CSE 163 Research prop

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University of Washington *

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163

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Sociology

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Feb 20, 2024

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Uploaded by JudgeDanger13368

Peter Tsirushkin Research Questions: • How has the population of Homeless individuals changed over the past 10 years in Seattle (if data isn’t readily available/if time is available, expand area of inquiry to west coast cities)? • Is there a correlation between weather and homeless population? • Is there a correlation between dominant political party and homelessness levels? • Is there a correlation between money invested in combating homelessness and homeless density? • How do homeless and crime levels correlate in Seattle? Motivation: It goes without saying that homelessness is a major issue in Seattle and many of the major West-Coast cities. Having lived here for the past 10 years, it appears to be getting worse with every passing year. This is a sentiment echoed by many political commentators as well as business owners and tax-paying residents in Seattle and much of the West Coast. These same individuals are also very quick to propose a mermaid of reasons as to why they believe homelessness is such a major issue in major West coast cities. Some of the more popular reasons/explanations for the homelessness epidemic are weather, investment into homeless infrastructure, political acceptance of homeless. I am curious to see if the change in homeless populations is superficial and what patterns can be correlated with what we observe in our populations. Data Sets This is a data set that tracks housing a ff ordability in the united states as a whole based o ff of census data. Its major limitations are that it is published only every four years. Additionally, it does not mention homelessness, which doesn’t advance my inquiry directly but is still relevant to my research as it can help me correlate housing a ff ordability with trends I observe in homelessness. https://www.huduser.gov/PORTAL/datasets/cp.html This data set is directly applicable to my research, it gives me historical data on homelessness by age for all the counties in California. Limitations are that it is not tabulated, it is a text file, and it only dates back to 2017. I need to find data sets that date further back to be better able to examine correlations with the other variables mentioned in my “motivation” section. https://data.ca.gov/dataset/c7ed1ae4-4f93-4fc7-b603-3cd07a55d862/resource/ b1a5ae24-5842-425c-b56c-aa90f8f1c767/download/ experiencing_homelessness_age_demographics.csv This data set gives me information on the geographic distribution of homelessness in the US. Limitations are extensive. The data is in PDF form, and it broken up into individidual PDFs based on Contiuum of Care program districts. It would require an impressive web scrapping program or further research into the origins of the data. https://www.hudexchange.info/programs/coc/coc-homeless-populations-and- subpopulations-reports/? &filter_year=&filter_scope=&filter_state=&filter_coc=&current_page=5

Challenge Goals • Multiple Data sets • Machine Learning (not sure how i would implement this) • Messy Data (potentially) Method • Collect relevant data into python (ether through web scraping or file reading) • Process the data and plot it in several di ff erent ways (bar chart, geographical chart, etc.) • Repeat for other data sets used to establish correlations (multiple data sets) Plan • Find historical data on homelessness and plot it against time (2-5 hours) • Identify trends/anomalies (30 seconds) • Plot data tracking other variables (2-4hours) • Identify anomalies(30 seconds) • find explanation to anomalies in the news based on public policy/major socioeconomic events (2-4 hours).

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