Vocab!

Question 1

Difference between a node and a point

Answer 1

both represent locations in a spatial dataset but their roles and significance differ

• Points are basic geometric entities representing a specific location with a set of coordinates. They exist independently and do not inherently define relationships with other features
• Nodes, however, are specific points that play a crucial role in defining topological relationships. They occur at the intersection of two or more lines (arcs) or mark the beginning and end points of a line. Nodes in a topological structure serve as connection points, defining how lines connect to form a network or how they enclose areas to create polygons
• all nodes are points, but not all points are nodes - point becomes a node when it participates in defining the connectivity and structure of a topological network

Question 2

Visual hierarchy

Answer 2

Visual Hierarchy: Just like variations in symbol size, colour, or shape, variations in typographic elements (font, size, style, weight) can be used to establish a visual hierarchy on the map. This hierarchy guides the viewer’s attention, highlighting the most important elements and differentiating between different types of information. Example:

• Larger or bolder fonts can emphasize major cities or features.
• Smaller fonts can be used for less prominent locations or details.
• Italic fonts can be used to differentiate water features from land features.

Question 3

Sliver polygon

Answer 3

A sliver polygon is a narrow, elongated polygon that often arises as an undesirable artifact during data digitization or overlay operations.
Sliver polygons typically occur when:

• There are slight discrepancies in the digitization of common boundaries between adjacent polygons
• Data from different sources are overlayed, and the boundaries don’t align perfectly

Question 4

Sequential-sequential color scheme

Answer 4

These schemes combine two sequential schemes, resulting in a mix of colours based on two hues. The hue mixtures can create a third hue. For instance, magenta and cyan sequences can produce various purple-blue hues.

Question 5

Sequential-Sequential-Sequential Color Scheme

Answer 5

Use when we need to present 3 numerial variables at a same time. It is based on 3 hues. It is a kind of three axel graph that is all combination of color in 3 sequential schemes

Question 6

Face complexity

Answer 6

face complexity (CF) as a measure of map complexity that can be used in optimal classification methods to determine the best number of classes for a map.

The CF is calculated as the ratio of actual polygons to potential polygons. Higher CF values indicate that the map is more complex.

CF = actual polygons / potential polygons

This formula suggests that a map with many small polygons will have a higher face complexity than a map with fewer, larger polygons. The sources do not elaborate on the definition of “potential polygons.”

Question 7

Oriented line topology

Answer 7

Oriented line topology is a way of structuring spatial data to define the relationships between lines and points in a network. Lines are digitized as arcs with two nodes and an identifier. In an oriented network, each arc has a begin node and an end node, indicating the direction of flow or movement.

This type of data structure is used to represent networks like roads, rivers, or utility lines. It facilitates network analysis, such as determining the shortest distance between points, identifying connectivity, and analyzing neighbourhood effects

Question 8

Cumulative frequency diagram and it’s components - Value, Frequency, CF, CF%

Answer 8

A cumulative frequency diagram is a graph that shows the total number of observations that are less than or equal to a particular value in a dataset. It is useful for understanding the distribution of data and identifying potential break points for data classification.

The components of a cumulative frequency diagram include:

• Value: The values in the dataset, typically plotted along the horizontal axis.
• Frequency: The number of times each value appears in the dataset.
• CF (Cumulative Frequency): The running total of frequencies, indicating the number of observations less than or equal to a specific value.
• CF% (Cumulative Frequency Percentage): The CF expressed as a percentage of the total number of observations.

Question 9

Douglas-Peucker data reduction algorithm

Answer 9

The Douglas-Peucker algorithm is used to simplify lines by reducing the number of points required to represent them. This simplification process is important for reducing data storage requirements and improving the efficiency of spatial analysis.

The algorithm works by:
1. Selecting the first and last points of a line as anchor points.
2. Creating a buffer zone around the line segment connecting the anchor points. The width of the buffer is defined by a tolerance distance (e).
3. Identifying the point farthest from the line segment within the buffer zone.
4. If a point lies outside the buffer, it is retained as a new anchor point. The line is split at this point, and the process repeats for the two resulting line segments.
5. If all points lie within the buffer zone, the points between the anchor points are eliminated.

This process continues until the line is simplified to the desired level of detail.

Question 10

Diverging color scheme

Answer 10

A diverging colour scheme emphasizes the progression of data values away from a critical midpoint in the data range. It combines two sequential colour schemes that share a light colour for the critical midpoint and progress to darker colours of different hues at each extreme.

This type of colour scheme is well-suited for representing data that deviates above and below an average or median value, like deviations in death rates from a disease.

The sources suggest using two Munsell colours to create a diverging scheme, as this system ensures that equal steps in the colour model represent equal perceptual steps.

Question 11

Cartogram

Answer 11

A cartogram is a map that distorts the size and shape of geographic areas to represent data values. For example, in an area-proportional cartogram, the size of each enumeration area is adjusted to be proportional to the value of a secondary dataset, such as population.

This type of map can be used to visually represent non-area-related phenomena, like population density or election results, in a way that highlights the distribution of the data rather than the actual size of the areas

Question 12

Typographic variables

Answer 12

Typographic variables are the characteristics of text that can be manipulated to enhance the visual hierarchy and communication effectiveness of a map. These variables include:

• Font (Typeface): The design of the characters (e.g., Helvetica, Times New Roman).
• Size: The height of the characters, typically measured in points.
• Weight: The thickness of the character strokes, often described as light, regular, or bold.
• Form: Whether the text is roman (upright) or italic (slanted).
• Colour: The hue, saturation, and value of the text.
• Spacing: The distance between characters, words, and lines.
• Case: Whether the text is uppercase, lowercase, or a combination

Question 13

GADF

Answer 13

The Goodness of Absolute Deviation Fit (GADF) is a measure used to evaluate the effectiveness of different data classification schemes, particularly in the context of creating choropleth maps. It helps determine the optimal number of classes for a given dataset.

GADF = (ADAM - ADCM) / ADAM

• The GADF value ranges from 0 to 1.
• A higher GADF value indicates a better classification, meaning the classes effectively group similar values.
• A GADF value of 1 would mean there’s no variation within classes, which is ideal but rarely achievable.

Question 14

ADAM

Answer 14

ADAM (Sum of Absolute Deviations Around the Median for the Entire Dataset)

ADAM measures the spread or dispersion of the entire dataset around its median. It tells you how much individual data points deviate from the central tendency of the dataset as a whole.

To calculate ADAM:
1. Calculate the median of the entire dataset. The median is the middle value when the data is sorted in ascending order. If the dataset has an even number of values, the median is the average of the two middle values.
2. Calculate the absolute difference between each data point in the dataset and the median. The absolute difference is the value of the difference, ignoring its sign. It is calculated as: |data point – median|.
3. Sum all the absolute differences calculated in step 2. This sum is your ADAM value

Question 15

ADCM

Answer 15

ADCM (Sum of Absolute Deviations Around the Class Median)

ADCM measures the spread or dispersion of data within each class around that class’s median. It tells you how much individual data points within a class deviate from the central tendency of that class.

To calculate ADCM:
1. Divide your data into classes. You can use any classification method for this (equal interval, quantile, natural breaks, etc.).
2. Calculate the median for each class.
3. Calculate the absolute difference between each data point in the class and the median of that class. Again, this is: |data point – class median|.
4. Sum all the absolute differences calculated in step 3 for each class. These sums are your ADCM values for each class.

Question 16

Raster Data

Answer 16

A spatial data model that represents geographic features as a grid of equally sized cells (pixels). Each cell contains a value that represents the attribute of the feature at that location.

Question 17

Vector Data

Answer 17

A spatial data model that represents geographic features as points, lines, and polygons defined by their coordinates

Question 18

Graphic Variables

Answer 18

Visual properties of symbols that can be manipulated to represent data, including size, shape, colour, texture, orientation, and arrangement.

Question 19

Visual Hierarchy

Answer 19

The arrangement of map elements according to their importance, using graphic variables to guide the viewer’s attention.

Question 20

Figure-Ground Relationship

Answer 20

The perceptual separation of map elements into figures (important objects) and ground (background), achieved through visual contrast and other design principles.

Question 21

Map Lettering

Answer 21

The use of text on a map to provide information and enhance readability, considering factors like font, size, weight, placement, and style

Question 22

Typographic variables

Answer 22

Characteristics of text that can be manipulated to convey information and create visual hierarchy, such as font, size, weight, form, colour, spacing, and case.

Question 23

Isoline map

Answer 23

This map uses lines, known as isolines, to connect points with equal values, effectively representing continuous data such as elevation or temperature.

The assumption behind isoline maps is that the phenomenon being mapped has a continuous distribution.

Question 24

Dot map

Answer 24

A map that uses dots to represent data values, where each dot represents a specific quantity.

Question 25

Multivariate Mapping

Answer 25

The simultaneous representation of multiple variables on a single map

Question 26

Diverging colour scheme

Answer 26

A colour scheme that emphasizes data values diverging from a central value, suitable for representing deviations from an average or median.

Question 27

Choropleth Map

Answer 27

This map utilizes colour or shading to represent data values associated with distinct areas. It effectively communicates data like population density or election results.

Relative values are crucial for choropleth maps, as using absolute values can lead to misinterpretations.

Question 28

Chorochromatic Map (Mosaic Map)

Answer 28

Similar to choropleth maps, chorochromatic maps employ colours or patterns to depict nominal (categorical) data for areas. For instance, they can differentiate soil types or geological formations across a region. Challenges arise when representing non-area-related phenomena, as the visual emphasis might be skewed by the size of the areas rather than the actual data.

Question 29

Area-Proportional Cartogram

Answer 29

This unique map type distorts the size and shape of geographic regions in proportion to a specific data value. It is particularly useful for highlighting the distribution of non-area-related phenomena. For example, the size of countries could be adjusted based on their population or GDP.

Question 30

Nominal Point Data Map

Answer 30

This type of map employs symbols that differ in shape, orientation, or colour to represent nominal (categorical) data associated with specific point locations. For example, different symbols could represent various types of businesses or landmarks.

Figurative symbols utilize associations for easy recognition, while geometric symbols are better suited for abstract phenomena due to their enhanced legibility.

Question 31

Absolute Proportional Map

Answer 31

This map uses symbol size to represent discrete absolute values associated with point locations or areas. Geometric shapes are preferred over figurative symbols for better legibility and comparability. Common symbol choices include bars, circles, and 3D symbols.

The map can also utilize isotypes (repetitions of the same symbol) to represent varying data values.

Question 32

Diagram map

Answer 32

This map incorporates diagrams, such as line diagrams, pie charts, or bar graphs, within a geographic context. However, diagrams might be visually distracting on a map.

It is often more effective to place them adjacent to the map or to summarize the trend in the diagram and map nominal values instead.

Question 33

Areal Diagram

Answer 33

A specific type of diagram map that uses a pie-chart-like representation, but the actual distribution of the phenomenon is simulated to some extent within the area.

Question 34

Flow line map

Answer 34

This map effectively depicts movement by employing arrow symbols. It can communicate information about the route, direction, and volume of movement.

Proportional arrow symbols convey the magnitude of the flow. In cases where the quantity of goods or people needs visualization, amounts can be proportionally represented at their origin with standard arrows.

Special cases of flow line maps include those representing fluxes without direction (e.g., traffic) and vector maps that illustrate the magnitude and direction of forces at specific points.

Question 35

Statistical Surface Map

Answer 35

This map presents quantitative data in a 3D format, similar to representing terrain elevation. It offers a dramatic and memorable visualization of data trends.

However, the complexity of the 3D representation might obscure certain areas or make it difficult to read precise values.

Question 36

Chorematic Map

Answer 36

A schematic map representing a territory, primarily focusing on conveying geographic knowledge. It provides a visual summary of spatial databases and was developed by Roger Brunet.

Question 37

Numerical Variables

Answer 37

Numerical Variables (Quantitative Variables): Variables with values that have a quantitative meaning and can be ordered. They can be further classified into:
- Interval Variables: Differences between values are meaningful, but there is no true zero point (e.g., temperature in Celsius).
- Ratio Variables: Differences between values are meaningful, and there is a true zero point (e.g., height, weight, income).

Question 38

Non-numerical Variables

Answer 38

Non-numerical Variables (Categorical Variables): Variables with values that do not have a quantitative meaning and cannot be ordered (e.g., colours, names, types of land use). They can be further classified into:

• Nominal Variables: Categories have no inherent order (e.g., land cover types, soil types).
• Ordinal Variables: Categories have a natural order or ranking (e.g., levels of education, satisfaction ratings).

Question 39

Equal interval

Answer 39

This method divides the data range into a specified number of classes, where each class has the same range of values. It is straightforward to calculate but might not accurately represent the true distribution of data, especially if the data is skewed. For example, if the data ranges from 0 to 100 and five classes are desired, each class would have a range of 20 (0-20, 21-40, 41-60, 61-80, 81-100)

Question 40

Quantiles

Answer 40

This method divides the number of observations equally among the desired number of classes. This ensures that each class has the same number of data points, regardless of the data distribution.

This method is suitable for ordinal data as well. Common quantile classifications include quartiles (four classes), quintiles (five classes), and deciles (ten classes).

Quantiles can effectively represent data distribution but might result in classes with significantly different value ranges

Question 41

Natural Breaks

Answer 41

This method utilizes the inherent patterns and clusters within the data to define class boundaries. It involves visually identifying “natural breaks” or gaps in the data distribution. This can be achieved by plotting the data in ascending order (observation series) or using a frequency diagram, particularly for larger datasets.

While this method considers data distribution and offers flexibility, it can be subjective and less replicable, making comparisons across multiple maps challenging

Question 42

mean vs median

Answer 42

Mean (Average): Sum of all values divided by the number of values.

Median: The middle value in a sorted dataset.

The terms mean and average are often used interchangeably and refer to the same concept in statistics: the sum of all values in a dataset divided by the total number of values. It’s a measure of central tendency that represents the typical value in a dataset.

The median, on the other hand, is the middle value in a sorted dataset. If the dataset has an odd number of values, the median is the middle value.

If the dataset has an even number of values, the median is the average of the two middle values. The median is less sensitive to extreme values or outliers compared to the mean.

the concept of “mean” in the context of data classification methods, specifically the “Mean - Standard Deviation” method. This method uses the mean and standard deviation to determine class boundaries, which is helpful for normally distributed data.
The sources also mention the “median” when discussing the Optimal Classification Methods. In this context, they refer to “minimizing the sum of the absolute deviations about class medians (ADCM)” as a parameter for classification error.

Question 43

Contiguity

Answer 43

Contiguity is the topological concept that allows the vector data model to determine adjacency. Polygon topology defines contiguity.

Polygons are contiguous to each other if they share a common arc. This is the basis for many neighbor and overlay operations.