Introduction

New York City is experiencing an increase of accidents amongst cyclist every year and as a results the summer of 2020 reported the highest number of bicycle accidents. In a report created on September 2020, I created a graph detailing the number of accident each month from August 2017 to August 2020. The graph contained information on the accidents happening in each month but did not have any information on the areas in which the accidents were occuring. The purpose of this lab is to create a map that will allow viewers to identify the regions that have the highest number of bicycle accidents.

Inspirations

In my previous report, I was inspired by a heat map of the accidents in NYC. While the heat map pointed out the areas that are high in accidents, the map was not specified in the areas accidents occured and also did not have any other bit of information for the viewers to read.

Methodology

The dataset used for this lab was used previously in a report I created counting the number of accidents per month from August 2017 to August 2020. The data was reviewed and processed through Openrefine and analyzed with Tableau.

This lab specifically focuses on illustrating the number of accident in each zip code using Carto which is a Software that provides GIS, web mapping, and spatial data science tools.

To add another layer of information, A Dataset on bicycle routes was obtained through data.cityofnewyork.us/. The data contains the Geojson information that would create the bike routes found in the final results of this lab.

The base layer in Carto contained a basic map of New York City, in order to illustrate the zip code and its boundaries the information had to be obtained using data.cityofnewyork.us/.

Results

The result produced a map that contained nyc’s map divided by it zip code borders. Combining the three different datasets in Carto allows the creation of a interactive map that the viewers can click around and selecting a location to see the name of the borough, population and the zip code of the selected area. Carto also can create widget with informations from the csv. The widget displays information on the top contributing factor of accidents.

Reflections

While examining the map I noticed that the regions with the highest number of accident usually occur near the entrances of the bridges. In a previous lab I created a heatmap that highlighted the hours in which accidents occur. Taking in these sets of information I would like to observe the flow of traffic near the entrances of the bridges during the hours of highest accident.