Solved by verified expert:Please replay to 2 of my classmate discussion each one 100 words. (total 200 words)Please make sure to tell me which one you are replaying to.I attached the discussion.Thanks,
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The method of k-nearest neighbors is fundamentally similar to the “First Law of Geography”:
the idea that events close in place and time are more similar to one another than events far
separated in place and time. Give an example where you’ve known people to be judged based
on the assumption that they will behave similar to others who are “close” to them on some
metric. In the case of your example, was this a good prediction, or not? Why?
The underlying concept of this method resembles the clustering strategy in retail marketing
where you see similar businesses operating in close proximity (e.g., car dealerships,
restaurant rows, furniture stores, shopping malls, etc.) in hopes to capitalize on existing
customer volume and demographics. This can at times be a very effective strategy because
people generally tend to behave in a similar manner given certain variables.
For example, in the two maps below, we can see how closely two national pizza chains are
clustered near each other. Domino’s Pizza was created back in 1960, and Papa John’s Pizza
was founded more recently in 1984. Papa John’s began a major expansion in the late 1990s
and strategically opened locations in relatively close proximity to Domino’s Pizza. In
addition to positioning themselves near major population bases, the Papa John’s brand made
the assumption that people that already buy pizza from a national chain brand will probably
be open to buying from their national brand as well. The operating metrics show that this
was a good prediction. While the Papa John’s chain has struggled recently due to some PR
missteps, during their growth phase in the 1990s when they were opening pizza stores near
other national chains, their same-store sales growth averaged an industry-leading 7.7%
Throughout my life I have had the opportunity to travel and visit many countries. With
these experiences I have always noticed that no matter where I go car dealerships always
seem to be located very close by to one another. I have always found this very interesting
because overtime, more and more dealerships appear in the vicinity of the ones that are
already there. This allows the customers to find the best prices and models and to be able to
compare them between dealerships as easy as they can. I found this very interesting map of
dealerships located in the US, it shows a clear trend around the country that dealerships are
usually located by one another. We can see that in several locations there are clusters of
around 500 dealerships within 25 miles of distance which is a large number! These locations
are very likely to keep growing over time since due to the First Law of Geography
My first impression of the k-nearest neighbor application in real life was to relate that
concept to car sales in California. California is one of the most restrictive states in the United
States when it comes to emissions laws, and many CA cities are stereotyped for driving
economically friendly vehicles such as hybrids and electric cars. According to the California
New Car Dealers Association (CNCDA), CA dominated the new light vehicle registrations in
the United States for the past ten years. The assumption that consumers and companies are
more likely to buy and sell light vehicles in CA compared to other states is validated by the
CNCDA. This can be applicable in all categories of vehicles, trucks, cars, SUV’s: light vehicles
consistently outsell all other categories in California year after year. (See attached image).
Before I even researched the topic, I would have assumed that it’s a good prediction that the
larger cities in CA attribute to the growth of sales in the light vehicle department. The
emissions, large cities, and culture of California all play a role in the growth of this industry.
For this discussion, I thought of the age-old Pop vs. Soda vs. Coke debate, which is based on a
regional dialect. I’ve lived all over the United States, and I’ve found this map to be an
accurate depiction of this phenomenon, and a good prediction of the terminology that will
be used by the people in the corresponding areas. I’ve also found that the dialect stays
regional, affecting the people who move into the region, rather than being affected by the
people. When I lived in Minnesota, the word was “Pop”, when I lived in Florida, it was
“Coke” or “Soda”, in North Texas is was “Coke” and if you were caught saying “Pop” in either
of those places, people would call you out! As you move into neutral territory as displayed on
this graphic, deviations are more acceptable and terminology is interchangeable.
After looking through the discussion board at the examples that people have posted in
relation to this topic, my first thought for how this applies in real life was much different.
The idea of the k-nearest neighbors concept, grouping things that are similar to one another,
has taken real form in stereotypes. People make judgments about others who are similar to
someone else, based on metics of attractiveness, hair color, race, sexuality, etc., the list really
could go on. Particularly looking at stereotypes this dose not at all create a good prediction.
People who are similar to someone else does not at all mean that they are like that other
In a business application or in any of the examples listed above the metrics being used are
better predictors. But by acknowledging that it is not a good method of prediction, people
and myself still stereotype all the time. Thinking about people who have similar “metrics” or
qualities, and making pre-judgments of the person before even getting to know them.
I picked this map which shows depicts the debate of what to call a semi truck.
I live in the NW, and clearly, I call them semi trucks, however some other variants across the
country are tractor trailer on the East Coast or 18 wheeler in the South. Generally, we would
expect people in these regions to call a truck by the common name in their region, but there
is always exceptions, just like the soda vs. pop vs. coke vs. soft drink debate. In both cases,
we are talking about the same item, but refer to it by a different name. It seems that usually
these are good predictors, but outliers do exist.
I decided to go with baseball team preferences with locations. I have always been a Red Sox
fan my entire life and growing up in the Pacific Northwest you don’t get the best looks from
baseball people around here. Everyone always assumes that you are either a Mariners fan or
Giants fan depending on what area of Oregon you are in. When I wear any of my Red Sox
stuff to a sports place I always get looks. From this map it would be a good example of how
people would judge based on the closeness of teams in the area.
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