{ "cells": [ { "cell_type": "markdown", "id": "e0c96097-460e-46d1-a3af-afd18c04db30", "metadata": {}, "source": [ "## Project Assignment 3 " ] }, { "cell_type": "markdown", "id": "d8ac4b13-8479-4f72-87f7-fe31d454fec0", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "id": "fb886891-195e-4a82-933b-d253a37775f6", "metadata": {}, "source": [ "The overall goal of my project is to analyze the Social Vulnerability Index (SVI) for counties in the U.S. using the 2022 SVI dataset. The Social Vulnerability Index measure how vulnerable each community to disaters, economic stress, and public health problems. With analyzing this dataset, I can find patterns in vulnerability across counties and find locations that might need more resources and support. Learning to understand these patterns can allow policymakers and emergency planners improve disater readiness and community resilience." ] }, { "cell_type": "markdown", "id": "1727f203-e21a-453f-8ccd-72cab18a9f48", "metadata": {}, "source": [ "## Dataset Description" ] }, { "cell_type": "markdown", "id": "8c2ecb89-0fbc-4c97-8156-6b2e575ecded", "metadata": {}, "source": [ "The dataset that I used for this project is the 2022 Social Vilnerability index dataset at the county level. This dataset was made by the CDC and has vulnerability scores for counties all across the U.S. Each county is given a score from 0 to 1, with the higher values meaning larger vulnerability. The main variable I will analyze is the RPL_THEMES, which represents the overall social vulnerability score." ] }, { "cell_type": "code", "execution_count": 1, "id": "58aa9d99-e3f4-4eb8-976d-ad804a3575b5", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "id": "1223bceb-e2ce-4f54-816c-68604a7a066d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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STSTATEST_ABBRSTCNTYCOUNTYFIPSLOCATIONAREA_SQMIE_TOTPOPM_TOTPOP...EP_ASIANMP_ASIANEP_AIANMP_AIANEP_NHPIMP_NHPIEP_TWOMOREMP_TWOMOREEP_OTHERRACEMP_OTHERRACE
01AlabamaAL1001Autauga County1001Autauga County, Alabama594.454786587610...1.10.40.10.10.00.13.31.00.20.3
11AlabamaAL1003Baldwin County1003Baldwin County, Alabama1589.8618172334200...0.90.10.20.10.00.13.10.40.40.3
21AlabamaAL1005Barbour County1005Barbour County, Alabama885.007619248770...0.50.10.30.10.00.11.80.71.20.8
31AlabamaAL1007Bibb County1007Bibb County, Alabama622.469286222510...0.30.40.10.10.00.21.71.00.10.1
41AlabamaAL1009Blount County1009Blount County, Alabama644.890376590770...0.20.20.10.10.20.22.80.70.10.1
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5 rows × 158 columns

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" ], "text/plain": [ " ST STATE ST_ABBR STCNTY COUNTY FIPS LOCATION \\\n", "0 1 Alabama AL 1001 Autauga County 1001 Autauga County, Alabama \n", "1 1 Alabama AL 1003 Baldwin County 1003 Baldwin County, Alabama \n", "2 1 Alabama AL 1005 Barbour County 1005 Barbour County, Alabama \n", "3 1 Alabama AL 1007 Bibb County 1007 Bibb County, Alabama \n", "4 1 Alabama AL 1009 Blount County 1009 Blount County, Alabama \n", "\n", " AREA_SQMI E_TOTPOP M_TOTPOP ... EP_ASIAN MP_ASIAN EP_AIAN MP_AIAN \\\n", "0 594.454786 58761 0 ... 1.1 0.4 0.1 0.1 \n", "1 1589.861817 233420 0 ... 0.9 0.1 0.2 0.1 \n", "2 885.007619 24877 0 ... 0.5 0.1 0.3 0.1 \n", "3 622.469286 22251 0 ... 0.3 0.4 0.1 0.1 \n", "4 644.890376 59077 0 ... 0.2 0.2 0.1 0.1 \n", "\n", " EP_NHPI MP_NHPI EP_TWOMORE MP_TWOMORE EP_OTHERRACE MP_OTHERRACE \n", "0 0.0 0.1 3.3 1.0 0.2 0.3 \n", "1 0.0 0.1 3.1 0.4 0.4 0.3 \n", "2 0.0 0.1 1.8 0.7 1.2 0.8 \n", "3 0.0 0.2 1.7 1.0 0.1 0.1 \n", "4 0.2 0.2 2.8 0.7 0.1 0.1 \n", "\n", "[5 rows x 158 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svi = pd.read_csv(\"SVI_2022_US_county.csv\")\n", "svi.head()" ] }, { "cell_type": "markdown", "id": "7003cb4f-0ed8-4eb5-a779-8b9a01e0dc4a", "metadata": {}, "source": [ "## Inspecting the Dataset" ] }, { "cell_type": "code", "execution_count": 3, "id": "3b656c47-93a7-453a-adce-c96732d0ae4e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 3144 entries, 0 to 3143\n", "Columns: 158 entries, ST to MP_OTHERRACE\n", "dtypes: float64(75), int64(79), object(4)\n", "memory usage: 3.8+ MB\n" ] } ], "source": [ "svi.info()" ] }, { "cell_type": "code", "execution_count": 4, "id": "377996ca-74d4-4eff-a0ff-e97c2e0a093b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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STSTCNTYFIPSAREA_SQMIE_TOTPOPM_TOTPOPE_HUM_HUE_HHM_HH...EP_ASIANMP_ASIANEP_AIANMP_AIANEP_NHPIMP_NHPIEP_TWOMOREMP_TWOMOREEP_OTHERRACEMP_OTHERRACE
count3144.0000003144.0000003144.0000003144.0000003.144000e+033144.0000003.144000e+033144.0000003.144000e+033144.000000...3144.0000003144.0000003144.0000003144.0000003144.0000003144.0000003144.0000003144.0000003144.0000003144.000000
mean30.26431330368.18702330368.1870231123.8128561.053109e+055.6870234.482939e+0466.3673663.999248e+04378.694975...1.4096060.3871821.7091920.4135180.0919850.3569343.1058210.8744910.2646950.423282
std15.15266815170.42748415170.4274843592.7736063.337924e+0529.9246061.320087e+0564.2586911.214871e+05367.587221...2.8891200.7669137.4895941.1197860.4724471.1423091.9684231.2036680.3840441.354489
min1.0000001001.0000001001.0000002.0464425.000000e+010.0000005.500000e+0111.0000003.200000e+0120.000000...0.0000000.1000000.0000000.1000000.0000000.1000000.0000000.1000000.0000000.100000
25%18.00000018174.50000018174.500000430.9686491.083575e+040.0000005.238250e+0330.0000004.139750e+03173.000000...0.3000000.1000000.1000000.1000000.0000000.1000002.0000000.4000000.0000000.100000
50%29.00000029174.00000029174.000000615.6257862.578450e+040.0000001.231600e+0447.0000009.944000e+03276.000000...0.6000000.2000000.2000000.1000000.0000000.1000002.8000000.6000000.2000000.200000
75%45.00000045079.50000045079.500000924.2459806.807975e+040.0000003.153475e+0477.0000002.643475e+04449.000000...1.3000000.4000000.5000000.4000000.1000000.3000003.7000001.0000000.4000000.400000
max56.00000056045.00000056045.000000145575.4917489.936690e+06328.0000003.599561e+06931.0000003.363093e+064811.000000...41.60000020.50000090.90000037.50000012.00000037.50000023.40000037.5000009.20000041.900000
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8 rows × 154 columns

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" ], "text/plain": [ " ST STCNTY FIPS AREA_SQMI E_TOTPOP \\\n", "count 3144.000000 3144.000000 3144.000000 3144.000000 3.144000e+03 \n", "mean 30.264313 30368.187023 30368.187023 1123.812856 1.053109e+05 \n", "std 15.152668 15170.427484 15170.427484 3592.773606 3.337924e+05 \n", "min 1.000000 1001.000000 1001.000000 2.046442 5.000000e+01 \n", "25% 18.000000 18174.500000 18174.500000 430.968649 1.083575e+04 \n", "50% 29.000000 29174.000000 29174.000000 615.625786 2.578450e+04 \n", "75% 45.000000 45079.500000 45079.500000 924.245980 6.807975e+04 \n", "max 56.000000 56045.000000 56045.000000 145575.491748 9.936690e+06 \n", "\n", " M_TOTPOP E_HU M_HU E_HH M_HH ... \\\n", "count 3144.000000 3.144000e+03 3144.000000 3.144000e+03 3144.000000 ... \n", "mean 5.687023 4.482939e+04 66.367366 3.999248e+04 378.694975 ... \n", "std 29.924606 1.320087e+05 64.258691 1.214871e+05 367.587221 ... \n", "min 0.000000 5.500000e+01 11.000000 3.200000e+01 20.000000 ... \n", "25% 0.000000 5.238250e+03 30.000000 4.139750e+03 173.000000 ... \n", "50% 0.000000 1.231600e+04 47.000000 9.944000e+03 276.000000 ... \n", "75% 0.000000 3.153475e+04 77.000000 2.643475e+04 449.000000 ... \n", "max 328.000000 3.599561e+06 931.000000 3.363093e+06 4811.000000 ... \n", "\n", " EP_ASIAN MP_ASIAN EP_AIAN MP_AIAN EP_NHPI \\\n", "count 3144.000000 3144.000000 3144.000000 3144.000000 3144.000000 \n", "mean 1.409606 0.387182 1.709192 0.413518 0.091985 \n", "std 2.889120 0.766913 7.489594 1.119786 0.472447 \n", "min 0.000000 0.100000 0.000000 0.100000 0.000000 \n", "25% 0.300000 0.100000 0.100000 0.100000 0.000000 \n", "50% 0.600000 0.200000 0.200000 0.100000 0.000000 \n", "75% 1.300000 0.400000 0.500000 0.400000 0.100000 \n", "max 41.600000 20.500000 90.900000 37.500000 12.000000 \n", "\n", " MP_NHPI EP_TWOMORE MP_TWOMORE EP_OTHERRACE MP_OTHERRACE \n", "count 3144.000000 3144.000000 3144.000000 3144.000000 3144.000000 \n", "mean 0.356934 3.105821 0.874491 0.264695 0.423282 \n", "std 1.142309 1.968423 1.203668 0.384044 1.354489 \n", "min 0.100000 0.000000 0.100000 0.000000 0.100000 \n", "25% 0.100000 2.000000 0.400000 0.000000 0.100000 \n", "50% 0.100000 2.800000 0.600000 0.200000 0.200000 \n", "75% 0.300000 3.700000 1.000000 0.400000 0.400000 \n", "max 37.500000 23.400000 37.500000 9.200000 41.900000 \n", "\n", "[8 rows x 154 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svi.describe()" ] }, { "cell_type": "markdown", "id": "ffaa1cd7-644e-40e5-b75f-e235f0925b3f", "metadata": {}, "source": [ "Checking for NA values and exploring important variables" ] }, { "cell_type": "code", "execution_count": 5, "id": "7abdc0b1-2bcf-4258-8064-5165f9fe2825", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ST 0\n", "STATE 0\n", "ST_ABBR 0\n", "STCNTY 0\n", "COUNTY 0\n", " ..\n", "MP_NHPI 0\n", "EP_TWOMORE 0\n", "MP_TWOMORE 0\n", "EP_OTHERRACE 0\n", "MP_OTHERRACE 0\n", "Length: 158, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svi.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 6, "id": "9f86d7d3-d408-42ad-9294-664ccfe34bdc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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STATECOUNTYRPL_THEMES
0AlabamaAutauga County0.2663
1AlabamaBaldwin County0.3487
2AlabamaBarbour County0.9927
3AlabamaBibb County0.8451
4AlabamaBlount County0.6166
\n", "
" ], "text/plain": [ " STATE COUNTY RPL_THEMES\n", "0 Alabama Autauga County 0.2663\n", "1 Alabama Baldwin County 0.3487\n", "2 Alabama Barbour County 0.9927\n", "3 Alabama Bibb County 0.8451\n", "4 Alabama Blount County 0.6166" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svi[['STATE','COUNTY','RPL_THEMES']].head()" ] }, { "cell_type": "markdown", "id": "310f53b9-ee07-43fd-a4d9-22bbc1d78626", "metadata": {}, "source": [ "## Distribution of Social Vulnerability Scores" ] }, { "cell_type": "code", "execution_count": 7, "id": "fda98f6d-9012-4b8b-9cfd-b2a44b2086f9", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.hist(svi['RPL_THEMES'], bins=10, edgecolor='black')\n", "plt.title(\"Distribution of Social Vulnerability Index Scores\")\n", "plt.xlabel(\"SVI Score\")\n", "plt.ylabel(\"Number of Counties\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "757b230b-fcae-4b59-9035-603db66af2a9", "metadata": {}, "source": [ "Because the SVI variable is a percentile ranking, the values are almost distrubited equally from 0 to 1. WHen the data is a uniform distribution, each bin contains roughly the same number of observations. " ] }, { "cell_type": "markdown", "id": "0ea99ba8-9625-498e-9b6e-c5ec7c3df8e7", "metadata": {}, "source": [ "## Identifying Counties with the Highest Vulnerability and Visualization" ] }, { "cell_type": "markdown", "id": "40b1b4ad-a120-49f5-9b05-1a02cf81f0b7", "metadata": {}, "source": [ "Sorts the dataset to allow me to find the counties with the largest vulnerability score." ] }, { "cell_type": "code", "execution_count": 8, "id": "414ec3ba-7ab8-4e85-91d3-2af82b0491ff", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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STATECOUNTYRPL_THEMES
1147LouisianaMadison Parish1.0000
1813New MexicoLuna County0.9997
2588TexasDimmit County0.9994
1134LouisianaEvangeline Parish0.9990
120ArkansasChicot County0.9987
1945North CarolinaLenoir County0.9984
334FloridaDeSoto County0.9981
111ArizonaYuma County0.9978
1484MississippiYazoo County0.9975
1478MississippiWashington County0.9971
\n", "
" ], "text/plain": [ " STATE COUNTY RPL_THEMES\n", "1147 Louisiana Madison Parish 1.0000\n", "1813 New Mexico Luna County 0.9997\n", "2588 Texas Dimmit County 0.9994\n", "1134 Louisiana Evangeline Parish 0.9990\n", "120 Arkansas Chicot County 0.9987\n", "1945 North Carolina Lenoir County 0.9984\n", "334 Florida DeSoto County 0.9981\n", "111 Arizona Yuma County 0.9978\n", "1484 Mississippi Yazoo County 0.9975\n", "1478 Mississippi Washington County 0.9971" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_counties = svi.sort_values(by='RPL_THEMES', ascending=False)\n", "top_counties[['STATE','COUNTY','RPL_THEMES']].head(10)" ] }, { "cell_type": "markdown", "id": "c0a9029c-8b0d-4cf5-9dd7-d5e13c479a6f", "metadata": {}, "source": [ "Since the top ten counties have very similar score close the 1, the x-axis is zoomed in to show the difference between counties. The chart show that Madison Parish In Louisiana has the highest vulnerability score and then goes down a the list to show the others counties scores." ] }, { "cell_type": "code", "execution_count": 13, "id": "a95e4db2-0af4-42b7-8b53-747193cacdad", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,6))\n", "plt.barh(top_counties.head(10)['COUNTY'], top_counties.head(10)['RPL_THEMES'], color = 'steelblue')\n", "plt.title(\"Top 10 Counties with Highest Social Vulnerability\")\n", "plt.xlabel(\"SVI Score\")\n", "plt.xlim(0.995, 1.001)\n", "plt.gca().invert_yaxis()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "2a6c4c10-9a45-4ded-a489-f0fa92afbabd", "metadata": {}, "source": [ "## State-Level Vulnerability Patterns or Visualization" ] }, { "cell_type": "markdown", "id": "4060aba5-73a5-454a-92b5-c64930b8bacc", "metadata": {}, "source": [ "This groups the counties by states and finds the average vulnerability by each state." ] }, { "cell_type": "code", "execution_count": 11, "id": "b3e89680-6c3c-49c9-a091-2cdbb37dd0cf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "STATE\n", "Arizona 0.829213\n", "Mississippi 0.806721\n", "New Mexico 0.794094\n", "Louisiana 0.772586\n", "South Carolina 0.763543\n", "Florida 0.708306\n", "Texas 0.706484\n", "Georgia 0.706448\n", "California 0.690778\n", "Arkansas 0.690255\n", "Name: RPL_THEMES, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "state_avg = svi.groupby('STATE')['RPL_THEMES'].mean().sort_values(ascending=False)\n", "state_avg.head(10)" ] }, { "cell_type": "markdown", "id": "f7ffc069-5164-4e2c-83e9-13e97136282a", "metadata": {}, "source": [ "The visualization show exactly which states have the highest average vulnerability levels across the different counties." ] }, { "cell_type": "code", "execution_count": 12, "id": "50774476-899b-4fca-a649-fe26e60a9cb8", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,6))\n", "state_avg.head(10).plot(kind='bar')\n", "plt.title(\"States with Highest Average Social Vulnerability\")\n", "plt.ylabel(\"Average SVI Score\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "019b4ab1-cb27-4a81-9967-449fd9f26036", "metadata": {}, "source": [ "## Results" ] }, { "cell_type": "markdown", "id": "42056014-4fa3-4041-8997-f3aff9ae2a92", "metadata": {}, "source": [ "The first graph that I made shows the SVI scores for counties in the U.S. These values are from 0 to 1, and the bars are almost the same height across the full graph. This is becasue the SVI score is actually based on percentile rankings, which spreads the counties fairly across the range of values." ] }, { "cell_type": "markdown", "id": "6e7fb77d-0ee6-4e11-9cf6-21c91af75a87", "metadata": {}, "source": [ "The second graph that I made shows the top ten counties with the highest social vulnerability score. I found that Madison Parish in Louisiana has the highest score followed by Luna County in New Mexico and Dimmit County in Texas. All of the top ten counties have scores very close to 1 with the losest one being higher the 0.997, which means they all rank among the most vulnerable counties in the country. This counties might be facing different or similar challenges like poverty, limited healthcare, and weak infrastructure. " ] }, { "cell_type": "markdown", "id": "d5b61c62-c065-4ff7-9989-e7267b3c688a", "metadata": {}, "source": [ "The last graph that I made shows the states with the largest average social vulnerability scores. The graph shows that Arizona has the biggest average score, followed by Mississippi and New Mexico. Some other states also appear in the top ten like Louisiana, South Carolina, Florida, and Texas. This implies that vulnerability could be more common in certain areas of the country." ] }, { "cell_type": "markdown", "id": "08117b61-049d-4474-8db7-34c06a178d49", "metadata": {}, "source": [ "Overall, the visualization and analysis shows that while SVI scores are evenly spread across the counties, some counties and states continue to appear consistently among the most vulnerable. " ] }, { "cell_type": "markdown", "id": "4329e2bd-bea9-4a83-8ece-8fbada64896f", "metadata": {}, "source": [ "## Conclusion" ] }, { "cell_type": "markdown", "id": "cdc3f839-d592-45c8-a397-f186081468e6", "metadata": {}, "source": [ "In conclusion for this project I analyzed the Social Vulnerability Index (SVI)for counties in the U.S. using the 2022 dataset. The graph I made helped show how vulnerability scores are distributed across counties and which counties have the highest scores. Counties like Madison Parish in Louisiana, Luna City in New Mexico, and Dimmit County in Texas had the highest vulnerability score." ] }, { "cell_type": "markdown", "id": "27dcae4d-eccb-4cb7-9a4e-f9fdf412fde5", "metadata": {}, "source": [ "The state-level results showed that certain states, like Arizona, Mississippi, and New Mexico have the largest average when it comes to vulnerability levels. This shows that vulnerability could be more common in certain regions of the country. In geography, places nearby tend to have similar characteristics. Because of this, counties that are nearby one another may show similar vulnerability patterns. By looking at these spatial patterns it could help find regions where communities may need more support and resources." ] }, { "cell_type": "code", "execution_count": null, "id": "4b8d0937-3482-43fd-8fb6-47bf2bc5b380", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3-0.8.0", "language": "python", "name": "python3-0.8.0" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 5 }