Uber Spatial Pricing - Agent Guidelines

Project Overview

Industrial organization paper scaffold for studying spatial pricing, demand, and market structure in Chicago using Uber-style pricing data plus neighborhood and transit inputs.

Canonical Project Reference

• uber_spatial_pricing.md is the canonical research master document for this project
• Treat it as the ground-truth reference for research design, empirical priorities, paper framing, and task sequencing whenever those choices are ambiguous

Data Analysis Workflow

• Task-based workflow identical to the spatial project pattern used elsewhere
• Every task has a dedicated folder in tasks/ with code/, input/, and output/ subfolders
• Every task also has a temp/ folder created through shared make rules
• Each task has its own Makefile
• Tasks use symlinking to connect upstream outputs to downstream inputs
• Never use mkdir explicitly in a task makefile; rely on ../../generic.make to create directories for all task makefiles, and call it in every single task makefile after the all target
• Prefer R over Python for new analysis, data-prep, estimation, and mapping tasks
• Use Python only when there is a clear technical reason to do so, and still follow the same task and make conventions
• Avoid ad hoc root-level scripts; new work should live inside a task with a task-specific Makefile

Project Structure

• tasks/ - Analysis tasks, data prep tasks, and model-building tasks
• paper/ - Minimal paper scaffold and future writeup
• tasks/*/code/ - Task entry scripts and Makefiles

Running Tasks

• Always execute tasks by running make from the code folder within any task
• All paths should be relative to the code folder
• Run the full project from the root with make or make tasks

Key Files

• tasks/shell_functions.make - Make helpers for R and Python execution
• tasks/shell_functions.sh - Shared shell functions
• tasks/generic.make - Common task directory rules
• tasks/setup_environment/code/packages.R - R package bootstrap (source this in all R scripts)
• tasks/setup_environment/code/requirements.txt - Python package reference for bootstrap checks

Research Design

• Focus on Chicago spatial pricing rather than the Red Line welfare model
• Build reusable geographic and transit inputs before ingesting proprietary or licensed pricing data
• Organize empirical work as separate tasks for data preparation, panel construction, descriptive outputs, and baseline estimation
• Prefer outputs that are easy to inspect and reuse across tasks: csv, geojson, parquet, rds, and simple figures

Data Sources

1. Chicago tract and city boundaries via Census or local GIS sources
2. CTA transit network inputs and stop locations
3. Uber-style trip or pricing microdata supplied separately by the researcher
4. Derived crosswalks between geographic units, stops, and market definitions

Initial Empirical Goal

Use Chicago spatial and transit data to build a reproducible pricing panel that can support IO-style descriptive analysis and a baseline estimating specification before the substantive paper design is finalized.