IB Geography F.5.4 Representing Health Patterns and Disease Diffusion Notes

The Variable Determines the Suitable Map or Graph

1. A visual should match whether the data describe location, rate, total, movement, timing or composition.
2. A map is suitable when spatial distribution matters, while a graph is suitable when change, comparison or relationship is the main question.
3. Rates allow comparison between populations of different sizes, while totals show the absolute service or response burden.
4. Time intervals must be consistent before a pattern of diffusion or mortality is inferred.
5. A title, date, unit, source and readable legend are part of the evidence rather than decorative additions.
6. The strongest visual makes one geographic relationship clear and keeps uncertainty visible.

Choropleth and Proportional-Symbol Maps Answer Different Questions

1. A choropleth shades administrative areas and is suited to standardised values such as incidence per 100,000 people.
2. Using raw case totals in a choropleth can make populous regions appear more affected simply because more people live there.
3. Class boundaries influence the visible pattern because the same data can look clustered or even under different classifications.
4. Large rural units dominate map area even when most cases occur in a small settlement within them.
5. Proportional symbols show totals at points or areas and are useful for comparing caseloads between cities.
6. Overlapping symbols can conceal dense clusters, while symbol area must scale with the value rather than symbol radius.
7. Using both maps can separate population risk from the absolute burden placed on services.

Map Sequences, Isolines and Flow Lines Show Diffusion

1. A sequence of maps at fixed intervals shows how the affected area and intensity change over time.
2. Changing the interval or classification between frames creates a false impression of acceleration or decline.
3. Isolines can join places reached on the same date and reveal the direction and speed of a spreading wave.
4. Sparse surveillance makes arrival-date isolines uncertain between observation points.
5. Flow lines show routes of movement, while line width can represent traveller numbers, cases or another volume.
6. A route map suggests a possible transmission connection but does not prove that infection moved along every displayed flow.
7. Annotations should identify barriers, transport hubs or policy changes that alter the pattern.

Epidemic Curves Reveal Timing and Pressure, Not Geography by Themselves

1. An epidemic curve plots new cases against time and reveals onset, growth, peak and decline.
2. Peak height approximates maximum reported case pressure per interval, while area under the curve represents total reported cases.
3. A flatter curve can indicate slower transmission, but it does not prove that fewer people were infected unless the area also falls.
4. A sudden vertical jump may reflect a reporting backlog or case-definition change rather than transmission on that day.
5. Testing access and willingness affect curve shape because undetected infections remain absent.
6. Separate curves for regions or social groups can reveal spatial and social inequality that one national curve hides.
7. Maps add place to the time pattern, while the curve adds temporal structure to a map.

Line and Compound-Bar Graphs Track Changing Health Burdens

1. A line graph is suited to mortality, incidence or prevalence measured repeatedly over time.
2. Age-standardised rates are preferable when age structures differ because ageing alone can raise crude mortality or disease prevalence.
3. A compound bar shows how causes of death or disease contribute to a total at one or more dates.
4. Percentage bars show composition but can hide growth in the total number of deaths.
5. Absolute bars show service burden but make populations of different sizes difficult to compare.
6. A clear answer states whether the pattern concerns share, rate or number before explaining change.

Visual Evaluation Begins with the Data-Generating Process

1. Surveillance quality varies with testing, diagnosis, reporting delay and access to health services.
2. A low reported rate may indicate low disease, weak detection or both.
3. Political boundaries group unlike places and can conceal neighbourhood-scale clusters.
4. Missing values should remain visible rather than being coloured as zero.
5. Correlation between food insecurity and disease does not establish the direction of causation.
6. Uncertainty ranges, source notes and methodological breaks help readers distinguish measurement change from real change.
7. A sophisticated visual is not stronger if its encoding makes the evidence harder to interpret.

A Reliable Workflow Moves from Description to Explanation

1. First read the title, axes, units, date, scale and legend.
2. Second describe the dominant pattern using clusters, direction, peak, rate of change or contrast.
3. Third identify an exception or discontinuity that needs explanation.
4. Fourth connect the pattern to a mechanism such as transmission, access, migration, policy or reporting.
5. Fifth state one limitation linked to the specific method or dataset.
6. Finally combine the visual with placed evidence before reaching a judgement.