A Level H2 Geography Map Graph Data Skills Quiz

A-Level Geography H2 Quiz - Map Graph Data Skills (Answer Key)

Section A: Cartographic & Map Skills

1. Definition: The acquisition of information about an object or phenomenon without making physical contact (usually via satellites or aircraft). Application: Using NDVI (Normalized Difference Vegetation Index) to detect changes in forest cover over time. [3]
2. Choropleth: Uses shaded/colored areas to represent average values within predefined administrative boundaries. Proportional Symbol: Uses symbols (e.g., circles) of varying sizes placed at specific points to represent absolute quantities. [4]
3. Process: 1. Find the difference in height (vertical interval) between point A and B using contour lines. 2. Measure the horizontal distance between A and B using the map scale. 3. Divide the height difference by the horizontal distance ($\text{Gradient} = \frac{\text{Rise}}{\text{Run}}$). [3]
4. Errors: 1. Over-generalization/loss of detail in small-scale maps. 2. Misinterpretation of symbols due to scale distortion or lack of precision in measurement on small-scale maps. [4]
5. Mercator Projection: It distorts area as latitude increases (stretching poles). In sustainability studies, this makes high-latitude regions (e.g., Greenland/Russia) appear disproportionately larger than equatorial regions (e.g., Africa), misleading the viewer regarding the actual spatial extent of environmental issues. [4]
6. Layers: GIS stores different types of spatial data (e.g., land use, temperature, vegetation) as separate thematic layers. Analysis: By overlaying these layers (e.g., overlaying a 'concrete surface' layer with a 'surface temperature' layer), geographers can identify correlations and hotspots of the Urban Heat Island effect. [5]
7. Terrain: Indicates a steep slope. Mass Movement: Steeper slopes increase the gravitational shear stress on materials, making the area more susceptible to landslides, rockfalls, or slumps, especially when saturated with water. [4]

Section B: Graph Interpretation & Data Analysis

8. Identification: 1. Check temperature: Coldest month must be $>18^\circ\text{C}$. 2. Check precipitation: All months must have precipitation $>60\text{mm}$ (no distinct dry season). [4]
9. Positive Correlation: As GDP increases, Carbon Emissions generally increase. Strong Positive: The data points cluster very tightly around the trend line, indicating a very consistent and predictable relationship. [4]
10. Purpose: A line that represents the general direction and average relationship between two variables. Prediction: By extending the line (extrapolation) or finding a point on the line (interpolation), one can estimate the value of Y for a given value of X. [4]
11. Method: Look at the segment of the bar representing organic waste for each city. Divide the value of the organic waste segment by the total height of the bar for that city. The city with the highest resulting ratio/percentage has the highest proportion. [4]
12. Histogram: Uses adjacent bars to show frequency within continuous intervals (bins); better for seeing the "bulk" of the data. Frequency Polygon: Uses points connected by lines; better for comparing multiple distributions on one graph. [4]
13. Calculation: Subtract the lowest monthly mean temperature from the highest monthly mean temperature ($\text{Max Temp} - \text{Min Temp}$). [3]
14. Logarithmic Scale: Used when data spans several orders of magnitude (e.g., from 10,000 to 10,000,000 people). It prevents the largest values from compressing the smaller values into an unreadable flat line at the bottom of the graph, allowing growth rates to be visualized more clearly. [5]

Section C: Statistical Skills & Fieldwork Data

15. Definition: A non-parametric measure of rank correlation (monotonic relationship). Range: $-1.0$ (perfect negative) to $+1.0$ (perfect positive), with $0$ indicating no correlation. [3]
16. Advantage: Ensures an even spread of data across the entire gradient/transect, capturing spatial variation. Disadvantage: May miss "random" anomalies or hotspots that fall between the fixed sampling intervals. [5]
17. Concept: The probability that the observed correlation occurred by chance. A $p\text{-value} < 0.05$ typically means the result is "statistically significant," meaning there is less than a 5% chance the relationship is accidental, allowing the researcher to reject the null hypothesis. [6]
18. Process: Identifying and handling missing values, removing obvious entry errors (outliers caused by mistakes), and ensuring units are consistent. Necessity: "Dirty" data can skew the mean and correlation coefficient, leading to false conclusions or an insignificant result. [5]
19. Mean: Sum of all velocities divided by 5. Median: The middle value when velocities are arranged in ascending order. Sensitivity: The mean is more sensitive to outliers (one extremely high velocity will pull the mean up significantly). [6]
20. Scatter Graph: Best for showing the relationship or correlation between two continuous variables (e.g., Income vs. House Price). Box-and-Whisker: Best for comparing the distribution (median, quartiles, range) of a single variable (Income) across two categories (Zone A vs. Zone B). For comparing distributions, the Box-and-Whisker is superior as it highlights inequality and skewness. [8]