v4 - Boston Crash Map
A map of Boston displays red points marking crash locations from the city’s crash dataset, with an outline of the state drawn from hardcoded GeoJSON. A point is plotted for each record’s latitude and longitude. Using a D3 geoMercator projection and geoPath, the code renders the map, and D3’s data join appends circles to the SVG. The crashes.csv file supplies the coordinates for each marker.
AI-generated descriptionData
The data I propose to visualize for my project is from a Boston Crash Dataset. This Dataset is pulled from data.boston.gov, provided as part of the Vision Zero Boston program, contains records of the date, time, location, and type of crash for incidents requiring public safety response which may involve injuries or fatalities. All records are compiled by the Department of Innovation and Technology from the City's Computer-Aided Dispatch (911) system and verified as having required a response from a public safety agency. To protect the privacy of individuals involved in these incidents, we do not indicate the severity of specific crashes or whether medical care was provided in any specific case.
I want to understand the geographic distribution of crashes. I want to understand how crashes have changed of time (time of day, time of week, time of year, year-after-year, etc.) I want to identify subsets of the data where crashes have a high value. I want to identify crash rates for location types. I want to identify crash rates for mode/vehicle type.
Questions & Tasks
The following tasks and questions will drive the visualization and interaction decisions for this project:
How does the amount of crashes vary over geographic location? Is there any patterns (correlation) between the number of crashes and the time of day? day of week? time of year? from year-to-year? Do different locations have varying crash rates? DO different vehicle types have higher crash rates?