Database Created in ArcView 8 For
Seven Counties in
Here are some charts I made in ArcView and Excel analyzing the fatality data supplied by the South Dakota Department of Transportation.†
Part of the work of creating this GIS database was
accomplished during a GIS Research 393 course during Spring
This GIS databasae has been formatted using descriptive terminology that will allow anyone to perform queries.† Iíll give an example.†
This screen capture shows how I select the accidents involving a pedestrian from the layer with all fatal accident data from 7 counties (Pennington is not complete and is not included in the database yet).† The 7-county layer is called ACC_7CNTY.† The ACC_TYPE field contains the entries shown on the right, BICYCLE DRIVER, PEDESTRIAN, AND VEH/DRIVER.† The map shows the location of all the pedestrian accidents.† I create the expression ACC_TYPE = PEDESTRIAN and perform the query.
Note the cluster between Pine Ridge and White Clay; this probably represents people walking down to White Clay to buy alcohol and coming back to Pine Ridge and being hit on the road.† There is another cluster around Sturgis, too, which may represent inebriated people walking around during the August rally.† It would be easy to chart those by month, too, to confirm that suspicion.
Doing the search selects all entries from the ACC_7CNTY layer that had PEDESTRIAN in the ACC_TYPE field.† As shown at the bottom, that was 44 out of the 430 total fatal accidents entered here.†
I can click the ďSelectedĒ button to view only these Pedestrian accidents, then I can save this out to a new layer.
I can do many analyses on this new Pedestrian layer since it contains all the data available for each of those 44 accidents.† Here Iím going to summarize the accidents by county.
I get this *.dbf table showing the number of accidents by county which I import to Excel and graph.
Here is the pedestrian fatality data.†
Here is the percentage of all 430 accidents in the seven counties just looking at the different possible road conditions and summarizing by that field.† I would have thought that bad road conditions would have accounted for more of the accidents, but 86% of them occurred on dry pavement.
And here are all accidents summarized by collision type, mostly head-on accidents on the 2-lane roads common in this area.
Here I summarized the alcohol and drug related accidents by
percent of total among all counties (blue) and then by percent of the total
accidents in each county (purple).† The ďresĒ counties, Bennett and Shannon, have the highest
percentage of accidents with alcohol and
Summarizing the alcohol-related accidents by month and county is also interesting.† The percentages are of total accidents in a given month among all counties.† Itís a busy chart, though, so I broke it out below for comparisons with a couple of counties at a time.
Here are the same data but just showing Shannon and
Here I looked at overturn fatalities.† Again the percentages are of total accidents during
a given month among the seven counties.†
Most counties donít show any trend here probably due to the fact that the
data are not extensive here for all 12 months and 7 counties, only 123
accidents.† But the chart does show a
pretty high rate of overturn fatalities in
Here I looked at time of day, dividing the day into 2-hour increments.† These data are sufficient over the 18 years to create a meaningful analysis.† Here Iím looking at actual numbers of fatal accidents in each county spread over the 24 hour day to indicate if thereís a trend in what time of day accidents in that county occur.
Look at the difference in the number of fatal accidents in
Shannon and Custer counties.†
Lawrence, Meade, and Shannon show pretty similar trends in accidents by time of day, the going home from work and school time being the worst.† That supports having and enforcing those lower speed signs in school zones.
I looked at sex and age next.†
To make a clearer picture of male and female participation, this graph shows the 20-40 year old males are the main ones involved in fatal accidents, with males 40-60 next, and females 20-40 third.
Here I looked at male drivers under 20 in all counties.† The highest percentage of male teenage driver
accidents was in
These data donít show any real difference by county for female teenager drivers.
Looking at percentages of accidents caused by male and
female drivers of all ages combined (in vehicle 1, the primary one causing most
accidents) shows males in
Comparing the number of accidents with fatal and non-fatal outcomes when the driver was not wearing a seatbelt, there were significantly more fatality accidents than non-fatal ones (Chi-Square = 3.28, p < 0.001).† When seatbelts were worn, the number of fatal accidents was significantly less than the number of non-fatal accidents (Chi-Square = 10.52, p < 0.001).††
Lawrence, Meade, and Shannon counties lead the way in accidents in which seatbelts were not used.†
Below I analyze all fatal accidents that occurred on a curve in the roadway to see if there were any outstanding factors associated with this type of accident.
††††††††††† ††††††††††† †
Most of the accidents occurring on road curves took place in
clear weather with dry road conditions.†
Where wet, icy, snowy road conditions were a factor in the accident,
With regard to the first harmful event, DOT terminology for the main cause of injury in an accident, collision with a fixed object (such as a tree, power pole, or fence post) and overturns were the main causes of injury.† These would result from leaving the roadway, an obvious hazard on a curve.
Looking at all fatal accidents occurring on curves by time interval throughout the day, the raw numbers donít show much because of different numbers of total accidents occurring during each time interval.† But looking at the fatal accidents on curves as a percentage of total accidents during a particular time interval, we see that the interval to has the most curve fatalities, 63% of the fatal accidents during that time period were related to curves in the roadway.† The next most frequent accidents on curves occurred during the intervals , and in decreasing order.†
Many more types of analyses are possible using this database.† We welcome the comments of any interested persons who might want to do research using this highway fatality database.† I also encourage any student wishing to gain experience building this type of database in a GIS to contact me.† The work remaining to incorporate fatality data from Pennington County into this database would make a great learning experience and would serve to complete a required three semester hour Research 393 or 493 course at Oglala Lakota College.
Jim Taulman, Conservation Biology instructor