Safeguard screening of a project footprint
Alignments and sites are overlaid on protected areas, key biodiversity areas, settlements and land tenure layers to set the classification and the scope of assessment.
GRA · Development Finance Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Geospatial analysis for development finance
Geospatial methods place projects, hazards, people and habitats on a common map. In development finance they support site screening, exposure of assets to climate hazards, safeguard footprints and the location of beneficiaries, with care taken over what can be published.
01Contents
Geospatial analysis for development finance in five parts.
02Application
What geospatial information systems does here.
Geospatial information systems capture, store, analyse and present location-referenced data. They provide the common spatial frame for combining hazard, exposure and vulnerability information. In development finance, critical infrastructure and economic development, geospatial analysis identifies where hazards overlap critical assets and vulnerable populations. It supports location-based planning across infrastructure, health and finance. The canonical description is on the STEM Commons.
Physical climate exposure of assets
Asset locations are intersected with flood, heat, drought and cyclone hazard layers by return period to estimate exposure and downtime for design and credit review.
Locating beneficiaries and gaps
Gridded population, service locations and travel time are combined to show who a programme reaches and where coverage gaps remain.
03Spatial finance
From a satellite tile to an exposure update.
How satellite radar turns a flood into an exposure update. Synthetic scene; no real event or place.
04Data
What the method consumes.
Datasets and records used by geospatial information systems on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Gridded population | Exposure and beneficiary counts | ESTIMATED |
| Hazard layers by return period | Physical risk to assets | MODELLED |
| Protected area and biodiversity layers | Safeguard screening | Published statistics |
| Administrative boundaries | Reporting units | Published statistics |
| Project location data from IATI and MDB portals | Where finance goes | REPORTED |
05Monitor
Spatial model.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
06Validation
How results are checked.
Positional and attribute accuracy are checked against surveyed control points and reference datasets. Topology rules and metadata standards ensure consistency.
| Check | How it is done |
|---|---|
| Positional accuracy | Project locations are checked against documents; geocoding precision is recorded for each point |
| Common projection | All layers are reprojected to a common equal-area grid before overlay |
| Disclosure review | Published maps are generalised so that no household, complainant or sensitive site can be identified |
Errors in projection, scale and data currency propagate through analysis. Aggregation to administrative units can mask local variation.
07Scenario model
Policy paths and what they do to the risk.
How three families of instruments change a risk outcome over time with adoption lags, coverage and compliance, the policy scenarios behind transition and physical risk assessments. Scenario: named assumptions about the future, not a forecast. The map on this page shows Southern Africa; the model itself is not tied to a place.
Controls
- Baseline trend of the outcome
- 2.0% a year
- Coverage of the instrument
- 70%
- Compliance
- 75%
- Effect size, against the teaching value
- 100%
- Years ahead
- 20 years
Readouts
- Largest reduction at the horizon
- Regulation
- Its reduction against the baseline
- 21%
Numbers, methods and sources
| Year | Baseline | Regulation | Economic incentive | Information and capacity |
|---|---|---|---|---|
| 0 | 100.0 | 99.5 | 98.8 | 99.1 |
| 1 | 102.0 | 101.2 | 100.1 | 99.8 |
| 2 | 104.0 | 102.5 | 101.1 | 99.9 |
| 3 | 106.1 | 103.5 | 101.6 | 100.0 |
| 4 | 108.2 | 103.7 | 101.8 | 100.7 |
| 5 | 110.4 | 103.2 | 101.7 | 102.1 |
| 6 | 112.6 | 102.0 | 101.6 | 103.9 |
| 7 | 114.9 | 100.4 | 101.6 | 105.9 |
| 8 | 117.2 | 99.2 | 102.1 | 108.0 |
| 9 | 119.5 | 98.6 | 102.9 | 110.1 |
| 10 | 121.9 | 98.9 | 104.2 | 112.3 |
| 11 | 124.3 | 99.7 | 105.7 | 114.5 |
| 12 | 126.8 | 101.1 | 107.4 | 116.8 |
| 13 | 129.4 | 102.7 | 109.4 | 119.2 |
| 14 | 131.9 | 104.5 | 111.4 | 121.6 |
| 15 | 134.6 | 106.5 | 113.5 | 124.0 |
| 16 | 137.3 | 108.5 | 115.7 | 126.5 |
| 17 | 140.0 | 110.7 | 118.0 | 129.0 |
| 18 | 142.8 | 112.9 | 120.4 | 131.6 |
| 19 | 145.7 | 115.1 | 122.8 | 134.2 |
| 20 | 148.6 | 117.4 | 125.2 | 136.9 |
Methods
- Tools of government Hood (1983), The Tools of Government, Macmillan
- Carrots, sticks and sermons Bemelmans-Videc, Rist and Vedung (1998), Transaction
- Diffusion of innovations Rogers (2003), Diffusion of Innovations, Free Press
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Borders Natural Earth
- Cities Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Development Finance Nexus in Foundry
- Tested at Development Finance Nexus in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Development Finance Nexus in Reports
- Taught in Faculty of Finance and Economics in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Trace a shock in geospatial analysis for development finance to the balance sheet.
Assembled from the Nexus Standards area profile Disaster and climate risk finance: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
Hazards
- Tropical cyclone
- Earthquake
- Ice-jam flood
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Dangerous surf (set off downstream)
- Widespread power outage (set off downstream)
- Mass displacement (set off downstream)
- Coastal flood (set off downstream)
- Aftershock sequence (set off downstream)
- Fault surface rupture (set off downstream)
- Soil liquefaction (set off downstream)
- Slope slide (set off downstream)
- Rockfall (set off downstream)
- Tsunami (set off downstream)
- Building collapse (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-F8Parametric and index-based risk transferNRS-F7Disaster risk finance and pre-arranged financeNRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-H114Hazard family: Macroeconomic and fiscal shocks (NHS-1 soc.macro-fiscal)NRS-D1Risk-informed development and project screeningNRS-H28Hazard family: Climate-state shifts (NHS-1 eco.climate-state)
Observation
- ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
- CHIRPS quasi-global gridded precipitation (listed for reference)
- USGS earthquake GeoJSON summary feeds (listed for reference)
- Total public international support for national disaster risk reduction actions (Sendai indicator F-1)
- Direct economic loss attributed to disasters in relation to global GDP
Example geography
- Western Europe: 152 hexagonal cells
- Rain rate, last half hour (IMERG), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
10Stewardship
Who maintains the method.
Space and Earth Observation stewards geospatial information systems for the Consortium and reviews how it is applied across platforms.
11Exposure, loss and capital
What geospatial analysis for development finance means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Transmission channels
How a shock in geospatial analysis for development finance reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
12Connected
Where this page leads across the Consortium.
13Questions
Questions and answers.
What are the limits of geospatial information systems in this field?
Errors in projection, scale and data currency propagate through analysis. Aggregation to administrative units can mask local variation.
How are results checked?
Positional and attribute accuracy are checked against surveyed control points and reference datasets. Topology rules and metadata standards ensure consistency.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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