Investigating the relationship between affluent neighbourhoods and access to affordable and highly rated food establishments in New York City. <Ng Yim Chew Klo’e, Klo-e, kyn227> 1. IntroductionNeighbourhoods in New York City play an important role of place-making, where various neighbourhoods have a different reputation (for example the Financial district is known for great museums and historical sites and Times Square having a reputation of congregating of theatre and entertainment of all sorts) that collectively make New York City an attractive place to be in. More importantly, neighbourhoods give residents a sense of home at a more granular scale, and are often a determinant for potential house owners when sourcing for a place to stay in. There are various ingredients that contribute to good neighborhoods, such as accessibility to public facilities such as parks and schools, low crime rate and range of retail stores provided in the vicinity. Food and Beverages (F&B) establishments are also an indicator, where popular establishments mean more footfall in the neighbourhood and more “eyes on the street”, leading to public safety. Conversely , poor urban neighborhoods have been labeled “food deserts” with few grocery stores and mainly fast food restaurants ( Schuetz et al , 2012). In a Seminal working paper published by Glaeser, Luca and Kim (2018), yelp data was utilised to quantify neighbourhood change in cites, understand and predict gentrification. This paper thus seeks to utilise yelp data to investigate if whether there is a relationship between median rent of households and ‘cheap and good’ F&B establishments. 2. DataThere were a couple of key sources of data used to piece the dataset together for investigation:(1) Information about businesses – ratings (on a scale of 5), price (on a scale of 4), business location (longitude and latitude) from YELP (2) Neighbourhood boundaries of New York City from NYC Open Data(3) Demographic information on each Neighbourhood from NYC Planning department(4) Neighbourhood locations 2.1. Limitations and Issues with Data:YELP data was limited in a way that the query results will only return the first 1000 results (of food establishments in New York City) which was too little for my analysis. I went around this problem by locating the centroids of neighbourhoods and ran the code such that i will get the YELP of each individual neighbourhood instead of the whole New York City. Additionally, neighbourhoods are defined rather differently across entities (such as YELP, AirBnB as well as the New York Planning Department). While i was able to eventually match the neighbourhoods between NYC planning and YELP, there was inevitably an eventual loss in data set (lesser neighbourhoods).
Abstract Citibike, a bike sharing system, was introduced into several parts of New York City in 2013 and have since been available in many areas around with its iconic blue bicycles.Various city dwellers use it, varying from age of users to gender of users. These data are captured in the Citibikes System Data retrievable online. The team set out to investigate if the average trip duration of female bikers is longer than or equals to the average trip duration of males on average in 2016. The one-tailed t-test was used to compare the means of the two samples.Introduction CitiBike is a privately owned bicycle sharing system that serves New York City, Jersey City and New Jersey. It officially opened in 2013 and serves to provide an alternative transportation choice for a city plagued with transportation woes. We would like to investigate if there is a difference in trip duration on the Citibike between Males and Females which may give insights on usage patterns due to possible gender factors that may help Citibike improve its system and increase usage of its bike in terms of total trip duration. Some factors that contribute to gender difference includes differences in attire (hence affecting trip duration usage). H0: Average trip duration of females is longer than or equal to average trip duration of males \(Trip\ Duration_F\ -\ Trip\ Duration_M\ \ge\ 0\)H1: Average trip duration of females is shorter than average trip duration of males \(Trip\ Duration_F-Trip\ Duration_M\ <0\) Significance level: 0.05DataData was extracted from the official Citibikes System Data retrievable at https://www.citibikenyc.com/system-data. The data set for 2016 is available in four different quarters (Jan-Mar; Apr-Jun;Jul-Sep;Oct-Dec). The headings for the month of October were logged in differently from the rest of the months and we renamed the columns for consistency. We then concatenated the four different quarters into a single data frame. Data was then separated by gender (Male == 1 and Female == 2) to run the analysis.