What Is Geodesign?
Geodesign is a six-phase iterative framework for landscape-scale spatial decisions, developed by Carl Steinitz at the Harvard Graduate School of Design. Each phase asks a different question. The cycle repeats with increasing depth.
The framework arranges six "models" into two triads. The Assessment Triad (left) asks how the landscape works. The Intervention Triad (right) asks how it might be changed. Three feedback loops connect them — each loop represents a full pass through the analysis at increasing depth.
This study ran all three loops. What follows is the story of that process.
The key insight of the framework is that the six phases are not a linear pipeline. They form feedback loops. When a scenario fails stress-testing (Loop 2), you go back and redesign. When a design reveals new data needs (Loop 3), you go back and collect. The three loops in this study each represented a full pass of increasing depth — from screening to stress-testing to detailed design.
Adaptation note. The implementation here is a custom software-and-data adaptation of Steinitz's framework, developed independently and tailored to each geodesign engagement. Site-specific spatial-data pipelines, scenario simulation, MCDA scoring, stakeholder evaluation, and the nested-loop structure are bespoke per project — assembled from open-source GIS, custom Python tooling, and project-specific data integration rather than off-the-shelf software. The diagram above adds explicit nested loops, MOD-SIM-VIS cycles at each model, and stakeholder participation at every phase to Steinitz's original six-phase model.
The Data Foundation
Before building any model, we assembled the most comprehensive public-data picture of this site ever constructed. All data is from authoritative public sources. Nothing was paywalled. Everything is reproducible.
Data Acquisition Timeline
The data collection itself followed the geodesign phases — each loop required deeper data.
Loop 1: The Scaffold
The first pass through the geodesign framework is deliberately coarse. The goal is not precision but triage: build the simplest credible models, generate a rough ranking, and identify which scenarios deserve deeper analysis.
What we built
- Stormwater model (SCS-CN method with SSURGO soils) — 17 soil map units, 5 storm recurrence intervals, curve numbers for forest (CN=58.7), open space (64.2), and impervious (98)
- Financial model (30-year cash flow) — Three-perspective split: public cost, university revenue, developer return. Property tax, ecosystem services, and subsidy requirements for each scenario
- MCDA framework — 20 objectives across 4 domains (environmental, economic, social, governance). Equal-weight scoring for initial screening. 13 scenarios (A through K, plus subscenario variants A2 and C2)
- Alternative site inventory — 134,436 parcels classified, 221 public parcels scored for stadium and housing suitability
Loop 1 Headline Finding: The stadium scenario (A) ranks last of 13 under every weighting scheme. It is Pareto-dominated — at least one alternative is better on every dimension simultaneously. Public parcels in Buncombe County score 90/100 as alternative stadium sites. The leading viable candidate is 1568 Brevard Rd (123 ac, County) — cleared, outside the floodplain, no forest impact. Two algorithmic top scorers were excluded on review: 53 Birch St (Riverside Cemetery adjacency) and 226 Fairway Dr (Asheville Muni Golf Course, NRHP-listed).
Five scenarios advanced to Loop 2: H (forest preserved, housing elsewhere), E (land swap, minimal clearing), F (scaled-down stadium, land swap), I (conservation + research emphasis), and A (stadium, kept as a baseline for comparison).
Loop 2: The Stress Test
Loop 1 produced a clear ranking. But rankings are only as good as the weights behind them. If someone who wants the stadium assigns weights that favor economic development, does H still win? We needed to stress-test the entire framework.
What we did
- 1,000 Monte Carlo draws — Random weights sampled uniformly across all 20 objectives. No human judgment in the weighting. Pure exploration of the objective space.
- Adversarial scoring — Gave the stadium the most generous possible scores on every objective. What if every assumption breaks in the developer's favor?
- Minimax regret analysis — For each scenario, what is the worst-case regret across all 1,000 draws? Which scenario minimizes maximum regret?
- 10-order cascade analysis — Traced impacts through 10 cascading orders: direct, transportation, stormwater, property values, tax revenue, tourism, ecology, equity, governance, resilience.
- 4-scale spatial analysis — Evaluated each scenario at site (45 acres), neighborhood (Five Points), watershed (Reed Creek), and city scale.
Loop 2 Headline Finding: Scenario H wins 1,000 out of 1,000 Monte Carlo draws. There is no weighting of the 20 objectives — no matter how much you value economic development over ecology — that makes the stadium rank first. Even under adversarial conditions designed to maximize the stadium's score, H dominates.
The 10-order cascade analysis confirmed the result from a different angle: the stadium scenario produces net harm at all four spatial scales (site, neighborhood, watershed, city). The minimax regret winner is H1, a hybrid variant that adds a community sports field to the forest-preserved scenario.
Loop 3: The Design
Loops 1 and 2 established that preservation beats stadium development under every analysis. But "preserve the forest" is not a plan. Loop 3 takes the top two scenarios and designs them in detail: spatial layout, phasing, 30-year financial pro formas, and stakeholder trade-off analysis.
What we designed
- H1: Community Field + Housing + Forest Preserved — The minimax-regret winner. 45 acres fully preserved. 2,000-seat community field on already-cleared Zillicoa MC land. 402 housing units (80% market-rate, 20% affordable) on MC parcels. $0 public subsidy. Breaks even by Year 3.
- E2: Research Reserve + Conservation Housing — UNCA-centered variant. Forest becomes a formal research reserve with conservation easement. Housing emphasizes faculty/student/senior mix. Partnership with USFS Southern Research Station formalized.
- 30-year pro forma for each — Year-by-year cash flows, NPV at 3 discount rates, cumulative tax generation, and comparison to Scenario A (stadium).
Loop 3 Headline Finding: The NPV gap between H1 (community plan) and A (stadium) is $200.7 million at a 5% discount rate over 30 years. H1 breaks even by Year 3, requires zero public subsidy, preserves 100% of the forest, and generates $120M in property tax for the city (taxed on both land and improvements). The stadium never breaks even, requires $29M+ in public support, and clears the entire forest. Its underlying UNC land is tax-exempt under G.S. 105-278.1, but the ~$200M structure would generate property tax to the developer as lessee — an order-of-magnitude estimate of ~$25–30M NPV over 30 years (not a vetted developer pro forma). Even with that recapture, the gap with H1 remains decisive.
The H1 design includes a 2,000-seat community field on the already-cleared Zillicoa MC parcel — giving UNCA a sports venue without touching the forest. 402 housing units across two phases (80% market-rate, 20% affordable) address the city's housing shortage while generating permanent tax revenue.
What We Got Wrong
Every analysis has errors. The question is whether you find them or your opponents do. We found four claims that required correction during the analysis and corrected them before publication.
Why Self-Correction Matters
An advocacy document hides its weaknesses. An analytical document exposes them. By catching these four errors before publication, the analysis gained something more valuable than the claims it lost: credibility.
Consider the first correction. The original claim — "this is the last significant urban forest in Asheville" — sounded powerful. But when we checked the Esri 2020 10m Land Cover dataset (Sentinel-2-derived, ESA WorldCover-aligned), filtering for contiguous tree-cover patches of 45+ acres within the Asheville-MSA bounding box, we found 65 such patches. The claim was false. The reproducibility details: bounding box approximately 35.42–35.72°N by 82.40–82.75°W; tree-cover pixels grouped by 8-connected adjacency; minimum patch size 45 acres (~182,109 m²). Anyone with a copy of the Esri 10m Land Cover layer can re-run the count. If an opponent had found this first, every number in the analysis would have been suspect.
The revised claim — "increasingly rare and demonstrably valuable" — is weaker as rhetoric but stronger as evidence. It is also true.
2026 update strengthening the "valuable" half of the revised claim: Zou et al. (Nature Ecology & Evolution, 15 May 2026), "Larger forest patches have greater per-area productivity", find a positive relationship between forest patch size and per-area productivity using global remote-sensing data and counterfactual modelling. The implication for this case is direct: the existence of 65 other 45-acre-plus patches in the Asheville metro does not make the UNCA forest redundant on a per-acre productivity basis — and reducing or fragmenting any large patch costs more ecosystem function than the proportional acreage loss would suggest. [DOI]
Quality Review Summary
The full analysis passed through five review passes (v1.0 through v6.0). Each pass:
- Checked all numerical claims against source data files
- Verified methodology references (SCS-CN, MCDA) against published standards
- Applied adversarial reading: "What would the developer's lawyer attack?"
- Distinguished analysis-supported claims from assumptions
- Flagged every claim as asserted (data-supported), estimated (model-derived), or hypothesized (plausible but unverified)
The integrity principle: An analysis that self-corrects is more trustworthy than one that appears perfect. We are publishing the correction log because it is evidence that the remaining claims survived testing.
What Remains Unknown
Honest analysis requires honest boundaries. These are the things this study cannot answer and the caveats readers must carry forward.
7 Uncertainties
- Actual stormwater volumes under Helene conditions. Our SCS-CN model is screening-level. The 3.7-7.0 million gallon range spans the reasonable parameter space, but HEC-HMS modeling with actual Helene rainfall hyetographs would provide engineering-grade estimates. We have filed a USGS data request for Reed Creek peak discharge to improve calibration.
- Dendrochronological age of the forest. The 150-year-old oaks are estimated from diameter-at-breast-height growth models. Only increment core sampling can confirm actual age and provide the climate proxy record we hypothesize exists. No dendrochronology study has been commissioned.
- USFS Southern Research Station interest in a formal partnership. The institutional proximity is real (Bent Creek Experimental Forest is 5 miles away), but the current federal administration's priorities and potential agency restructuring create genuine uncertainty. We downgraded this from "likely" to "worth exploring."
- Two algorithmic top scorers disqualified on review. 53 Birch St (100/100) is disqualified due to probable Riverside Cemetery adjacency — the current-use field in county records indicates "Cemeteries / Burial." 226 Fairway Dr (90/100) is the Asheville Municipal Golf Course (a 1927 Donald Ross design listed on the National Register of Historic Places). The leading viable alternative is 1568 Brevard Rd (123 ac, County, 90/100).
- Developer financial capacity. The developer entity (~5 employees) has no completed stadium project. We identified zero comparable completions nationally. But we have not obtained financial statements, and a well-capitalized silent partner could change the risk profile.
- Political feasibility of land-swap scenarios. Scenarios E-H require the UNC system to exchange forest land for MC parcel development rights. This is legally permissible but politically complex. Board of Governors preferences, state legislative dynamics, and UNCA leadership transitions are all unknowns.
- Climate trajectory effects on forest value. Our ecosystem services valuation assumes current conditions. If Asheville experiences more Helene-scale events (which climate science suggests is likely), the stormwater absorption and heat mitigation value of the forest increases nonlinearly. We have not modeled this amplification.
5 Mandatory Caveats
- Scores reflect one analyst's judgment and have not been verified with stakeholders. We invite each stakeholder group to assign their own scores — we will re-run the analysis with any alternative scoring.
- Financial comparisons are directional, not precise. The stadium's $204M construction cost is primarily the developer's investment, not a public expenditure. The direct public cost is the $29M subsidy sought. Financial figures show relative differences between scenarios, not audited projections.
- Alternative site scenarios depend on feasibility. 53 Birch St (100/100) and 226 Fairway Dr (90/100) were disqualified on review (cemetery and historic golf course, respectively). The leading viable candidate is 1568 Brevard Rd (90/100, 123 ac, County) — site-specific assessment is still needed before committing.
- Stormwater figures are screening-level estimates (NRCS SCS-CN method with SSURGO soil data). They show relative differences between scenarios, not engineering-grade volumes. For design, HEC-HMS modeling with actual Helene rainfall data is recommended.
- This analysis was completed in approximately 40 hours using publicly available data. It is a structured framework for discussion, not a substitute for professional environmental impact assessment or fiscal impact study. Its value is in making the trade-offs explicit and the reasoning transparent.
The bottom line: Even with all seven uncertainties resolved in the stadium's favor, the structural conclusion holds. The ~$200M NPV gap (modeled at 5% discount rate over a 30-year horizon; screening-level, not engineering-grade), the zero tax revenue on UNC-exempt land, the floodway overlap with FEMA Special Flood Hazard Area boundaries, and the Pareto dominance of preservation scenarios under every weighting scheme tested are robust findings. What remains unknown can change the details but not the direction.