Computational tools help planning teams make better-informed choices by converting large, disconnected datasets into models that people can inspect, discuss and revise. They also give stakeholders a practical way to contribute and let clients compare physical alternatives before committing money or schedule.
Three projects described by NBBJ in its May 8, 2023 article, “3 ways computational tools empower better decision-making”, show how that works in campus planning, urban redevelopment and research-facility budgeting. The examples are reported project accounts, not independent tests of a particular software product.
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What computational tools add to a planning process
Traditional planning already involves surveys, maps, engineering records, cost information and conversations with users. The difficulty is seeing how those pieces interact when a decision affects an entire campus, district or research site. Computational workflows provide a shared model in which different data types can be located, filtered and viewed together.
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That shared model changes the questions a team can ask. Instead of discussing a building, road or open space in isolation, planners can examine its relationship to land use, ground conditions, circulation, vegetation, existing facilities and potential future development. A visual scenario can then make an abstract policy legible to people who do not work with plans every day.
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The technology does not replace professional judgment. Designers and planners still decide which evidence matters, how assumptions should be treated and what trade-offs a community can accept. The benefit is a larger and more understandable set of options for that human judgment.
Three project examples at a glance
| Project | What was modeled or visualized | How people interacted | Decision supported |
|---|---|---|---|
| Louisiana State University campus master plan | More than a terabyte of land-use, groundwater, topography, tree, building-use and room-use/condition data | Students and staff used a 24/7 portal to report travel routes, modes and experiences | Renovation or replacement choices, investment sites, capital priorities and budgeting |
| Wilburton Commercial Area, Bellevue, Washington | 2D land-use ideas converted into alternative three-dimensional city forms | Citizens Advisory Committee members marked up a map; their annotations became inputs to the 3D process | Rapid comparison and discussion of different responses to an upzoning study |
| Oak Ridge National Laboratory Experimental Gas-Cooled Reactor campus | Interactive 3D GIS scenarios for a 300-acre site | Client users manipulated physical development alternatives | Testing potential sites and comparing feasibility and cost implications for multi-year planning and budgeting |
1. LSU: turning a very large campus dataset into capital priorities
The planning problem
Louisiana State University needed a long-term campus master plan grounded in evidence. A campus contains many overlapping systems: buildings and rooms, circulation, landscape, utilities and environmental conditions. Looking at each inventory separately can hide conflicts or opportunities, especially when the question is whether to repair, renovate, replace or build.
How the model was assembled
NBBJ reports that the project team translated more than one terabyte of information into models. The dataset covered land use, groundwater information, topography, trees, and building and room use and condition. “More than a terabyte” describes the scale of this particular LSU project; it is not a standard amount of data required for every campus plan or an independently verified industry benchmark.
Once those sources were represented spatially, the team could examine the consequences of planning choices across the campus rather than relying on disconnected spreadsheets or static drawings. A proposed investment could be viewed alongside terrain, tree cover, existing use and the condition of nearby facilities. The model exposed spatial patterns that are difficult to see when records remain in separate departments.
What decisions the model informed
According to the article, the resulting views helped the team assess which facilities might need renovation or replacement, identify sites for investment, establish capital priorities and inform budgeting. The model therefore served as a decision aid: it organized evidence so that university leaders could discuss where limited funding would have the greatest strategic effect.
That role is different from allowing an algorithm to choose a project automatically. The model can show relationships and consequences, but university officials still have to weigh mission, timing, risk, available funding and the experience of people who use the campus.
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Participation through a travel portal
LSU’s process also included a 24/7 data-exchange portal. Students and staff could describe how they traveled across campus, including typical routes, travel modes and how they felt while moving through those routes. This added lived experience to physical inventories.
Travel feedback is most useful when it can be located and compared with the rest of the campus model. A frequently used route, a difficult crossing or an uncomfortable segment can then be considered with topography, buildings, trees and planned work. The portal illustrates an important design choice: participation was built into the same information environment used for analysis, rather than treated as a separate comment period with no connection to the model.
2. Bellevue: converting hand-drawn ideas into testable city forms
Starting with a familiar public exercise
For an upzoning study in Bellevue’s Wilburton Commercial Area, members of a citizens advisory committee marked a two-dimensional map with crayons. The method was intentionally accessible. Participants could indicate where they wanted different land-use patterns without needing specialist drafting or modeling skills.
From annotations to 3D alternatives
The article says those marks became inputs to tools that generated different three-dimensional city forms. A committee member’s broad idea could therefore be translated into a visible arrangement of buildings and land uses. Participants and planners could see how an interpretation of the same map might produce a different urban shape.
Rapid generation mattered because the group could iterate. Instead of waiting for a single polished plan, the process allowed alternative forms to be produced, reviewed and adjusted while discussion was still underway. Seeing the consequences of different inputs made disagreements more specific: people could respond to height, massing or distribution as represented in a scenario rather than debating only abstract words such as “density.”
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What this example does—and does not—prove
NBBJ presents the Bellevue process as an account of rapid visualization and discussion. It does not provide a measured participation rate, approval outcome or independent evaluation showing that the 3D process caused a better zoning decision. The defensible lesson is narrower: a computational workflow can preserve public annotations as structured inputs and turn them into alternatives that a group can inspect together.
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3. ORNL: comparing development scenarios before committing a site
A multi-year planning context
Oak Ridge National Laboratory’s Experimental Gas-Cooled Reactor campus covers 300 acres. The article describes an interactive, three-dimensional, GIS-based decision-support tool for planning and budgeting over multiple years.
Manipulating physical options
Users could alter physical planning scenarios in the interface and test potential development sites as needs arose. Rather than viewing one fixed master plan, the client team could examine several ways to accommodate future requirements. The visual environment made the physical implications of each alternative available during a planning conversation.
Feasibility and cost comparisons
The tool supported comparisons of feasibility and cost implications. That does not mean it produced a guaranteed estimate or an automatic ranking. It gave the client a structured way to ask what would be possible at a site, what constraints an option would encounter and how the financial consequences might differ from another option.
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This is particularly useful for a research campus whose requirements can change over a multi-year budget cycle. A scenario tool can be revisited when a new need appears, allowing the team to test it against existing development assumptions instead of restarting the analysis from a blank map.
A practical framework for using computational tools
Taken together, the three cases suggest a repeatable way to plan a computational workflow. The sequence below is a practical framework derived from what the projects emphasize; it is not a claim that every project used identical steps.
- Define the decision first. State whether the team is deciding about renovation, replacement, a new development site, land-use form, circulation or a budget priority. A model is useful only when its outputs answer a real choice.
- Inventory the evidence. List the data that can change the decision. The LSU example combined land use, groundwater, topography, trees and facility-use and condition records. Other projects may need fewer sources, but each source should have a clear purpose.
- Make assumptions visible. Record the date, condition and intended use of each dataset. Showing where information came from helps users distinguish observed conditions from planning assumptions.
- Design participation as data input. Decide how residents, staff, students or clients will contribute. LSU collected routes, modes and travel experiences through a portal; Bellevue used annotated maps. The format should match participants’ abilities and the decision being discussed.
- Build alternatives, not just a single picture. Generate multiple physical responses so participants can compare consequences. Bellevue’s 3D forms and ORNL’s manipulable scenarios demonstrate why an alternative is more informative than a single rendering.
- Compare agreed criteria. For each option, examine the measures that matter to the client: feasibility, cost implications, facility condition, access, environmental constraints or capital timing. Keep the criteria explicit so a visually attractive option is not mistaken for the most workable one.
- Use the model in a live decision cycle. Let users revise an option when new information or a new requirement appears. ORNL’s tool was used as needs arose, while Bellevue’s process relied on rapid iteration during discussion.
- Keep human review at the final gate. A model can reveal patterns and make trade-offs visible, but leaders and communities must judge priorities, equity, risk and acceptability. Document the reasoning behind the selected path.
Capabilities to specify when selecting or commissioning a tool
The examples point to four practical capability areas. They are evaluation questions for a project team, not a ranking of commercial products.
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Data integration
Ask whether the workflow can bring together the kinds of information that drive the decision: land use, environmental conditions, terrain, vegetation, buildings, rooms and condition records. Confirm how location, update dates and data ownership will be handled so that a large volume does not become an untraceable collection of layers.
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Check how nontechnical participants can contribute and whether their input remains linked to a place or scenario. A portal for travel experiences and a map-annotation exercise serve different audiences, but both make participation part of the model rather than a detached narrative.
Scenario manipulation
Users should be able to change relevant physical assumptions and see the resulting form. For an urban study, that may mean generating alternative massing or land-use arrangements. For a campus, it may mean testing sites and development sequences.
Comparison and communication
Look for views that allow options to be compared on the criteria the client actually uses, including feasibility and cost implications where those are available. The interface also needs to be understandable to decision-makers who are not model authors.
Project-specific customization
None of the three examples identifies a commercial software product. Their value came from tools configured around a particular campus, district or laboratory and the decisions its client needed to make. A procurement brief should therefore describe the users, data sources, scenarios and outputs before naming a platform.
Limits, safeguards and realistic expectations
The NBBJ article is organizationally authored and reports the outcomes of these projects; the underlying project records were not independently checked for this account. The examples should not be read as controlled evidence that computation always produces better decisions, faster approvals or lower costs.
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Data quality remains a constraint. A model can display an outdated building condition, incomplete travel response or uncertain groundwater record with great visual clarity. Teams need ownership rules, update schedules and a way to flag uncertainty so that confidence in the interface does not exceed confidence in the evidence.
Participation also requires interpretation. A portal response or a crayon mark expresses an experience or preference, not a complete design brief. Planners must explain how comments were translated into scenarios and which constraints limited the result.
Finally, visualization can make an option persuasive before its feasibility has been established. Keep cost, constructability, operational effects and policy requirements in the comparison process, and preserve a written record of assumptions and decisions alongside the model.
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Computational tools are most valuable when they connect three activities: integrating evidence, inviting meaningful input and comparing realistic alternatives. LSU shows how a very large campus dataset can inform renovation and capital priorities. Bellevue shows how public map annotations can become discussable 3D forms. ORNL shows how an interactive site model can support changing requirements and multi-year budget decisions.
The common advantage is not automation for its own sake. It is the ability to make relationships and trade-offs visible early enough for clients, designers and communities to improve the decision together.
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