What Are Map Data?

Mapping, an ancient craft, has been extensively transformed by advances in computers and information systems. Today, map data, also referred to as spatial data (data that reference location), are the fuel for location-enabled applications. Good data—precise, accurate, and reliable—are the essential foundation for useful information. With robust map data underpinning their software applications, users can view maps and do much more:

• Plot a route to a destination—the fastest way, the most scenic way, avoiding freeways, and so on.
• Sort categories, such as businesses, by time and distance from the user's location.
• Dynamically modify a route to avoid obstructions, such as heavy traffic.
• Pinpoint the locations of fleet vehicles, and dispatch the nearest vehicle or the one most likely to arrive first.

Correct decisions often rely on complete, accurate, reliable, and up-to-date map data for navigation, traffic, point of interest (POI) lookup, social networking, entertainment and leisure, business applications like geomarketing, and industry vertical types of applications like telecom, mobile resource management, and the like.

Map data consist of topologically consistent vector or raster data that can be organized in various ways, such as in a relational database. These data are not only essential for calculating routes, avoiding traffic, and finding a nearby destination or POI, but are also useful for displaying or printing maps or for other types of geographic analysis.

In simplest terms, spatial data consists of points, lines, and/or polygons. Each of these data elements can be associated with one or more attributes that define a quality or characteristic of the element. The Britannica online dictionary defines an attribute as "A quality or characteristic inherent in or ascribed to someone or something." In a map context, an attribute can be anything that can be ascribed directly or indirectly to a location at a point, about a line, or about a polygon. These attributes include both static (relatively stable) dimensions, such as roads, signs, stores, landmarks, and rivers, as well as dynamic (changing frequently) dimensions, such as traffic and weather conditions. Click here for more information on attributes.

For database management, these points, lines, polygons, and associated attributes have data structures, additional data elements, and/or processing methods that make their data processing faster, less resource intensive, and easier to manage/update in order to complete their transition to map data. These include:

• Spatial Topology – These spatial data or geographic data consist of structuring and maintaining line features such as road networks, polygon features such as district and county boundaries, and point features such as city and hospital locations.
• Geocoding – This process assigns a map position to an address record. The process matches and links records in two databases: an address database (without map position information) and a reference street map, thus tagging the address with the correct map position, such as latitude-longitude coordinates.
• Linear Reference – Data on utility and transportation networks are kept using a measurement from a fixed reference point along a route. Methods used include complete route mileage, geographic coordinates, or GPS (Global Positioning System) coordinates.
  • Dynamic Segmentation – This function locates a point or line segment by interpolating the distance between two known points and allows the recording of information along linear features, typically using GPS.
• Overlaying – Combining two or more maps within the same geodetic reference is called overlaying. Historically, overlaying was done by transferring two maps to clear sheets and literally overlaying them on a light board. Digital mapping makes this process much more powerful by overlaying multiple layers of data and integrating them with other related business information. Two categories of map data best handled with layering are:
  • Routing network data, which are stratified into layers of successive detail, by functional class and speed category, to optimize data retrieval for fast route calculation
  • Cartographic data, such as water features, railroad networks, city limits, and political boundaries, which are stratified into layers to simplify and speed the data retrieval for rendering maps and for collecting and maintaining map data.

An example of overlaying a number of data layers is shown below.

In the future, location-enabled applications will increasingly include three-dimensional imagery. Many industry experts believe three-dimensional imagery is the future of map-based applications, enabling users to virtually access distant places—with either actual photographic imagery or mockups—such as "walking" on Fifth Avenue in Manhattan, "looking" at the shops, and "entering" a store, all in three dimensions. In addition, the layout and placing would no longer be restricted to the real world; for example, video games or jewelry could float in the air instead of staying inside glass cases.

While three-dimensional imagery has been tried in the past, technology advancements such as very-high-speed Internet connections, powerful graphics cards and processors, and improved compression software can now support the massive data transmission, processing, and management capabilities required for three-dimensional imagery. In just a few years, two-dimensional Web sites and applications may actually be considered "old-hat" applications that need to be upgraded.

To find out more about NAVTEQ^® map data, click here. To find out about the benefits of becoming a NAVTEQ registered developer and to register, click here.