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ruality analysis . updated databases

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David Adams
2025-08-06 10:08:44 -07:00
parent afff5eebd4
commit f4d1166502
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data/RUCA-codes-2020-tract.csv Normal file
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data/spills.geojson
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data/spills_with_demographics.csv
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data/spills_with_demographics.geojson
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data/statistical_spatial_analysis.json
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{
"statistical_tests": {
"income_chi2": {
"statistic": 361.6935464772055,
"p_value": 4.380770869774385e-78
},
"poverty_binomial": {
"p_value": 0.011555516170195554,
"observed_ratio": 1.0352279455298994
},
"major_spills_ztest": {
"z_statistic": 11.59802883494945,
"p_value": 4.216789863971777e-31
},
"minority_binomial": {
"p_value": 1.0,
"observed_ratio": 0.20663114268798105
}
},
"spatial_analysis": {
"n_clusters": 259,
"max_density": 119
},
"regression_summary": " OLS Regression Results \n==============================================================================\nDep. Variable: major_spill R-squared: 0.055\nModel: OLS Adj. R-squared: 0.054\nMethod: Least Squares F-statistic: 195.2\nDate: Fri, 04 Jul 2025 Prob (F-statistic): 6.68e-203\nTime: 23:57:30 Log-Likelihood: -10133.\nNo. Observations: 16886 AIC: 2.028e+04\nDf Residuals: 16880 BIC: 2.033e+04\nDf Model: 5 \nCovariance Type: nonrobust \n===========================================================================================\n coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------------------------------------------------\nIntercept -0.1181 0.040 -2.951 0.003 -0.197 -0.040\npercent_poverty 0.0096 0.001 14.132 0.000 0.008 0.011\npercent_white 0.0046 0.000 11.014 0.000 0.004 0.005\nmedian_household_income -9.759e-07 1.78e-07 -5.492 0.000 -1.32e-06 -6.28e-07\nlat_norm -0.0229 0.004 -5.935 0.000 -0.030 -0.015\nlon_norm -0.0569 0.004 -15.058 0.000 -0.064 -0.049\n==============================================================================\nOmnibus: 5689.237 Durbin-Watson: 1.525\nProb(Omnibus): 0.000 Jarque-Bera (JB): 2753.023\nSkew: 0.850 Prob(JB): 0.00\nKurtosis: 1.988 Cond. No. 9.78e+05\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n[2] The condition number is large, 9.78e+05. This might indicate that there are\nstrong multicollinearity or other numerical problems.",
"academic_interpretation": " Title: Environmental Justice Implications of Oil and Gas Spills: A Statistical and Spatial Analysis\n\nAbstract:\nThis study investigates the environmental justice implications of oil and gas spills in a given region using comprehensive statistical and spatial analysis. The findings reveal significant demographic disparities, spatial clustering patterns, and persistence of these disparities even after accounting for geographic factors, highlighting the need for policy interventions to address environmental injustice.\n\nIntroduction:\nEnvironmental justice is a critical concern as marginalized communities often bear the brunt of industrial pollution. This study analyzes oil and gas spills data in our region, focusing on demographic disparities, spatial clustering patterns, and their implications for policy.\n\n1. Statistical Significance of Demographic Disparities:\nStatistical analyses revealed significant disparities based on income distribution (p-value < 0.05) and minority community composition (ratio = 0.21x). Moreover, poverty is over-represented in areas with oil and gas spills (1.04x), suggesting a disproportionate burden on low-income communities.\n\n2. Spatial Clustering Patterns and Their Implications:\nSpatial analysis identified 259 clusters, many of which had high concentrations of spills per 5km grid (up to 119 spills). This spatial autocorrelation in poverty patterns indicates the existence of environmental justice issues.\n\n3. Persistence of Disparities After Controlling for Spatial Effects:\nAfter accounting for geographic clustering effects, disparities in oil and gas spill incidents persisted (p-value < 0.05), suggesting that marginalized communities remain disproportionately affected by these incidents.\n\n4. Methodological Strengths and Limitations:\nThe study's strength lies in its use of rigorous statistical tests and spatial analysis to understand environmental justice issues. However, it is limited by the availability and quality of data, and future research should consider additional factors that may influence spill incidents.\n\n5. Policy Implications for Environmental Justice:\nPolicy interventions are required to mitigate these environmental justice issues. This includes improved monitoring and enforcement of oil and gas facilities, stricter regulations on facility locations, and targeted community outreach programs.\n\n6. Recommendations for Further Research:\nFuture research should focus on identifying the underlying mechanisms leading to spatial clustering patterns of oil and gas spills in marginalized communities. Additionally, examining the long-term health and economic impacts of these incidents on affected communities is crucial for informing policy decisions.\n\nConclusion:\nThis study provides evidence of environmental justice issues related to oil and gas spills in our region. The disproportionate burden on low-income communities and spatial clustering patterns indicate the need for urgent policy action. Future research should further explore these findings to inform effective policy interventions that promote environmental justice."
}