Census Data to Improve City Neighborhoods about 3 to 5 lines each You must read the article that the post is about. You must add to the discussion with som

Census Data to Improve City Neighborhoods about 3 to 5 lines each You must read the article that the post is about. You must add to the discussion with something interesting from the article that was not already mentioned, or by explaining why you found the article interesting or topical. Posts like “great article” will not receive credit. It must be clear from your response that you read the article. Article 1
I read an article from phys.org, entitled “Census Data Could Be Used To Improve
City Neighborhoods,” posted by the University of Bristol on January 9th, 2019.
https://phys.org/news/2019-01-census-city-neighbourhoods.html (Links to an
external site.)Links to an external site.
According to an analysis of the 2011 census, researchers have concluded that the
social differences among populations in various cities greatly influence how
neighborhoods are shaped.
Professors in the Department of Engineering Mathematics at the University of
Bristol, Dr. Thilo Gross and Dr. Edmund Barter used a new algorithm to study
characteristics and gain more knowledge into city neighborhoods. According to
the study, “in order to improve city life, an understanding of where social
differences come from and how different neighborhoods acquire their distinct
characteristics in essential” (Barter and Gross). The mathematicians utilized an
algorithm called “diffusion maps” to analyze the census. They asked which
neighborhoods answer census questions in a similar way. Rather than focusing on
specific census questions, they focused on identifying the main underlying
features of neighborhoods from the entirety of the census. When looking at
Bristol, the “algorithm detected that in specific areas scattered throughout the city,
people answered the census in a similar way” (Barter).
The census provides a vast amount of information, but is not user friendly. The
method of diffusion mapping turns that information into useful data that can
improve lives. The researchers plan to extend the study to more cities in the UK
and other countries where the census is used.
A diffusion map is a dimensionality reduction or feature extraction algorithm
which computes a family of embeddings of data set into a Euclidean space whose
coordinates can be computed from the eigenvectors and eigenvalues of a diffusion
operator on the data.
I found this topic to be very interesting not only because we are learning about
the census in class, but 2020 is the census year where our districts will eventually
be redrawn. I definitely think that if we adopted the method of diffusion mapping,
our district lines would be drawn more fittingly and reflect our neighborhoods’
more accurately.
Article 2
My article was titled “New mathematical model can help save endangered species”
and was published on January 11, 2019 from the University of Southern Denmark.
They published through
ScienceDaily. https://www.sciencedaily.com/releases/2019/01/190111112844.htm (
Links to an external site.)Links to an external site.
I was drawn to the article due to the fact that I love animals and want to
see endangered species make a comeback in my lifetime.
It was found that using math and statistic models we can recreated the dynamics
of survival and reproduction of most endangered species therefore, giving them a
higher chance of survival and comeback. It was found that the quality of each
model needs to be spot on to help have a higher success rate or accuracy. By
recreating the animals population mathematically we can study and see the
birthrates, death rates, and how the population numbers have increased or
decreased over time, further helping the understanding on what the main issue of
the species truly is. The mathematical models also showcase the environment of
the species which also plays a factor into the animals survival rates.
One of the scientists in the article, Colchero, used all of mathematical models,
statistics, and computer simulations with the given data to see if they could
simulate population growth. There were 24 different species tested and each
model ended up with an improved and plentiful population growth. These models
then show us and others what we need to help improve those species in need.
With the correct data collected and the numbers then plugged into each model we
can see that they truly do end up showing a positive change in the environment
with these species that are dying or close too. Hopefully we have a boost in those
who are passionate about helping these animals and more models are made to help
protect them.

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