妇女的流动性联系起来

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-04 DOI: 10.1140/epjds/s13688-023-00430-5

Hugo Contreras, Cristian Candia, Rodrigo Troncoso, Leo Ferres, Loreto Bravo, Bruno Lepri, Carlos Rodriguez-Sickert

{"title":"Linking physical violence to women’s mobility in Chile","authors":"Hugo Contreras, Cristian Candia, Rodrigo Troncoso, Leo Ferres, Loreto Bravo, Bruno Lepri, Carlos Rodriguez-Sickert","doi":"10.1140/epjds/s13688-023-00430-5","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00430-5","url":null,"abstract":"Despite increased global attention on violence against women, understanding the factors that lead to women becoming victims remains a critical challenge. Notably, the impact of domestic violence on women’s mobility—a critical determinant of their social and economic independence—has remained largely unexplored. This study bridges this gap, employing police records to quantify physical and psychological domestic violence, while leveraging mobile phone data to proxy women’s mobility. Our analyses reveal a negative correlation between physical violence and female mobility, an association that withstands robustness checks, including controls for economic independence variables like education, employment, and occupational segregation, bootstrapping of the data set, and applying a generalized propensity score matching identification strategy. The study emphasizes the potential causal role of physical violence on decreased female mobility, asserting the value of interdisciplinary research in exploring such multifaceted social phenomena to open avenues for preventive measures. The implications of this research extend into the realm of public policy and intervention development, offering new strategies to combat and ultimately eradicate domestic violence against women, thereby contributing to wider efforts toward gender equity.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"30 6","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices 计算社会科学正在成长：青春期为何包括接受测量验证、理论发展和开放科学实践

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-01 DOI: 10.1140/epjds/s13688-023-00434-1

Timon Elmer

引用次数: 0

Individual mobility deep insight using mobile phones data 利用手机数据深入洞察个人移动性

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-01 DOI: 10.1140/epjds/s13688-023-00431-4

C. Mizzi, Alex Baroncini, Alessandro Fabbri, Davide Micheli, Aldo Vannelli, Carmen Criminisi, Susanna Jean, Armando Bazzani

引用次数: 0

Structural gender imbalances in ballet collaboration networks 芭蕾合作网络中的结构性性别失衡

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-11-23 DOI: 10.1140/epjds/s13688-023-00428-z

Yessica Herrera-Guzmán, Eun Lee, Heetae Kim

{"title":"Structural gender imbalances in ballet collaboration networks","authors":"Yessica Herrera-Guzmán, Eun Lee, Heetae Kim","doi":"10.1140/epjds/s13688-023-00428-z","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00428-z","url":null,"abstract":"Ballet, a mainstream performing art predominantly associated with women, exhibits significant gender imbalances in leading positions. However, the collaboration’s structural composition vis-à-vis gender representation in the field remains unexplored. Our study investigates the gendered labor force composition and collaboration patterns in ballet creations. Our findings reveal gender disparities in ballet creations aligned with gendered collaboration patterns and women’s occupation of more peripheral network positions than men. Productivity disparities show women accessing 20–25% of ballet creations compared to men. Mathematically derived perception errors show the underestimation of women artists’ representation within ballet collaboration networks, potentially impacting women’s careers in the field. Our study highlights the structural imbalances that women face in ballet creations and emphasizes the need for a more inclusive and equal professional environment in the ballet industry. These insights contribute to a broader understanding of structural gender imbalances in artistic domains and can inform cultural organizations about potential affirmative actions toward a better representation of women leaders in ballet.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"18 12","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Temperature impact on the economic growth effect: method development and model performance evaluation with subnational data in China 温度对经济增长效应的影响:基于中国次国家级数据的方法开发与模型绩效评价

2区计算机科学

EPJ Data Science Pub Date : 2023-10-27 DOI: 10.1140/epjds/s13688-023-00425-2

Yu Song, Zhihua Pan, Fei Lun, Buju Long, Siyu Liu, Guolin Han, Jialin Wang, Na Huang, Ziyuan Zhang, Shangqian Ma, Guofeng Sun, Cong Liu

{"title":"Temperature impact on the economic growth effect: method development and model performance evaluation with subnational data in China","authors":"Yu Song, Zhihua Pan, Fei Lun, Buju Long, Siyu Liu, Guolin Han, Jialin Wang, Na Huang, Ziyuan Zhang, Shangqian Ma, Guofeng Sun, Cong Liu","doi":"10.1140/epjds/s13688-023-00425-2","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00425-2","url":null,"abstract":"Abstract Temperature-economic growth relationships are computed to quantify the impact of climate change on the economy. However, model performance and differences of predictions among research complicate the use of climate econometric estimation. Machine learning methods provide an alternative that might improve the predictive effects. However, time series and extrapolation issues constrain methods such as random forests. We apply a simple thought experiment with national marginal GDP growth by aggregating subnational climate impact to alleviate the shortcomings in random forests. This paper uses random forests, multivariate cubic regression, and linear spline regression to examine the direct impacts of temperature on economic development and conducts a performance comparison of the methods. The model results indicate an optimal temperature of 15°C, 15°C or 21°C for each model. Furthermore, a thought experiment indicates that the marginal predictions of national GDP changes by approximately 1%, −3%, or −6% for models with 1°C warming. The performance comparison suggests that random forests have stable model performance and better prediction performance in bootstrapping. However, the extrapolation problem in random forests causes underestimation of climate impact in 5% of cells under 6°C warming. Overall, our results suggest that temperature should be considered in economic projections under climate change scenarios. We also suggest the use of more machine learning methods in climate impact assessment.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"13 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136262842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Does noise affect housing prices? A case study in the urban area of Thessaloniki 噪音会影响房价吗?以塞萨洛尼基市区为例

2区计算机科学

EPJ Data Science Pub Date : 2023-10-17 DOI: 10.1140/epjds/s13688-023-00424-3

Georgios Kamtziridis, Dimitris Vrakas, Grigorios Tsoumakas

{"title":"Does noise affect housing prices? A case study in the urban area of Thessaloniki","authors":"Georgios Kamtziridis, Dimitris Vrakas, Grigorios Tsoumakas","doi":"10.1140/epjds/s13688-023-00424-3","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00424-3","url":null,"abstract":"Abstract Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available a general purpose noise pollution dataset based on published studies conducted by the Hellenic Ministry of Environment and Energy for the city of Thessaloniki, Greece. Then, we train ensemble machine learning models, like XGBoost, on property data for different areas of Thessaloniki to investigate the way noise influences prices through interpretability evaluation techniques. Our study provides a new noise pollution dataset that not only demonstrates the impact noise has on housing prices, but also indicates that the influence of noise on prices significantly varies among different areas of the same city.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135994955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The structure of segregation in co-authorship networks and its impact on scientific production 合作作者网络中的隔离结构及其对科学成果的影响

2区计算机科学

EPJ Data Science Pub Date : 2023-10-09 DOI: 10.1140/epjds/s13688-023-00411-8

Ana Maria Jaramillo, Hywel T. P. Williams, Nicola Perra, Ronaldo Menezes

{"title":"The structure of segregation in co-authorship networks and its impact on scientific production","authors":"Ana Maria Jaramillo, Hywel T. P. Williams, Nicola Perra, Ronaldo Menezes","doi":"10.1140/epjds/s13688-023-00411-8","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00411-8","url":null,"abstract":"Abstract Co-authorship networks, where nodes represent authors and edges represent co-authorship relations, are key to understanding the production and diffusion of knowledge in academia. Social constructs, biases (implicit and explicit), and constraints (e.g. spatial, temporal) affect who works with whom and cause co-authorship networks to organise into tight communities with different levels of segregation. We aim to examine aspects of the co-authorship network structure that lead to segregation and its impact on scientific production. We measure segregation using the Spectral Segregation Index (SSI) and find four ordered categories: completely segregated, highly segregated, moderately segregated and non-segregated communities. We direct our attention to the non-segregated and highly segregated communities, quantifying and comparing their structural topologies and k -core positions. When considering communities of both categories (controlling for size), our results show no differences in density and clustering but substantial variability in the core position. Larger non-segregated communities are more likely to occupy cores near the network nucleus, while the highly segregated ones tend to be closer to the network periphery. Finally, we analyse differences in citations gained by researchers within communities of different segregation categories. Researchers in highly segregated communities get more citations from their community members in middle cores and gain more citations per publication in middle/periphery cores. Those in non-segregated communities get more citations per publication in the nucleus. To our knowledge, this work is the first to characterise community segregation in co-authorship networks and investigate the relationship between community segregation and author citations. Our results help study highly segregated communities of scientific co-authors and can pave the way for intervention strategies to improve the growth and dissemination of scientific knowledge.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135095076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0