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