After completing this lesson, you’ll be able to:
In this lesson, you will:
Raster data comprises pixels, where a pixel's value (or color) represents the value of a phenomenon, e.g., the average annual precipitation. Standard imagery uses the visible spectrum like a regular photograph. However, other kinds of imagery are available. For example, imagery in the near-infrared band can be used to measure the prevalence of vegetation beyond what the human eye can detect.
Raster data is good for representing continuous data that varies over an entire area, such as elevation.
FME stands out in its robust support for spatial data among data integration tools.
Spatial data can be stored in vector or raster formats. Vector spatial data consists of points, lines, and polygons. At its core, it consists of lists of coordinates on a plane and information regarding how they are connected.
Raster data comprises pixels, where a pixel's value (or color) represents the value of a phenomenon, e.g., the average annual precipitation. Standard imagery uses the visible spectrum like a regular photograph. However, other kinds of imagery are available. For example, imagery in the near-infrared band can be used to measure the prevalence of vegetation beyond what the human eye can detect.
Raster data is better for representing continuous data that varies over an entire area, such as elevation. Vector data is better for representing discrete objects, such as the outlines of buildings (polygons).
| Geometry type | Illustration | Examples |
| Point |  |
Cell towers Community centers Fire hydrants Oil wells |
| Line |  |
Electricity distribution network Pipelines Roads Trails Water distribution network |
| Polygon |  |
Administrative borders Building footprints Service areas Water bodies Zoning districts |
| Raster |  |
Classified land use Elevation Orthophoto (a satellite or aerial photograph adjusted so the scale is uniform) Scanned documents |
Learn more about FME’s geometry model in the documentation or the Work with Geometry course.
Spatial data contains geometry data that describes the actual location of the data. It also usually includes attribute data that describes the features. For example, here is a dataset of point locations of community centers that includes attributes such as “CentreName” and “CentreAddress.”
Having geometry and attributes in the same dataset lets you query or filter the data. For example, you could filter the dataset of community centers to select the point with a “CentreName” that equals “Mount Pleasant.”
Spatial data can be used to create maps or analyze patterns like clusters. However, it must be integrated with other data sources to gain maximum value. For example, retail businesses combine existing store locations, road networks, and neighborhood demographic data to identify the best places to build new stores.
Integrating spatial with nonspatial data, such as spreadsheets or database tables, is also possible. This integration is possible if the nonspatial data has an attribute with spatial information. Many nonspatial datasets contain addresses, coordinates, or other identifiers. Combining these with spatial data allows you to unlock new insights. For example, retail businesses combine customer transaction data (containing their zip or postal code) with neighborhood demographic data to understand their customers and market their products or services more effectively.
Data inspection is an essential step in the data transformation process. Inspecting the output dataset to ensure the process's success is crucial. Sven wants to use Data Preview to learn more about his spatial data.
What if you want to change the color and size of the points shown in the Graphics View to make the points easier to see against the background map?
Sven could check layers on and off here but leaves them both enabled.
Clicking on the grid icon next to BusinessOwners opens the Geometry Styles dialog. Here, Sven can change the display symbology and color of the points. Sven selects red, increases the point size to 8, and then turns the background map back on.
Sven toggles off Display Control and Table View to allow more space for the Graphics View.
Map tiles © Stadia Maps, © OpenMapTiles, © OpenStreetMap contributors, © Stamen Design
Sven clicks on Zoom to Full Extent to see all of the data points.
Map tiles © Stadia Maps, © OpenMapTiles, © OpenStreetMap contributors, © Stamen Design
If you don't see Zoom to Full Extent button, click the two right-pointing arrows in the top right of the Graphics pane to view hidden toolbar buttons:
Data Preview and the stand-alone application Data Inspector are not a Geographic Information System; they can not be used to create polished cartographic output, conduct interactive spatial analysis, or edit data. The purpose of Data Preview and Data Inspector is to inspect data.
Over lunch, Sven tells his colleague Jennifer that he is working with the public art data. She asks him if he knows the title of a specific sculpture she likes near Stanley Park. He decides to use FME to find the title.
Map tiles © Stadia Maps, © OpenMapTiles, © OpenStreetMap contributors, © Stamen Design
