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Business Patterns in Puerto Rico (2012-2022)

Ironhack Data Analysis Project by Danielle Steede and Vanessa Jimenez

Overview

Exploring business patterns in Puerto Rico from 2012 to 2022 across different industries. Analysis will identify sectors resilient or vulnerable to major events like Hurricane Maria and COVID-19. Aim to provide valuable insights for Puerto Rican policymakers, investors, and entrepreneurs.

Problem/Hypothesis

In this project we aim to analyze data sets of Business Patterns in Puerto Rico from 2012-2022 and answer the following questions:

  1. What are the industries that have had the most growth in Puerto Rico over the past 10 years?
  2. How have business locations changed in Puerto Rico over the past 10 years?

The Accommodation & Food Services industry has experienced the most significant fluctuations over the past 10 years. The most notable fluctuations will be in the years following Hurricane Maria and the COVID-19 pandemic

Analysis

Our hypothesis was refuted: While the restaurant/food industry experienced a reduction over time, it generally maintained stability over the 10-year period with minimal variation, even after Hurricane Maria and the pandemic.

Key Findings

  1. Industry growth: The manufacturing sector saw the most significant growth, almost tripling between 2012 and 2022.
  2. Business location Shifts: There was a slight trend of businesses moving away from the metro area. However, it's too early to determine if this pattern will continue significantly.

Biggest Discoveries

  1. Stark distribution towards the Metro Area: We expected the metro area to have the highest concentration of businesses, but the extreme disparity in distribution, as shown on the heatmap, was surprising.
  2. Pandemic impact: Contrary to our expectations, the pandemic had a less severe impact on businesses outside the metro area compared to those within it.

Potential Implications

  1. Over-Concentration of Industry:
  • Regional Disparities: Concentrating industry and jobs in a few towns can exacerbate regional inequalities.
  • Vulnerability to Economic Shocks: Such concentration makes the state more susceptible to economic shocks.
  • Social Cohesion Challenges: Disparities in wealth and opportunities between urban and rural areas can lead to social divisions and resentment.
  1. Over-Dependence on the Tourism Industry:
  • Economic Sensitivity: The economy is highly sensitive to external factors, including economic downturns, natural disasters, and travel trends.
  • Neglect of Other Industries: Other potentially viable industries may be neglected.
  • Seasonal Employment: Reliance on seasonal employment that depends on tips can lead to economic instability.

Features

  • Data Set Folder: Copies of the original data sets are found here
  • Exploratory Data Analysis and Visualizations Folder: Visualizations of the data are found here
  • Reference Info Folder: Copies of the NAICS and FIPS codes used in the data sets and their full explanations are found here
  • Biz_Set_Functions.py: The functions we created and used during this project
  • Clean and Concat DFs.ipynb: The Jupyter notebook where we cleaned and concatenated all of our data
  • Graphs from Clean Data.ipynb: The Jupyter notebook where we created our visualizations
  • pr_biz_dataset_clean.csv: The final clean data set

Data Sets

The data sets used in this project were obtained from:
estadisticas.pr
mercadolaboral.pr

Other Relevant links

For this project we used a kanban board in Trello to track and assign tasks. The board can be found here
This project was presented on May 16th during Ironhack Data Analytics Course. Google Slides