Geographic Information System | Geology Optional for UPSC PDF Download

Introduction

• Geographic Information System (GIS) serves as a powerful research tool for handling geographic information, and it can also be viewed as a science and technology in its own right. At its core, GIS is a computer-based system with the capability to perform various tasks such as data capture and preparation, data management, data manipulation and analysis, and data presentation.
• The effectiveness of GIS in carrying out these operations relies on the integration of computer systems, software, data, infrastructure, and the users of GIS technology. Since its inception in the 1970s, the application of GIS has expanded significantly, and it is now utilized across a wide range of fields and industries.
• GIS operates based on different data models, primarily the vector and raster data models. These models define how geographic data is represented and analyzed within the system.
• In this unit, we will explore the various components of GIS, delve into the intricacies of data models used in GIS, and examine vector and raster data analysis in detail.

Objectives

After studying this unit, you should be able to:

• explain the components of GIS;
• classify different types of data models; and
• explain vector data and raster data analysis.

Definition of GIS

GIS stands for Geographical Information System. Since GIS combines various fields, there isn't a single agreed-upon definition. However, one widely accepted definition from the National Center for Geographic Information and Analysis (NCGIA) is:

"A GIS is a system of hardware, software, and procedures to facilitate the management, manipulation, analysis, modeling, representation, and display of georeferenced data to solve complex problems related to planning and resource management."

Breaking down the term GIS:

• Geographic: Refers to spatial location (where in the world).
• Information: Refers to the specificity of the location.
• System: Refers to the integration of different information about various locations.

In essence, GIS stores information about the world as a collection of thematic layers that are connected through geography.

Components of GIS

There are five basic components of GIS.

• Hardware: Hardware component consists of a computer, data storage, and display.
• Software: Software includes the tools for management, analysis, display and dissemination of spatial data and spatial information. There exists several GIS software such as TerrSet, ArcGIS (ArcView, ArcEditor, Arc/ Info) GeoMedia; MapInfo; ERDAS (imagery analysis), AUTOCAD MAP (drafting and design) MicroImages; Manifold, GRASS; PCI; ENVI; ER Mapper
• Data: Data is fundamental requirement for any GIS system. It can be spatial (geographical location) or tabular (non-spatial) data. GIS combines spatial and non-spatial part of data through database management system (DBMS).
• Method: For efficient working of a GIS system, one need to have knowledge of how to utilize GIS technology. Methods involve how data is acquired, stored, processed, analyzed and displayed in a GIS system.
• People: People constitute technical personnel (e.g. GIS analyst, manager, programmers) who design, maintain and use GIS.

History of GIS

The term "GIS" was first coined in the early 1960s by Canadian geographer Roger Tomlinson, who is considered the pioneer of the field. Tomlinson was the first to store, collate, and analyze data on land usage in Canada. While GIS as a concept and discipline emerged in the 1960s, the roots of spatial analysis can be traced back to 1832 when Charles Picquet created a map showing the cholera outbreak in Paris. This idea was later used by John Snow in 1854 to depict a similar outbreak in London.

The evolution of GIS can be divided into three phases:

• Early Experimentation (late 1960s to mid-1980s): This phase was characterized by a focus on the conceptual and theoretical development of GIS.
• Take-Off Phase (mid-1980s to early 1990s): During this period, the emphasis shifted to application and technology transfer, with rapid software development.
• Maturation and Professional Establishment (mid-1990s to present): This phase has been marked by technology consolidation and advancement, with a growing focus on professional standards and applications.

Data Models in GIS

In GIS, data models are essential for defining and representing spatial features and their relationships within a database. When transitioning from real-world entities to digital representations, it is crucial to understand the concepts of entities and features.

• Entities refer to the things in the real world that we want to represent in a digital system, such as rivers, buildings, or soil types.
• Features are our representation of these entities in the system, which includes both geometric information (spatial data) and descriptive information (tabular or non-spatial data).

To move from entities to features, a data model is used. A data model provides a consistent way to define and represent spatial features in a database, along with the relationships between them.

There are two main types of data models in GIS:

Vector Data Model

The vector data model represents spatial features using points and their associated coordinates (X and Y) to define vertices. There are three basic types of vector models in GIS:

• Point:. point is a zero-dimensional feature represented by a single coordinate (X and Y). Examples include the locations of wells, buildings, and sample sites.
• Line:. line is a one-dimensional feature made up of explicitly connected points. Lines are used to represent linear features such as roads, railways, streams, and faults.
• Polygon:. polygon is a two-dimensional feature created by multiple lines that loop back to form a closed shape. In a polygon, the first coordinate pair (point) of the first line segment is the same as the last coordinate pair of the last line segment. Polygons are used to represent geographic features with area, such as forests, administrative boundaries (e.g., states, countries), geological formations, and lakes.

Raster Data Model

• The raster data model is made up of rows and columns of equally sized picture elements known as pixels, which are interconnected to form a flat surface.
• Pixels are the building blocks of raster images, and they represent geographical features such as buildings, forests, and parks.
• The size of each pixel determines the smallest geographical feature that can be seen in the raster data. For example, if the pixel size is 1 meter, any feature smaller than 1 square meter cannot be viewed in the raster data.
• It is possible to convert between raster and vector data models and vice versa.

Advantages of Data Models

Data models are crucial in GIS as they provide a structured framework for organizing and managing spatial data. Here are some advantages of using data models in GIS:

• Efficient Data Management: Data models help in organizing spatial data efficiently, making it easier to store, retrieve, and manage large datasets.
• Enhanced Data Analysis: With a clear structure, data models enable advanced analytical capabilities, allowing users to perform complex spatial analyses and generate insights.
• Improved Data Quality: Data models facilitate better data quality management by enforcing standards and consistency in data representation.
• Flexibility and Scalability: Data models can be adapted to various types of spatial data and can scale to accommodate growing datasets, making them suitable for diverse GIS applications.
• Support for Various Applications: Different data models support various GIS applications, from urban planning to environmental monitoring, ensuring that the right model is used for the right purpose.

Vector Data Analysis

Vector data analysis involves various steps to work with geographic information system (GIS) data represented as points, lines, and polygons.

Data Acquisition

• Digitization: Vector data can be digitized from images or other maps, converting visual information into digital geographic features.
• Field Coordinate Measurement: GPS technology is used to collect data in the field, capturing both the geographic coordinates and related attributes of specific locations.
• Importing through Excel: Data available in reports, such as the incidence of malaria at different locations, can be imported into the GIS system by providing location coordinates and associated information.

Data Query

Data querying involves extracting information from the attributes (tabular data) associated with geographic features or directly from the features themselves. There are two main types of queries:

• Attribute-based Query: This type of query extracts information from a table based on specific conditions. For example, determining the area of forest in a given landscape or identifying countries with populations exceeding one billion. Attribute queries in GIS are performed using Structured Query Language (SQL). The result is a set of records that meet the specified criteria, which can be used for further analysis, such as exporting records to a new table, calculating statistics, assigning new values, or generating reports.
• Location-based Query: This query type extracts information based on the geographic location of features. For instance, finding all cities within a certain distance from a river or identifying all parcels of land located within a specific boundary.

Query Based on Location (Spatial Query)

A spatial query is used to understand the spatial relationship between features. There are three fundamental types of spatial relationship:

• Intersection: This refers to the intersecting boundaries of two features. For example, finding out if any road crosses a particular forest patch.
• Containment: This describes whether a feature is contained within another feature. For instance, determining if a road is located inside a geological unit.
• Proximity: This indicates the closeness between features. For example, finding out how many schools are located within 200 meters of a riverbank.

Geoprocessing of Vector Data

Geoprocessing involves the processing of geographical information through spatial analysis by transforming the dataset. The geoprocessing can be done on one dataset or multiple datasets depending on the types of analysis. There are three classes of tools:

• Breaking features into smaller features. This requires the use of more than one dataset. Examples include clip, intersect, and union.
• Aggregating features into larger features. This can be done on a single dataset (dissolve) or by combining datasets (merge).
• Creating new features through buffering. This is performed on a single dataset and involves operations like buffer.

The analysis done by using more than one dataset is called overlay GIS operation. This is one of the most important operations where spatial and attribute information from multiple data are combined into a single dataset.

• Union: Union combines features of two or more themes. The extent needs to be the same for the union operation. The operation can only be done on polygon datasets and not on line and point data. The result contains a new set of polygons obtained by breaking down features. For example, to determine the different soil types in land parcels, we can use union between soil type polygon dataset and a dataset containing the boundary of land parcels.
• Intersection: Intersection results in the area that is common in both datasets. This operation can be carried out by overlaying polygon over polygon/line/point. The output type will vary depending on the type of vector feature used: Polygon intersected with polygon will result in a polygon output. However, polygon intersected with line (point) will result in a line (point) output.
• Clip: Clip geoprocessing operation is carried out to extract those features from lines, polygons, or point layers which fall within the spatial extent of another polygon layer. Example: to identify those flood plains which are inside a district of a state.
• Dissolve: Dissolve operation combines adjacent polygons to form one polygon based on a predetermined attribute. For example, if there is a land parcel layer with boundaries representing different owners. One of the attributes in the layer is soil type. If the interest is to combine land parcels with respect to soil types, then we can use dissolve with the predefined attribute “Soil Type”. This will combine adjacent polygons with the same soil type.
• Merge: Merge operation requires more than one layer that is spatially adjacent to each other. The two layers need to have the same attribute to combine them. For example, if there exist land use layers for two neighboring districts, merge can be used to combine them as one layer.
• Buffering: Buffering operation is carried out on a single dataset that could be point, line, or polygon feature. The operation creates zone (zones) of specified width around the input feature. Example: To identify the plant species that are found within a 5 km distance from the road, one can use buffer operation on the road layer.

All these geoprocessing operations can be combined to achieve a goal. To demonstrate the geoprocessing operations that we have discussed, let us assume a hypothetical situation, wherein an environmental scientist is interested in finding areas that are near deer wintering areas and water bodies but far from traffic for a region.

The data available with the researcher are:

• Polygon layer of deer wintering areas
• Polygon layer for water bodies
• Road layer (line feature)

One can use most of the geoprocessing operations just now learned in this exercise. Below is the flowchart demonstrating the steps:

Raster-Based Analysis

Just like with vector data, there are various geoprocessing operations that can be performed on raster data. These operations include both single layer analysis and multiple layer analysis.

Single Layer Analysis

Single layer analysis involves operations using only one raster layer. One of the most common operations in this context is reclassification or recoding. For instance, consider an elevation raster where each pixel represents an elevation value ranging from 10 to 1000 meters. If an environmental scientist wants to categorize this landscape into low, medium, and high elevation areas, they can reclassify the raster as follows:

• Low Elevation (code 1): 10 to 30 meters
• Medium Elevation (code 2): 30 to 60 meters
• High Elevation (code 3): above 60 meters

By assigning these codes to the pixels based on their values, the scientist can calculate frequency statistics to determine how many cells fall into each category. Since each raster pixel represents a specific area, multiplying the pixel area by the frequency allows for the calculation of the total area under each elevation class.

Multi-layer Operation

Multi-layer operations include raster to raster and raster to vector operations.

• Clipping. Similar to vector clipping, raster clipping can be performed using the spatial extent of a vector layer. For example, if there is a global elevation raster and the goal is to extract data for a smaller region, such as a district, this can be achieved by overlaying the district boundary onto the elevation raster and using the clip operation.
• Multi-raster Mathematical Overlay: This operation requires all the rasters to have the same spatial extent and pixel size. Various arithmetic operations such as addition, subtraction, multiplication, and division can be performed on a set of rasters. For instance, to assess changes in vegetation over a 10-year period using rasters from 2000 and 2010, where pixel values represent vegetation status on a scale from 0 to 1 (0 indicating no vegetation and 1 indicating maximum vegetation), subtracting the two rasters can reveal changes in vegetation status. Positive or negative values in the result indicate increases or decreases in vegetation, respectively.

Applications of GIS

Geographic Information System (GIS) has a wide range of applications in various fields, including climate change, disaster management, natural resource management, land use/land cover analysis, and irrigation mapping. Let's explore some of these applications in detail:

Land Use/Land Cover Application

Land cover refers to the physical characteristics of the landscape, such as forests, water bodies, and urban areas, while land use pertains to how the land is utilized, like agriculture, housing, or industry. For instance, an environmental scientist might use GIS to study how changes in land use activities, such as urban expansion or deforestation, impact the habitat of specific animal species. By analyzing satellite imagery and other geospatial data, researchers can assess the extent of land cover changes and their implications for biodiversity and ecosystem services.

Disaster Management

GIS plays a crucial role in disaster management by helping identify areas that are more susceptible to natural or man-made disasters. For example, GIS can be used to analyze historical data on floods, earthquakes, or industrial accidents to pinpoint high-risk zones. This information is vital for emergency preparedness, response planning, and resource allocation. During a disaster, GIS can provide real-time data to aid in rescue operations and assess damage. After an event, GIS helps in recovery and rebuilding efforts by evaluating the impact and planning for future resilience.

Natural Resource Management

GIS is widely used in natural resource management for mapping and monitoring various resources. For instance:

• Forestry: GIS helps foresters assess forest health, monitor changes in forest cover, and plan sustainable management practices.
• Agriculture: Agricultural scientists use GIS to evaluate crop yields, identify crop types, and monitor pest infestations. This information is crucial for improving agricultural productivity and ensuring food security.
• Water Resources: GIS can be employed to map the geographical distribution of water resources, such as rivers, lakes, and groundwater aquifers, and monitor their availability and quality.

Irrigation Mapping

Irrigation is vital for crop production, and GIS can help manage water resources for irrigation effectively. By analyzing data on soil moisture, crop water requirements, and weather patterns, GIS can assist in planning and optimizing irrigation schedules. This ensures efficient water use and reduces waste, contributing to sustainable agricultural practices.

Let Us Sum Up

In summary, Geographic Information System (GIS) is a powerful tool for capturing, storing, analyzing, and displaying geospatial data. It has evolved significantly since its introduction in the 1960s, with advancements in technology, software, and applications. GIS is used as a research tool to work with geographic information and has various components, including hardware, software, data, methods, and users. Different data models, such as vector and raster data models, are used in GIS, along with analysis techniques for both vector and raster data.