Please send your presentation (e.g. Colab, Datastudio, Tableau) on your quantitative analysis of CA-18 congressional 2022 candidates head of time. This should have the pros/cons and opportunities of each candidate using the voting and census data. Here is a suggested process:
Step 1: Gather precinct-level voting data for Congressional District 18. There are three counties in district 18. However, use Santa Clara only for this mini-project.
Step 2: Gather block-level US Census data from the website for Santa Clara County. You can find the link here: [login to view URL]
From that link, you can get a table for the feature (e.g. age, gender) that you want that looks something like this (selecting as many geographies as you want): [login to view URL],060855003002,060855004002&tid=ACSDT5Y2019.B01001&hidePreview=true
Step 3: Join data from Step 1 and Step 2 for at least one city in district 18. Cross-reference the election day map: [login to view URL]
and the census data map: [login to view URL]
This is done by visually looking at the map between the voting precinct and the census CBP. They will not match up perfectly.
Example for Palo Alto:
Precinct # CBP
2046 5114 (1, 2, 3) and 5113.01 (1, 2)
2009 5109 (1, 2) and 5107 (1) and 5115 (2)
2025 5108.01 (1, 2, 3) and 5109 (3)
2098 5110 (1, 2, 3, 4, 5)
2057 5113.02 (1, 2, 3) and 5112 (1, 2, 3, 4)
2068 5046.01 and 5111 (1, 2)
2013 5108.02 (1, 2) and 5108.03 (1, 2)
2108 5106 (1, 2, 3)
2043 5111 (3, 4) and 5114 (4)
2014 5107 (3) and 5117.01 (1)
2002 5116.09 and 5115 (1)
2005 5117.05 (1)
2125 5094.01 (2)
2018 5093.02 and 5093.04 (1)
2006 5094.01 (1)
Step 4: Analyze Step 3 joined data for the 2020 Congressional Race between Anna Eshoo and Rishi Kumar. What trends do you see across different precincts? Graph out these trends.
Step 5: Create a model that predicts this trend and measure the error.
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