v5 - Boston Crash Map

v6 - Boston Crash Map

This visualization shows the geographic distribution of traffic crashes across Boston, with individual points colored by mode type: red for motor vehicles, blue for pedestrians, and green for bicycles. It also provides a legend identifying these categories. The map uses D3's `geoMercator` projection and `geoPath` to render Boston neighborhood boundaries from embedded GeoJSON data. Crash locations from the provided `crashes.csv` are plotted as circles, with coordinates projected and positioned using the same projection function.

93% match

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.

92% match

v7 - Boston Crash Map

This map shows the geographic distribution of traffic crashes in Boston, colored by mode type: motor vehicle (red), pedestrian (blue), and bicycle (green). Interactive checkboxes let viewers filter crash points by type. The visualization renders Boston’s neighborhood boundaries as GeoJSON paths using `d3.geoPath` with a `geoMercator` projection, and overlays individual crash locations from the city’s 911 dispatch dataset as SVG circles. Crash data is loaded directly from a CSV file.

v3 - MA Crash Map

This map shows the geographic distribution of traffic crashes across Boston, utilizing point data from the Boston Crash Dataset. The visualization plots each incident as a red circle, with the state and city boundaries of Massachusetts rendered as an outline. The code uses `d3.geoMercator` for projection and `d3.geoPath` to draw the map paths, while importing crash data from a CSV file to position the individual crash points.

v4 - Boston Crash Map

This visualization displays a geographic map of Boston using hardcoded GeoJSON data of the Roslindale neighborhood, rendered with a Mercator projection via `d3.geoPath`. Crash records from the Boston Crash Dataset, including time, location, and mode type, are plotted as points on the map. The code imports CSV data directly and uses `d3.select` to set up the SVG container, with a projection centered on Boston's coordinates. This version focuses on integrating the neighborhood boundary geometry with the crash data for spatial analysis.

v8 - Boston Crash Map

This map visualizes Boston crash data from data.boston.gov, displaying incidents as colored points based on crash type: red for motor vehicle, blue for pedestrian, and green for bicycle. Users can toggle crash types on and off via interactive checkboxes in the legend. The visualization uses d3.geoPath with a geoMercator projection to render Boston neighborhood boundaries from GeoJSON, and positions crash points from CSV data using the same projection system.

v6 - Boston Crash Map

A geospatial treemap of Boston crash records from the city’s Vision Zero program maps point locations from a CSV onto a GeoJSON boundary. The visualization uses `d3.geoMercator` with a center on Boston and `d3.geoPath` to render the neighborhood polygon, while individual crashes are projected as points. The code imports crash data via `crashes.csv` and draws them in an SVG container, showing the geographic distribution of incidents by their `dispatch_ts` and `mode_type` attributes.

90% match

Boston Crashes

This interactive visualization combines a geographic map of Boston with crash data from the city’s Vision Zero program, plotting each incident as a colored circle based on mode type—red for motor vehicles, blue for pedestrians, and green for bicycles. A toggleable time series chart below aggregates monthly crash counts using `d3.bin` and `timeMonth`, while checkboxes allow filtering by mode to update both views. The map uses `d3.geoMercator` and `d3.geoPath` to render Boston neighborhood boundaries from a GeoJSON file, with crash locations projected via longitude and latitude coordinates.