Data Visualization

This page is for presenting our data and how we analyzed and utilized it.

Data Analysis

Analysis of both Static and Continuous Data Features

Raw Heatmap

Raw Crash Data

This is a graphical plot of the raw crash data from Utah. The green is a crash, and we used coordinates to plot it. It is very explicit in showing how concentrated the crashes are in certain areas, and the vast amount of crashes are shocking as well. Note these are real crashes, not predicted ones.

Raw Crashes

Crashes per Road

This graphical visualization shows the streets and roads on a map, with latitude and longitude. The purple and yellow display roads and streets, and it shows where crashes occur. The blank is areas without roads. This is more representative of what city planners would create by hand, but we automate the process and include raw crash data.


Crash Frequency, by Hour and Month

This shows the frequency of crashes on an hourly basis per month. We plotted this using our raw CSV crash files. It is clear that there are distinct spikes in crashes during rush hour, and specifically in Utah, during Jan and Feb(winter), there is more ice and snow, causing increases in accidents.

Major Crashes

High Intensity and Magnitude Crashes

This graphic is similar to to the first few, as it shows the streets and raw crashes. However, this isolates for crashes labeled as "high magnitude", and shows how frequently and how often they occur. It is clear that they occur near intersections or on long highways, which could be indicative of poor signage.


Map with Temperature Data

This map shows the temperature variance in the area analyzed for crashes. This shows the blueish area as colder. This data is collected from local weather stations dating back to 2010, collected by our web scraper. This helps us understand how temperature and weather play a role in crashes.


Billboards and Crashes

This plot shows the location of signs, billboards and other distractions that are along these roads. These can be a big factor in why crashes occur, as people may lose focus as they will view these billboards while driving, causing more accidents. Plotted against crashes and predicted crashes, this can help city planners understand where to locate billboards going forward.


Crashes at Intersections

This graphic displays intersections and where crashes have occurred on them. It is clear that there are lots of crashes that occur at an intersection, considering how dangerous they may be. With predicted crashes and data like this, city planners can determine how to make intersections more safe by possibly removing blind spots or possibly adding more stop signs.


Crashes on Windy Roads

As expected, increased curvature on roads would lead to more crashes, which is evident in this visualization. With increased curvature and windiness, drivers may lose focus or control of their vehicle, meaning crashes occur more frequently. City planners can take advantage of graphics like this, and to address the problem, they could enforce stricter speed limits, increase signage and warnings, or even redirect very windy roads.