To discuss information visualization on the maps, it is vital to talk first about the types of data that we may need to visualize on the maps. Let’s make it here. But with important note: the selection of types is not comprehensive and touches only the most important types, but I will try to expand it in new editions.
As the general task of visualization imples, the data that we want to visualize is somehow connected with locations. For instance, these can be air temperatures across multiple places, a voltage measurement of some sensor located at some spot, position of a ship or an airplane, their route or past track, some forbidden for drone flights zone and so on. We do not cover the needs to visualize the data, but if the data is not connected to some location or area, then it can not be visualized on the map. This is why the term “geodata” is used — the data, that is related to the geographical location.
Explicit and implicit geodata
Some data, like position of the delivery car, is explicit. But in many cases the data is not explicitly related to geodata. For instance, we may have a status of some system, that is installed on the airplane. Let’s say connectivity status, if the navigational system is online or not. It is status, that has nothing related to the position. But we may need to track airplanes, and the statuses of their systems, and this is where this status may become geodata — we may visualize the positions of aircrafts on the map, and change the color of airplane icon according to connectivity status. So, the connectivity status implicitly becomes geodata — now it is related to some object on the map and is visualized.
Static and dynamic geodata
It is also clear, that we may have static geodata, that is not changing in time or is changing so slow, that we can ignore it for the purpose of our system. For instance, the position of the building or shore lines are static in most cases.
But we may have dynamic data, that is changing in time sufficiently, like aforementioned position of the car or weather. In many cases time is an important factor, and requires specific UX-solutions, considering time of update, playback and playahead, among other things.
Two-dimensional and three-dimensional geodata
In most cases we will have two dimensional data, that corresponds to specific geographic locations, that are defined by latitude and longitude. But in some more rare cases the geodata is three-dimensional, where additional component of altitude or depth is added. This is used in weather forecasts (for example, wind and clouds at different heights), in aviation, geological surveys, maritime data and so on.
General types of geodata by geometry
Now let’s highlight the most basic, abstract types of geodata, based on geometry. We will spend much more time discussing them, but right now we will just mention them:
- Point objects — points, that correspond to some object or measurement with specified coordinates. There can be a single point, like our personal location) or set of independent points, like weather stations. In addition to coordinates, every point may have multiple other values, like measurement value, unit, time of measurement, status and so on.
- Polylines — set of points, connected by lines. It can be a road, past track, planned route of the ship. Polylines have a beginning and end position. It is important to mention, that polyline doesn’t have any area, even if the starting point is the same as the beginning point.
- Polygons — set of points, connected by lines, that enbound some area. This means, that the last point is always connected with the first, and the area inside has some semantic meaning. Polygons may correspond to forbidden no-go areas, countries, areas of operation and so on.
It is also important to mention the following, less abstract additional type of geodata, that is used actively. It is called grid geodata. It is a combination of points, that are located on some geographical grid with specific steps. The simplest example is a satellite image, where we have a grid of pixels, that correspond to some coordinates. Another example — weather forecasts, that define for every point expected wind speed and direction, air and/or water temperature, humidity and so on. Technically, grid data is an array of values or objects.