zachpinto/walmart-proximity

This project explores the proximity of populations to Walmart locations, integrating geospatial data and statistical analysis.

Walmart Proximity Analysis

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This project explores the proximity of populations to Walmart locations, integrating geospatial data and statistical analysis.

Introduction

• Background:
  • Walmart claims that over 90% of Americans live within 10 miles of a Walmart location.
• Objectives:
  • To analyze the correlation between Walmart proximity and factors such as median household income and (RUCA) rural-urban classifications.
  • Develop models to predict driving distances and driving times to Walmart locations.
• Research Questions:
  • Can we accurately predict driving time and distance to Walmarts for different populations?
  • How does the proximity of Walmarts vary with various economic predictor variables?

Data

• Data Sources:
  • Scraped Walmart location data from the Walmart store locator webpage
  • Sourced zip code and RUCA classification from the USDA RUCA Codes
  • Geospatial coordinates for Walmart stores and zip code areas were integrated for proximity analysis using the GeoPy library for python
• Data Attributes:
  • Analyzed attributes include zip code populations, median household incomes, and geospatial data for Walmarts and zip code areas.

Methodology

• Data Processing:
  • Data from the sources were cleaned, merged, and transformed for analysis.
  • RUCA codes were encoded, and great circle distances were calculated for proximity analysis.
• Geospatial Analysis:
  • Utilized geospatial data to assess the proximity of populations to Walmart stores.
  • The general assumption is that great circle distance serves a generally strong predictor of actual driving distance/time distance.
  • Created visualizations to explore spatial relationships and distributions.
• Statistical Analysis:
  • Developed linear regression models to predict driving distances and times using great cirlce distance
  • Conducted correlation and descriptive analyses to understand pfdcitor impacts on Walmart accessibility.
• User Interface:
  • Leveraged Tableau for dynamic visualizations and interactive data exploration.

Analysis

• Descriptive Analysis:
  • Examined demographic distributions and Walmart locations to identify trends and patterns.
• Proximity Analysis:
  • Analyzed the physical distance of populations to Walmart stores.
• Correlation Analysis:
  • Investigated the relationship between socio-economic factors and the number of Walmarts within a 10-mile radius.

Results

• Findings:
  • Walmart's own research claims that 90% of Americans live within 10 miles of a Walmart store
  • My findings show that between 79% and 86% of Americans live within 10 miles of a Walmart store
  • According to the model
• Visualizations
  • Created comprehensive charts and maps to illustrate findings and support insights.

Discussion

• Interpretation:
  • Discussed the implications of Walmart accessibility on various communities.
  • Highlighted the importance of location in retail strategies.
• Limitations:
  • Acknowledged data limitations and potential biases.
  • Addressed the constraints of the predictive models used.
• Future Work:
  • Proposed further studies with additional variables and refined models.
  • Recommended exploring other retail chains for comparative analysis.

Project Organization

---

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data