From Accessibility to Allocation: An Integrated Workflow for Land-Use Assignment and FAR Estimation

Urban land use and building intensity are often planned without a direct, auditable link to network accessibility, limiting ex-ante policy evaluation. This study asks whether multi-radius street centralities can be elevated from diagnosis to design lever to allocate land use and floor area in a transparent, optimization-ready workflow. We introduce a three-stage pipeline that connects configuration to program and intensity. First, multi-radius accessibility is computed on the street network and translated to blocks to provide scale-legible measures of reach. Second, these measures structure nested service basins that guide a rule-based placement of land uses with explicit priorities and minimum parcel footprints, ensuring reproducibility. Third, within each use, floor-area ratio (FAR) is assigned by an accessibility-weighted linear model that satisfies global construction totals while anchoring the average FAR, thereby tilting height toward better-connected blocks without pathological extremes. The framework supports multi-objective policy search via sampling and Pareto screening. Applied to a real urban district, the workflow reproduces corridor-biased commercial siting and industrial belts while concentrating intensity on highly connected blocks. Policy sampling via multi-objective screening yields Pareto-efficient plans that reconcile accessibility gains with deviations from target land-share and construction-share structures. The contribution is twofold: methodologically, it translates familiar space-syntax measures into cluster-aware, rule-governed land-use and FAR assignment with explicit guarantees (scale-legible radii, parcel minima, and an average-FAR anchor). Practically, it offers planners a transparent instrument for counterfactual testing and negotiated trade-offs at neighborhood/district/city scales.

💡 Research Summary

The paper tackles a fundamental disconnect in urban planning: the separation of street‑network configuration, land‑use allocation, and building intensity (FAR) into sequential, siloed decisions. Because accessibility is usually treated as a post‑hoc performance metric, planners lack an auditable, ex‑ante lever to shape where and how intensely development occurs. Sun et al. propose a three‑stage, fully reproducible workflow that elevates multi‑radius street centralities from diagnostic tools to design levers, integrating them directly into land‑use placement and FAR assignment.

Stage 1 computes several centrality measures (angular integration, choice, impedance‑weighted gravity) on the street network for a set of user‑defined radii (e.g., 400 m, 800 m, 1.6 km). Segment‑level scores are aggregated to the block level, producing a multi‑dimensional accessibility vector for each block. By offering both fine‑scale (local convenience) and coarse‑scale (regional connectivity) perspectives, the authors ensure “scale‑legible” accessibility that can inform decisions at neighborhood as well as city level.

Stage 2 translates these vectors into hierarchical service basins through hierarchical clustering. Each basin corresponds to a particular radius tier and is only eligible for allocation if the sum of remaining parcel area within its constituent blocks exceeds a predefined minimum parcel threshold, preventing fragmentation. A rule‑based allocator then processes land‑use categories in a priority queue. “Good” uses (commercial, service, public) are placed first on the most accessible basins, while “bad” uses (industrial, low‑density residential) are assigned later. The allocator respects explicit policy inputs: target land‑use shares, minimum parcel sizes, and a ranking of uses. Because the algorithm updates remaining area after each assignment, later decisions are automatically conditioned on earlier ones, creating a transparent feedback loop.

Stage 3 assigns FAR within each allocated use using an accessibility‑weighted linear model. The model is calibrated to satisfy two global constraints simultaneously: a total construction target (e.g., total floor‑area) and a prescribed average FAR (e.g., 2.5). FAR for block i is expressed as FAR_i = α·Acc_i + β, where α and β are solved so that the sum of FAR_i·area_i equals the construction target and the mean FAR equals the prescribed average. Upper and lower bounds on FAR prevent pathological extremes, ensuring that high‑accessibility blocks receive higher-than‑average intensity while low‑accessibility blocks receive lower intensity, but the overall average remains fixed.

To explore the policy space, the authors sample combinations of radii, priority orders, parcel minima, and FAR weighting coefficients, then evaluate each scenario against multiple objectives: (1) accessibility gain, (2) deviation from target land‑use shares, (3) deviation from construction‑share targets, and (4) ancillary indicators such as travel‑demand or jobs‑housing balance. Non‑dominated solutions are extracted via Pareto screening, giving planners a set of trade‑off plans rather than a single “optimal” answer. The Pareto frontier reveals which constraints become binding (e.g., land‑share vs. construction‑share) under different parameterizations, allowing a “knee‑point” selection that balances competing goals.

The workflow is applied to a real‑world district in the northeastern United States. Results reproduce observed spatial patterns: commercial and high‑density residential uses concentrate along high‑accessibility corridors, while industrial belts occupy peripheral, lower‑accessibility zones. The multi‑objective exploration yields several Pareto‑efficient alternatives that improve overall accessibility while keeping land‑share and construction‑share deviations within acceptable limits. This demonstrates that the method can generate counterfactual scenarios that are both theoretically grounded (urban economics, space‑syntax) and practically usable (transparent rules, auditable steps).

Key contributions are twofold. Methodologically, the study bridges multi‑scale street centralities with block‑level allocation through explicit, parameterizable rules, turning accessibility into a design lever rather than a post‑hoc metric. Practically, it delivers a transparent, audit‑ready tool that planners can manipulate to test “what‑if” policies, negotiate trade‑offs, and communicate outcomes to stakeholders. By integrating configuration, program, and intensity in a single loop, the framework advances beyond black‑box cellular automata, pure optimization engines, or procedural grammars, offering a middle ground that retains interpretability while capturing complex emergent patterns. Future work could extend the approach to diverse urban typologies, incorporate real‑time mobility data, and embed multi‑actor decision‑making models for collaborative planning.