PALMSplus for R

Overview

palmsplusr is an extension to the Personal Activity Location Measurement System (PALMS). This R package provides a customisable platform to combine PALMS data with other sources of information (e.g., shapefiles or csv files). This enables physical activity researchers to answer higher-level questions, such as:

How much time is spent in parks?
How much MVPA is accumulated in parks?
What proportion of sedentary time is accumulated during vehicular travel?
What is the average distance of home-to-school trips?
How much MVPA is accumulated inside the schoolyard during school time?
What proportion of commuters trip chain different travel modes (e.g., walk-bus-walk)
What is the average speed of bicycle trips during peak travel times?

The PALMS data are combined with other input files to build the palmsplus simple features data frame. This can then be summarised two ways:

days provides a breakdown of information per day, per person (e.g., time spent in greenspace)
trajectories builds individual trips, and provides trip-level summaries (e.g., MVPA during the work commute). This can then be processed into multimodal trips if desired.

The user is able to specify how each data source is combined. This is done by creating field tables using highly customisable formulas.

Palms Workflow

Brief Example

This example demonstrates the most basic implementation with one participant:

library(palmsplusr)

palms <- read_palms("F:/data/csv/one_participant.csv")

palms_load_defaults(palms_epoch(palms))

The palms_load_defaults() function automatically populates the field tables with basic formulas. Each of these fields will be calculated and added to the palmsplus dataset. The default palmsplus_fields table looks like:

palmsplus_fields
#> # A tibble: 16 x 3
#>    name       formula                domain_field
#>    <chr>      <chr>                  <chr>       
#>  1 weekday    dow < 6                FALSE       
#>  2 weekend    dow > 5                FALSE       
#>  3 indoors    iov == 3               FALSE       
#>  4 outdoors   iov == 1               FALSE       
#>  5 in_vehicle iov == 2               FALSE       
#>  6 inserted   fixtypecode == 6       FALSE       
#>  7 pedestrian tripmot == 1           FALSE       
#>  8 bicycle    tripmot == 2           FALSE       
#>  9 vehicle    tripmot == 3           FALSE       
#> 10 nonwear    activityintensity < 0  TRUE        
#> 11 wear       activityintensity >= 0 TRUE        
#> 12 sedentary  activityintensity == 0 TRUE        
#> 13 light      activityintensity == 1 TRUE        
#> 14 moderate   activityintensity == 2 TRUE        
#> 15 vigorous   activityintensity == 3 TRUE        
#> 16 mvpa       moderate + vigorous    TRUE

There are four other field tables that can be customized by the user:

palmsplus_domains is used to specify aggregation domains for days
trajectory_fields is used to specify fields for trajectories
trajectory_locations is used to specify trip start and end locations (e.g., home-school trip)
multimodal_fields is used to specify the fields summarized in multimodal

Building datasets using these field tables is as simple as:

# Building palmsplus
palmsplus <- palms_build_palmsplus(palms)
#> [1/1] Computed palmsplus for: BC0627

# Building days
days <- palms_build_days(palmsplus)

# Building trajectories
trajectories <- palms_build_trajectories(palmsplus)

# Building multimodal trajectories
multimodal <- palms_build_multimodal(trajectories, 200, 10)
#> Calculating multimodal eligibility...done
#> Assigning trip numbers...done
#> Calculating fields...done

Results can easily be saved to csv or shapefile:

write_csv(days, "days.csv")
st_write(trajectories, "trajecories.shp")

Installation

The easiest way to install palmsplusr is using devtools:

library("devtools")
install_github("TheTS/palmsplusr")

Documentation and Examples

For further information and extensive examples, please see the GitHub documentation

Notes

This project is based on the palmsplus project originally written in PostgreSQL and PostGIS by Bernhard Snizek.

References

Klinker, C D, J Schipperijn, H Christian, J Kerr, A K Ersbøll, and J Troelsen. 2014. “Using Accelerometers and Global Positioning System Devices to Assess Gender and Age Differences in Children’s School, Transport, Leisure and Home Based Physical Activity.” International Journal of Behavioral Nutrition and Physical Activity 1 (11): 8.

Klinker, C D, J Schipperijn, M Toftanger, J Kerr, and J Troelsen. 2015. “When Cities Move Children: Development of a New Methodology to Assess Context-Specific Physical Activity Behaviour Among Children and Adolescents Using Accelerometers and Gps.” Health & Place 0 (31): 90–99.

Pizarro, A N, J Schipperijn, H B Andersen, J C Ribeiro, J Mota, and M P Santos. 2016. “Active Commuting to School in Portuguese Adolescents: Using Palms to Detect Trips.” Journal of Transport & Health 3 (3): 297–304.

Stewart, T, S Duncan, and J Schipperijn. 2017. “Adolescents Who Engage in Active School Transport Are Also More Active in Other Contexts: A Space-Time Investigation.” Health & Place 0 (43): 25–32.

TheTS / palmsplusr