Hazard and exposure overlays

Combine a hazard layer with occupied homes without turning ordinal categories into probabilities.

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Hazard and exposure overlays

The occupied-home count represents exposure. Hazard and vulnerability categories are ordinal labels; multiplying them does not produce a probability of loss. A screening rule is supplied for this fictional exercise.

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Fictional hazard and exposure layersA: High hazard, 180 occupied homes, vulnerability index 1; B: Medium hazard, 70 occupied homes, vulnerability index 1; C: High hazard, 90 occupied homes, vulnerability index 2; D: Low hazard, 180 occupied homes, vulnerability index 3. The vulnerability index is ordinal, not a probability.District AHazard: High180 occupied homesDistrict BHazard: Medium70 occupied homesDistrict CHazard: High90 occupied homesDistrict DHazard: Low180 occupied homesHazard category describes the layer, not a probability.Exercise screening: high hazard and at least 100 occupied homes.
DistrictHazard categoryOccupied homesVulnerability index (ordinal 1–3)
AHigh1801
BMedium701
CHigh902
DLow1803
Fictional hazard and exposure layers. Original fictional resource; not an official map or real dataset.
Resource description

A: High hazard, 180 occupied homes, vulnerability index 1; B: Medium hazard, 70 occupied homes, vulnerability index 1; C: High hazard, 90 occupied homes, vulnerability index 2; D: Low hazard, 180 occupied homes, vulnerability index 3. The vulnerability index is ordinal, not a probability.

1. How many occupied homes are in high-hazard districts altogether?

2. How many districts meet the stated high-hazard and 100-home screening rule?

3. Can these ordinal labels establish a numerical probability of loss?

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The maps and data are fictional. These exercises do not count as required fieldwork.

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State the unit and the part of the resource you used. A pattern in a fictional dataset is evidence for that exercise; it does not establish a fact about a real place.

Check whether an explanation is supported by the data or needs additional geographical knowledge. For statistical tasks, a calculated result does not establish causation or significance by itself.

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