Geographic information system stores spatial data on geographical features and attribute data associated with these features. The life cycle of a GIS starts with data acquisition, moves through different stages of data processing, and ends with representation of both spatial and attribute data to carry out analysis and decision-making processes for complex spatial problems. To use a GIS with all its advancements and potential, it is necessary to understand its core functions. Although a wide range of GIS packages are available in the market, all with their own specifications, some common core functions are implemented within all systems. GIS functionality is a logical sequence of data capture, data processing, data storage, data analysis, and representation to proliferate solutions for complex spatial problems. The logical flow of data for successful implementation of GIS projects takes the following sequence of processes.
Data Preprocessing
Data preprocessing is a sequence of processes carried out to acquire and embed error-free spatial and attribute data into the GIS. It is composed of four sequential steps.
1. Data capture: GIS has the power to capture different types of data from different sources. There are two separate categories of geo-data sources—primary data sources and secondary data sources (Figure 3.3). Primary data sources are digital data sources primarily focused on data acquisition for GIS application; for example, remote sensing to acquire geo-data without any direct physical touch with the object, that is, to remotely sense the data of a physical object on the earth’s surface. Secondary data sources acquire data sets in both digital and analogue forms and initially gather data for any other purpose such as manual cartography. They are then converted for GIS application using techniques such as projection. Again, based on the different types of geo-data (raster or vector), a different technique is employed for different sets of data. For example, ground survey is used to capture vector data, while scanning is used to acquire raster data. Digitization is a key step to be carried out at this stage. It is the process of converting primary data (analogue data such as paper maps) into primary digital data for direct use in GIS.
2. Data transfer: Data transfer involves transferring already acquired digital spatial data into GIS using an electronic network or some external media such as a pen drive or a magnetic disc. The transferred data may be of any format—system dependent such as ARC/INFO format or system independent such as TIGER.
3. Data edit: Data editing is the process of compiling the acquired data by making them error free and mapping the relation between spatial data and attribute data of distinct spatial features. GIS software allows correcting spatial and non-spatial data. Textual and graphical sets of data can be copied, moved, deleted, or updated using different functionalities provided by the GIS development software.
The resultant digital data files contain all spatial and attribute data present in the original data set but without distortions. Different spatial operations such as rubber sheeting, overlay, and buffering are carried out to manipulate the data and make them error free for further use.
4. Data storage: The compiled data are structured and efficiently stored (because of space constraints) in the GIS. The concept of relational database management system (RDBMS) is used to organize and manage the location and attribute data separately.
The unique structure of the database facilitates retrieval, analysis, and manipulation of topographical data. Stored map layers in the database need to be analysed, manipulated, and retrieved for analysis.
Prior to data storage, they need to be structured. Raster and vector data models are techniques to organize data in a structural form.
Structured data are easy to transfer and well organized.
Figure 3.3 GIS data acquisition method
Generalization
Geographic information system is used to model real world for analysis of geographical phenomena and features. The real-world features have a complex structure and numerous amounts of data are associated with those geographical features. However, due to constraints such as time, space, and processing speed of devices that are used for processing of spatial data, it is not advisable to store all data sets associated with the feature. Here the term “generalization” comes into play. Generalization is a technique to scale the information need to be represented on a map according to the scale of the display medium. It is the process to derive more relevant, purposeful, and less detailed data at a smaller scale from a larger set of data at a higher scale. Major operations for generalization are as follows.
• Smoothing: The process of reducing the angularity of line is called smoothing. Smoothing is a technique to simplify the map features, involving several other characteristics such as feature displacement and location shifting of generalization. The purpose of smoothing is to exhibit line work in a much less complicated and less visually grating way.
• Enhancement: A cartographer uses the enhancement technique to clarify specific elements that aid in map reading. Enhancement can be used to show the true attribute of the feature being represented and is often used to highlight domain-specific knowledge.
• Selection: Selection is a map generalization technique that facilitates the reduction of the complexities of the real world by deliberately reducing auxiliary and unnecessary details.
• Simplification: Generalization not only facilitates simplification of data but also provides an opportunity to reduce the complexity of data by simplifying the geospatial data. Conversion of large-scale data into small-scale data for detailed geospatial data is an example of simplification.
Analysis
Computer-based information systems are used to acquire, store, and manage information to deliver a digital product. However, GIS is special because of its capability to store spatial information and its potential to recognize the interrelationships and hidden patterns that exist among spatial data sets. Spatial analysis is a set of statistical, mathematical, and spatial operations to determine the existing patterns in the spatial data of a given domain. To no small degree, the recent quantitative
analysis in geography represents a study in depth of patterns of points, lines, areas, and surfaces depicted on maps of some sort or defined by coordinates in two- or three-dimensional space (Wilson and Bennett 1985; Hägerstrand 1973).
Spatial analysis establishes the link between traditional cartography and statistical, mathematical models to manipulate spatial and non- spatial data in GIS. The GIS software is nothing without its analysis toolbox. Spatial data can be stored as a point, line, or a polygon, and the interrelationship between these features needs to be established with different analysis techniques such as nearest neighbour method (point);
network analysis and autocorrelation (line); and surface analysis and Bayesian technique (polygon).
Analysis techniques provide support for planning, management, and decision-making applications by exploring the existing hidden patterns and deriving new spatial patterns from old ones.
Representation
Geographic information system produces a digital product as an output of processing and manipulating spatial data. Digital interactive maps, reports, graphs, and results of specified queries are distinct methods of representing spatial and non-spatial data (Figure 3.4). Usually, geographical data are represented in the form of maps and graphs, while attribute data are represented as tables and reports. At this stage, GIS works like computer-based cartography. It provides a collaborative and interactive platform for display of spatial data. Results of spatial queries are displayed as charts, tables, or reports on attribute data associated with spatial features. GIS software provides all these methods of data display. Although the representation of data plays a vital role in decision-making, planning, and other objectives of the GIS application, technically it is a less sophisticated stage than the earlier stages of a GIS life cycle.