Dataset for "Social and behavioral determinants of indoor temperatures in air-conditioned homes"
This dataset is associated with the manuscript by Wright et al. Social and behavioral determinants of indoor temperatures in air-conditioned homes to be published in 2020 in the journal Building and Environment. As part of the 3HEAT project (NSF award #1520803), this dataset includes indoor temperatures from Phoenix households (PHX_indoor_wide.csv), social survey responses (phx_survey_indoor_manuscript_v2.csv) and outdoor temperatures obtained from the local Phoenix Sky Harbor International Airport National Weather Service weather station (skyharbor.csv). The file 'README_file descriptions.csv' contains brief description of what each file contains and descriptions for each column header. Finally, 'Code for Wright et al Indoor temperatures.Rmd' is an R markdown file that contains the R code necessary to run analyses that appear in the associated manuscript. For a greater description of how these datasets were curated, please refer to the associated manuscript. In brief, indoor temperatures were collected from 46 study participants' homes in the summer of 2016 in Phoenix, Arizona, USA. Study participants were administered a social survey to find out how they use air conditioning, their limitations on air conditioning use, their set and preferred temperatures, and their household resources. The attached social survey data contain no personally identifiable information and the study protocol was reviewed and approved by the Institutional Review Board at Arizona State University (study number 2831). The R script was used to run exploratory analyses on the indoor temperature data and discover associations between the indoor temperatures and the social and behavioral variables from the survey data.
Steps to reproduce
To replicate study results in the associated manuscript in Building and Environment, download all files to the same file directory. Run the R markdown (.Rmd) file in RStudio using R version 3.5.x (R versions 3.6 and later produce slight differences in the output of analyses that require random processes (k-means, permutational ANOVA) because the way in which R software handles random numbers changed; however, these differences are minimal and do not significantly change the main findings of the research). The data cannot be perfectly reproduced because of its nature. However, the data collection process (collecting indoor temperatures and administering a survey) can be replicated by following the methodology outlined in the associated manuscript.