Overview
This lecture covers methods for mapping GIS data, focusing on symbology, types of data representation, and common pitfalls like the modifiable areal unit problem (MAUP).
Map Objects and Symbology
- Map objects refer to spatial data sets: points, lines, and polygons representing real-world features.
- Symbology uses colors, shapes, line types, patterns, and fonts to make maps meaningful.
- Changes in data category are shown by varying symbol shapes, colors, or line patterns.
- Changes in quantity are represented by varying symbol size, thickness, or color intensity.
Mapping Different Data Types
- Nominal data (labels/text) use single symbol maps (e.g., one symbol for all fire locations).
- Categorical/Ordinal data use unique values maps, assigning a different symbol or color to each category (e.g., volcano types, road types).
- Numeric data use graduated color, symbol, or dot density maps to show value magnitude (e.g., income levels, population density).
Thematic Maps and Data Types
- Thematic maps display attributes about locations using color and are tailored for the data type.
- For nominal data, use single symbol thematic maps, optionally with labels.
- For categorical data (e.g., land use), use categorical thematic maps with distinct colors for each category.
- Ordinal data (ranked values) are effectively shown with unique values maps.
- Interval data (regular numeric scale, e.g., pH) use equal interval categories for mapping.
- Ratio data (values normalized by another value, e.g., income per capita) reveal more accurate spatial patterns.
Classifying and Normalizing Data
- Numeric data should be divided into classes before mapping (e.g., income ranges, ranking intervals).
- Normalization (e.g., income per person) avoids misleading interpretations of raw totals.
- Choosing the right classification method is crucial for meaningful map interpretation.
Modifiable Areal Unit Problem (MAUP)
- MAUP occurs when results change based on arbitrary spatial unit choices (e.g., state vs. county).
- Large units can dominate map visuals and create analytical bias.
- Normalizing or using proportion maps addresses MAUP by adjusting for unit size differences.
Key Terms & Definitions
- Symbology — Visual representation of data using symbols, colors, and patterns.
- Thematic Map — A map focusing on a specific attribute or theme for mapped locations.
- Nominal Data — Data that names or labels categories without inherent order.
- Categorical Data — Data sorted by distinct categories, sometimes with an order (ordinal).
- Interval Data — Numeric data with equal intervals (no true zero); e.g., temperature.
- Ratio Data — Numeric data with a true zero, allowing for meaningful comparisons of magnitude.
- Graduated Color Map — Map showing numeric ranges with a spectrum of colors.
- Dot Density Map — Map representing quantities with distributions of dots.
- Modifiable Areal Unit Problem (MAUP) — Error or bias caused by arbitrary spatial unit selection.
Action Items / Next Steps
- Review examples of different map types (single symbol, unique value, graduated color).
- Prepare for the next lecture on raster data and its types.