NCERT Class 12 Practical Geography Chapter 6 Spatial Information Technology YouTube Lecture Handouts Part 4

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NCERT Class 12 Practical Geography Chapter 6: Spatial Information Technology GIS | CBSE | English

Sequence of GIS

Entering the Attribute Data

  • Attribute data define the properties of a spatial entity that need to be handled in the GIS, but which are not spatial. For example, a road may be captured as a set of contiguous pixels or as a line entity and represented in the spatial part of the GIS by a certain colour, symbol or data location. Information describing the type of road may be included in the range of cartographic symbols. The attribute values associated with the road, such as road width, type of surface, estimated number of traffic and specific traffic regulation may also be stored separately either as spatial information in the GIS in case of relational databases, or input along with spatial description with the object-oriented data bases. The attribute data acquired from sources like published record, official censuses, primary surveys or spread sheets can be used as input into GIS database either manually or by importing the data using a standard transfer format.

Data Verification and Editing

  • The spatial data captured into a GIS require verification for the error identification and corrections so as to ensure the data accuracy. The errors caused during digitization may include data omissions, and under/over shoots. The best way to check for errors in the spatial data is to produce a computer plot or print of the data, preferably on translucent sheet, at the same scale as the original.
  • The two maps may then be placed over each other on a light table and compared visually, working systematically from left to right and top to bottom of the map. Missing data and locational errors should be clearly marked on the printout. The errors that may arise during the capturing of spatial and attribute data may be grouped as under:

Spatial Data Are Incomplete or Double

The incompleteness in the spatial data arises through omissions in the input of points, lines, or polygons/area of manually entered data. In scanned data the omissions are usually in the form of gaps between lines where the raster vector conversion process has failed to join up all parts of a line.

Spatial Data at the Wrong Scale

The digitizing at the wrong scale produces input spatial data at a wrong scale. In scanned data, the problems usually arise during the geo-referencing process when incorrect values are used.

Spatial Data Are Distorted

  • The spatial data may also be distorted if the base maps used for digitizing are not scale correct. The aerial photographs, in particular, are characterized by incorrect scale because of the lens distortions, relief and till displacements. In addition, paper maps and field documents used for scanning or digitizing may contain random distortions as a result of having been exposed to rain, sunshine and frequent folding. Hence, transformation from one coordinate system to another may be needed if the coordinate system of the database is different from that used in the input document or image.
  • These errors need corrections through various editing and updating functions as supported directly by most GIS software. The process is time-consuming and interactive that can take longer time than the data input itself. The data editing is usually undertaken by viewing the portion of map containing the errors on the computer screen and correcting them through the software using the keyboard, screen cursor controlled by a mouse or a small digitizer tablet. Minor locational errors in a vector database may be corrected by moving the spatial entity through the screen cursor. In some GIS, computer commands may be used directly to move, rotate, erase, insert, stretch or truncate the graphical entities are required. Where excess coordinates define a line, these may be removed using β€˜weeding’ algorithms. Attribute values and spatial errors in raster data must be corrected by changing the value of the faulty cells. Once, the spatial errors have been corrected, the topology of vector line and polygon networks can be generated.

Data Conversion

  • While manipulating and analysing data, the same format should be used for all data. When different layers are to be used simultaneously, they should all be in vector or all in raster format. Usually, the conversion is from vector to raster, because the biggest part of the analysis is done in the raster domain.
  • Vector data are transformed to raster data by overlaying a grid with a user-defined cell size. Sometimes, the data in the raster format are converted into vector format. This is the case especially if one wants to achieve data reduction because the data storage needed for raster data are much larger than for vector data.

Spatial and Attribute Data Linkages

The linkages of spatial and the attribute data are important in GIS. Linking of attribute data with a non-related spatial data shall lead to chaos in ultimate data analysis. Similarly, matching of one data layer with another is also significant.


A GIS typically links different data sets. Suppose, we want to know the mortality rate due to malnutrition among children under 10 years of age in any state. If we have one file that contains the number of children in this age group, and another that contains the mortality rate from malnutrition, we must first combine or link the two data files. Once this is done, we can divide one figure by the other to obtain the desired answer.

Exact Matching

Exact matching means when we have information in one computer file about many geographic features (e. g. , towns) and additional information in another file about the same set of features. The operation to bring them together may easily be achieved using a key common to both files, i.e.. name of the towns. Thus, the record in each file with the same town name is extracted, and the two are joined and stored in another file.

Hierarchical Matching

Some types of information, however, are collected in more detail and less frequently than other types of information. For example, land use data covering a large area are collected quite frequently. On the other hand, land transformation data are collected in small areas but at less frequent intervals. If the smaller areas adjust within the larger ones, then the way to make the data match of the same area is to use hierarchical matching β€” add the data for the small areas together until the grouped areas match the bigger ones and then match them exactly.

Fuzzy Matching

  • On many occasions, the boundaries of the smaller areas do not match with those of the larger ones. The problem occurs more often when the environmental data are involved. For example, crop boundaries that are usually defined by field edges/boundaries rarely match with the boundaries of the soil types. If we want to determine the most productive soil for a particular crop, we need to overlay the two sets and compute crop productivity for each soil type. This is like laying one map over another and noting the combinations of soil and productivity.
  • A GIS can carry out all these operations. However, the sets of spatial information are linked only when they relate to the same geographical area.

Spatial Analysis

  • The strength of the GIS lies in its analytical capabilities. What distinguish the GIS from other information systems are its spatial analysis functions. The objective of geographic analysis is to transform data into useful information to satisfy the requirements of the decision-makers. For example, GIS may effectively be used to predict future trends over space and time related to variety of phenomena. However, before undertaking any GIS based analysis, one needs to identify the problem and define purpose of the analysis. It requires step – by – step procedures to arrive at the conclusions. The following spatial analysis operation may be undertaken using GIS:
    • Overlay analysis
    • Buffer analysis
    • Network analysis
    • Digital Terrain Model

Overlay Analysis Operations

GIS makes it possible to overlay two or more thematic layers of maps of the same area to obtain a new map layer. Example, urban land use during 1974 and 2001. When the two maps overlaid, the changes in urban land use have been obtained and the urban sprawl is mapped during the given time period

Buffer Operation

  • A buffer of a certain specified distance can be created along any point, line or area feature. It is useful in locating the areas/population benefitted or denied of the facilities and services, such as hospitals, medical stores, post office, asphalt roads, regional parks, etc. Similarly, it can also be used to study the impact of point sources of air, noise or water pollution on human health and the size of the population so affected. This kind of analysis is called proximity analysis. The buffer operation will generate polygon feature types irrespective of geographic features and delineates spatial proximity. For example, numbers of household living within one-kilometre buffer from a chemical industrial unit are affected by industrial waste discharged from the unit.
  • For example, by using appropriate commands of either of the available software, one can create buffers of 2,4, 6,8 and 10 kilometres around the cities having a major hospital located. As a case study, point location of Saharanpur, Muzaffarnagar, Meerut, Ghaziabad, Gautam Budh Nagar and Aligarh has been mapped and the buffer have been created from the cities where major hospitals are found. One can observe that the areas closer to the cities are better served, people living away from the cities have to travel long distances to utilise the medical services and their areas that are least benefitted

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