UNIT 10: PROJECTING DATA

Written by Rustin Dodson, Santa Barbara, California


Context


A map projection defines the manner in which three-dimensional information about the Earth is transformed to a two-dimensional surface for display and analysis. All spatial data stored in a GIS are associated with a map projection, either implicitly (so-called "unprojected" data stored with raw longitude, latitude coordinates) or explicitly (where the data have been transformed into a known map projection). Map projections are a particularly important feature of spatial data because they introduce error and distortion, since a sphere cannot be transformed to a plane without some stretching and twisting of the sphere's surface.

The typical GIS package supports anywhere from 5 to 50 map projection types, and most projections may be modified or customized by varying their parameters. This flexibility is important for two reasons: first, in order to use two or more layers together in a GIS for visualization or analysis, all data must be projected in the same way. Second, certain types of analyses, such as the measurement of area, are only valid when using certain types of projections (and conversely, are completely wrong when using an inappropriate projection).

An understanding of map projections is essential in order to confidently display, analyze, and interpret GIS data. The subject of map projections is vast and complex. This unit attempts to present an overview of projection issues that are likely to be encountered by a GIS user.


Example Application

As GIS analyst on a large ecological modeling project in the Pacific Northwest, you are constantly on the lookout for higher-quality data with which to upgrade your environmental database. On the World Wide Web you discover a new set of Digital Elevation Model (DEM) data: the GTOPO30 Database from the US Geological Survey, which provides high-quality elevation data over the entire globe at a 30-arc- second grid resolution.

You decide to upgrade your existing DEM data with the newer, more extensive, and more carefully documented GTOPO30 data, however your current environmental database is on an Albers equal-area conic map projection, while GTOPO30 uses a Plate Carree projection and a simple longitude, latitude coordinate system.

In order to use the new data with your existing environmental database, you will need to project it from Plate Carree to Albers equal-area conic, making appropriate decisions regarding the grid resolution and the resampling method.


Learning Outcomes

The following list describes the expected skills which students should master for each level of training, i.e. Awareness/Competency/Mastery.

Awareness:

Students should be able to discuss the issues of map projection and map distortion as they relate to GIS; and to demonstrate a working knowledge of some common map projections and associated parameters.

Competency:

Students should be able to project spatial datasets into a new map projection; to identify appropriate (and inappropriate) projections for certain GIS applications; and to create a custom map projection.

Mastery:

Students should understand the issues involved with projecting raster data, including the choice of grid cell size and resampling methods.


Preparatory Units

Recommended:

Complementary:


Awareness


Learning Objectives:

The student will be able to:

  1. Define "map projection" and the basic classes of map projections
  2. Discuss the types of distortion inherent in map projections
  3. Demonstrate familiarity with some commonly-used map projections and coordinate systems
  4. Discuss the required parameters for several common map projections

Vocabulary:

Awareness Knowledge/Skills:


Competency


Learning Objectives:

After completing this section you should be able to:

  1. Identify an appropriate map projection for some example applications.
  2. Create a custom map projection.

Tasks:

  1. Application: A parcel delivery company with an air-freight hub in Memphis, Tennessee needs a map showing flight distances between the hub and the rest of its North American market.

    Projection: use an equidistant projection centered on the hub city, in order to show correct distances from the hub. A suitable projection for this task is the Azimuthal Equidistant.

    Memphis is located at:

    • 35 deg 6' 22" N
    • 90 deg 0' 25" W




  2. Application: The same company in the above example has added another hub in Mexico City.

    Projection: the Two Point Equidistant, a projection which presents true distance from any point on the map to either of two central points.

    Mexico City is at:





Mastery


Learning Objectives:

After completing this section you should be able to:

Tasks:

Projecting raster data

Raster, or gridded, datasets introduce additional issues which the GIS analyst must understand in order to properly execute map projection tasks. In general, vector data may be converted from projection A to projection B, and then back to projection A with essentially no loss of information (except for rounding and machine precision). This is because vector-based locations (points and vertices), can be unambiguously located in the coordinate system of a map projection. A raster, on the other hand, is constrained by the fact that it must reference a grid of equally-spaced grid cells. In addition, while a vector-based point is infinitely small, a raster grid cell is associated with an area of the Earth's surface. These two constraints--a regular grid and grid cells which reference an area--introduce ambiguities and uncertainties when raster data are coerced into a new map projection.

Some GIS software packages allow a raster to have rectangular grid cells (e.g. GRASS, Idrisi, IPW), while some require that grid cells be square (e.g. Arc/Info). The following discussion will assume that grid cells are square.

1. Grid cell size

For raster datasets using geographic coordinates (i.e. the plate carree projection), a raster image will have a constant cell size in map projection units, e.g. 30 arc-seconds, but these grid cells represent different areas of the Earth's surface. A distance of 30 arc-seconds in the east-west direction decreases with distance from the equator, finally reaching zero at the poles.

Let's compute the dimensions of a 30-arc-second grid cell at various locations on the Earth. We'll assume that the Earth is a sphere with radius 6,371 km (this is a commonly-used value for approximating the Earth as a sphere [Robinson, et al 1984]).

The circumference of the earth, 2*pi*R, is 2*3.14159*6371, or 40,030.17 km. A circle has 360 degrees of arc, so one degree of arc is 40,030.17 / 360, or about 111.19 km long. This is the (approximate) length of one degree of arc measured along a great circle (the Equator or a meridian). One degree contains 60 minutes * 60 seconds/minute, or 3,600 seconds, so 30 arc-seconds is 111.19 * (30/3600) = 0.9266 km or about 927 m.

Thus our 30 arc-second grid cell, at the equator, measures about 927 x 927 m. The north-south dimension of the grid cell, since it falls on a great circle, will always be this distance. The east-west dimension, however, decreases with latitude to a value of zero at the North and South Poles. The decrease varies with the cosine of the latitude [cos(0deg) = 1.0; cos(90deg) = 0.0]. So at 30deg latitude, the width of a grid cell is 927 * cos(30deg) = 927 * 0.8660 = 803 m. At 45deg latitude the cell width is 655 m, and at 60deg, the width is 464.

For a more concrete example, let's assume that you download 30 arc- second digital elevation data from the USGS GTOPO30 dataset, (http://edcwww.cr.usgs.gov/landdaac/gtopo30/gtopo30.html), and that you extract a DEM of the contiguous USA. The actual area of these 30 arc-second grid cells will vary from a maximum of 927 x 843 m at Key West, Florida (latitude 24.595deg); to a minimum of 927 x 604 m at the northern boundary of Minnesota (latitude 49.356deg).

The variable area of the 30 arc-second cells presents a dilemma if the data are converted to a different map projection. What is the appropriate choice of cell size if we project this DEM to an Albers equal-area conic projection? This question has no definitive answer. One approach would be to use the finest resolution of the input data in order to avoid losing any information. In this case we would perhaps choose a cell size of 604 x 604 m for the Albers grid. The downside of this approach is that it creates redundancy, a large number of output grid cells, and the illusion that the Albers dataset contains higher- resolution data than the original dataset. Another approach is to use the coarsest resolution of the input dataset and choose a cell size of 927 x 927 m. The downside of this approach is that information is lost in regions where the input data are of higher resolution (e.g. 927 x 604 m). A third approach would be to choose a cell size close to the average cell size of the input data, perhaps by computing the area of a cell in the center of the USA.

2. Resampling method

If you take a regular grid of points on a given projection, and project them to a different projection, the resulting point pattern will not line up perfectly with the original regular grid. Figure 7 illustrates this situation with a set of points spaced at 30 arc-minute intervals. These points were projected to the US Albers projection, and the points from both projections are plotted together for two regions of the US. Note how the Albers points do not coincide neatly with the regular spacing of the 30 arc-minute points.

Suppose that we have a 30 arc-minute grid of the USA (a raster version of the 30 arc-minute point set of Figure 7), and we want to project it to a US Albers projection with a 1000 x 1000 m cell size. We know from Figure 7 that there is no way to lay down a regular grid of 1000 x 1000 m cells so that each Albers cell matches up with a 30-minute cell. The method for determining the value of an output cell is known as resampling.

Most GIS packages support three resampling methods when projecting raster data:

1) Nearest neighbor: The output cell is assigned the value of the nearest input cell. For categorical data, this method is the only choice. 2) Bilinear interpolation: The output cell value is computed as the weighted average of the nearest 4 input cells. This method results in a smoother surface than the nearest- neighbor method. 3) Cubic convolution: The output cell value is computed by fitting a smooth surface to the nearest 16 input cells. This method tends to smooth data more than the bilinear interpolation method.

Figure 8 shows the results of projecting 30 arc-second cells in NW Oregon to a 100 m US Albers grid, using three different resampling methods. Note how bilinear interpolation and cubic convolution create a smoother surface than the nearest neighbor method.


Follow-up Units


Resources


Robinson, A. H., Sale, R. D., Morrison, J. L., Muehrcke, P. C., 1984. Elements of Cartography, Fifth Edition. New York: John Wiley & Sons.

Snyder, J.P., and R.M. Voxland, 1989. "An album of map projections", USGS Professional Paper 1453, 249 p.

Snyder, John P. 1987. Map Projections, A Working Manual. Washington, DC: US Government Printing Office.

A general introduction to map projections:
Map Projection Home Page, Hunter College, New York, NY.

This site has many, many images of various map projections and explanatory graphics:
Map Projection Overview, Peter H. Dana, Univ. of Texas at Austin.

A detailed treatment of map datums, also with many useful graphics:
Geodetic Datum Overview, Peter H. Dana, Univ. of Texas at Austin.

A comprehensive overview of geodesy, the study of the size and shape of the earth:
Geodesy for the Layman, National Imagery and Mapping Agency (NIMA).

This is a detailed exercise which guides the student through a series of visualization tasks using ArcView 2.1:
Map Projection Exercise (ArcView), David R. Maidment, Univ. of Texas at Austin



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Currently maintained by Steve Palladino
Created: May 14, 1997. Last updated: October 5, 1998.
Content comments to Rusty Dodson
Formatting comments to Steve Palladino