five

[Superseded] Built environment and transit use meta-analysis database

收藏
Research Data Australia2024-12-21 收录
下载链接:
https://researchdata.edu.au/superseded-built-environment-analysis-database/1945523
下载链接
链接失效反馈
官方服务:
资源简介:
This database is no longer maintained. For the up to date supporting data associated with publication listed below, please visit: https://github.com/Laura-k-a/BE-TU-Meta-analysis.Aston, L., Currie, G., Delbosc, A., Kamruzzaman, M., & Teller, D. (2020). Exploring built environment impacts on transit use - An updated meta-analysis. Transport Reviews. https://doi.org/10.1080/01441647.2020.1806941 Superseded database descriptionThis database contains a subset of another online database compiled by the authors (1). The purpose of this database is to provide traceability over the source data and methodology used to estimate elasticities for the relationship between indicators of the built environment and transit use.The 505 elasticity estimates contained in this workbook are sourced directly or derived using information available in 76 prior studies.Main contentOverview - Complete index of database content, include calculation stepsMetadata - Index of column headers describing attributes and corresponding levels in 'Database'Database - Database information for 505 data points from 76 studies. Study attributes and quantitative information relevant to screening and calculation steps is included. Calculation steps10_ Mean elasticities - Calculation of mean elasticities based on average of the weighted elasticities for data points of each indicator11_results_summary - Summary of mean elasticity and significance level for each indicatorSample_only Static table containing data for the 226 data points in the final sampleNotes1 - Aston, Laura; Currie, Graham; Delbosc, Alexa; Kamruzzaman, MD; O'Hare, Tyler; Teller, David (2019): Built environment and transit use empirical research database. figshare. Dataset. Available on figshare: https://doi.org/10.26180/5c3fe01b7fd7e
提供机构:
Monash University
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作