GIS

week 1-3: Research, Scale, Data Types and Graphic Design Basics and GIS Feature Visualization

map types

cognitive, reference, thematic, virtual

cognitive map

amental map of neighborhoods, not very accurate

reference map

shows boundaries/names of geographic areas (roads/coastlines)

thematic map

shows specific data (qualitative/quantitative)

virtual map

exists on digital devices, gps, uber, ect

3 major tasks

visualization, data collection, data analysis

visualization

representing geographic area through mapping, design, visualization

data collection

way of obtaining information without physical contact

data analysis

using statistical methods for understanding/manipulating/displaying primary data or secondary data sources

qualitative (categorical) scale

nominal scale, ordinal scale

quantitative scale

interval scale, ratio scale

nominal scale

no hierarchy, just categories. Assigns #’s as labels, but the numbers don’t have any value. Just used to identify categories on the map.

ordinal scale

used to determine whether an object has more or less of an attribute than another. Has no scale, just categories. EXAMPLE: if showcasing the average income level of each area of a state, it will start with the lowest income level category and go up from there. Each category is a range of numbers

interval scale

Has a gradient scale ex: temperature

ratio scale

indicates the absolute lack of the property being measured. EXAMPLE: if looking at income levels of a state, it will start at $0 and go from there. This has a scale, and is ranked

Points - drawing style

shows data as a point - example: CA universities

lines - drawing style

can be used to display streets

polygons - drawing style

can be used to display categorical data. example: redlining

choropleth map

data based on predefined data - good for displaying statistical data like population

dot distribution

each dot has same # value that has been assigned to them. Shows spatial patterns/distribution

Isoline map

uses line symbol to display temperature/elevation/distance

proportional symbol/graduated symbol

symbol size varies based on the value is represents

5 design principles

1. contrast

2. hue v value

3. color wheel

4. visual hierarchy

5. color scale (monochromatic/dichromatic)

Contrast

the greater the different of value btw data sets, the greater the contrast

hue v value

1. hue: basic color

2. value: amt of black in the color (saturation/intensity)

color wheel

adjacent colors for harmony - opposite colors to differentiate data more easily

visual hierarchy (bright/drab)

content organized to visually communicate order/importance - creates depth

1. bright (figure): assign to important features

2. drab (ground) graphic elements that provide context

monochromatic

same hue with different values (intensity)

dichromatic

2 monochromatic scales combined (ex: hot/cold)

3 types of points (symbolizing data)

1. unique points: no correlation to each other

2. graduated points: ranking system based on size

3. icons: easily identifiable points (grocery store icon)

scale

ratio of map distance to ground distance

large scale map

detailed map - should have graphic scales

small-scale map

highly generalized map

visibility base map

• simplifies countries to polygon shapes

• inaccurate representation of boundaries/coastlines

• makes smaller states much more legible/identifiable

intensity data vs count data

• intensity data: choropleth maps - light/dark color values indicate the correlation to each other

• count data: graduated symbols to represent the #’s

dot array map

distinguishable dots on a map separated by clear state boundaries. Good for count data

bivariate correlation

combines 2 different data sets to reveal patterns/relationships - ex: rural vs urban population