Settings

Ecology & MCDA Settings

MCDA Weights

CBA Parameters

Feasible Space Limits

Unit Costs (€)

Show equations & assumptions

MCDA score: Score = wU · UHI + wN · Vegetation deficit + wC · Corridor proximity + wA · Age + wR · Area

NPV: ∑t=1T (Annual benefits)/(1 + r)t − Capex

BCR: Present value of benefits ÷ Capex

Budgets: Feasible space and budget caps limit the maximum area or funds allocated to interventions.

Stakeholders & user stories

Stakeholders: residents, local businesses, schools, NGOs, municipality, utilities.

Users: municipal planners, sustainability officers, housing association asset managers, consultants/researchers.

User story 1: A municipal planner adjusts weights and budgets to compare greening scenarios under a €X budget.

User story 2: A housing corporation prioritises buildings for facade greening and green roofs based on MCDA weights.

User story 3: A community group explores tree planting options and trade‑offs using customised weights.

Ecology Digital Twin | Twekkelerveld
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Layers

Tree Inventory (Open)
Tree Points (Muni)
Green Spaces
Buildings (LOD 2.2)

Advanced Metrics

Well‑being Corridors
Heatmap (UHI)
Legend
Urban Forest Simulator
0
New Trees
0
Green Facades

Performance Trade-offs

Scores are normalised between 0 and 100. Higher values indicate greater positive impact.

Financial Tradeoffs

Enter a budget and objective → get a plan with clear trade‑offs. (This is advisory; you still place trees/facades/polygons on the map.)

Select Tree Species

🌳 Oak €250
🌿 Birch €100
🌲 Pine €150

Select Green Facade Type

🪴 Ivy €3.5k
🧱 Living Wall €6k
🌿 Trellis €2k

Impact Overview

0
CO₂
0
Water
0
Cooling
0
Investment
0
Biodiversity
Tree Removal Temp Cost +0.000°C
Scenario Comparison
CO₂ Sequestration Baseline 0 → 0
Cooling (UHI) Baseline 0 → 0°C
Stormwater Baseline 0 → 0 L/yr
Investment Baseline €0 → €0

Intervention Prioritization (MCDA)

No priorities yet
Uses building attributes (hidden) + UHI + NDVI + corridor proximity. Run the wizard.

Draw Greenery Scenario

Draw a polygon anywhere → choose greenery type → updates biodiversity proxy + cooling + stormwater.
🌱 Grass
🌿 Shrubs
🌳 Trees
🌾 Mixed
💧 Rain Garden
🏠 Green Roof
🛣️ Permeable
🏖️ Shading
🚧 Remove Impervious
Tip: In draw mode, hold SHIFT and drag = smooth freehand

Temporal & Physics Simulation

Growth Projection Year 1
Shadow Time 12:00
Shadows ON
Turn on shadows and adjust the shadow time slider to see shaded areas. This feature provides visual guidance only and is not a validated analysis.
AI Assistant
This assistant provides explanations and suggestions only. It does not generate geometry, validate policy decisions, or guarantee investment outcomes.
Detailed Ecological Summary
1. Urban Heat Island (UHI)

Description: The Urban Heat Island (UHI) layer uses surface temperature proxies to highlight pockets of heat across the city. It shows relative temperatures based on the underlying raster; cooler areas appear green while hotter areas trend toward browns. UHI arises from heat‑absorbing materials, limited vegetation and anthropogenic heat sources.

Priority rule: This digital twin uses UHI values above 35 °C combined with NDVI below 0.20 at a building’s centroid to flag heat‑stressed buildings for cooling interventions.

Analysis tip: Overlay the UHI heatmap with vegetation (NDVI) to quickly identify where planting trees or installing green roofs can reduce temperatures.

2. Well‑being Corridors & Movement

Description: Well‑being corridors are polylines linking parks and other green spaces to illustrate ecological connectivity and provide walkable routes for residents. Connected habitats improve genetic diversity and species richness, and corridors reduce plant extinction rates while boosting colonisation and mental well‑being.

Dynamic arrows: The corridor lines feature animated arrowheads moving along the direction of travel. These arrows symbolise flow and make it easier to see the intended direction of ecological or pedestrian movement.

Use case: Turn on well‑being corridors together with green spaces to understand how parks, lawns and trees can be connected into continuous networks that benefit both biodiversity and human well‑being.

3. Scenario Polygons (Smooth Draw)

Functionality: Scenario polygons let you draw intervention areas for grass, shrubs, trees or mixed vegetation. The drawing is smoothed automatically to produce natural boundaries.

Impacts: Each vegetation type modifies NDVI, cooling effect, stormwater infiltration and biodiversity proxies based on predefined parameters. After drawing, gauges and comparison panels update to reflect your scenario’s ecological benefits.

Tip: Use mixed vegetation to balance cooling, biodiversity and water management, or experiment with different types to see trade‑offs.

4. 3D Context + Shadows

Details: Level‑of‑Detail 2.2 (LOD 2.2) models offer realistic roof and wall geometry for buildings. They provide context for proposed interventions and allow shadow‑casting simulations.

Hidden attributes: Each building contains metadata such as BAG ID, construction year and footprint area (from the LOD 1 layer). Select a building to reveal these attributes in the popup.

Shadows: Shadows are computed in real time. Use them to assess how building form and orientation influence potential tree planting and facade greening.

Layer Info
Decision Support Wizard (MCDA)

How it works: The wizard ranks buildings by combining five criteria — UHI, vegetation deficit, proximity to green corridors, building age and footprint area — into a single score. Each criterion is first normalized to a 0–1 range so that different units are comparable. You then assign weights to indicate relative importance. The final score is a weighted sum of the normalized values (this method is called a weighted linear combination). Weights are automatically scaled to sum to one before scoring. See WLC guidance, normalisation guidelines and weighting best practice for details.

Prioritizes buildings for interventions using UHI, NDVI, corridor proximity, footprint area and building age (attributes remain hidden).
0.46
0.28
0.10
0.08
0.08

Normalization note: Each criterion is scaled to a 0–1 range so that different units (°C, NDVI deficit, age, area) are comparable. Higher values correspond to higher priority for all criteria shown here (UHI, vegetation deficit, corridor proximity, age, footprint).

Show MCDA assumptions & equations

Criteria & direction: UHI (°C, benefit), vegetation deficit (unitless, benefit), corridor proximity (0/1, benefit), age (years, benefit), footprint area (m², benefit).

Normalization: For each criterion \(x_{s,j}\), compute a normalized value \(x'_{s,j}\) on 0–1 scale. For benefit criteria: \(x'_{s,j} = (x_{s,j} - \min(x_j)) / (\max(x_j) - \min(x_j))\). For cost criteria: \(x'_{s,j} = (\max(x_j) - x_{s,j}) / (\max(x_j) - \min(x_j))\).

Weighted linear combination: The overall score is \(S_s = \sum_j w_j \cdot x'_{s,j}\) where weights \(w_j\) sum to 1. Adjust the sliders to change weights; they are normalized internally.

Spatial de-clustering: The algorithm ensures recommended buildings are spread out by enforcing a minimum distance between selected priorities.

No double counting: Each criterion captures a distinct aspect (temperature mitigation, vegetation deficit, corridor proximity, age and footprint). MCDA best practice recommends avoiding highly correlated criteria to prevent double counting.

References: Weighted linear combination method explanation, normalisation to avoid “apples vs pears” comparisons and weighting guidance where importance weights are scaled to 0–1 and sum to one.

Score uses: (UHI high) + (NDVI low) + (corridor proximity) + (age) + (footprint area). Results are also spatially de-clustered.

Object

Action Complete
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