Climate-Migration Readiness in Atlanta

Missing-Middle Housing + Resilient Infrastructure Simulations

Independent Study – M.S. Urban Design

Author: Nitiksha Mota - LinkedIn

Institution: Georgia Institute of Technology

Advisors: Prof. Brian Stone, Prof. Patrick Kastner

1. Overview

This repository documents a replicable workflow for evaluating how missing-middle housing infill can support climate-migration readiness while balancing environmental resilience in Atlanta neighborhoods.

The study focuses on:

• Translating zoning and overlay codes into buildable scenarios

• Testing density increases through ADUs, townhomes, and small multifamily housing

• Evaluating microclimate, energy, wind, and stormwater impacts using simulation tools

This repo is intended as a guide for future students who want to:

• Run neighborhood-scale simulations

• Compare multiple density scenarios

• Produce policy-relevant, climate-informed urban design analysis

2. Research Question

How can existing zoning frameworks in climate-migration receiving cities like Atlanta be leveraged to increase housing supply while minimizing environmental and microclimatic impacts?

3. Case Study Neighborhoods

|————-|————-|———————|

4. Tools Used

Modeling & Simulation

• Autodesk Revit – massing + context modeling

• Autodesk Forma – climate and performance simulations

  • Sun hours

  • Heat stress

  • Wind comfort

  • Solar panel potential

Planning & Policy

• Atlanta Zoning Ordinance (R-5, SPI-19, SPI-16)

• BeltLine Overlay District regulations

• MARTA proximity and TOD guidelines

5. Workflow Summary

Step 1 — Define Site Type

Each neighborhood is categorized by growth logic:

• Retrofit (Reynoldstown): limited vacancy, backyard infill

• Rebuild (Vine City): block-scale redevelopment

• Reference (Midtown): contextual benchmark

Step 2 — Translate Zoning Into Design Rules

For each site:

• Maximum units per lot

• Height and setback limits

• Parking requirements or reductions

• ADU permissions

• Overlay intent (TOD, walkability, affordability)

This step ensures proposals are code-aligned.

Step 3 — Create Density Scenarios

Each site includes:

• Existing condition

• Proposal 1 (moderate infill)

• Proposal 2 (higher density infill)

Scenarios differ in:

• Number of units

• Roof area

• Tree canopy loss

• Ground permeability

6. Simulation Workflow (Core of the Study)

6.1 Revit Preparation

Goal: Comparable, clean massing models.

Best practices:

• Use simple massing (avoid detailed components)

• Keep ground plane constant across scenarios

• Maintain consistent building heights

• Represent trees as simplified canopy objects

• One Revit file per scenario

6.2 Import to Autodesk Forma

• Create a Forma site using real address

• Import Revit massing

• Verify:

  • Orientation

  • Scale

  • Terrain alignment

⚠️ Important: Do not change simulation settings between scenarios.

7. Simulations Performed

A. Sun Hours Analysis

Purpose: Measure daylight tradeoffs caused by density.

Run conditions:

• Winter Solstice (Dec 21)

• Summer Solstice (Jul 21)

• Daytime window (e.g. 8:00–16:00)

Recorded outputs:

• % ground with ≥ 3 hours sun

• % facades with ≥ 3 hours sun

• % roofs with ≥ 3 hours sun

Interpretation focus:

• Backyard livability

• Passive solar access

• Roof usability for PV

B. Microclimate / Heat Stress

Purpose: Evaluate thermal comfort impacts of added density.

Run condition:

• July 21, 2 PM (peak summer heat)

Metrics recorded:

• % strong heat stress

• % moderate heat stress

• Spatial concentration of heat

Key insight:

Heat stress changes were driven more by tree canopy loss than by density alone.

C. Wind Comfort Analysis

Purpose: Understand pedestrian-level comfort and ventilation.

Run condition:

• Prevailing east wind

Outputs:

• Comfort categories (sitting → uncomfortable)

• Wind stagnation vs channeling

• Block interior ventilation

Key tradeoff:

• Trees improve comfort but can reduce cooling airflow

D. Solar Panel Potential

Purpose: Evaluate energy resilience benefits of added density.

Metrics recorded:

• Total roof surface area

• Panel placement area

• Estimated annual energy output (kWh)

Finding:

Higher density increases total solar capacity, even if per-square-foot performance remains similar.

E. Stormwater + Runoff (Interpretive Analysis)

While Forma does not directly model runoff volume, geometry-based conclusions were drawn:

Observed impacts:

• Increased roof area → increased runoff volume

• Reduced parking helps lower impervious surface per unit

• Tree loss reduces interception and evapotranspiration

Mitigation strategies proposed:

• Mandatory rainwater harvesting

• Cool / reflective roofs

• Green roofs

• Bioswales + green streets

• Permeable paving

• Tree replacement ratios

8. Writing Findings (Reusable Template)

For every analysis slide:

Findings

• Quantitative results (% or area)

• Spatial patterns

Interpretation

• Comfort impacts

• Environmental tradeoffs

• Policy relevance

• Equity implications

9. Key Results Summary

Reynoldstown (Retrofit Model)

• ~230 new units via ADUs + small MF

• Minimal heat increase with canopy preservation

• Stormwater manageable with lot-scale mitigation

• Low displacement risk

Vine City (Rebuild + TOD Model)

• 196–263 new units across scenarios

• Heat stress remains similar across densities

• Significant solar + housing gains

• Stormwater impacts require district-scale solutions

10. Resilience Toolkit (Recommended Requirements)

For missing-middle infill:

• Rainwater harvesting on all new roofs

• Cool or green roofs

• Bioswales + curbside green infrastructure

• Permeable paving

• Tree replacement (1–2 new trees per removal)

11. Future Extensions

• Quantitative stormwater modeling (SWMM / SCS-CN)

• Canopy sensitivity testing

• Neighborhood-to-neighborhood comparison toolkit

• Policy scenario testing (density bonuses tied to resilience)

12. Citation

This repository is based on the Independent Study submission:

“Climate-Migration Readiness in Atlanta: Housing + Resilient Infrastructure”

Georgia Institute of Technology, 2025

13. For Future Students

If you are continuing this work:

• Keep simulation settings constant

• Always compare Existing vs Proposal

• Pair density increases with required mitigation

• Use simulations to support policy arguments, not just visuals

Source

Link to the repository.