Spatial data can be obtained from various sources. It can be collected from scratch, using direct spatial-data acquisition techniques, or indirectly, by making use of existing spatial data collected by others. The first source could include field survey data and remotely sensed images. To the second source belong printed maps and existing digital data sets.
One way to obtain spatial data is by direct observation of relevant geographic phenomena. This can be done through ground-based field surveys or by using remote sensors on satellites or aircraft. Many Earth science disciplines have developed specific survey techniques as ground-based approaches remain the most important source of reliable data in many cases.
Data that are captured directly from the environment are called primary data. With primary data, the core concern in knowing their properties is to know the process by which they were captured, the parameters of any instruments used, and the rigour with which quality requirements were observed.
In practice, it is not always feasible to obtain spatial data by direct capture. Factors of cost and available time may be a hindrance, and sometimes previous projects have acquired data that may fit a current project’s purpose.
In contrast to direct methods of data capture, spatial data can also be sourced indirectly. This includes data derived by scanning existing printed maps, data digitized from a satellite image, processed data purchased from data-capture firms or international agencies, and so on. This type of data is known as secondary data. Secondary data are derived from existing sources and have been collected for other purposes, often not connected with the investigation at hand.
Over the past two decades, spatial data have been collected in digital form at an increasing rate and stored in various databases by the individual producers for their own use and for commercial purposes. More and more of these data are being shared among
There are several related initiatives in the world to supply base data sets at national, regional and global levels, as well as those aiming to harmonize data models and definitions of existing data sets. Global initiatives include, for example, the Global Map, the
An important problem in any environment involved in digital data exchange is that of data formats and data standards. Different formats have been implemented by various
Describe and explain standard spatial (and non-spatial) data input techniques (non RS) including the management of the data collection process (level 1 and 2).