In the last chapter, we covered the location component: mapping. However, GIS mapping requires more than coordinates.

GIS mapping requires data.

In this chapter, we’ll cover the two basic data types (vector and raster), common GIS file formats, and our top five GIS data sources.

• Vector data
  • a. Points
  • b. Lines
  • c. Polygons
• Raster Data
• Shapefiles
• Common GIS Data Types
• LiDAR Data
• Sourcing GIS Data

Vector Points

Vector points are basically x,y coordinates. They don’t have dimensions and usually represent single data points.

In GIS mapping, vector points illustrate features too small to be drawn at scale.

For example, if you're creating a map of a specific city, you'd use lines to draw the city's boundaries. However, create a map of the entire country and that boundary is no longer visible - so a labeled point is used instead.

This image of Colorado illustrates this concept perfectly: the state capital being representated as a labeled point.

Vector Lines

Vector lines are a series of connected vector points.

They have distinct start and end points and, though they can intersect with one another, a single line will not intersect with itself.

Vector lines are used to represent linear features such as rivers, roads, and trails.

Color, thickness, and line type (solid or dashed) are used to denote unique features, or unique attributes of the same feature.

For example, a heavily trafficked highway might be drawn with a thick line, whereas the residential roadway would be much thinner. Moreover, streets could be solid black lines, while the river might be dotted and blue.

Stylistic choices like these are at the map makers discretion, but can add depth and visual interest to the map.

Polygons

Polygons are lines in which the first point is also the last: creating a shape.

Polygons represent features with distinct boundaries: states, property lines, lakes, etc.

Though they're most frequently used to represent perimeter, with modern GIS, polygons can also be used to measure a feature’s area.

In terms of GIS mapping, raster data comes in two types: discrete and continuous.

Discrete data can only take specific values, whereas continuous data can take any value within a range. For example:

The number of people in a room is a discrete value. You can have any number of people, but you can’t have half a person. You’re limited to whole numbers: no decimals or percents.

Continuous data is more flexible, including values such as height, weight, and length. A person's height can be any value within the range of human heights. In fact, most people’s height is not exact to the inch or foot.

Continuous and discrete data are complementary, but do have different applications.

The map on top illustrates discrete raster data.

Each value is assigned a different color, while each cell has only one data type and one color: there’s no gradation of either.

In contrast, the map on the bottom represents continuous data.

Each grid cell contains some level of gradation. Continuous rasters are often used to represent data that experiences gradual change: temperature, population, elevation, etc.

Shapefiles are, by far, the most common GIS file type.

Developed by GIS powerhouse ESRI, shapefiles are a simple way to store and share GIS vector data.

Shapefiles combine non-topological data with associated attributes.

To breakdown what that means, let’s return to our original definition of GIS: the intersection of data and location.

Non-topological data is the location. It consists of x,y coordinates and does not include a third dimension (the z coordinate).

Examples of non-topological data include street, state, or area maps.

Associated attributes are the data.

Consider an elevation map. The non-topological data (x,y coordinates) illustrate the base terrain, while the associated attributes (z coordinates) represent the elevation profile.

Topological data isn’t just limited to linear data such as elevation.

Drought conditions in the United States are a good example of a map that could be stored and shared as a shapefile. The map of the United States would be the non-topological data, while the drought conditions data would be the attributes.

Point clouds are highly detailed 3D maps, illustrating everything from a downtown core to a national forest.

Unlike radar and sonar, LiDAR is not necessarily inhibited by object interference. One LiDAR emission can complete multiple returns, meaning it will bounce multiple times between the LiDAR system and any objects it meets.

This makes LiDAR particularly useful for mapping vegetated areas. For example, when surverying a national forest, the LiDAR emissions won't stop at the top of the tree canopy: they will make returns until hitting the ground.

The two most common LiDAR maps are digital elevation models (DEM) and canopy height models (CHM): both made possible by the multiple returns property.

For DEMs, you would take a full LiDAR scan and then filter for the last return: remembering that the last return generally represents ground points. With these filtered data points you can then create a bare earth map, one that excludes all but the surface of the Earth itself.

For CHMs, the idea is similar. Filter for the first return (in this case, the top of the tree) and then subtract the final return (the ground). This leaves the height of each tree in the area, allowing you to create a full canopy height map.

Natural Earth Data

Best for: Cultural, physical, and basemap data

Natural Earth Data (NED) is an especially excellent resource for cartographers, topping several lists for best open-source GIS database.

NED offers a combination of both vector and raster data sets, most of which are available in three different size scales.

Supported by the North American Cartographic Information Society, NED hosts data on a global scale and should be your first stop for beautiful GIS map making.

OpenStreetMap Data

Best for: High spatial resolution vector data

OpenStreetMap (OSM) offers high spatial resolution vector data. What differentiates OSM from other GIS data sources, is that all the data on OSM is crowdsourced from cartographers and other GIS map makers.

This means there is a massive amount of detailed information available.

The downside of the crowdsource format is that nothing is vetted beforehand. Verifying data accuracy is difficult and some data sets are incomplete.

That said, most anecdotal evidence points to a high degree of accuracy. As the data is coming primarily from GIS professionals, it’s in everyone’s interest to upload quality data that increases the quality of the database as a whole.