Showing posts with label algebraic topology. Show all posts
Showing posts with label algebraic topology. Show all posts
Friday, August 16, 2024
"Persistent Homology for Resource Coverage: A Case Study of Access to Polling Sites"
One of my paper was published in final form last week. Here are some details.
Title: Persistent Homology for Resource Coverage: A Case Study of Access to Polling Sites
Authors: Abigail Hickok, Benjamin Jarman, Michael Johnson, Jiajie Luo, and Mason A. Porter
Abstract: It is important to choose the geographical distributions of public resources in a fair and equitable manner. However, it is complicated to quantify the equity of such a distribution; important factors include distances to resource sites, availability of transportation, and ease of travel. We use persistent homology, which is a tool from topological data analysis, to study the availability and coverage of polling sites. The information from persistent homology allows us to infer holes in a distribution of polling sites. We analyze and compare the coverage of polling sites in Los Angeles County and five cities (Atlanta, Chicago, Jacksonville, New York City, and Salt Lake City), and we conclude that computation of persistent homology appears to be a reasonable approach to analyzing resource coverage.
Title: Persistent Homology for Resource Coverage: A Case Study of Access to Polling Sites
Authors: Abigail Hickok, Benjamin Jarman, Michael Johnson, Jiajie Luo, and Mason A. Porter
Abstract: It is important to choose the geographical distributions of public resources in a fair and equitable manner. However, it is complicated to quantify the equity of such a distribution; important factors include distances to resource sites, availability of transportation, and ease of travel. We use persistent homology, which is a tool from topological data analysis, to study the availability and coverage of polling sites. The information from persistent homology allows us to infer holes in a distribution of polling sites. We analyze and compare the coverage of polling sites in Los Angeles County and five cities (Atlanta, Chicago, Jacksonville, New York City, and Salt Lake City), and we conclude that computation of persistent homology appears to be a reasonable approach to analyzing resource coverage.
Wednesday, September 16, 2020
"Spatial Applications of Topological Data Analysis: Cities, Snowflakes, Random Structures, and Spiders Spinning Under the Influence"
One of my papers was just published in final form today. Here are some details.
Title: Spatial Applications of Topological Data Analysis: Cities, Snowflakes, Random Structures, and Spiders Spinning Under the Influence
Authors: Michelle Feng and Mason A. Porter
Abstract: Spatial networks are ubiquitous in social, geographical, physical, and biological applications. To understand the large-scale structure of networks, it is important to develop methods that allow one to directly probe the effects of space on structure and dynamics. Historically, algebraic topology has provided one framework for rigorously and quantitatively describing the global structure of a space, and recent advances in topological data analysis have given scholars a new lens for analyzing network data. In this paper, we study a variety of spatial networks—including both synthetic and natural ones—using topological methods that we developed recently for analyzing spatial systems. We demonstrate that our methods are able to capture meaningful quantities, with specifics that depend on context, in spatial networks and thereby provide useful insights into the structure of those networks. We illustrate these ideas with examples of synthetic networks and dynamics on them, street networks in cities, snowflakes, and webs that were spun by spiders under the influence of various psychotropic substances.
Title: Spatial Applications of Topological Data Analysis: Cities, Snowflakes, Random Structures, and Spiders Spinning Under the Influence
Authors: Michelle Feng and Mason A. Porter
Abstract: Spatial networks are ubiquitous in social, geographical, physical, and biological applications. To understand the large-scale structure of networks, it is important to develop methods that allow one to directly probe the effects of space on structure and dynamics. Historically, algebraic topology has provided one framework for rigorously and quantitatively describing the global structure of a space, and recent advances in topological data analysis have given scholars a new lens for analyzing network data. In this paper, we study a variety of spatial networks—including both synthetic and natural ones—using topological methods that we developed recently for analyzing spatial systems. We demonstrate that our methods are able to capture meaningful quantities, with specifics that depend on context, in spatial networks and thereby provide useful insights into the structure of those networks. We illustrate these ideas with examples of synthetic networks and dynamics on them, street networks in cities, snowflakes, and webs that were spun by spiders under the influence of various psychotropic substances.
Monday, January 27, 2020
"Quantifying “Political Islands” with Persistent Homology"
Here is a new expository article (in SIAM News) by my Ph.D. student Michelle Feng and me about our work on spatial topological data analysis.
You may also be interested in our associated research article and our recent follow-up article.
I finally get to share my new @TheSIAMNews article (by @michellehfeng and me): "Quantifying “Political Islands” with Persistent Homology"https://t.co/ocpZN43EHg
— Mason Porter (@masonporter) January 28, 2020
You may also be interested in our associated research article and our recent follow-up article.
Saturday, January 18, 2020
Quote of the Conference: Geodesic Spaces of Normal Shrinkage
"I call it 'geodesic spaces of normal shrinkage', in honor of George Costanza."
This may be the quote of the conference. The talk in question is this one.
This may be the quote of the conference. The talk in question is this one.
Labels:
algebraic topology,
amusing,
conferences,
mathematicians,
mathematics,
quotes,
Seinfeld,
shrinkage
Thursday, November 28, 2019
My TDA (Topological Data Analysis) Origin Story
Based on what I saw as an undergraduate, I thought that algebraic topology was hopelessly abstract, and then I encountered Konstantin Mischaikow's work when I was a postdoc at Georgia Tech. He was using these ideas to analyze experimental data from areas like fluid mechanics. This stuck in my head, but I didn't work on these topics for many years. However, it stuck in the back of my head for about a decade, as this had made an impression on me. (I was aware of work of some others as well, but this is the one that made an impression, because of the close collaboration with experimentalists.) I was spending a bunch of time on granular networks as well as on generalizing network analysis from graphs to various more complicated structures (and I also had the desire to look more at "higher-order" interactions more generally).
During one of my daily arXiv routines, I noticed a paper by Konstantin and collaborators that used topological data analysis (TDA), so I saw that we were looking at the same systems, but in different ways. I contacted him, visited him early in 2013, and we started a joint TDA project --- but it turned out to be on spreading dynamics on networks, rather than on granular networks. Our first paper (which was led by Dane Taylor and coauthored with many other excellent people, including my Oxford colleague Heather Harrington) was published in final form in Nature Communications in 2015. I viewed this as just one paper; I never intended to start a large new direction in my research program. Back at Oxford, one student saw that I was part of that and wanted to work with Heather and me on applications of TDA. Then more students saw the 2015 paper and what this student was doing, and they wanted to work with us on TDA.
After I moved to UCLA, more students (starting with Michelle Feng) saw that I had some papers on TDA and wanted to work with me on those topics, partly because they wanted to do things with applications but also wanted to continue pursuing more theoretical mathematical subjects as well. I also really like the idea of taking "traditionally pure" areas of mathematics and bringing more and more of them into applications. It's a really exciting thing to do. And the work on applications also yields really great insights into the mathematical theory. (Because it does go in both directions, after all.)
Most recently, at least among people who have officially joined my group, Abby Hickok saw the work that Michelle and I have been doing, and she has ideas for building further on that work. And now TDA (along with work involving the intersection of dynamics, networks, and simplicial complexes) has become an important part of my research program,
Anyway, it was an all an accident.
During one of my daily arXiv routines, I noticed a paper by Konstantin and collaborators that used topological data analysis (TDA), so I saw that we were looking at the same systems, but in different ways. I contacted him, visited him early in 2013, and we started a joint TDA project --- but it turned out to be on spreading dynamics on networks, rather than on granular networks. Our first paper (which was led by Dane Taylor and coauthored with many other excellent people, including my Oxford colleague Heather Harrington) was published in final form in Nature Communications in 2015. I viewed this as just one paper; I never intended to start a large new direction in my research program. Back at Oxford, one student saw that I was part of that and wanted to work with Heather and me on applications of TDA. Then more students saw the 2015 paper and what this student was doing, and they wanted to work with us on TDA.
After I moved to UCLA, more students (starting with Michelle Feng) saw that I had some papers on TDA and wanted to work with me on those topics, partly because they wanted to do things with applications but also wanted to continue pursuing more theoretical mathematical subjects as well. I also really like the idea of taking "traditionally pure" areas of mathematics and bringing more and more of them into applications. It's a really exciting thing to do. And the work on applications also yields really great insights into the mathematical theory. (Because it does go in both directions, after all.)
Most recently, at least among people who have officially joined my group, Abby Hickok saw the work that Michelle and I have been doing, and she has ideas for building further on that work. And now TDA (along with work involving the intersection of dynamics, networks, and simplicial complexes) has become an important part of my research program,
Anyway, it was an all an accident.
Friday, February 15, 2019
Topological Data Analysis of Geographic Distance to Donuts
Clearly, it must be done. It must be done!
Often from a single position you could see two @dunkindonuts #redditDataIsBeautiful pic.twitter.com/SDfHVXmKub
— Suzanne Sindi (@SuzanneSindi) February 16, 2019
Tuesday, September 11, 2018
A Very Exciting (and Dangerous?) Mathematics Conference
Some mathematics conferences are more dangerous than others.
— Mason Porter (@masonporter) September 11, 2018
(Bernadette Stolz-Pretzer is apparently attending a particularly exciting conference. Thanks for posting this picture and letting me share it.) pic.twitter.com/wKiax2gWOA
P.S. I wonder what they actually have in mind with that session? I'm at a loss. I suppose it's clearer to the people who are actually attending the conference?
Labels:
algebraic topology,
amusing,
conferences,
executions,
mathematics,
students
Friday, March 02, 2018
Proposal: Use Computational Topology to Study Aversion to Pictures of Holes
Clearly, we need to use topological data analysis (in which one tries to algorithmically compute things like holes and their generalizations) to study aversions to images of clusters of holes.
For science!
For science!
Wednesday, November 01, 2017
Mathematician Wins 2017 Dance Your Ph.D. Contest!
That's right: The top overall video in the 2017 Dance Your Ph.D. contest was for an explanation of braid groups! Awesome!
Labels:
algebra,
algebraic topology,
awesome,
braids,
dancing,
mathematicians,
mathematics,
prizes,
topology,
videos
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