Census Data to Improve City Neighborhoods

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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.

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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.

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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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agneta
School: UT Austin

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According to the article on “Census data could be used to improve city neighborhoods,”
the use of diffusion maps seems interesting, in the sense t...

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Anonymous
Good stuff. Would use again.

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