Tuesday, September 24, 2013

Buck Analytics for Dummies (Part 2)

Segmenting Data

The days of "stay and pray" hunting may be over. Segmenting allows us to layer and group like data to reduce the number of unproductive days in the field and to select the most likely places to hunt on any given day.

Things to consider when segmenting data are:

1) deer density

2) feed and bedding locations

3) daytime travel corridors

4) location of licking branches

5) weather patterns ( including barometric pressure, wind direction, humidity)

6) date

Visualizing Results

Hunters need to make decisions - and fast. Transforming these predictable insights into visuals is critical in making key decisions quickly and effectively.

Segmenting your hunting intelligence is essential because it allows you to quickly see

  • Underlying reasons for the deer’s actions
  • Relationships between data collected
  • What-if situations in real time
  • If new locations will be productive

For example:

There are three funnels on a piece of land I hunt. I have set trail cameras near licking branches on all three funnels.

Camera 1 has pictures of several deer and images of a monster buck. Problem; The buck comes through only hours after dark.

Camera 2 has pictures of deer, but the majority of these pictures are on sunny days with the wind

blowing from the southwest.

Camera 3 has pictures of deer on rainy days with a north wind.

Most hunters would hunt the funnel where camera 1 is located and pray the buck will show during shooting hours. By analyzing all the data, the smart hunter would pick one of the other funnels and hunt when the weather conditions are most favorable to see deer during the day. Remember that during thee rut, the bucks are doing exactly what you are doing. They are hunting other deer.

Behavioral data (frequency where we see deer) is good place to start segmenting. But in order to get a complete view of the deer in your area you’ll need to incorporate environmental data as well.
More on segmenting deer data

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