COVID-19 Regional Analytics — from administrative-region population to hotspot graphs
Act as an analyst in a fictional outbreak situation room and connect region, population, patient, clinic, and hotspot data to explain which area needs attention first.
0/8 chapters complete
9:00 a.m. in a fictional regional outbreak situation room
Simultaneous follow-up requests arrive for Junggye 4-dong, Seongnam-dong, and Junggye Bon-dong. Patient records, population, clinics, and movement-intersection hotspots live in separate tables, so patient count alone cannot determine the next review order. Today you are the regional public-health data analyst.
When patients, population, clinics, and hotspots are considered together, which area should be checked first?
Decision criteria for this mission
- Perspectives in play — Regional data analysis aligns different administrative levels and metrics, while situation-room coordination communicates the order and evidence for three follow-up requests.
- Decision constraints — Do not declare a high-risk area from patient count or one graph connection. Apply the same rule to the synthetic data and state that movement intersection is the current review purpose.
- Completion signals — Confirm that Junggye 4-dong and Seongnam-dong each meet two signals, review Junggye 4-dong first for its five hotspots, follow with Seongnam-dong at 14.22 patients per 10,000, and keep Junggye Bon-dong under monitoring.
Workshop goals
- Align sido, sigungu, and dong names into one regional key.
- Distinguish and compare resident and floating population for the same area.
- Connect patients, clinics, and hotspots to the regional hierarchy and trace their relationships.
- Explain a priority area from several pieces of evidence rather than one number.
Eight decisions
Loading the diagram. Mermaid source:
flowchart LR
accTitle: Eight stages for choosing a regional follow-up order and explaining the solution
accDescr: Frame the situation-room question, align region names, compare population and health relationships, choose a priority, check the evidence in a quiz, and reflect on the solved problem.
q["1. Frame the question"] --> key["2. Align region names"]
key --> pop["3. Compare populations"]
pop --> connect["4. Connect health data"]
connect --> expand["5. Expand from one dong"]
expand --> explain["6. Explain the priority"]
explain --> check["7. Check the evidence"]
check --> reflect["8. Reflect on the solution"]Every chapter follows question → reason → action → observation → interpretation → next decision. The implementation inventory is in Deep dive in the first chapter, so the core path is sufficient.
Prerequisites
- A D.Hub Portal account with Editor access or higher
- A browser that can download the scenario ZIP
- 95 minutes of practice time
You do not need a terminal or repository clone. If import is new to you, complete Import and tour a complete hands-on scenario first.
Learning journey
- Frame the situation room's first question15 minImport the COVID-19 training scenario and define the analysis question and asset boundaries.
- Align region names to one standard15 minCombine sido, sigungu, and dong into a composite key that connects tables safely.
- Compare resident and floating population15 minCompare both populations in the joined result and distinguish the question each answers.
- Connect patients, clinics, and hotspots15 minMaterialize the ontology and inspect relationships between public-health data and regions.
- Expand from one dong to the surrounding region15 minExpand patients and hotspots in Graph Explorer, then trace the path from one dong to its sigungu and sido.
- Choose the first area to investigate10 minCompare population, patients, clinics, and hotspots for three areas by the same criteria and explain the follow-up order.
- Check the regional priority reasoning5 minBefore the final explanation, review the regional keys, relationship aggregation, graph boundaries, and follow-up order in four questions.
- Explain the problem you solved and finish the workshop5 minReturn to the opening situation-room problem and summarize the regional analysis flow and learning outcome.