Project 3: Property Value Choropleth with Price Prediction

Build a choropleth map of property values and a simple predictive model using spatial features.

Quick Reference

Attribute Value
Difficulty Intermediate
Time Estimate 1-2 weeks
Language Python
Prerequisites Pandas, basic statistics
Key Topics choropleths, spatial joins, feature engineering, regression
Output Interactive map + model report

Learning Objectives

By completing this project, you will:

  1. Join property prices to geographic boundaries.
  2. Design a choropleth with meaningful class breaks.
  3. Engineer spatial features like distance, density, and area.
  4. Train a baseline regression model and evaluate it.
  5. Interpret spatial bias and data quality issues.
  6. Communicate results visually and statistically.

The Core Question You’re Answering

“How does location become a number I can model and a pattern I can visualize?”

Real estate prices vary spatially because of proximity, accessibility, and land use. This project forces you to quantify those forces.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Choropleth design Bad color scales mislead GIS visualization guides
Spatial joins You must attach prices to polygons GeoPandas docs
CRS for distance Distance requires projection GeoPandas CRS guide
Regression metrics You must measure model quality ML intro (MAE/RMSE)
Feature leakage Model quality can be fake ML validation basics

Key Concepts Deep Dive

  1. Choropleth Class Breaks
    • Quantiles vs equal intervals lead to different patterns.
    • Use a scale that matches your data distribution.
  2. Spatial Feature Engineering
    • Distance to downtown, density of amenities, average income.
    • These turn geography into predictors.
  3. Join Strategies
    • Use ID-based joins when possible.
    • Spatial joins are for when IDs are missing.

Theoretical Foundation

Spatial Data Flow

Prices (CSV) + Boundaries (GeoJSON)
        |
        v
   Spatial Join
        |
        v
 Choropleth Map + Feature Table
        |
        v
 Regression Model + Error Metrics

Choropleth Pitfalls

  • Outliers skew color ranges.
  • Small polygons can look too important.
  • Missing data must be handled explicitly.

Project Specification

What You Will Build

A notebook that creates a choropleth of median property values by area and trains a simple regression model using spatial features.

Functional Requirements

  1. Load boundaries (tracts/ZIPs) and price data.
  2. Join price data to polygons.
  3. Build a choropleth with legend and tooltips.
  4. Engineer at least three spatial features.
  5. Train a regression model and report MAE or RMSE.

Non-Functional Requirements

  • Accuracy: Use projected CRS for distances.
  • Clarity: Map and model outputs must be explained.
  • Reproducibility: Notebook runs end-to-end.

Example Usage / Output

Choropleth: Median price by ZIP
Legend: $150k - $250k - $350k - $450k - $550k
Model MAE: $42,500

Real World Outcome

You open the notebook and see:

  1. A choropleth map with tooltips for each area.
  2. A feature table showing distance, density, and area.
  3. A model evaluation section with MAE/RMSE.
  4. A short note listing undervalued areas by model residuals.

Solution Architecture

High-Level Design

Boundaries + Prices -> Join -> Choropleth
                      |
                      v
              Feature Engineering -> Model

Key Components

Component Responsibility Key Decisions
Joiner Attach prices to polygons ID join vs spatial join
Mapper Render choropleth Class breaks and palette
Feature builder Create predictors Distance, density, area
Modeler Fit regression Baseline model choice

Data Structures

GeoDataFrame  # polygons with attributes
DataFrame     # engineered features

Algorithm Overview

  1. Read boundaries and price data.
  2. Join by ID or spatial intersection.
  3. Create choropleth map.
  4. Compute features (distance, density, area).
  5. Train regression and compute error metrics.

Complexity

  • Spatial join: O(n log n)
  • Regression: O(n * f)

Implementation Guide

Development Environment Setup

python -m venv geo-env
source geo-env/bin/activate
pip install geopandas folium scikit-learn

Project Structure

project-root/
├── data/
│   ├── boundaries.geojson
│   └── prices.csv
├── notebooks/
│   └── choropleth_model.ipynb
└── README.md

The Core Question You’re Answering

“How do spatial patterns become predictive signals?”

Concepts You Must Understand First

  1. Join integrity: Ensure every polygon has a price.
  2. Projection: Use projected CRS for distance.
  3. Validation: Hold out data for model testing.

Questions to Guide Your Design

  1. What is your target variable: median price or price per square foot?
  2. Which spatial features are likely predictive?
  3. How will you handle missing values or outliers?
  4. What does a “good” error metric mean for this domain?

Thinking Exercise

Pick two neighborhoods with different prices. List 3 spatial features that might explain the difference. Then decide how to compute each feature.

Interview Questions

  1. What is a choropleth and when is it useful?
  2. How do you join tabular data to polygons?
  3. How do you choose class breaks for a choropleth?
  4. What is MAE vs RMSE, and when would you use each?
  5. How do you avoid spatial leakage?
  6. How do you validate a spatial model?

Hints in Layers

  • Hint 1: Start with ID joins before spatial joins.
  • Hint 2: Use quantiles for class breaks if the distribution is skewed.
  • Hint 3: Normalize features to avoid scale bias.
  • Hint 4: Compare model error to a naive baseline.

Books That Will Help

Topic Book Chapter
Choropleths “Introduction to GIS Programming” Visualization
Spatial joins GeoPandas docs Joins
Regression “Hands-On Machine Learning” Regression

Implementation Phases

Phase 1: Join and Map (3-4 days)

  • Load boundaries and prices.
  • Join and validate coverage.
  • Render choropleth with tooltips.

Checkpoint: Map shows values for each polygon.

Phase 2: Feature Engineering (3-4 days)

  • Compute distances and densities.
  • Build feature table.

Checkpoint: Feature dataset aligned to polygons.

Phase 3: Modeling (2-3 days)

  • Train regression model.
  • Report metrics and residuals.

Checkpoint: Model results documented and interpreted.

Key Implementation Decisions

Decision Options Recommendation Rationale
Join type ID vs spatial ID when available More stable
Model Linear, tree Linear baseline Interpretability
Scale raw, log log for skew Stabilizes variance

Testing Strategy

Category Purpose Examples
Join accuracy All polygons populated Null count check
Map correctness Tooltips show correct values Spot checks
Model validity Error vs baseline Compare MAE

Critical cases:

  • Missing prices.
  • Outliers with extreme values.
  • Invalid geometry.

Common Pitfalls and Debugging

Pitfall Symptom Solution
Wrong CRS Distance features wrong Project to local CRS
Overfitting Low train error, high test Train/test split
Misleading color scale Map looks uniform Use quantiles or log

Extensions and Challenges

  • Add spatial lag features (neighbor averages).
  • Compare multiple years of prices.
  • Build a residual map to show under/over-valued areas.

Real-World Connections

  • Real estate analytics.
  • Urban planning investment analysis.
  • Policy studies of housing affordability.

Resources

  • GeoPandas: https://geopandas.org/
  • Folium: https://python-visualization.github.io/folium/
  • Scikit-learn: https://scikit-learn.org/

Self-Assessment Checklist

  • I can explain choropleth class breaks.
  • I can build spatial features and justify them.
  • I can evaluate a regression model in context.

Submission / Completion Criteria

Minimum Viable Completion

  • Choropleth map built with joined data.

Full Completion

  • Feature engineering + regression with metrics.

Excellence

  • Residual map and spatial lag features.

This guide was generated from GEOSPATIAL_PYTHON_LEARNING_PROJECTS.md. For the complete learning path, see the parent directory GEOSPATIAL_PYTHON_LEARNING_PROJECTS/README.md.