Assessing climate change and its effect on physical activity through NYC bike Share usage:
Heaney’s research article is about predicting the physical activity level in response to rising temperatures. They used Citi Bike’s bike sharing program to calculate the physical activity, and the meteorological data were collected from the NOAA, National Weather Service, and ASOS. They used the total hours ridden and average distance ridden, and compared it to temperature.
The authors start with assuming that people are more physically active during warmer seasons than colder season, and predicts that ridership might decline when temperatures become too hot. This assumption sounds valid. This data can also be seen in google trend when we look up for citi bike search, it shows the interest is more towards June, July and its least towards the end of the year when it gets the coldest. They tried to localize the temperature measurement using ASOS data for the Central Park, which is necessary to make accurate assessment because most of the Citi Bike are concentrated in Manhattan.
There were statistical adjustments made to gather missing data for the months of June through December 2013 in calculating the heat index using LaGuardia Airport’s hourly temperature and relative humidity values as inputs to the Rosthfusz equation. However, temperature pattern drop below 80 degree F after August, and according to the National Centers for Environmental Prediction (NCEP) the Rothfusz regression is not appropriate when conditions of temperature and humidity warrant a heat index value below 80 degrees F. This could have an effect on the accurate calculation of temperature projection for that time period. In addition, Citi Bike’s location are mostly concentrated in Manhattan, and on parts along the Hudson river of Queens and Brooklyn. The location of Citi Bike are strategically placed in Manhattan because it is a dense place and a popular place for tourists. These areas are cooler compared to other parts so, people are more likely to ride a bike in those areas, but the temperature data are collected based on central park, and the presence of river has an effect of sea breeze, which makes those areas cooler in the summer, and colder in the winter. These factors can influence and explains the increased number of riders in the summer and decrease towards the end of the year when it gets colder. This problem of temperature is solved when they conducted longitudinal study. They can use the pattern to make an accurate assessment of temperature pattern with CitiBike rider’s activity, but they cannot make precise relation of temperature with the CitiBike rider due to larger error value. The longitudinal study shows increasing pattern of CitiBike rides along with increasing temperature, but there is also history effect that causes an increase in ridership every year, which shows increased projection of hours ridden and average distance ridden. Citi Bike share program started in May of 2013 with 6000 bikes in Manhattan and some parts of Brooklyn, but next year they doubled that number, and the following year after they expanded to Long Island city, and more parts of Brooklyn. Also the map showing NYC CitiBike location shows the available bike location aligns with the current map of CitiBike station map. So their data did not account for history effect, which is one of the reasons for increasing citi bike rider, total hours ridden, and average distance ridden. Towards the end they did agree that to make an accurate assessment of bicycling behavior shifts in the future, the research must continue with holistic perspective taking into account individual, social, and other environmental influences, but failed to account and mention the history effect in their existing study.
Tenjing Sherpa 