Analyzing heart-pounding data: An Interview with Steve Strand, GIS pro and race car driver

By Catherine Burton

This interview with Steve Strand, GIS analyst for the Orange County Water District, was conducted and condensed by Catherine Burton. It originally appeared in the blog, Southland Spatial, and has been reprinted here by permission of the author.


Strand practices his rally driving skills at DirtFish Rally School.

I met Steve Strand at the USC Los Angeles Geospatial Summit. He presented his work on integrating homespun geospatial analysis and his own love of race car driving! Sick. I was instantly intrigued. Hope you enjoy his work as much as I do.

Q: What's your day job in 140 characters or less?

A: GIS at Orange County Water District, multitasker, water-use reporter, database builder, information disseminator, middleman eliminator

Q: How long have you been racing cars, and where and why did you initially get started in the sport?

A: I've been interested in motorsports since I was a little kid, but didn't have the opportunity to participate until I moved to Southern California to attend college.  In 2002, I started volunteering at local Sports Car Club of America rally events; working for a racing series allowed me to experience the inner workings of motorsports. After several years of volunteering, I occasionally entered races. Over the past 12 years, I’ve raced in autocross, rallycross, time attack and endurance events. In 2015, I’ll run a full season of autocross in a competitive car.

Q: As a race car driver/enthusiast, you knew there were mobile apps out there for plugging in data, and of course the professional racers capture all kinds of data. For your master’s thesis you decided to plug your own "homemade" race track GPS, heart rate and other cool data into a GIS. Can you tell the readers what inspired you to take it to the GIS level and then a little bit about the project?

A: I started using GIS in 2007 and from the beginning I was thinking about ways that GIS could help in motorsports. At first I was just making nice maps of race tracks. The first real project I did using GIS data was to generate stage notes for a rally event. Stage notes are a team’s handwritten notes, captured before an event, on how to best drive a route or course. My goal was to use GIS data to generate course notes that would be just as accurate. An ArcUser article by Mike Price is what got me started. By calculating the radius of a turn on a road, I was able to assign value ranges to equal specific rally notations for turns. That, along with basic distance calculations, allowed me to get pretty accurate notes from GIS. The only thing missing was notations for jumps, which would require very precise elevation data. In the future, I'd like to go back and work on it more.


Strand modeled the handling performance of a vehicle in relation to speed using autocross data and GIS.

My master’s thesis came together after I did a graduate school pilot project, in which I took autocross data and displayed it on a map to show the potential of GIS in motorsports. My professor enjoyed it and identified time geography visualizations as another possible way to display the data. I went one step further and used a 3D environment to create 3D time geography analysis of the data. I wanted to have the project be something that could be used at a race, which required the data be processed quickly. I used ESRI ArcMap ModelBuilder and Python to automate most of the work needed to analyze the data, which allowed for visualizations to be ready for viewing within minutes of downloading the data. 


Strand’s master’s thesis work shows the GIS-enabled heart rate data plotted in 3D as he circled the autocross course in his car.

 


Strand’s master’s thesis work shows the autocross course change in speed data plotted in 3D.

Q: What kinds of reactions do you get from your buddies who race; is it positive, negative or a mix?

A: All of the people I share my work with are impressed that data logging can be visualized in this way since they typically only work with the data in Excel or Google Earth. When they ask how it works and I start to talk about GIS and how the data is being processed, their eyes will glaze over.  I've also found that each person has their own idea of how to display the data they have logged.  I try a lot of the new visualization ideas people come up with and I always try to automate the workflow so they can see what they want quickly.  I continue to work on finding the perfect visuals for all users.


Strand races a Volvo 142s (front) in a 24 Hours of LeMons endurance race.

Q: If there were no "rules" or "limits," including laws, money or time, how would you take this work further?

A: GIS could be used in a number of applications in motorsports. Regarding my thesis, I would love to have an ArcGIS Server setup where people could send their log data directly to a database to then display on a desktop or mobile app. It could be used with all sorts of racing and would give drivers/teams the ability to look over data in near real time. 

Right now I am also trying to use my old stage/rally notes project for autocross, seeing if I can accurately model an autocross course and generate a plan of attack for a best lap time. I'll be testing this out for the first time at an event in late March and if it works I'll be looking for a way to offer it to other drivers. Combining a pre-race plan with data support during a race and also for post-race analysis would be a great thing to offer amateur drivers looking for some sort of edge on the competition. 

Another way GIS could be helpful would be using the geofencing service during a rally event. This could serve to track vehicles on each stage of a course, which could alert emergency crews when a car goes off course and doesn’t finish a stage. They would also have a specific location as to where the car went off course and would save time looking for the missing vehicle.


Strand is shown participating in a rallycross event.


Published Wednesday, April 22nd, 2015

Written by Catherine Burton


Published in

Location Intelligence


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