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Visualizing Arkansas traffic fatalities, Part 2

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A couple of weeks ago, I posted a map of the traffic fatalities in Arkansas in 2015. The data came from the NHTSA, and the graphic I posted was just scratching the surface. I've sliced the data a couple of different ways and created three more sets of visualizations about that data. For the next three posts, I'll show the visualizations, my interpretation, and then the code so that non-programmers will get the goods on the front end.

The Visualizations

The first set of visualizations maps the raw number of traffic fatalities in the US. You can click to zoom the image. Each band represents a single year between 2000 and 2015. Each row within the band is a day of the week. From left to right (or top to bottom on small devices), you have drunk driving fatalities, non-drunk driving fatalities, and total fatalities. We'll repeat this comparison a number of times, and the color coding in each set of graphs uses the same scale (so we compare apples to apples across the three visualizations).  

For me, there are two things that stand out in this set of visualizations. First, drunk driving fatalities are heavily weighted towards weekends. Second, New Years' Day (the left- and top-most block) is an especially dangerous time to be in the road.

As I will with the remaining posts, I repeated the same analysis on Arkansas-specific wreck information (this requires a single line of R code given below). The data is a little noisier, but the same results appear to hold. Note that this scale is different than the nationwide set. The scale is a little skewed towards lighter colors by one extremely bad Arkansas traffic Saturday in the fall of 2004.

The Code

Now to the code. First, we'll load in all the data from the NHTSA (available at ftp.nhtsa.dot.gov/fars/). Because the files added data points over time, we'll need to select just what we need for the visualization so we can combine them all.

# Read data elements
accidents_2015 <- read.dbf("Data/FARS2015NationalDBF/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2014 <- read.dbf("Data/FARS2014NationalDBF/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2013 <- read.dbf("Data/FARS2013NationalDBF/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2012 <- read.dbf("Data/FARS2012/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2011 <- read.dbf("Data/FARS2011/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2010 <- read.dbf("Data/FARS2010/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2009 <- read.dbf("Data/FARS2009/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2008 <- read.dbf("Data/FARS2008/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2007 <- read.dbf("Data/FARS2007/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2006 <- read.dbf("Data/FARS2006/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2005 <- read.dbf("Data/FARS2005/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2004 <- read.dbf("Data/FARS2004/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2003 <- read.dbf("Data/FARS2003/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2002 <- read.dbf("Data/FARS2002/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2001 <- read.dbf("Data/FARS2001/accident.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]
accidents_2000 <- read.dbf("Data/FARSDBF00/ACCIDENT.dbf")[,c("STATE", "COUNTY", "DAY", "MONTH", "YEAR", "FATALS", "DRUNK_DR")]

# Combine annual data
accidents <- rbind(accidents_2015, accidents_2014, accidents_2013, accidents_2012, accidents_2011, accidents_2010, accidents_2009, accidents_2008, accidents_2007, accidents_2006, accidents_2005, accidents_2004, accidents_2003, accidents_2002, accidents_2001, accidents_2000)

Next, if we're looking at just Arkansas data, we need this line of code:

# Subset Arkansas wrecks
accidents <- subset(accidents, STATE == 5)

We have to add an R-specific date column to be able to use date functions later on, as follows:

# Add date column
accidents$date <- as.Date(paste(accidents$YEAR, accidents$MONTH, accidents$DAY, sep='-'), "%Y-%m-%d")

Next, we need to write the code that will let us parse the data by whether or not any driver involved in the wreck was drunk. We do this by creating R vectors. The vectors allow us to aggregate the raw wreck data by day and then subset it by whether or not it is alcohol-related, as follows:

# Subset and aggregate wrecks by drunk/not drunk
accidents_drunk <- accidents$DRUNK_DR > 0
accidents_not_drunk <- accidents$DRUNK_DR == 0
summary <- aggregate(FATALS ~ date, accidents, sum)
summary_not_drunk <- aggregate(FATALS ~ date, accidents, sum, subset=accidents_not_drunk)
summary_drunk <- aggregate(FATALS ~ date, accidents, sum, subset=accidents_drunk)

Now, we need to create empty entries for those dates that don't have any fatal wrecks (not necessary for nationwide plots, but necessary for Arkansas-specific ones). We'll add this to the data we created earlier.

# get vector of all days in relevant data range
start_year <- format(min(summary_not_drunk$date), "%Y") # Calculate start_year from data
firstday <- format(Sys.Date(), paste(start_year, "-01-01", sep=""))
lastday <- format(Sys.Date(), "2015-12-31")
alldays <- seq(c(ISOdate(start_year,01,01)), by="day", length.out=as.Date(lastday) - as.Date(firstday) + 1)

alldays <- as.data.frame(alldays)
alldays <- rename(alldays, c("alldays"="date"))
alldays[["date"]] <- as.Date(alldays[["date"]], "%Y-%m-%d")

# Uses 53-week year when week 1 has < 4 days
alldays <- transform(alldays,
week = as.numeric(format(date, "%U")),
day = as.numeric(format(date, "%d")),
wday = as.numeric(format(date, "%w"))+1,
month = as.POSIXlt(date)$mon + 1,
year = as.POSIXlt(date)$year + 1900)

# initialize date vector columns to match data columns
alldays[,"FATALS"] <- NA

data <- rbind(summary, alldays)
data_drunk <- rbind(summary_drunk, alldays)
data_not_drunk <- rbind(summary_not_drunk, alldays)

Next, we'll create some additional columns to give us the week of the year, which is how we plot the bands. This code is adapted from the very helpful r-bloggers post I mentioned in the last visualization post. As an aside, if you're interested in data visualization, you should subscribe to r-bloggers, as those guys are always posting fascinating stuff.

data$yearmonth<-as.yearmon(data$date)
data$yearmonthf<-factor(data$yearmonth)
data <- ddply(data,.(yearmonthf),transform,monthweek=1+week-min(week)) # and now for each monthblock we normalize the week to start at 1
data <- subset(data, select = -c(yearmonth, yearmonthf)) # Then, drop extra columns
data$monthf<-factor(data$month,levels=as.character(1:12),labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE)
data$weekdayf<-factor(data$wday,levels=rev(1:7),labels=rev(c("S","M","T","W","Th","F","Sa")),ordered=TRUE)

data_drunk$yearmonth<-as.yearmon(data_drunk$date)
data_drunk$yearmonthf<-factor(data_drunk$yearmonth)
data_drunk <- ddply(data_drunk,.(yearmonthf),transform,monthweek=1+week-min(week)) # and now for each monthblock we normalize the week to start at 1
data_drunk <- subset(data_drunk, select = -c(yearmonth, yearmonthf)) # Then, drop extra columns
data_drunk$monthf<-factor(data_drunk$month,levels=as.character(1:12),labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE)
data_drunk$weekdayf<-factor(data_drunk$wday,levels=rev(1:7),labels=rev(c("S","M","T","W","Th","F","Sa")),ordered=TRUE)

data_not_drunk$yearmonth<-as.yearmon(data_not_drunk$date)
data_not_drunk$yearmonthf<-factor(data_not_drunk$yearmonth)
data_not_drunk <- ddply(data_not_drunk,.(yearmonthf),transform,monthweek=1+week-min(week)) # and now for each monthblock we normalize the week to start at 1
data_not_drunk <- subset(data_not_drunk, select = -c(yearmonth, yearmonthf)) # Then, drop extra columns
data_not_drunk$monthf<-factor(data_not_drunk$month,levels=as.character(1:12),labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE)
data_not_drunk$weekdayf<-factor(data_not_drunk$wday,levels=rev(1:7),labels=rev(c("S","M","T","W","Th","F","Sa")),ordered=TRUE)

Next, we'll look at the maximum and minimum number of fatalities across the three sets of data we'll be plotting. This will allow us to keep the same scale across all three plots.

max <- max(c(max(data["FATALS"], na.rm=TRUE), max(data_not_drunk["FATALS"], na.rm=TRUE), max(data_drunk["FATALS"], na.rm=TRUE)))
min <- min(c(min(data["FATALS"], na.rm=TRUE), min(data_not_drunk["FATALS"], na.rm=TRUE), min(data_drunk["FATALS"], na.rm=TRUE)))

We're finally done with our data processing. The next step is to set up a theme for the visualization. To do this, I installed the development version of ggplot2 from github to use some of the newer features like subtitles and moving the facets (year labels) around.

# Define theme
heat_map_theme <- theme(
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.spacing.x = unit(0, "points"),
panel.spacing.y = unit(1, "points"),
strip.placement = "outside",
strip.switch.pad.grid = unit(2,"points"),
strip.background = element_rect(fill="gray90", color=NA),
strip.text = element_text(color="gray5"),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(color="gray5", size=5),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.text = element_text(color="gray5"),
legend.title = element_text(color="gray5"),
plot.title = element_text(color="gray5", hjust=0.5),
plot.subtitle = element_text(color="gray5", hjust=0.5),
plot.caption = element_text(color="gray5", hjust=1, size=6),
panel.background = element_rect(fill="transparent", color=NA),
legend.background = element_rect(fill="transparent", color=NA),
plot.background = element_rect(fill="transparent", color=NA),
legend.key = element_rect(fill=alpha("white", 0.33), color=NA)
)

Finally, we'll perform the plot on the three datasets and save the output. The important things here are:

  • The switch command in facet_grid function puts the years on the left, not the right
  • the limits in the scale_fill_gradient function keeps the scale the same across all three plots
  • the expand=c(0,0) function in scale_x_continuous() eliminates the padding around the plot area 
# Plot and save all
ggplot(data, aes(week, weekdayf)) +
geom_tile(aes(fill=FATALS), na.rm = FALSE) +
facet_grid(year ~ ., drop = FALSE, switch="y") +
scale_fill_gradient(name="Fatalities", low="yellow", high="red", na.value = alpha("white", 0.25), limits=c(min,max)) +
scale_x_continuous(expand=(c(0,0))) +
labs(title = "2000-2015 Traffic Fatalities, Arkansas", x="", y="", subtitle="by Year", caption = "(based on data from NHTSA FARS: ftp://ftp.nhtsa.dot.gov/fars)") +
heat_map_theme

filename <- paste(c("Images/2000-2015_fatalities_calendar (AR, all).png"), collapse="")
ggsave(filename, bg = "transparent")

# Plot and save drunk
ggplot(data_drunk, aes(week, weekdayf)) +
geom_tile(aes(fill=FATALS), na.rm = FALSE) +
facet_grid(year ~ ., drop = FALSE, switch="y") +
scale_fill_gradient(name="Fatalities", low="yellow", high="red", na.value = alpha("white", 0.25), limits=c(min,max)) +
scale_x_continuous(expand=(c(0,0))) +
labs(title = "2000-2015 Traffic Fatalities, Arkansas", x="", y="", subtitle="by Year (drunk driving only)", caption = "(based on data from NHTSA FARS: ftp://ftp.nhtsa.dot.gov/fars)") +
heat_map_theme

filename <- paste(c("Images/2000-2015_fatalities_calendar (AR, drunk).png"), collapse="")
ggsave(filename, bg = "transparent")

# Plot and save not drunk
ggplot(data_not_drunk, aes(week, weekdayf)) +
geom_tile(aes(fill=FATALS), na.rm = FALSE) +
facet_grid(year ~ ., drop = FALSE, switch="y") +
scale_fill_gradient(name="Fatalities", low="yellow", high="red", na.value = alpha("white", 0.25), limits=c(min,max)) +
scale_x_continuous(expand=(c(0,0))) +
labs(title = "2000-2015 Traffic Fatalities, Arkansas", x="", y="", subtitle="by Year (excludes drunk driving)", caption = "(based on data from NHTSA FARS: ftp://ftp.nhtsa.dot.gov/fars)") +
heat_map_theme

filename <- paste(c("Images/2000-2015_fatalities_calendar (AR, not drunk).png"), collapse="")
ggsave(filename, bg = "transparent")

Conclusion

I hope you found these visualizations useful. I'd love your feedback on what you insight you gathered from the visualizations, or any critiques on how to make them more useful. 

I'm new to R, so if you're seeing these and have suggestions for how to do things more efficiently, I'd love to see you comment.

Visualizing Arkansas traffic fatalities

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Nathan here again with another guest post. I recently started a master's program at UALR in information science, so I've been following several blogs on statistical programming and visualization. One of the best sites I've found is R-bloggers, which is dedicated to the popular statistical programming language R.

A recent post on R-bloggers by Lucas Puente on mapping traffic fatalities in the US caught my eye. While in private practice, I helped several Arkansas counties through the election process of voting from dry to wet. One of the often-debated issues in those races was whether the highways are safer in dry counties or wet counties. Some people believe roads are safer when alcohol is less available; other people believe that less availability means more stockpiling and more driving drunk over long distances to liquor stores after knocking a few back.

So, I decided to adapt Mr. Puente's R program to create an Arkansas-centric map. In the R-bloggers tradition, I'll explain the code and then present the results.

Part I: The Data

The data comes from two separate sources. First, the traffic fatality data comes from National Highway and Transportation Safety Administration open records, available at ftp://ftp.nhtsa.dot.gov/fars/2015/National/. As instructed by Mr. Puente, I downloaded the file FARS2015NationalDBF.zip and unzipped it. 

Next, I had to get the wet/dry status of each Arkansas county. To do this, I created my own CSV (comma-separated value) file from the Arkansas Department of Finance and Administration's wet/dry status page. I uploaded it to a Github public repository so others can use it.

library(foreign) # required to use read.dbf method
accidents <- read.dbf("accident.dbf")
wet_status <- read.csv(file="Ark_counties_wet_dry_status.csv")

Part II: Subsetting and Summarizing the Data

As Mr. Puente did, we're only going to use a portion of the NHTSA's information. However, instead of using the lower 48 states, we're just going to use data for Arkansas. We'll then sum the number of fatalities for all wrecks by county.

ark_accidents <- subset(accidents, STATE == 5)
ark_summary_all <- aggregate(FATALS ~ COUNTY, ark_accidents, sum)

 Next, we'll create a vector from the wreck subset to identify just the drunk driving wrecks. We'll use that vector to sum drunk driving fatalities per county, then calculate a percentage of wrecks involving drunk driving fatalities for each county.

ark_accidents_drunk <- ark_accidents$DRUNK_DR > 0
ark_summary_drunk <- aggregate(FATALS ~ COUNTY, ark_accidents, sum, subset=ark_accidents_drunk)
rk_summary <- merge(ark_summary_all, ark_summary_drunk, by="COUNTY", all=TRUE)
ark_summary$percent_drunk <- ark_summary$FATALS.y / ark_summary$FATALS.x * 100

We'll merge that data with the wet/dry status of each county. So we can color the scale differently for wet counties and dry counties, we'll multiply the percentage by -1 for wet counties.

ark_summary <- merge(ark_summary, wet_status, by.x="COUNTY", by.y="FIPS")
ark_summary$percent_drunk <- ifelse(ark_summary$wet, ark_summary$percent_drunk * -1, ark_summary$percent_drunk)

Finally, we'll get the midpoint of the number of drunk drivers, which we'll use to color parts of our map later.

mid <- (which.max(accidents$DRUNK_DR) - which.min(accidents$DRUNK_DR)) / 2

Part III: Preparing the Map Data

The functions Mr. Puente describes in his tutorial have nice features that allow you to subset county and state map data by state.

county_map_data <- map_data("county", region = "arkansas")
state_map <- map_data("state", region = "arkansas")

I merged the county-level information with the wet-dry status. Then (and this is important), I reordered the path information in the county file. Otherwise the plotting function draws extra lines between counties.

county_map_data <- merge(county_map_data, ark_summary, by.x="subregion", by.y="county")
library(plyr) # necessary for arrange function
county_map_data <- arrange(county_map_data, order) # required to draw lines properly

Part IV: Creating the Map

The goal with the visualization was to show the wet/dry status of counties having drunk driving fatalities. So, the percent_drunk column we created earlier, which contains percentages from -100 to 100, serves as the fill. We'll define a continuous scale in different colors to differentiate between wrecks occurring in wet counties and dry counties; by choosing white as the midpoint, we'll be able to see which counties had no drunk driving wrecks in 2015, and it will also give us a gradient that shows some idea of how many wrecks occurred.

map<-ggplot() + 
# Add county borders:
geom_polygon(data=county_map_data, aes(x=long,y=lat,group=group, fill=percent_drunk), colour = alpha("grey", 1/4), size = 0.8) +
scale_fill_gradient2(name="Percentage of\nDrunk Drivers", midpoint=0, low="#5ab4ac", mid="white", high="#d8b365", na.value = "white", breaks=c(-100,0,100), labels=c("100% (wet county)", "No drunk driving\nfatalities", "100% (dry county)")) +
# Add state borders:
geom_polygon(data = state_map, aes(x=long,y=lat,group=group), colour = "grey", fill = NA) +

The next goal was to represent the number of fatalities in each wreck by the size of the point. The NHTSA dataset also contains an interesting data point for the number of drunk drivers involved, which we'll use for the color of the point. It appears that one wreck in Pulaski County involved 3 drunk drivers and killed several people.

# Add points (one per fatality):
geom_point(data=ark_accidents, aes(x=LONGITUD, y=LATITUDE, color=DRUNK_DR, size=FATALS), alpha=0.35) +
scale_color_gradient2(name="Number of\nDrunk Drivers\nInvolved", midpoint=mid, low="lightgoldenrod4", mid="firebrick1", high="blue3", na.value="yellow") +
scale_size(name="Number of \nFatalities", range=c(3,8)) +

Finally, we'll use Mr. Puente's other adjustments for cleaning up the map, and then plot it.

#Adjust the map projection
coord_map("albers",lat0=39, lat1=45) +

#Add a title:
ggtitle("Arkansas Traffic Fatalities in 2015") +

#Adjust the theme:
theme_classic() +
theme(panel.border = element_blank(),
axis.text = element_blank(),
line = element_blank(),
axis.title = element_blank(),
plot.title = element_text(size=40, face="bold"))
map

The result is a detailed graphic that shows in a glance that 21 of 75 counties had no drunk driving fatalities in 2015. I'd say that Jefferson County is the place you were most likely to die in a drunk-driving fatality in 2015. If you know anything about Arkansas highways, you can definitely see the outlines of I-30, I-40 west of Little Rock, and I-49 from Ft. Smith to Fayetteville. 

2015 Arkansas traffic fatalities. Graphic ©2016 Nathan Chaney.

What do you take away from this visualization?

Which Arkansas counties are the most litigious?

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I've posted before about the upcoming launch of Docket Dog, a case watching service for Arkansas state court cases. To me, one of the interesting things coming out of Docket Dog is the ability to look at different metrics for case filings in Arkansas.

I have been learning the Python programming language, which has some good visualization tools available for it. My latest exploration has been using Python to create state maps. I found this awesome tutorial by a guy with an equally-awesome name (Nathan, of course!). I also wanted to view metrics per capita, so I downloaded the latest US Census data.

I wanted to figure out which Arkansas counties were the most sue-happy. So, looked at the total number of cases filed in each county, divided by the population, and plotted the result. Each color band is a multiple of the average for that year.

2015 cases per capita. Click to enlarge.

2014 cases per capita. Click to enlarge.

2013 cases per capita. Click to enlarge.

2012 cases per capita. Click to enlarge.

2011 cases per capita. Click to enlarge.

2010 cases per capita. Click to enlarge.

What do you make of this? Clark County, my old stomping ground, is average from 2010 to 2012, but is well above that the last couple of years.

Why do you think certain counties are more litigious than others?

How long will my case take?

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Nathan here. I'm back for a guest post with some new tricks I've learned at my new job from some of the researchers at UAMS. I've having a blast getting an inside look at cutting-edge biomedical research. This post looks at some data visualization about the time it takes to resolve civil tort cases in Arkansas.

Background:

One of the researchers has a master's degree in computer science, and I picked his brain a little bit about what software packages he likes to use. He prefers python to Perl (which I like) because python's research libraries are easier to use.

I took his recommendations to heart, and I've been tinkering around with the Anaconda python distribution with data I've gathered for another project I'm working on releasing very soon: Docket Dog. It's an Arkansas state court notification system. I used the data mining application Orange to perform some data visualization on the types of civil cases my dad and brother handle.

Arkansas Tort Case Length Analysis:

I took a look at over 98000 tort cases available electronically from the Administrative Office of the Courts for which I could calculate an end date. This is what the time frames look like:

Pendency of Arkansas tort cases in years. The scale is 20 years wide. Click to enlarge.

As you can see, civil court cases can take several years to resolve. We'll see what the averages look like here in a few minutes with another chart.

In the meantime, there are several interesting patterns that appear in this chart. For instance, on the first line for product liability cases, there are several vertical bands around 9, 12, and 14–16 years. I haven't looked into this, but I suspect each band probably represents a settlement of a specific type of cases, like Firestone exploding tire cases, Pinto exploding car cases, or something similar.

The declaratory judgment (dec action) line is notably shorter overall than the others. Again, I haven't researched this further, but I would expect this is due to the fact that dec actions don't involve juries and are usually about a specific question of law. For instance, lots of dec actions involve whether there is insurance coverage for a particular event or not (the hilarious Luther Sutter v. Dennis Milligan dec action notwithstanding). 

Now, on to the next chart. This is called a box chart:

Comparison of median Arkansas tort case values over the last 20 years. Click to enlarge.

This chart is broken up into quartiles. The light blue box represents 50% of all cases. So, 50% of motor vehicle collision (MVC) cases are decided within 2 years, with the median value being 1.6 years. (Median means the middle value; if there were 101 cases, for instance, the median value would be the 51st value). The average MVC case length is shorter at just over 1 year.

The dark blue lines represent maximum values, excluding outliers. The dots out to the right of the graph represent those outliers, which extend out to 20 years.

What's the bottom line? For 3/4 of tort cases, you can expect resolution to take at least 6 months to 3 years. Another quarter of cases take up to 4 years or so. And, there are always outliers that can take many, many years to reach ultimate resolution.

What questions do you have about this analysis?

What Do Rice, Dark Money, and the Race for Chief Justice of the Arkansas Supreme Court Have in Common?

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Now that early voting has started for primaries and "non-partisan" judicial races, the mud slinging is getting thrown into another gear. If you are a follower of this blog, you have seen post in the past referring to the influence of money in judicial, supposedly "non-partisan" elections.

Unfortunately, the trend of judicial elections is taking an anti-citizen turn ... again. In 2014, Circuit Judge Robin Wynne defeated attorney Tim Cullen with help of a "dark money" group based out of Washington, D.C. "Dark money" refers to an organization who is not required to disclose the identity of its donors. The Law Enforcement Alliance of America spent hundreds of thousands of dollars in tv air time in the Wynne-Cullen race. The LEAA ad misrepresented a position taken by Cullen in an appeal brief, which can be seen here. The LEAA accused Cullen of arguing child pornography was a victimless crime. Cullen actually argued the sentence for the perpetrator should fit within the federal guidelines. The president of the National Association of Criminal Defense Lawyers had this to say about the LEAA ad:

“The idea of attacking someone simply for fulfilling the mandate of the United States Constitution to provide every accused person with a defense attorney is inconsistent with the nation’s fundamental values. The founders of this nation recognized that no person should ever stand alone when the government seeks to condemn, imprison, or kill a person. To suggest that one’s qualifications for the bench are diminished for having lived up to the ideal of the right to counsel is an ill-conceived line of attack.”
— http://www.nacdl.org/NewsReleases.aspx?id=33063&terms=cullen

Wynne won the election by getting 52% of the vote.

On March 1, 2016, Arkansas voters will decide between Associate Justice Courtney Goodson and Circuit Judge Dan Kemp for who will become the next Chief Justice of the Arkansas Supreme Court. Goodson's platform includes her experience as an Associate Justice on the Supreme Court, as well as her experience while serving as an appellate judge on the Arkansas Court of Appeals. Kemp's platform includes his faith, integrity, and following the constitution.

Earlier this year, a dark money group called the Judicial Crises Network ran the following tv ad against Goodson:

I recently saw this website come across my facebook page, and I soon realized what the JCN is all about. At the bottom of the page, there is a footnote to the following quote:  "on the Supreme Court Goodson consistently sides with the Trial Lawyers, issuing legal opinions that make them millions more in their lawsuits." The "opinion" (singular) refers to the opinion Goodson wrote in Bayer CropScience LP v. Schafer, 2011 Ark. 518.

For those unfamiliar with the facts of the case, Bayer Cropscience produced genetically modified strains of long grain rice in the '90s. The Food and Drug Administration never approved the use of these strains. No foreign governments authorized the commercial use of these strains for human consumption. In 2006, these strains of rice were found to exist in the United States' long grain rice supply. Prior to that time, 52% of the long grain rice grown in the country was exported to other countries. Arkansas is the leading producer of long grain rice in the United States.

The United States Department of Agriculture quickly granted approval of one of the strains of rice. However, they banned the use of another strain for the 2007-2008 crop year, and implemented efforts to eradicate the contaminated rice. The world-wide reaction was much more guarded due to foreign governments' resistance to genetically modified food sources. Several countries required certification or testing that the rice was not contaminated before being allowed within its borders. These countries included Mexico, Canada, Iraq, Korea, Taiwan, Saudi Arabia, Cuba, and Japan. The Philippines and Russia banned the import of American rice. The European Union (consisting of 27 countries and 1/6 of the American rice export market) imposed stringent testing requirements at every port of entry. American rice exports decreased by 622,972 metric tons between 2005 and 2008.

This financially ruined several rice farmers and small businesses across Arkansas. So they joined together and filed a lawsuit in Lonoke County. The rice farmers and small business owners alleged Bayer knew the majority of American rice was exported; Bayer knew other countries did not import genetically modified rice; and Bayer knew any contamination of the United States rice supply with genetically modified rice would depress the export market and negatively affect the price of American long-grain rice. The rice farmers and small business owners claimed Bayer was negligent for allowing the genetically modified strains of rice into the nation's rice supply. They alleged Bayer was not careful enough during the strains' testing to prevent contamination of genetically modified seed with conventional seed. The rice farmers and small business owners claimed Bayer's negligence caused economic harm by driving down the market price for American long grain rice. They also claimed punitive damages to punish and deter future similar conduct by Bayer since Bayer knew or should have known that its conduct would probably result in damages to the rice farmers and small business owners; and because Bayer continued to pursue their negligent conduct with a reckless disregard of the consequences.

During the course of the case, the rice farmers and small business owners asked the circuit judge to declare unconstitutional a cap on punitive damages because it violated the Article 5, section 32 of the Arkansas Constitution, which states as follows:

“The General Assembly shall have power to enact laws prescribing the amount of compensation to be paid by employers for injuries to or death of employees, and to whom said payment shall be made. It shall have power to provide the means, methods, and forum for adjudicating claims arising under said laws, and for securing payment of same. Provided, that otherwise no law shall be enacted limiting the amount to be recovered for injuries resulting in death or for injuries to persons or property; and in case of death from such injuries the right of action shall survive, and the General Assembly shall prescribe for whose benefit such action shall be prosecuted.”

The circuit judge agreed and found the punitive damages cap unconstitutional. The jury returned a verdict for compensatory damages in the amount of $5.975 million dollars collectively for the rice farmers and small business owners. The jury also returned a punitive damages verdict in the amount of $21 million dollars.

Bayer appealed to the Arkansas Supreme Court, and said the circuit judge made a mistake by holding the punitive damages cap unconstitutional. Goodson, relying on a case from 1998, 1954, and a case from 1950, found the legislature "may limit tort liability only where there is an employment relationship between the parties." Because the punitive damages cap limited the amount of recovery outside of an employment relationship in violation of Article 5, Section 32 of the Arkansas Constitution, Goodson held the punitive damages cap enacted by the Arkansas legislature was unconstitutional. 

 

Goodson followed and enforced the law. In doing so, she ensured hard-working Arkansas citizens were not financially ruined by the actions of a multi-national conglomerate corporation.

Goodson also maintained the integrity of the civil justice system in this state. Every Arkansan has a constitutional right to a jury trial. That means the people in our communities get to decide our disputes. This is the definition of local control, and what separates our system of government from other countries. Goodson's ruling sends a message to legislators that the Arkansas Constitution does not tolerate government overreach into the decision making of Arkansas citizens who serve on juries.

Instead of correcting the JCN's misrepresentations, Kemp embraced the message of the JCN in a February 10, 2016 campaign email. After a JCN mailer criticized Goodson for being part of a holding striking down a Voter I.D. law, Kemp said he didn't "believe there is any place in our judiciary for this type of politics."

Regardless for whom you vote, do your research and ask questions of the candidates. If you see an ad sponsored by an organization, ask questions about the organization. Misleading advertisements have no place in judicial or any other type of election.