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| schema: foundry-doc-v1 | schema: foundry-doc-v1 |
| title: "Location intelligence UX design philosophy" | title: "Location intelligence UX design philosophy" |
| slug: location-intelligence-ux | slug: location-intelligence-ux |
| category: applications | category: applications |
| type: topic | type: topic |
| quality: complete | quality: complete |
| status: active | status: active |
| audience: public | audience: public |
| bcsc_class: public-disclosure-safe | bcsc_class: public-disclosure-safe |
| language_protocol: PROSE-TOPIC | language_protocol: PROSE-TOPIC |
| last_edited: 2026-05-19 | last_edited: 2026-05-19 |
| editor: pointsav-engineering | editor: pointsav-engineering |
| short_description: "The PointSav Location Intelligence interface is engineered for decision-maker clarity, using a Conclusion-First design philosophy that communicates site-selection confidence through visual hierarchy and structural grading rather than raw data volume." | short_description: "The PointSav Location Intelligence interface is engineered for decision-maker clarity, using a Conclusion-First design philosophy that communicates site-selection confidence through visual hierarchy and structural grading rather than raw data volume." |
| paired_with: location-intelligence-ux.es.md | paired_with: location-intelligence-ux.es.md |
| --- | --- |
| The [[location-intelligence-platform]] interface is engineered to prioritize decision-maker clarity over raw data volume. By utilizing a Conclusion-First design philosophy, the platform communicates site-selection confidence through visual hierarchy and structural grading. | The [[location-intelligence-platform]] interface is engineered to prioritize decision-maker clarity over raw data volume. By utilizing a Conclusion-First design philosophy, the platform communicates site-selection confidence through visual hierarchy and structural grading. |
| ## Quality benchmark: the professional map | ## Quality benchmark: the professional map |
| The interface draws inspiration from professional-grade spatial platforms, where complex multi-parameter models are rendered as intuitive, layered navigation surfaces. Key design patterns adopted from this benchmark include: | The interface draws inspiration from professional-grade spatial platforms, where complex multi-parameter models are rendered as intuitive, layered navigation surfaces. Key design patterns adopted from this benchmark include: |
| - **First-class layer toggles:** Analytical layers (Clusters, Catchment, OD Study) are presented as primary navigation controls, not secondary legend items. | - **First-class layer toggles:** Analytical layers (Clusters, Catchment, OD Study) are presented as primary navigation controls, not secondary legend items. |
| - **Decision-driven visualization:** The map renders conclusions (for example, "This node is Tier 5") rather than individual data points, allowing for rapid cross-market comparisons. | - **Decision-driven visualization:** The map renders conclusions (for example, "This node is Tier 5") rather than individual data points, allowing for rapid cross-market comparisons. |
| - **Scale-adaptive legibility:** Visual detail adapts dynamically to zoom level, ensuring a coherent national overview without sacrificing street-level precision. | - **Scale-adaptive legibility:** Visual detail adapts dynamically to zoom level, ensuring a coherent national overview without sacrificing street-level precision. |
| ## Design differentiation: cluster grade as primary unit | ## Design differentiation: cluster grade as primary unit |
| Unlike commercial GIS products that default to individual points on a map, the PointSav platform utilizes **Cluster Grade** as the primary visual and analytical unit. This differentiation represents a core design principle: | Unlike commercial GIS products that default to individual points on a map, the PointSav platform utilizes **Cluster Grade** as the primary visual and analytical unit. This differentiation represents a core design principle: |
| 1. **Confidence ramp:** Sites are encoded using a single-hue color ramp (pale to deep amber). Darker, larger markers indicate higher levels of capital-validated convergence. | 1. **Confidence ramp:** Sites are encoded using a single-hue color ramp (pale to deep amber). Darker, larger markers indicate higher levels of capital-validated convergence. |
| 2. **Structural guardrails:** The interface enforces a strict visual hierarchy where Tier 5 and Tier 4 nodes dominate the national view, guiding the user toward the most defensible commercial nodes. | 2. **Structural guardrails:** The interface enforces a strict visual hierarchy where Tier 5 and Tier 4 nodes dominate the national view, guiding the user toward the most defensible commercial nodes. |
| 3. **Contextual index cards:** Clicking a cluster activates a side drawer — not a modal — that provides immediate municipal ranking, operator chips, and institutional support counts without losing map context. | 3. **Contextual index cards:** Clicking a cluster activates a side drawer — not a modal — that provides immediate municipal ranking, operator chips, and institutional support counts without losing map context. |
| ## Component architecture | ## Component architecture |
| The GIS surface utilizes a standardized component set built on [[app-orchestration-gis]] (the analytics engine) and [[pointsav-gis-engine]] (the rendering layer), designed for rapid re-provisioning across deployments. | The GIS surface utilizes a standardized component set built on [[app-orchestration-gis]] (the analytics engine) and [[pointsav-gis-engine]] (the rendering layer), designed for rapid re-provisioning across deployments. |
| ## See also | ## See also |
| * [[location-intelligence-platform]] | * [[location-intelligence-platform]] |
| * [[app-orchestration-gis]] | * [[app-orchestration-gis]] |
| * [[pointsav-gis-engine]] | * [[pointsav-gis-engine]] |