[OC] Panel Regression with Fixed Effects - Shootings per Neighborhood in Rio Visualization

What This Visualization Shows

This data visualization displays "[OC] Panel Regression with Fixed Effects - Shootings per Neighborhood in Rio" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on NegativeBinomial Regression Results ============================================================================== Dep. Variable: y No. Observations: 1368 Model: NegativeBinomial Df Residuals: 1358 Method: MLE Df Model: 9 Date: Fri, 19 Jun 2026 Pseudo R-squ.: 0.02560 Time: 22:34:19 Log-Likelihood: -4792.0 converged: False LL-Null: -4917.9 Covariance Type: cluster LLR p-value: 4.195e-49 ============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ const -3.6334 1.056 -3.439 0.001 -5.704 -1.563 hdi -4.3635 1.244 -3.509 0.000 -6.801 -1.926 year_2018 0.3801 0.069 5.524 0.000 0.245 0.515 year_2019 0.0711 0.083 0.857 0.392 -0.091 0.234 year_2020 -0.3883 0.085 -4.551 0.000 -0.555 -0.221 year_2021 -0.4204 0.108 -3.899 0.000 -0.632 -0.209 year_2022 -0.4602 0.133 -3.451 0.001 -0.722 -0.199 year_2023 -0.6527 0.135 -4.823 0.000 -0.918 -0.387 year_2024 -0.8031 0.135 -5.934 0.000 -1.068 -0.538 year_2025 -0.7968 0.179 -4.444 0.000 -1.148 -0.445 alpha 1.0039 0.093 10.775 0.000 0.821 1.187 ============================================================================== The coefficient 'const' is statistically significant (p-value: 0.0006) The coefficient 'hdi' is statistically significant (p-value: 0.0005) The coefficient 'alpha' is statistically significant (p-value: 0.0000)

log(E[Y_it / Pop_it]) = B_0 + B_1(HDI_i) + Σ γ_t(D_t)

Model Coefficients: B_0 = -3.6334 (p-value: 0.0006, significant) B_1 (HDI) = -4.3635 (p-value: 0.0005, significant)

Year Fixed Effects (γ_t): γ_2018 = +0.3801 (p-value: 0.0000, significant) γ_2019 = +0.0711 (p-value: 0.3916, not significant) γ_2020 = -0.3883 (p-value: 0.0000, significant) γ_2021 = -0.4204 (p-value: 0.0001, significant) γ_2022 = -0.4602 (p-value: 0.0006, significant) γ_2023 = -0.6527 (p-value: 0.0000, significant) γ_2024 = -0.8031 (p-value: 0.0000, significant) γ_2025 = -0.7968 (p-value: 0.0000, significant)

Per Capita Shooting Rate = exp(-3.6334 -4.3635 · HDI + year effects)

A 0.1 increase in HDI is associated with a reduction of 35.4% in per capita shooting occurrence rate.

Following the series of [posts](https://www.reddit.com/r/dataisbeautiful/comments/1u91djc/oc_the_routine_of_rio_de_janeiros_shooting/) regarding my work with [Instituto Fogo Cruzado](https://fogocruzado.org.br)'s Rio de Janeiro city-wide shooting data, worked on a panel regression approach to learn more about the neighborhood specific dynamics.

This regression is using population as an offset (that way, the effect a more populated neighborhood has in its shooting data is removed) and also, treating for year related fixed effects.

Thoughts on the results? Visualization made with matplotlib + seaborn., which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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Data Analysis and Insights

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