Objective
Demonstrate how data-driven geospatial analytics can help governments identify gaps in public infrastructure access, prioritize new investments, and track progress toward equity-driven coverage targets — with no coding or GIS expertise required.
At a Glance
Format
3–4 Hours
Audience
Task Teams, PIM units, health sector staff
Level
Intermediate — hands-on
Platform
pim-pam.net / Geospatial Hub
Sector
Health (expandable to education, water, roads)
Agenda
08:45 – 09:00
Welcome & Framing
Registration, objectives of the Master Class, and participant introductions.
Chair
09:00 – 09:25
InfraGov 2.0 Overview
Modular framework design to support better public investment outcomes across sectors.
WBG Lead
09:25 – 09:30
GPB Digital Tools Overview Video (5-min video — to be recorded)
A 5-minute visual overview of all PIM/MEGA digital tools: PIA, eCBA, CCS, LDT, GoAT, AI Knowledge Coach, PIM Policy Repository, Portfolio Doctor, CLAD, and Country Demos. Sets the scene for where PIA sits in the ecosystem.
Video
09:30 – 10:25
1. The Access Problem — Why Geography Matters
Healthcare access is geographic. Walking vs. driving time catchments. Why road-based analysis alone misses gaps. Country evidence from Timor-Leste and Zambia.
Geospatial Lead
10:25 – 10:55
1. GBP/MEGA Ecosystem Context
Where PIA sits within the broader suite of digital tools. Links to eCBA, CCS, LDT, and the Geospatial Hub.
WBG Lead
10:55 – 11:25
3. Live Demo — Coverage Mapping
Hands-on walkthrough: loading facility, generating coverage maps, and identifying underserved populations at national and sub-national level.
WBG Economist / Facilitator
11:25 – 11:40
Coffee Break
Networking and informal Q&A.
11:40 – 12:10
4. Optimization — Where to Invest Next?
Simulating thousands of potential facility locations. Visualizing incremental coverage gains and diminishing returns. Prioritizing investments across scales.
WBG Economist / Facilitator
12:10 – 12:30
5. Existing Challenges in methodology
Challenges in mapping geospatial data; existing methods (Euclidean, topography, raster-based calculation, isochrone / catchment area) Incorporating facility construction costs into placement optimization.
Data Scientist
12:30 – 12:50
6. From Insight to Action
How PIA outputs feed into budget proposals, capital investment plans, and WBG project preparation. Integration with eCBA and GPB tools for full investment appraisal.
WBG Lead
12:50 – 13:10
7. Hands-On Exercise
Participants run their own country scenario: define a coverage target, identify top facility placement candidates, and generate a decision-ready output.
WBG Economist / Facilitator
13:10 – 13:30
Synthesis & Q&A
Key takeaways, open discussion, and next steps for participants.
Chair
Participants Will Leave Able To
- Interpret coverage maps showing who can access care by walking or driving time
- Identify underserved populations at national, regional, and facility-catchment level
- Simulate new facility placements and evaluate incremental coverage gains
- Understand diminishing returns and how to prioritize when resources are limited
- Connect PIA outputs to budget proposals and WBG project preparation processes
- Link PIA analysis to eCBA, CCS, and other GPB tools for end-to-end appraisal
Delivered By
Governance staff along with the DIME team at DEC and the Innovation team, in collaboration with external experts and knowledge partners.
About the pim-pam.net GPB PIA Tool
The Geospatial Planning and Budgeting (GPB) Public Infrastructure Access (PIA) tool supports governments in identifying where healthcare centers exist today, who they serve, and — most importantly — where new investment will have the greatest impact. First piloted in Timor-Leste in 2021 and scaled to much larger countries such as Zambia (nearly 50× bigger), PIA is now a living system hosted on the World Bank's Geospatial Hub under the MEGA initiative. At its core, PIA answers two questions: Who is currently served? And where should new facilities be placed to reach the most people? It models both walking and driving catchments, simulates thousands of potential placement scenarios, and translates complex geospatial analytics into decision-ready outputs — no GIS or coding skills required.