L2.06: GEOINT Data


The NGA doctrine said that GEOINT data "is any data used to create GEOINT." This doctrine also goes on the say that geospatial information, imagery, and imagery intelligence are the main source of data for GEOINT. We take a more general view that GEOINT data are data and information collected to understand the human and physical qualities of a location. So, what is GEOINT data? GEOINT data, including geospatial information, imagery, and imagery intelligence, fundamentally consists of:

  • structured and unstructured geospatial data, and
  • information about the physical and human qualities of a location on the Earth.

Table 2.1 illustrates these general data types and the relation between them:

Table 2.1: Data Content versus Geospatial Data Organization
  Data Organization:
Structured Geospatial Data
Data Organization:
Unstructured Geospatial Data
Data Content:
Physical Geography
Digital imagery of a land feature
Report describing facts about a land feature
Data Content:
Human Geography
Geospatial data of incidences of bacterial infections
Scholarly article describing
facts associated with bacterial infections at a location

Structured GEOINT Data are geospatial data organized to be immediately usable by technologies, such as Geographic Information Systems (GIS). A formal definition is:

Structured geospatial data is information about locations and shapes of geographic features and the relationships between them. It is usually stored as coordinates and topology with a high degree of organization to be readily searchable.

What often goes unstated and unappreciated is a broad class of data known as “unstructured geospatial data,” a catchall term because much of the data included under that term actually has elements of structure. Email, for instance, may contain a street address, senders, times, and the like. A definition for unstructured geospatial data is:

Unstructured geospatial data refers to geographic information that either does not have a predefined data model or is not organized in a predefined manner. Unstructured geospatial data may be text containing geographic information such as street addresses and site descriptions. Unstructured data is not readily searchable.

While unstructured geospatial data may be organized into a digital file, the data are still "unstructured" because they cannot be easily accessed for mapping. However, unstructured data are extremely important when completing an analysis and help to complete the partial picture provided by heavily structured data. Some suggest that between 50 and 80 percent of the data in an organization is unstructured. If this is correct, then most of the data that an analyst might encounter is unstructured. However, extracting geographic features from unstructured data into defined fields for analysis poses a challenge. To illustrate, I will compare three common datasets encountered in developing GEOINT:

  • a vector data file of streets with building addresses,
  • an orthorectified satellite image with a known resolution stored as a raster image, and
  • a text report discussing construction types of the individual buildings, including the building addresses.

The vector data files and satellite image represent structured data. With the vector file, we can use a GIS to geocode a list of street addresses, plot these as points on the satellite image, and determine the extent of vegetation around each house. However, the report of construction types is unstructured geospatial data. We cannot immediately import it into into a GIS and map the building construction types on the street map and satellite image.

GEOINT Data can be divided into two other major data content categories of physical and human geospatial data. Physical geospatial data is a record of the spatial characteristics of the various natural phenomena associated with the Earth's hydrosphere, biosphere, atmosphere, and lithosphere. Examples are:

  • Landforms that are a result of the natural processes
  • Vegetation
  • Climate and weather

Human geospatial data record the imprint of human activity on Earth. Examples include:

  • Cities, buildings, roads, bridges, power/utility lines, and airfields
  • Farms, orchards, irrigation channels, deforestation, dams, and mining
  • Birth and death rates, population clusters, media accessibility, political culture, medical facilities, education, tribal boundaries, and other cultural details.

The World-Wide Human Geography Data Working Group (WWHGD WG) is a voluntary partnership around human geography data focused on the general principle of making appropriate human information available to promote human security. This data helps us understand the behavior of people during different times and in different places and informs human security and humanitarian assistance initiatives. Human geography data enables us to understand why people do what they do and where they do it. WWHGD Working Group builds voluntary partnerships around geospatial datasets for human geography to support human security, humanitarian assistance, disaster relief and emergency preparedness, and response and recovery efforts globally. The WWHGD Working Group has catalogued more than 1200 data sources and links. This global mapping community has more than 1500 members with participating organizations representing the Department of Defense (DoD), civil agencies, academia, non-government organizations (NGO), international organizations, and private corporations.

Commercial firms also amass human geography data and transform it into a structured form for easy access and use by Geographic Information Systems. Their products are made possible by the fact that the original data exist in digital form, and because the companies have developed systems that enable them to structure the data efficiently.

Try This!

Try out the demo of what Claritas used to call the "You Are Where You Live" tool. The Nielson Company has acquired Claritas and the tool is now called "MyBestSegments." Use the following link to access the My Best Segments - ZIP Code Look-up page. Unfortunately this tool only works for locations within the United States; if you don't live in the United States, consider entering a ZIP code for a town or city you are familiar with or try Penn State's Zip code: 16802.

If you do live in the US, enter the ZIP code for your home town and then enter the security code provided on the page and click the Submit button. You will see a list of lifestyle segments listed on the left. Click on some of the lifestyle segment names to see if they seem accurate for the community you selected.

Does the market segmentation match your expectations?

The key point is that human, physical, structured, and unstructured data are used together to create a picture of place. The use of human geography data and imagery in the response to Ebola is an example. Analysts utilize this data to better understand where infrastructure is located, where the disease has the greatest risk of transmission, and what populations are most at risk.