Tracking Development through Satellite Images & Cartography | Economic Survey & Government Reports - UPSC PDF Download

 Page 1


CHAPTER
11
Tracking Development through 
Satellite Images & Cartography
An important theme of this year’ s Economic Survey is the use of new forms of data and information 
for tracking economic activity and development. Chapter 1 looked at the use of high-frequency 
data for the real-time management of an economy through uncertain times. This chapter looks at 
the use of another kind of data – geo-spatial data and cartographic techniques – to track, compare 
and represent longer term developments. Geospatial maps not only lets users visualize data but 
also helps users to better understand trends, relationships and patterns. The use of maps is not 
entirely new and previous Economic Surveys have used them for years, but there is now a plethora 
of information from satellites, drones, mobile phones and other sources. Moreover, there has also 
been a dramatic improvement in cartographic technology that allows for better representation of 
the information. This chapter illustrates some of the interesting ways of depicting geospatial data. 
Using satellite images, India’s night-time luminosity is compared between 2012 and 2021 in 
Figures 1A and 1B. Night-time luminosity provides an interesting representation of the expansion 
of electricity supply, the geographical distribution of population and economic activity, urban 
expansion as well as growth of ribbon developments between urban hubs. Similarly, using 
geospatial and cartographic techniques, the subsequent maps show the extent of physical as well 
as financial infrastructure development in India. This includes expansion of national highways, 
airports, commercial bank branches, metros, etc. The maps in Figure 5A & 5B depict change in 
net sown area of India over the last 15 years. The maps have been created by combining satellite 
data over the course of a 12 month period in each year. 
The images in Figures 6A & 6B compare the Kharif crop cycle in Moga district, Punjab during 
2005 and 2021. The images show that Kharif sowing cycle has shifted ahead by around two-to-three 
weeks causing the Kharif harvest to almost coincide with Rabi sowing in November . The closing of 
the gap is a likely factor that encourages farmers to burn stubble and may be related to restrictions 
on early transplanting of Kharif paddy. These restrictions were introduced in 2009 in order to reduce 
pumping of ground-water but may have had the unintended consequence of damaging air quality.
Satellite imagery is used to show annual water storage cycle at Stanley Reservoir, Tamil Nadu 
in Figure 7A & 7B. Using new geo-spatial methods, population density of select Indian cities 
is compared over time, showing the extent of urban expansion in Delhi-NCR and Bangalore 
between 2001 and 2021. Finally, using satellite imagery, Figures 18A, 18B, 19A & 19B illustrate 
wasteland redeployment in Andhra Pradesh and Gujarat. 
While this chapter has restricted itself to static two-dimensional images due to practical considerations 
of publication, readers will be aware that dynamic and multi-dimensional cartography is now 
commonplace for every-day activities like ordering a taxi or looking for an address.
Page 2


CHAPTER
11
Tracking Development through 
Satellite Images & Cartography
An important theme of this year’ s Economic Survey is the use of new forms of data and information 
for tracking economic activity and development. Chapter 1 looked at the use of high-frequency 
data for the real-time management of an economy through uncertain times. This chapter looks at 
the use of another kind of data – geo-spatial data and cartographic techniques – to track, compare 
and represent longer term developments. Geospatial maps not only lets users visualize data but 
also helps users to better understand trends, relationships and patterns. The use of maps is not 
entirely new and previous Economic Surveys have used them for years, but there is now a plethora 
of information from satellites, drones, mobile phones and other sources. Moreover, there has also 
been a dramatic improvement in cartographic technology that allows for better representation of 
the information. This chapter illustrates some of the interesting ways of depicting geospatial data. 
Using satellite images, India’s night-time luminosity is compared between 2012 and 2021 in 
Figures 1A and 1B. Night-time luminosity provides an interesting representation of the expansion 
of electricity supply, the geographical distribution of population and economic activity, urban 
expansion as well as growth of ribbon developments between urban hubs. Similarly, using 
geospatial and cartographic techniques, the subsequent maps show the extent of physical as well 
as financial infrastructure development in India. This includes expansion of national highways, 
airports, commercial bank branches, metros, etc. The maps in Figure 5A & 5B depict change in 
net sown area of India over the last 15 years. The maps have been created by combining satellite 
data over the course of a 12 month period in each year. 
The images in Figures 6A & 6B compare the Kharif crop cycle in Moga district, Punjab during 
2005 and 2021. The images show that Kharif sowing cycle has shifted ahead by around two-to-three 
weeks causing the Kharif harvest to almost coincide with Rabi sowing in November . The closing of 
the gap is a likely factor that encourages farmers to burn stubble and may be related to restrictions 
on early transplanting of Kharif paddy. These restrictions were introduced in 2009 in order to reduce 
pumping of ground-water but may have had the unintended consequence of damaging air quality.
Satellite imagery is used to show annual water storage cycle at Stanley Reservoir, Tamil Nadu 
in Figure 7A & 7B. Using new geo-spatial methods, population density of select Indian cities 
is compared over time, showing the extent of urban expansion in Delhi-NCR and Bangalore 
between 2001 and 2021. Finally, using satellite imagery, Figures 18A, 18B, 19A & 19B illustrate 
wasteland redeployment in Andhra Pradesh and Gujarat. 
While this chapter has restricted itself to static two-dimensional images due to practical considerations 
of publication, readers will be aware that dynamic and multi-dimensional cartography is now 
commonplace for every-day activities like ordering a taxi or looking for an address.
390 Economic Survey 2021-22
Figure 1A: India Night-time Luminosity, 2012
Source: NASA, MapmyIndia
Page 3


CHAPTER
11
Tracking Development through 
Satellite Images & Cartography
An important theme of this year’ s Economic Survey is the use of new forms of data and information 
for tracking economic activity and development. Chapter 1 looked at the use of high-frequency 
data for the real-time management of an economy through uncertain times. This chapter looks at 
the use of another kind of data – geo-spatial data and cartographic techniques – to track, compare 
and represent longer term developments. Geospatial maps not only lets users visualize data but 
also helps users to better understand trends, relationships and patterns. The use of maps is not 
entirely new and previous Economic Surveys have used them for years, but there is now a plethora 
of information from satellites, drones, mobile phones and other sources. Moreover, there has also 
been a dramatic improvement in cartographic technology that allows for better representation of 
the information. This chapter illustrates some of the interesting ways of depicting geospatial data. 
Using satellite images, India’s night-time luminosity is compared between 2012 and 2021 in 
Figures 1A and 1B. Night-time luminosity provides an interesting representation of the expansion 
of electricity supply, the geographical distribution of population and economic activity, urban 
expansion as well as growth of ribbon developments between urban hubs. Similarly, using 
geospatial and cartographic techniques, the subsequent maps show the extent of physical as well 
as financial infrastructure development in India. This includes expansion of national highways, 
airports, commercial bank branches, metros, etc. The maps in Figure 5A & 5B depict change in 
net sown area of India over the last 15 years. The maps have been created by combining satellite 
data over the course of a 12 month period in each year. 
The images in Figures 6A & 6B compare the Kharif crop cycle in Moga district, Punjab during 
2005 and 2021. The images show that Kharif sowing cycle has shifted ahead by around two-to-three 
weeks causing the Kharif harvest to almost coincide with Rabi sowing in November . The closing of 
the gap is a likely factor that encourages farmers to burn stubble and may be related to restrictions 
on early transplanting of Kharif paddy. These restrictions were introduced in 2009 in order to reduce 
pumping of ground-water but may have had the unintended consequence of damaging air quality.
Satellite imagery is used to show annual water storage cycle at Stanley Reservoir, Tamil Nadu 
in Figure 7A & 7B. Using new geo-spatial methods, population density of select Indian cities 
is compared over time, showing the extent of urban expansion in Delhi-NCR and Bangalore 
between 2001 and 2021. Finally, using satellite imagery, Figures 18A, 18B, 19A & 19B illustrate 
wasteland redeployment in Andhra Pradesh and Gujarat. 
While this chapter has restricted itself to static two-dimensional images due to practical considerations 
of publication, readers will be aware that dynamic and multi-dimensional cartography is now 
commonplace for every-day activities like ordering a taxi or looking for an address.
390 Economic Survey 2021-22
Figure 1A: India Night-time Luminosity, 2012
Source: NASA, MapmyIndia
391 Tracking Development through Satellite Images & Cartography
Figure 1B: India Night-time Luminosity 2021
Source: NASA, MapmyIndia
Page 4


CHAPTER
11
Tracking Development through 
Satellite Images & Cartography
An important theme of this year’ s Economic Survey is the use of new forms of data and information 
for tracking economic activity and development. Chapter 1 looked at the use of high-frequency 
data for the real-time management of an economy through uncertain times. This chapter looks at 
the use of another kind of data – geo-spatial data and cartographic techniques – to track, compare 
and represent longer term developments. Geospatial maps not only lets users visualize data but 
also helps users to better understand trends, relationships and patterns. The use of maps is not 
entirely new and previous Economic Surveys have used them for years, but there is now a plethora 
of information from satellites, drones, mobile phones and other sources. Moreover, there has also 
been a dramatic improvement in cartographic technology that allows for better representation of 
the information. This chapter illustrates some of the interesting ways of depicting geospatial data. 
Using satellite images, India’s night-time luminosity is compared between 2012 and 2021 in 
Figures 1A and 1B. Night-time luminosity provides an interesting representation of the expansion 
of electricity supply, the geographical distribution of population and economic activity, urban 
expansion as well as growth of ribbon developments between urban hubs. Similarly, using 
geospatial and cartographic techniques, the subsequent maps show the extent of physical as well 
as financial infrastructure development in India. This includes expansion of national highways, 
airports, commercial bank branches, metros, etc. The maps in Figure 5A & 5B depict change in 
net sown area of India over the last 15 years. The maps have been created by combining satellite 
data over the course of a 12 month period in each year. 
The images in Figures 6A & 6B compare the Kharif crop cycle in Moga district, Punjab during 
2005 and 2021. The images show that Kharif sowing cycle has shifted ahead by around two-to-three 
weeks causing the Kharif harvest to almost coincide with Rabi sowing in November . The closing of 
the gap is a likely factor that encourages farmers to burn stubble and may be related to restrictions 
on early transplanting of Kharif paddy. These restrictions were introduced in 2009 in order to reduce 
pumping of ground-water but may have had the unintended consequence of damaging air quality.
Satellite imagery is used to show annual water storage cycle at Stanley Reservoir, Tamil Nadu 
in Figure 7A & 7B. Using new geo-spatial methods, population density of select Indian cities 
is compared over time, showing the extent of urban expansion in Delhi-NCR and Bangalore 
between 2001 and 2021. Finally, using satellite imagery, Figures 18A, 18B, 19A & 19B illustrate 
wasteland redeployment in Andhra Pradesh and Gujarat. 
While this chapter has restricted itself to static two-dimensional images due to practical considerations 
of publication, readers will be aware that dynamic and multi-dimensional cartography is now 
commonplace for every-day activities like ordering a taxi or looking for an address.
390 Economic Survey 2021-22
Figure 1A: India Night-time Luminosity, 2012
Source: NASA, MapmyIndia
391 Tracking Development through Satellite Images & Cartography
Figure 1B: India Night-time Luminosity 2021
Source: NASA, MapmyIndia
392 Economic Survey 2021-22
Figure 2A: India’s National Highway Network (As of August 2011)
Source: Ministry of Road Transport and Highways, MapmyIndia
Page 5


CHAPTER
11
Tracking Development through 
Satellite Images & Cartography
An important theme of this year’ s Economic Survey is the use of new forms of data and information 
for tracking economic activity and development. Chapter 1 looked at the use of high-frequency 
data for the real-time management of an economy through uncertain times. This chapter looks at 
the use of another kind of data – geo-spatial data and cartographic techniques – to track, compare 
and represent longer term developments. Geospatial maps not only lets users visualize data but 
also helps users to better understand trends, relationships and patterns. The use of maps is not 
entirely new and previous Economic Surveys have used them for years, but there is now a plethora 
of information from satellites, drones, mobile phones and other sources. Moreover, there has also 
been a dramatic improvement in cartographic technology that allows for better representation of 
the information. This chapter illustrates some of the interesting ways of depicting geospatial data. 
Using satellite images, India’s night-time luminosity is compared between 2012 and 2021 in 
Figures 1A and 1B. Night-time luminosity provides an interesting representation of the expansion 
of electricity supply, the geographical distribution of population and economic activity, urban 
expansion as well as growth of ribbon developments between urban hubs. Similarly, using 
geospatial and cartographic techniques, the subsequent maps show the extent of physical as well 
as financial infrastructure development in India. This includes expansion of national highways, 
airports, commercial bank branches, metros, etc. The maps in Figure 5A & 5B depict change in 
net sown area of India over the last 15 years. The maps have been created by combining satellite 
data over the course of a 12 month period in each year. 
The images in Figures 6A & 6B compare the Kharif crop cycle in Moga district, Punjab during 
2005 and 2021. The images show that Kharif sowing cycle has shifted ahead by around two-to-three 
weeks causing the Kharif harvest to almost coincide with Rabi sowing in November . The closing of 
the gap is a likely factor that encourages farmers to burn stubble and may be related to restrictions 
on early transplanting of Kharif paddy. These restrictions were introduced in 2009 in order to reduce 
pumping of ground-water but may have had the unintended consequence of damaging air quality.
Satellite imagery is used to show annual water storage cycle at Stanley Reservoir, Tamil Nadu 
in Figure 7A & 7B. Using new geo-spatial methods, population density of select Indian cities 
is compared over time, showing the extent of urban expansion in Delhi-NCR and Bangalore 
between 2001 and 2021. Finally, using satellite imagery, Figures 18A, 18B, 19A & 19B illustrate 
wasteland redeployment in Andhra Pradesh and Gujarat. 
While this chapter has restricted itself to static two-dimensional images due to practical considerations 
of publication, readers will be aware that dynamic and multi-dimensional cartography is now 
commonplace for every-day activities like ordering a taxi or looking for an address.
390 Economic Survey 2021-22
Figure 1A: India Night-time Luminosity, 2012
Source: NASA, MapmyIndia
391 Tracking Development through Satellite Images & Cartography
Figure 1B: India Night-time Luminosity 2021
Source: NASA, MapmyIndia
392 Economic Survey 2021-22
Figure 2A: India’s National Highway Network (As of August 2011)
Source: Ministry of Road Transport and Highways, MapmyIndia
393 Tracking Development through Satellite Images & Cartography
Figure 2B: India’s National Highway Network (As of August 2021)
Source: Ministry of Road Transport and Highways, MapmyIndia