This guide is intended only as a guide. The actual content of the mid-term is not limited to the topics provided here. Any material covered in the lectures or readings may be included on the mid-term.

Introduction to GIS/Data Structures

Terms:

raster, vector, topology, attribute, resolution, accuracy, precision, point, line, polygon, node, segment, ellipsoid, projection, nominal data, ordinal data, ratio data, GIS, cartography

Concepts:

Advantages/disadvantages of raster and vector data structures.

How resolution affects representation of raster data.

How to make vector datasets topological.

What the difference is between GIS and cartography.

Projections/Coordinate Systems

Terms:

projection, coordinate system, UTM, latitude/longitude, geodesy, ellipsoid, scale, map scale, scale factor, cartesian coordinate system, geographic lat/long coordinate system

Concepts:

How changing map projections changes the representation of spatial data on a map

Basic types/properties of map projections (e.g. azimuthal, conformal...)

Representing Earth's surface on a sphere and on a flat surface

Database Construction/Data Acquisition

Terms:

Digitizing, common field (key-field, id-field), relational database, snapping, island polygon, adjacency, contiguity, connectivity, point, line, polygon, attribute table, field, primary data, secondary data, data documentation, georeferencing, registration, rectification

Concepts:

How attribute data is tied to spatial features in raster and vector data structures

What information needs to be documented for spatial data

Process of taking hard copy data and creating vector datasets with coordinate system and projection defined

Spatial Data Representation

Scale effects

Raster cell size/resolution

Representing features as points, lines, polygons, surfaces

Sources of error in raster, vector data, database construction

GPS

• Structure of the US DOD constellation of satellites, how a position is established by GPS units
• Sources of error in GPS units
• differential correction, real-time differential correction
• WAAS

Metadata

• What it is, why it's important
• The fundamental aspects to always document in a dataset
• The Main sections of the FGDC metadata standard

Cartography

• How cartographic design can change the representation of data on a map
• How maps can lie
• Fundamental elements that all maps should have

Some Highlights from the Labs

UTM is a projected coordinate system, "geographic" latitude/longitude (or GCS in ArcGIS terminology) is a spherical system.

Spherical coordinates are commonly measured in DD or DMS. UTM in meters. State Plane in feet.

The area of least distortion in a map projection is the point (or points) of tangency

A secant projection is a case where the projected surface intersects the Earth's sphere.

The UTM system is comprised of a series of zones, is an implementation of a cylindrical projection, and has the most distortion in extreme east-west locations far from the point of tangency of the transverse cylinder.

No single map projection can eliminate all types of map distortion.

Spatial datasets are composed of a series of spatial objects linked to attribute tables through a unique identifier

Tables may be joined together using a unique field. These joins are case and type sensitive (text vs. integer)

RMS is the Root Mean Squared Error produced in the registration and rectification of a dataset produced from pairs of map and file coordinates. It is calculated from the individual errors of each registration point and is an estimate of the average spatial error in an image. Some areas on the rectified image will have higher and some lower error than the RMS value. Registration points should be distributed around an image to try to minimize the maximum error in a rectified image.

Factors that contributed to errors in the change dataset produced in the the digitizing lab included: 1) spatial and coding errors in 1980 source data, 2) spatial error introduced in the registration/rectification process, 3) human error in the digitizing process, 4) potential differences in the rules used to code features in the 2006 vs. 1980 datasets. Why might an area that shows up as "reforestation" in the change dataset not really be reforestation in the real world?

Example exam questions:

1. You are creating a GIS for the City of Bloomington to implement a 911 system (emergency system dispatch). One of your goals with the GIS is to be able to tell drivers the fastest way to get from their dispatch area to the right address. This GIS is best implemented using a vector data structure. Why?
2. Why might the GIS mentioned in #1 above not provide an accurate measure of the actual travel time from the dispatch area to the address (i.e. what error is there between the spatial representation and the real world?)?
3. Someone gives you two datasets for Indiana with the following coordinates for the location of the Student Building a) 541000, 4335201 and b) –86.48, 39.13. Which one is in UTM and which one is Lat/Long? What are the units for each?
4. Is it possible to have the same x,y coordinates for two different locations with the UTM coordinate system? Why or why not?
5. Describe the steps (bulleted list) for taking a hardcopy topographic map and digitizing vector features to create a digital spatial database that can be integrated with other spatial datasets. Assume you have a UTM grid (intersections) on the map.
6. What sources of error exist in the process described in #5.
7. Define what a GIS is, what a GIS does... How is a GIS different from a cartographic or mapping package?
8. Describe what qualities/characteristics make a) a vector line dataset topological and b) a vector polygon dataset topological.
9. List the rules that can be used to assign cell values when converting a vector polygon dataset to a raster dataset. Use diagrams to support your answer indicating how different rules may result in different cell assignments depending on the rule used.