Data Science and Management最新文献_第6页

Effects of economic factors on median list and selling prices in the U.S. housing market 经济因素对美国房地产市场中位价和售价的影响

Data Science and Management Pub Date : 2023-09-01 DOI: 10.1016/j.dsm.2023.08.001

Durga Vaidynathan , Parthajit Kayal , Moinak Maiti

{"title":"Effects of economic factors on median list and selling prices in the U.S. housing market","authors":"Durga Vaidynathan , Parthajit Kayal , Moinak Maiti","doi":"10.1016/j.dsm.2023.08.001","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.08.001","url":null,"abstract":"<div><p>This study investigates the effects of key economic factors on the median list price and median selling price in the U.S. housing market. Key economic factors such as interest rates, unemployment rates, inflation rates, real gross domestic product, money supply, mortgage rate, Standard & Poor’s (S&P) 500, and government expenditure are investigated to understand their relationships with housing prices. Conventional econometric models are typically used for housing market analysis; however, advancements in data science and machine learning allow these relationships to be examined more accurately. This study employs a decision tree regressor, k-nearest neighbors, random forest, and gradient boosting to enhance analysis accuracy and feature selection, thus enriching literature pertaining to machine learning in the housing market domain. The significance of housing market data as an indicator of economic growth is emphasized, and its effect on the overall economy, consumer spending, investment patterns, and financial stability is discussed. By utilizing a robust dataset and performing rigorous preprocessing, this study aims to provide valuable insights for policymakers, investors, and individuals involved in the housing sector.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71784311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revolutionizing spatial data analysis: unveiling a cutting-edge approach for batch coordinate transformation 革命性的空间数据分析:揭示批坐标转换的前沿方法

Data Science and Management Pub Date : 2023-07-06 DOI: 10.1016/j.dsm.2023.07.001

Waruna Buddhika , Kumesha Premawansha , Thushara R. Bandara , Lakdinu Samaranayake , Viraj Dayananda , Chameera Mudannayaka , Shyama Priyadarshani

{"title":"Revolutionizing spatial data analysis: unveiling a cutting-edge approach for batch coordinate transformation","authors":"Waruna Buddhika , Kumesha Premawansha , Thushara R. Bandara , Lakdinu Samaranayake , Viraj Dayananda , Chameera Mudannayaka , Shyama Priyadarshani","doi":"10.1016/j.dsm.2023.07.001","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.07.001","url":null,"abstract":"<div><p>Spatial data have become indispensable across various disciplines and provide crucial insights. These data are associated with coordinates and different coordinate systems. However, the diversity of geospatial data formats and disparate coordinate systems present challenges in harmonizing them for analysis. This study addresses the pressing need for an improved approach to the batch transformation of commonly used coordinate systems in Sri Lanka. First, we examine different coordinate transformation systems and identify their limitations. Subsequently, we present a comprehensive procedure for seamless coordinate transformations between various systems. To demonstrate the practical applications of our approach, we have developed a user-friendly desktop application capable of simultaneously converting input coordinates into multiple systems. This application streamlines the process for users unfamiliar with sophisticated geographic information system (GIS) applications and datum transformations. We validate the output coordinates transformed using our application by comparing them with those obtained from established applications such as ArcGIS and epsg.io. The results, which have been assessed based on the root mean squared error (RMSE) and mean absolute error (MAE), indicate high levels of accuracy, with a maximum RMSE of approximately 0.013 and a maximum MAE of approximately 0.008. A performance evaluation reveals that our approach is exceptionally efficient, outperforming ArcGIS and epsg.io by 40x and 60x, respectively. Moreover, the proposed pipeline holds potential as an infrastructure for developing web applications, mobile applications, and plugins for popular GIS platforms such as ArcGIS and QGIS.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000322/pdfft?md5=902e0547dd52f80eb999b91669690d7e&pid=1-s2.0-S2666764923000322-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91773965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revolutionizing spatial data analysis: Unveiling a cutting-edge approach for batch coordinate transformation 革命性的空间数据分析:揭示批坐标转换的前沿方法

Data Science and Management Pub Date : 2023-07-01 DOI: 10.1016/j.dsm.2023.07.001

Waruna Buddhika, Kumesha Premawansha, Thushara R. Bandara, Lakdinu Samaranayake, Viraj Dayananda, Chameera Mudannayaka, Shyama Priyadarshani

引用次数: 0

A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management 一种机器学习方法，利用震级和后果的层次来形成地震类别，以指导应急管理

Data Science and Management Pub Date : 2023-06-28 DOI: 10.1016/j.dsm.2023.06.005

Donald Douglas Atsa'am , Terlumun Gbaden , Ruth Wario

{"title":"A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management","authors":"Donald Douglas Atsa'am , Terlumun Gbaden , Ruth Wario","doi":"10.1016/j.dsm.2023.06.005","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.06.005","url":null,"abstract":"<div><p>This study deployed <em>k</em>-means clustering to formulate earthquake categories based on magnitude and consequence, using global earthquake data spanning from 1900 to 2021. Based on patterns within the historical data, numeric boundaries were extracted to categorize the magnitude, deaths, injuries, and damage caused by earthquakes into low, medium, and high classes. Following a future earthquake incident, the classification scheme can be utilized to assign earthquakes into appropriate categories by inputting the magnitude, number of fatalities and injuries, and monetary estimates of total damage. The resulting taxonomy provides a means of classifying future earthquake incidents, thereby guiding the allocation and deployment of disaster management resources in proportion to the specific characteristics of each incident. Furthermore, the scheme can serve as a reference tool for auditing the utilization of earthquake management resources.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000310/pdfft?md5=d091cab3db8db2f195cb54b6af5a5125&pid=1-s2.0-S2666764923000310-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90015335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time series clustering of COVID-19 pandemic-related data COVID-19大流行相关数据的时间序列聚类

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.003

Zhixue Luo , Lin Zhang , Na Liu , Ye Wu

引用次数: 1

A trajectory data warehouse solution for workforce management decision-making 用于劳动力管理决策的轨迹数据仓库解决方案

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.002

Georgia Garani, Dimitrios Tolis, Ilias K. Savvas

{"title":"A trajectory data warehouse solution for workforce management decision-making","authors":"Georgia Garani, Dimitrios Tolis, Ilias K. Savvas","doi":"10.1016/j.dsm.2023.03.002","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.03.002","url":null,"abstract":"<div><p>In modern workforce management, the demand for new ways to maximize worker satisfaction, productivity, and security levels is endless. Workforce movement data such as those source data from an access control system can support this ongoing process with subsequent analysis. In this study, a solution to attaining this goal is proposed, based on the design and implementation of a data mart as part of a dimensional trajectory data warehouse (TDW) that acts as a repository for the management of movement data. A novel methodological approach is proposed for modeling multiple spatial and temporal dimensions in a logical model. The case study presented in this paper for modeling and analyzing workforce movement data is to support human resource management decision-making and the following discussion provides a representative example of the contribution of a TDW in the process of information management and decision support systems. The entire process of exporting, cleaning, consolidating, and transforming data is implemented to achieve an appropriate format for final import. Structured query language (SQL) queries demonstrate the convenience of dimensional design for data analysis, and valuable information can be extracted from the movements of employees on company premises to manage the workforce efficiently and effectively. Visual analytics through data visualization support the analysis and facilitate decision-making and business intelligence.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Challenges, opportunities, and advances related to COVID-19 classification based on deep learning 与基于深度学习的新冠肺炎分类相关的挑战、机遇和进展

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.005

Abhishek Agnihotri, Narendra Kohli

{"title":"Challenges, opportunities, and advances related to COVID-19 classification based on deep learning","authors":"Abhishek Agnihotri, Narendra Kohli","doi":"10.1016/j.dsm.2023.03.005","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.03.005","url":null,"abstract":"<div><p>The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities, i.e., computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches highlighted a future research possibility.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Hotspots and trends of environmental, social and governance (ESG) research: a bibliometric analysis 环境、社会与治理(ESG)研究的热点与趋势:文献计量分析

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.001

Guochao Wan , Ahmad Yahya Dawod , Somsak Chanaim , Siva Shankar Ramasamy

引用次数: 4

A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management 一种机器学习方法，利用震级和后果的层次来形成地震类别，以指导应急管理

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.06.005

D. Atsa’am, T. Gbaden, R. Wario

引用次数: 0

Cross-border issues and technology and management solutions during COVID-19 COVID-19期间的跨境问题以及技术和管理解决方案

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.004

Jae Kyu Lee , Shengsheng Huang , Yasin Ceran , Haibing Lu , Shan Liu , Wei Huang , Jian Mou

引用次数: 0