Complete work for Milestone 2
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README.md
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README.md
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# Group YY - {Short snappy Title of your project}
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- Your title can change over time.
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# Group 44 - An Economic Reconsideration of Pride
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## Milestones
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@ -8,17 +6,20 @@ Details for Milestone are available on Canvas (left sidebar, Course Project).
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## Describe your topic/interest in about 150-200 words
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{Add your stuff here}
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On a very high level, we hope to perform an analysis on the ways in which pride correlates with different economic and social factors geographically. In particular, we've chosen to bring political inclinations and taxation into our consideration. As queer people, we have "skin in the game," so to speak, and making our culture statistically relevant is existentially important in a world that operates more and more strictly in terms of information (specifically, information first passed through the sieve of institutions designed to ignore or actively eliminate us). We're interested in this topic because we believe that through the application of data science, we can gain insights into queerness in the context of the United States' polarized geography.
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Geography is a subject neither of us have a formal background in, although it's something that interests us both. Geography has become deplatformed as a subject in a certain regard since the near-universalization of globalism, creating a world that, at least on its surface, seems completely interconnected. Despite this, understanding our local community is more important than ever for marginalized groups. So, we hope that this project will enable us to reestablish that perspective on a more macro level.
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## Describe your dataset in about 150-200 words
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{Add your stuff here}
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The "gaybourhoods" data set we're using for our project was produced by The Pudding for their 2018 article [Men are from Chelsea, Women are from Park Slope](https://pudding.cool/2018/06/gayborhoods/). Although published in 2018, the data was actually collected in 2015. The article cites its purpose as being an attempt to properly quantify what they call "gaybourhoods," which is "an overarching term to describe areas with a visible LGBTQ and queer presence." There's different reasons why this may or may not be something we want. For one, it provides us with perspective on the geographic imprint of queer people on the United States. The data was collected from a composite of sources, including local pride organizations and the federal government.
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To quantify gaybourhoods, writers and data analysts from The Pudding collected information on the number of tax-filing households classified as "same-sex," whether or not pride parades march through a given zip code, and the number of bars tagged as "gay bar" in the region on Yelp. This is, of course, very limited in quantifying the diversity of the queer community, but it provides us with a base to work on.
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## Team Members
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- Person 1: one sentence about you!
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- Person 2: one sentence about you!
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- Person 3: one sentence about you!
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- Nat Scott
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- Sami Almuallim
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## Images
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@ -2,27 +2,58 @@
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Your Name Here"
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],
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"metadata": {}
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"# Nat Scott"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Research question/interests\r\n",
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"\r\n",
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"Briefly describe your research question or interests here."
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],
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"metadata": {}
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"## Research question/interests\n",
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"\n",
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"Is there a correlation between political alignment & living in neighbourhoods with large quantities of LGBT people?\n",
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"\n",
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"- The gaybourhoods data set does not include data on residents political alignments, however, there is a wealth of electoral data available freely online that we intend on incorporating into this project. The primary difficulty then will be developing a geographic \"compatibility layer\" between the data sets so that the data can be understood in the same context. To build this, we intend on working with the OpenStreetMap API to create an additional column representing observations position space in a more neutral way, such as their coordinates.\n",
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"- Alternatively, we've also considered working with an additional data set that links US zip codes to their longitude and lattitude positions. As such, incorporating this data would be as easy as merging the two tables.\n",
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"\n",
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"\n",
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"Is there a correlation between geographical stratums & being LGBT?\n",
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"\n",
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"- Once again, representing this data visually will require determining the coordinates associated with each observation.\n",
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"- The gaybourhoods data set defines a \"gaybourhood index\" which effectively measures how friendly a given neighbourhood is to queer people. Since this index is entirely subjective, we will need to closely evaluate it's usefulness for our project and investigate different ways to quantify \"queer-friendliness\"\n",
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"- In addition to the last point, since, of course, no matter what choice of observations we make, the measurement will still be subjective, answering this research question will come more so in the form of comparing and contrasting different measurements to see what they tell us.\n",
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"- Obviously, visualizing this among many aspects of the other research questions would involve projecting the data onto a map of the United States, so this research question would establish the motivation for subsequent inquiries."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"orig_nbformat": 4,
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "plaintext"
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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"nbformat_minor": 4
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}
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Your Name Here"
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],
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"metadata": {}
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"# Sami Almuallim"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Research question/interests\r\n",
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"\r\n",
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"Briefly describe your research question or interests here."
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],
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"metadata": {}
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"## Research question/interests\n",
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"\n",
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"Is there a positive or a negative correlation between taxes paid and the pride of a given queer neighbourhood?\n",
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"\n",
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"- Similar again to the first research question posed, we'll need to find another data set containing geographically located tax data, which should be easy to acquire from the US government (for example, [in our cursory research, we found this data set from the IRS](https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-statistics-2018-zip-code-data-soi)).\n",
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"- This would bring the number of data sets used in this project up to three, which might present some challenges in terms of the amount of data wrangling necessary to bring it all together.\n",
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"- To measure this, we would rank the neighbourhoods presented in the gaybourhoods data set by pride (an open question which we will explore in a separate research question)\n",
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"\n",
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"\n",
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"Is there a correlation between pride flags & parades & gay bars in a given region? In other words, how are the different metrics of pride represented in this data set correlated?\n",
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"\n",
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"- This will probably be the simplest research question, requiring only the data contained in our original data set. To explore this topic, we will use different visualization methods discussed in class to develop a better understanding of the data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"orig_nbformat": 4,
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "plaintext"
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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"nbformat_minor": 4
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}
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