GIS Final

Local raster operations

Changing the values of individual raster cells. 

Zonal statistics

Calculates statistics on values of a raster within the zones of another dataset.

Map algebra

Combining map layers using mathematical or statistical operations (aka raster map overlay)

Neighborhood raster operations

Spatial filtering: can be used to improve the quality or appearance of raster grids by reducing “noise” or enhancing features. 

Low pass/frequency filtering: used to smooth out local details and emphasize general trends. High pass/frequency filtering: used to enhance edges and extreme values. 

Slope

In 2 dimensions. Slope represents the Y differences divided by the X differences between two points. “Rise over run”. In 3 dimensions, it is calculated using a raster cell’s “neighborhood” aka the 8 raster cells surrounding it. Unit can be percent or degrees. 

Aspect

The direction that slope faces. Unit is angle in degrees.

DEM

Digital Elevation Model. A type of raster data representation of Earth’s surface that provides elevation information. 

Hill shading

Drawing shadows on a map to stimulate sunlight over terrain; creates a 3 dimensional effect. 

Triangulated Irregular Networks (TINs)

A TIN is a type of GIS data that defines geographic spaces as a set of contiguous triangles of different sizes. Can be created directly from sample points, and points can be irregularly distributed. 3 dimensional data (usually elevation) in vector format. 

Spatial interpolation

The process of using points with known values to estimate values at other points. Predicting unknown values using a sample of known values.

Nearest-neighbor interpolation

Assuming that an unknown point shares the values of its nearest control point. 

Thiessen polygons

Aka Voronoi Diagrams. Each polygon only contains one point. Any location  within the polygon is closer to that point than points in any other polygon.

Inverse distance weighting

Assumes that the value of a variable at an unsampled point is influenced more by nearby known points than by distant ones.

Mode: Most common value in dataset

Median: Middle values of an ordered dataset.

Mean: Sum of all values divided by the total number of values in the dataset.

Standard deviation

Shows how much the data varies around the mean

Statistical distributions

Used to model the spatial and temporal variability of geographic phenomena.

Centroid/mean center

Measure of the center of a geographic distribution. AKA center of mass or balance point.

Median center

Another measure of central tendency. Minimizes the distance to all other features. 

Standard distance

Measures how features are concentrated or dispersed around the mean center.

How to calculate the mean center

The mean center is the mean X and Y coordinate of all the points in a study area.

Spatial Pattern; Random: Neither clustered nor dispersed

Spatial Pattern; Clustered: Points are concentrated in groups

Spatial Pattern; Dispersed: Points are scattered across the distribution and tend to not be

located nearby each other.

Average nearest neighbor analysis

Describe the overall distribution of a set of points. 

Spatial autocorrelation

Measures spatial dependency of  geographic data. Shows how similar neighboring features are to each other. 

Tobler’s First Law of Geography

“Everything is related to everything else, but near things are more related than distant things”

Moran’s I test

Measures overall spatial autocorrelation in a GIS dataset. -1, 0, +1. 

Nodes: Represent points or locations in a GIS network, such as intersections, stations, or service points.

Edges: Represent the connections or paths between nodes, such as roads, rivers, or utility lines

Shortest path analysis

Edge weights: cost of traversing network segments. Distance, time, or other values. To calculate shortest path first create a weights matrix with costs for all edge segments. 

Geocoding

The process of finding a geographic location from an address or text. Address matching: plotting the street addresses as points on a map. 

Visual hierarchy

Organizes the content of a map to visually communicate order and importances

Visual cues that create visual hierarchy

Size, position, figure-ground, color. Allows you to obtain the most important information quickly. 

Visual variables

Size, shape, color, shade, pattern.

Figure-ground

Figures: objects that stand out from the background; where your eyes “settles”. Ground: everything else. 

Color perception

The human brain perceives shades and color differently depending on context and the use of color/shading. Choice of color is important.

Colorblind-safe color schemes

Typically impaired with reds and greens.

Critical map reading

Don’t take maps at face value. Use your cartographic knowledge. Understand, who is the author? Who is the intended audience? What choices did the author make and why?

Ethical implications of geospatial technology

We leave an electronic “trail” of personal data. Credit cards, social media. Potential for misuse.

GIS code of ethics

Obligations to Society

Obligations to Employers and Funders

Obligations to Colleagues and the Profession

Obligations to Individuals in Society