An Introduction to Geographical Information Systems: A Primer for the Novice
Mapping has transfigured how we contemplate about location. Maps are important decision-making tools. They help us get to places, and are becoming more immersed in our day-to-day lives. Let me introduce you to a burgeoning technological field- Geographic Information System or Geographic Information Science that incorporates geographical features that eventually got evolved from quantitative cartography and geography in the 19th century to map, analyse, and assess real-world problems.
The history of Geographical Information Systems is remarkable which uses mapping technology as a powerful medium in deriving complex ideas. It’s been influenced by key people and gone through stages of technology development. There is nearly an unlimited number of applications that are relevant to GIS because virtually all human interactions, natural and man-made features, resources, and populations have a geographic component.
Roger Tomlinson termed as the Father of Geographic Information Systems (GIS) has rightly expressed: “The early days of GIS were very lonely. No-one knew what it meant.”
Now, Let’s understand the word ‘Geographic Information Systems’
A Geographic Information System (GIS) is a set of tools for gathering, overseeing, and investigating information. It breaks down the spatial area and sorts out layers of data into representations utilizing maps and 3D scenes. GIS as a whole can be described as a system consisting of hundreds of tools in a single environment.
(Source: Presentation on “Geographical Information System: An Overview” by Dr. Sameer Saran, IIRS)
Characteristics of Geographic Data:
- Spatial data: features orientation shape, size & structure
- Non-Spatial data: Information about various attributes like area, length & population
Characteristics of Spatial Data:
- spatial reference ————— where?
- attributes ————————-what?
- spatial relationships ———— how?
- temporal component ———– when?
Spatial data models
Two fundamental approaches:
Raster model– The entity information is explicitly recorded for a basic data unit (cell, grid or pixel)
Vector model– In a vector-based GIS data are handled as:
– Points (X,Y) or (longitude, latitude) coordinate pair + label
– Lines series of points
– Areas line(s) forming their boundary (series of polygons)
Raster data model versus Vector data model
|Raster model||Vector model|
|· Simple data structure|
· Easy and efficient overlaying
· Compatible with Remote Sensing imagery
· High spatial variability is efficiently represented
· Simple for programming by user Same grid cell definition for various attributes
· Inefficient use of computer storage
· Errors in perimeter and shape Difficult to perform network analysis
· Inefficient projection transformations
· Loss of information when using large pixel sizes
· Less accurate and less appealing map output
|· Complex data structure|
· Difficult to perform overlaying
· Not compatible with RS imagery
· Inefficient representation of high spatial variability
· Compact data structure
· Efficient encoding of topology
· Easy to perform network analysis
· Highly accurate map output
Characteristics of Non-Spatial Data:
Different kinds of data values which we can use to represent different “phenomena”
- Qualitative Data
(i) Nominal/ Categorical Data – describe data of different categories (e.g. soil data)
(ii) Ordinal Data – differentiate data by a ranking relationship (e.g. soil erosion, road network)
- Numeric Data
(iii) Interval Data – data having known interval between values (e.g. temperature)
(iv) Ratio Data – data having absolute values (e.g. population density)
Jack Dangermond, an American billionaire businessman and environmental scientist, co-founded (with his wife Laura) in 1969, the Environmental Systems Research Institute (Esri), in his quote: –
“As we continue advancing and leveraging GIS and as we keep bringing in new generations of technology as well as new generations of people. My sense is we’re going to achieve extraordinary things”.- Jack Dangermond
Advancements in Geographical Information Systems (GIS) was the result of several technologies. Databases, computer mapping, remote sensing, programming, geography, mathematics, computer-aided design, and computer science all played a key role in the development of GIS. Within the last five decades, GIS has evolved from a concept to a science. The phenomenal evolution of GIS from a fundamental tool to a modern, powerful platform for understanding and planning our world is marked by several key milestones. Geographic Information Science and Technology (GIST) also plays a vital role in scientific research, with a broad array of applications for spatial data and visualizations in earth science.
“By 2000, the rate of GIS development had risen above the normal growth trend of institutional management skills. This means that systems are now more capable than people, and the ordinary incremental growth rate in skills within an organization does not keep up with developments in technology. Recently, the relative curve of GIS development has leveled off somewhat, but management still has a lot of institutional learning to do before truly making use of the full capabilities of GIS.”
― Roger Tomlinson, Thinking About GIS: Geographic Information System Planning for Managers, Fifth edition
Feature Image Source: https://forestrypedia.com/download/geographic-information-system-gis-seminar/
ITC (2009). Principles of Geographic Information Systems: An introductory textbook. Otto Huisman, Rolf A. de By (eds.), ITC Educational Textbook Series, Fourth Edition.
Chang, K.T. (2008). Introduction to Geographic Information Systems. The McGraw-Hill Companies, Inc..
Here are my details:
Post Graduate Diploma Student at Centre for Space Science & Technology Education in Asia and the Pacific, Indian Institute of Remote Sensing, Dehradun
I want to specially thank Prudhvi Goud Katta, Application Developer (GIS/DevOps) at Tata Consultancy Services, and Sashi Rekha, Aerospace Engineer at Honeywell for helping and guiding me in creating the content.