Spatial Analysis and Map Design

These flashcards cover key concepts from the lecture on spatial analysis and map design, including definitions of terms and methods used in geospatial data handling.

Spatial Analysis

Asking and answering questions about spatial data.

Query

Selecting data based on criteria.

SQL

Structured Query Language used to query databases.

Boolean Expression

Logical conditions used in queries (AND, OR, NOT).

Buffer

Area around a feature, for example, a 1-mile buffer around schools.

Hot Spot Analysis

Finds clusters of high or low values in spatial data.

Spatial Query

Selects features based on spatial relationships, such as 'within 500m'.

Overlay

Combining two or more layers of spatial data.

Intersect

Keeps only the overlapping areas of features in spatial overlay.

Union

Combines all features from multiple layers in spatial overlay.

Map Algebra

Mathematical operations on raster layers, e.g., adding rainfall and elevation.

Site Suitability

Determining the best locations based on multiple criteria.

Representative Fraction (RF)

A ratio representing scale, e.g., 1:50,000.

Large Scale

Represents a small area with more detail.

Small Scale

Represents a large area with less detail.

Generalization

Simplifying map features for clarity.

Map Elements

Components such as title, legend, scale bar, north arrow, data source.

Typography

Style and placement of text for map readability.

Map Types

Different categories of maps, such as general reference and thematic.

General Reference Map

Shows overall features like roads and boundaries.

Thematic Map

Focuses on a specific topic, such as population or income.

Choropleth Map

Uses shades to represent different values in areas.

Graduated Symbol Map

Varies the size of symbols based on values.

Classification Method - Natural Breaks (Jenks)

Minimizes variation within groups for data classification.

Classification Method - Quantile

Distributes an equal number of features into each class.

Classification Method - Equal Interval

Divides the data into classes with the same range.

Classification Method - Standard Deviation

Classes data based on its deviation from the mean.