4.0 Information Visualizations - WayneWang86/UDistrict-Real-Time-Fire-Response GitHub Wiki

  • Heat Map:

Fire Call Frequency Map

For the research question “What parts of the U-district are at higher risk of incidents necessitating a fire call? What about at different times of the year?”, our main focus in the locations. Using a map would give the users a clear and direct observation of our research focus. In order to show the frequency of Fire 911 calls, heatmap would be the most suitable approach to do so. Users could learn about the incidents’ geographic distribution with one glance. It is easy to spot the location with the high frequency of incidents (with red-ish color), and low frequency of incidents (with blue-ish color). To make the heatmap interactive, we allow users to change the range of longitude and latitude (within the area around U-District) and the season. With the interactive features, users could see the incident frequency in a specific area and how the frequency changes through the seasons.

  • Bar Plot:

Number of Incidents Calls, Histogram

For the research question “What types of fire call incidents are most common, and how is this changing?”, we are focusing on the types of fire calls as well as the frequency trends through time. In order to better present the comparison through time, we decided to make a barplot. For the barplot, we would first generate the top 10 most common types of 911 fire calls. Users could select the type of their interest from a dropdown menu in the web interface. Given the type of incident, a barplot will be generated, showing the number of incidents with this specific type in each year. In each season, the barplot will group the data by seasons. This display could help users to make cross-comparison among years and seasons to find the basic trends.

  • Line Graph:

Incident Frequency in Hours, AM Incident Frequency in Hours, PM

For the research question “What types of fire call incidents are most common, and how is this changing?”, we are focusing on the types of fire calls as well as the frequency trends through time. In previous, we used a bar plot to show the comparison of frequency based on seasons and years. For this visualization, we decided to use Multiple Line Plots to give users a better sense of how the frequency of the different types of incident calls would change through times in a day. In addition, we separate the data from 2017, 2018 and 2019, so the users could make cross-comparison among years. Using multiple lines could also provide more evidence to the frequency tendency through times.