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

TL;DR

The paper presents an auditable, end-to-end workflow that turns multi-radius street centralities into a design lever for land-use allocation and FAR estimation. By computing space-syntax–based accessibility at multiple radii, clustering blocks into nested service basins, and applying a rule-based land-use allocator before fitting an accessibility-weighted FAR, the approach yields transparent, policy-friendly plans that can be explored via Pareto optimization. A West Oakland case study demonstrates corridor-focused commercial siting and intensity concentration on well-connected blocks, while a Pareto frontier reveals trade-offs among accessibility gains, land-use target adherence, and jobs–housing balance. This framework offers planners a counterfactual testing instrument capable of negotiating trade-offs at neighborhood to city scales with explicit guarantees such as scale-legged radii and parcel minima.

Abstract

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.

Figures (7)

• Figure 1: The flow diagram and visualization of land use allocation and FAR calculation on site. a) The site with its original street network. b) Accessibility analysis for each tier of radius. c) Transferring segment accessibility scores to blocks. d) Hierarchical clustering of blocks for each tier. We are showing 3 tiers of clusters, therefore, 3 cluster colors in the plot. e) Land use allocation. f) FAR calculation with each lot being assigned a FAR value.
• Figure 2: Three instances of Pareto policies by respectively selecting the least $D_{Total}$ (left), $1-AU$ (middle), and $JH_{penalty}$ (right). Each polyline across the parallel coordinates indicates one solution. The corresponding polylines of the selected optimal solutions are highlighted.
• Figure 3: Comparison of observed land use and generated land use map with minimum inputs. a) Current land use map used for validation. It is the snapshot from the interactive zoning map of City of Oakland, California oakland_experiencebuilder. The legend is zoning code. OS: open space; U:education; HBX: housing and business mix; CIX: industrial and business mix; RM: mixed housing type residential; CC: community commercial; RU: urban residential. b) Baseline simulation result using land use percentages share vector $s^{obs}$ retrieved by color segmentation analysis from Plot a. The land use sequence order specified in $s^{obs}$ is also applied. The legend follows our land use class. G: open space; E: education; A: administration; F: restaurant; R: residential; B: business; I: business and industrial mix; T: transportation. Note that, in plot a, we notice there are transportation facilities in both southwest (Oakland Bart station) and northwest (bus station) corner but are categorized as community commercial and industrial/business mix. Therefore, we add transportation land use in our simulation. Its target land use share can be set to 0 if necessary.
• Figure 4: 3D scatter of the policy design space showing all sampled solutions (circles, faint) and the Pareto frontier (triangles, opaque). Axes represent the three objectives used for non-dominated sorting --- $(1-AU)$, $D_{Total}$, $JH_{penality}$ --- so points nearer the origin are preferred. The frontier traces the best attainable trade-offs among accessibility benefit, aggregate deviation, and jobs–housing balance.
• Figure 5: Frontier trade-off slices. Two-dimensional trade-off views comparing all sampled policies (faint) to the Pareto frontier (opaque), with the knee solution highlighted (green star). Each panel isolates a pair of objectives to reveal curvature and local trade-offs.