The dataset we are using to create these visualizations comes from the Fire Department Calls for Service.
We filtered our data to only include the latest 3 months of data which begins on December 1st, 2018 and ends on Febuary 28th, 2019.
To create our charts, we used these columns:
Call Type - whether the incident call was categorized as Alarms, Citizen Assist/Service Call, Electrical Hazard, Gas Leak (Natural and LP Gas), Medical Incident, Other, Outside Fire, Structure Fire, Traffic Collision, or Water Rescue.
Call Date - Date the call is received at the 911 Dispatch Center. Used for reporting purposes.
Received DtTm - Date and time of call is received at the 911 Dispatch Center.
Entry DtTm - Date and time the 911 operator submits the entry of the initical call information into the CAD system
Dispatch DtTm - Date and time the 911 operator dispatches this unit to the call.
On Scene DtTm - Date and time the unit records arriving to the location of the incident
Transport DtTm - If this unit is an ambulance, date and time the unit begins the transport to the hospital
Hospital DtTm - If this unit is an ambulance, date and time the unit arrives at the hospital.
This is the pre-processing that we performed for the following visualizations:
Darianne:
Pre-feedback - For the first Visualization I did not have to do much wrangling with the data besides calculating the number of incidents which I calculated by counting the number of Call Type for each Call Type, since every incident had a Call Type associated with it.
Final - After recieving some feedback about regrouping my area chart, I had to do some data wrangling. Originally my csv file looked similar to the Fire Department Calls for Service data set
in the sense that the calltype was a column of different incident categories (I did have way fewer columns and limit the date range of the data of course),
but for my code I needed to have each category of call type have its own column with its own data for each day of the date interval:
December 1st, 2018 - February 28th, 2019. After doing this my data looked like this which made it easier to use D3's stack implementation for my area chart.
Emmit:
Pre-feedback - For this visualization, I first prefiltered the call types to only include the types that fall under "Medical Incident". Next, I filtered the data to omit all incidents
that had empty on-scene datetimes. Lastly, I calculated the duration of time between dispatch and on-scene time into minutes then I filtered the data to exclude all negative
values and values under one minute.
Final - In the final version, although it had one of the highest average travel times, I omitted the Lincoln Park data due to the lack of data that was provided.
I also implemented a heatmap instead of a line chart due to the feedback I received. I included a heatmap example in the prototypes below the line chart prototype. The
heatmap is sorted from the largest travel time averages (starting with Treasure Island) to the least travel time averages (ending with the Financial District / South Beach).
Olivia:
Pre-feedback - For my visualization, the only calculation I had to make was calculating the travel time from the scene to the hospital, which I did by subtracting the “Hospital DtTm” category from the “Transport DtTm” category. This calculation gave me the travel time since both these fields are labeled as type Date and Time in Tableau.