📊 Los Angeles County: Equity-Based Resource Allocation Model

🎯 Project Overview & Community Value

This project develops an analytical model to generate a Resource Allocation Priority Score for every census tract in Los Angeles County. This score is designed to identify areas of highest vulnerability and need based on key indicators of socioeconomic disadvantage. The primary deliverable is a Choropleth map and a ranked list, serving as an objective, data-driven tool for government agencies and NGOs to ensure equitable resource distribution.

Model Goal: Minimize bias in funding decisions by identifying communities where cumulative disadvantage creates the greatest need.

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⚙️ Technical Methodology

This analysis follows an end-to-end data science pipeline:

1. Data Acquisition: Utilizes the U.S. Census Bureau's American Community Survey (ACS) 5-year Estimates (2020) via a direct API connection.
2. Key Vulnerability Metrics: Selected indicators based on common factors contributing to systemic disadvantage:
   • Poverty Rate (Fails to meet basic needs)
   • Lack of Vehicle Access (Limits access to jobs, health, and education)
   • Educational Attainment (Predicts future earning potential and stability)
   • Renter Occupied Households (Indicates potential housing instability)
3. Composite Indexing: A PRIORITY SCORE is generated for each census tract by normalizing the four vulnerability metrics and creating a weighted sum, with a higher score indicating a greater need for resources.
4. Data Visualization: A Choropleth map is created using GeoPandas/Matplotlib to visually demonstrate the geographic spread of high-need areas across Los Angeles County.

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🚀 Key Deliverables & Insights

Metric	Highest Score Value	Rationale
Poverty Rate	Highest poverty tracts	Directly correlates with financial hardship.
No Vehicle PCT	Highest % of households without a vehicle	Indicates transportation barriers and isolation.
PRIORITY SCORE	Top 10 High-Need Tracts	Highest cumulative socioeconomic vulnerability.

The map visualization clearly shows a concentration of high-priority tracts in the [Suggest a geographical region based on your map, e.g., South/Southeast LA], guiding immediate resource focus.

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📚 Project Files & Setup

File Name	Description
DataEquityLAResourceAllocationPriorityScale.ipynb	The complete analysis notebook (data connection, calculation, and visualization generation).
requirements.txt	List of all necessary Python packages.

Setup and Installation

1. Clone the Repository

   git clone [https://github.com/pandakitty/LA-Equity-Resource-Allocation.git](https://github.com/pandakitty/LA-Equity-Resource-Allocation.git)
   cd LA-Equity-Resource-Allocation

2. Install Dependencies You will need a specific library (census) not included in standard DS installations.

   pip install -r requirements.txt

3. Run the Analysis

   jupyter notebook DataEquityLAResourceAllocationPriorityScale.ipynb