research-article

Tracking Socio-Economic Development in Rural India over Two Decades Using Satellite Imagery

Authors:

Anant Gulgulia,

Akshay P Sarashetti,

Aaditeshwar SethAuthors Info & Claims

ACM Journal on Computing and Sustainable Societies, Volume 1, Issue 2

Article No.: 12, Pages 1 - 31

https://doi.org/10.1145/3615361

Published: 06 December 2023 Publication History

Abstract

Longitudinal analysis of socio-economic development at sub-national scales can reveal valuable insights about which areas tend to develop faster than others and why. However, such analysis is difficult to conduct with traditional data sources such as censuses and surveys which are not repeated frequently and may require assumptions for imputation of values at non-surveyed locations. Indicators of socio-economic development based on satellite data have emerged as a proxy to track development at fine spatial and temporal scales. We build a model using daytime and nightlights satellite data to estimate an index of socio-economic development at the village level in India. We evaluate our model for temporal robustness and use it to produce estimates at three time points over a two-decade period. We then use these estimates to understand the effect on village-level development of factors such as the geographic distance of a village to hubs of economic activity and the inequality of development in the district. Our findings provide evidence of the possible impact that policy changes during this period have had on village development.

References

[1]

D. J. Peters. 2012. Income inequality across micro and meso geographic scales in the midwestern United States, 1979–20091. Rural Sociology 77, 2 (2012), 171–202.

[2]

A. Kalaiyarasan and M. Vijayabaskar. 2021. The Dravidian Model: Interpreting the Political Economy of Tamil Nadu. Cambridge University Press.

[3]

A. Kohli. 2012. State and redistributive development in India. In Growth, Inequality and Social Development in India: Is Inclusive Growth Possible?, R. Nagaraj (Ed.). Palgrave Macmillan, London, UK.

[4]

C. Hall. 2022. Nighttime lights. Earthdata. Retrieved September 5, 2023 from https://www.earthdata.nasa.gov/learn/backgrounders/nighttime-lights

[5]

A. Prakash, A. K. Shukla, C. Bhowmick, R. Carl, and M. Beyer. 2019. Night-time luminosity: Does it brighten understanding of economic activity in India? Reserve Bank of India Occasional Papers 40, 1. https://www.researchgate.net/publication/334811462_Nighttime_Luminosity_Does_it_Brighten_Understanding_of_Economic_Activity_in_India

[6]

N. Jean, M. Burke, M. E. Xie, W. M. Davis, D. B. Lobell, and S. Ermon. 2016. Combining satellite imagery and machine learning to predict poverty. Science 353, 6301 (2016), 790–794.

[7]

Y. S. Lee. 2018. International isolation and regional inequality: Evidence from sanctions on North Korea. Journal of Urban Economics 103 (2018), 34–51.

[8]

F. Bickenbach, E. Bode, P. Nunnenkamp, and Mareike Soder. 2016. Night lights and regional GDP. Review of World Economics 152 (2018), 425–447.

[9]

C. Bansal, A. Jain, P. Barwaria, A. Choudhary, A. Singh, A. Gupta, and A. Seth. 2020. Temporal prediction of socio-economic indicators using satellite imagery. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. 73–81.

Digital Library

[10]

W. Hu, J. Patel, Z. Robert, P. Novosad, S. Asher, Z. Tang, M. Burke, D. Lobell, and S. Ermon. 2019. Mapping missing population in rural India: A deep learning approach with satellite imagery. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society.

Digital Library

[11]

K. Ayush, B. Uzkent, M. Burke, D. B. Lobell, and S. Ermon. 2020. Generating interpretable poverty maps using object detection in satellite images. In Proceedings of the 29th International Joint Conference on Artificial Intelligence Special Track on AI for CompSust and Human Well-Being. 4410–4416.

[12]

S. Asher, T. Lunt, R. Matsuura, and L. Novosad. 2021. Development research at high geographic resolution: An analysis of night-lights, firms, and poverty in India using the SHRUG Open Data platform. World Bank Economic Review 35, 4 (2021), 845–871.

[13]

E. Aiken, S. Bellue, D. Karlan, C. Udry, and J. E. Blumenstock. 2022. Machine learning and phone data can improve targeting of humanitarian aid. Nature 603, 7903 (2022), 864–870.

[14]

Landsat NASA. 2022. Landsat 7 | Landsat Science. Landsat Science | A Joint NASA/USGS Earth Observation Program. Retrieved September 5, 2023 from https://landsat.gsfc.nasa.gov/satellites/landsat-7/

[15]

M. Wong. 2023. Visible Infrared Imaging Radiometer Suite (VIIRS). Earthdata. Retrieved September 4, 2023 from https://earthdata.nasa.gov/earth-observation-data/near-real-time/download-nrt-data/viirs-nrt

[16]

D. H. Fabini, D. P. De Leon Barido, A. Omu, and J. Taneja. 2014. Mapping induced residential demand for electricity in Kenya. In Proceedings of the 5th ACM Symposium on Computing for Development (ACM DEV-5’14). ACM, New York, NY, 43–52.

Digital Library

[17]

Deepanshu Mohan, Soumya Marri, Bilquis Calcuttawala, Malhaar Kasodekar, Aniruddh Bhaskaran, and Hemang Sharma. 2023. Modi govt’s fiscal policy on welfare: Trends so far and what to expect. The Wire. Retrieved September 4, 2023 from https://thewire.in/economy/modi-govts-fiscal-policy-on-welfare-trends-so-far-and-what-to-expect

[18]

G. S. Fields. 2001. Distribution and Development: A New Look at the Developing World. MIT Press, Cambridge, MA.

[19]

R. W. Fogel. 1987. Some Notes on the Scientific Methods of Simon Kuznets. NBER Working Paper No. w2461. National Bureau of Economic Research.

[20]

C. E. Utazi, J. Thorley, V. A. Alegana, M. J. Ferrari, S. Takahashi, B. Roche, J. Lessler, and A. J. Tatem. 2018. High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries. Vaccine 36, 12 (2018), 1583–1591.

[21]

A. Elmustafa, E. Rozi, Y. He, G. Mai, S. Ermon, M. Burke, and D. B. Lobell. 2022. Understanding economic development in rural Africa using satellite imagery, building footprints and deep models. In Proceedings of the 30th International Conference on Advances in Geographic Information Systems.

Digital Library

[22]

A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS’12). 1097–1105. http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf

[23]

S. Lathuilière, P. Mesejo, X. Alameda-Pineda, and R. Horaud. 2020. A comprehensive analysis of deep regression. IEEE Transactions on Pattern Analysis and Machine Intelligence 42, 9 (2020), 2065–2081.

Digital Library

[24]

S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He. 2017. Aggregated residual transformations for deep neural networks. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 5987–5995.

[25]

Y. Cui, M. Jia, T. Lin, Y. Song, and S. Belongie. 2019. Class-balanced loss based on effective number of samples. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19). 9260–9269.

[26]

R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. 2017. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV’17). 618–626.

[27]

M. D. Zeiler and R. Fergus. 2014. Visualizing and understanding convolutional networks. In Computer Vision—ECCV 2014. Lecture Notes in Computer Science, Vol. 8689. Springer, 818–833.

[28]

N. Jethani, M. Sudarshan, Y. Aphinyanaphongs, and R. Ranganath. 2021. Have we learned to explain?: How interpretability methods can learn to encode predictions in their interpretations. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 1459–1467.

[29]

N. Otsu. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9, 1 (1979), 62–66.

[30]

S. Kuznets. 1955. Economic growth and income inequality. American Economic Review 45, 1 (1955), 1–28. http://www.jstor.org/stable/1811581

[31]

S. Asher, K. Nagpal, and P. Novosad. 2018. The Cost of Distance: Geography and Governance in Rural India. World Bank Working Paper. World Bank. https://thedocs.worldbank.org/en/doc/499571527990962709-0010022018/original/B1administrativeremotenesspaper20180223.pdf

[32]

Z. Chen, B. Yu, C. Yang, Y. Zhou, S. Yao, X. Qian, C. Wang, B. Wu, and J. Wu. 2021. An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data 13, 3 (2021), 889–906.

[33]

X. Chen and W. D. Nordhaus. 2010. The Value of Luminosity Data as a Proxy for Economic Statistics. Cowles Foundation Discussion Paper No. 1766. Cowles Foundation.

[34]

C. D. Elvidge, K. E. Baugh, S. B. Anderson, P. J. Sutton, and T. K. Ghosh. 2012. The night light development index (NLDI): A spatially explicit measure of human development from satellite data. Social Geography 7, 1 (2012), 23–35.

[35]

Giorgia Giovannetti and Elena Perra. 2019. Syria in the Dark: Estimating the Economic Consequences of the Civil War through Satellite-Derived Night Time Lights. Working Papers—Economics wp2019. Universita degli Studi di Firenze. https://ideas.repec.org/p/frz/wpaper/wp2019_05.rdf.html

[36]

World Bank. 2023. World Bank Open Data. Retrieved September 5, 2023 from https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS

[37]

T. Baskaran, B. Min, and Y. Uppal. 2015. Election cycles and electricity provision: Evidence from a quasi-experiment with Indian special elections. Journal of Public Economics 126 (2015), 64–73.

[38]

A. Sharma. 2023. Electrification still a challenge in rural India. Frontline. Retrieved September 5, 2023 from https://frontline.thehindu.com/the-nation/electrification-still-remains-a-challenge-in-rural-india/article66493576.ece

[39]

A. Perez, C. Yeh, G. Azzari, M. Burke, D. Lobell, and S. Ermon. 2017. Poverty prediction with public Landsat 7 satellite imagery and machine learning. In Proceedings of the NIPS 2017 Workshop on Machine Learning for the Developing World. http://arxiv.org/abs/1711.03654

[40]

S. Pandey, T. Agarwal, and N. C. Krishnan. 2018. Multi-task deep learning for predicting poverty from satellite images. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence Conference, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI’18/IAAI’18/EAAI’18). Article 957, 6 pages.

[41]

R. Rustowicz, R. Y. Cheong, L. Wang, S. Ermon, M. Burke, and D. B. Lobell. 2019. Semantic segmentation of crop type in Africa: A novel dataset and analysis of deep learning methods. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 75–82. https://openaccess.thecvf.com/content_CVPRW_2019/papers/cv4gc/Rustowicz_Semantic_Segmentation_of_Crop_Type_in_Africa_A_Novel_Dataset_CVPRW_2019_paper.pdf

[42]

R. Goldblatt, A. R. Ballesteros, and J. Burney. 2017. High spatial resolution visual band imagery outperforms medium resolution spectral imagery for ecosystem assessment in the Semi-Arid Brazilian Sertão. Remote Sensing 9, 12 (2017), 1336.

[43]

P. Helber, B. Bischke, A. Dengel, and D. Borth. 2019. EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, 7 (2019), 2217–2226.

[44]

W. Wu, Z. Lin, P. Oghazi, and P. C. Patel. 2022. The impact of demonetization on microfinance institutions. Journal of Business Research 153 (2022), 1–18.

[45]

A. Sen, D. Ghatak, K. Kumar, G. Khanuja, D. Bansal, M. Gupta, K. Rekha, S. Bhogale, P. Trivedi, and A. Seth. 2019. Studying the discourse on economic policies in India using mass media, social media, and the parliamentary question hour data. In Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS’19). ACM, New York, NY, 234–247.

Digital Library

[46]

S. K. Basu, S. Ganguly, S. Mukhopadhyay, R. DiBiano, M. Karki, and R. R. Nemani. 2015. DeepSat: A learning framework for satellite imagery. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’15). ACM, New York, NY, Article 37, 10 pages.

Digital Library

[47]

Internal Labour Organization. n.d. Informal Economy in South Asia. Retrieved September 5, 2023 from https://www.ilo.org/newdelhi/areasofwork/informal-economy/lang--en/index.htm

[48]

J. Drèze and A. Sen. 2013. An Uncertain Glory: India and Its Contradictions. Princeton University Press, North Oxford, UK. https://press.princeton.edu/books/hardcover/9780691160795/an-uncertain-glory

[49]

D. Goswami, S. B. Tripathi, S. Jain, S. Pathak, and A. Seth. 2019. Towards building a district development model for India using census data. In Proceedings of the ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS’19).

Digital Library

Cited By

Koirala AR nair STai X(2024)Mapping Opium Poppy Cultivation: Socioeconomic Insights from Satellite ImageryACM Journal on Computing and Sustainable Societies10.1145/36484352:2(1-29)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3648435
Mehta SAshish Solanki MSeth A(2024)A Characterization of Land-use Changes in the Proximity of Mining Sites in IndiaACM Journal on Computing and Sustainable Societies10.1145/36247742:1(1-23)Online publication date: 13-Jan-2024
https://doi.org/10.1145/3624774

Index Terms

Tracking Socio-Economic Development in Rural India over Two Decades Using Satellite Imagery
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

Recommendations

Analysis and evaluation of socio-economic development of coastal municipalities in the West Pomeranian Voivodeship
Abstract
The paper presents the results of the study, the purpose and practical input of which was the analysis and evaluation of municipal governments, including the identification of municipalities best developed in socio-economic terms in conditions of ...
A qualitative inquiry of ict based socio-economic development in developing countries: the case of pakistan
Explaining caste-based digital divide in India
Highlights
- Caste is an important and unique axis of inequality in India. Caste based digital divide can exacerbate the existing socioeconomic divisions between ...
Abstract
With the increasing importance of information and communication technologies in access to basic services like education and health, the question of digital divide based on caste assumes importance in India where large socioeconomic ...

Comments

Information & Contributors

Information

Published In

cover image ACM Journal on Computing and Sustainable Societies

ACM Journal on Computing and Sustainable Societies Volume 1, Issue 2

December 2023

230 pages

EISSN:2834-5533

DOI:10.1145/3613673

Editor:
Lakshminarayanan Subramanian
New York University, United States

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2023

Online AM: 18 August 2023

Accepted: 06 July 2023

Revised: 15 June 2023

Received: 14 February 2023

Published in ACMJCSS Volume 1, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
313
Total Downloads

Downloads (Last 12 months)253
Downloads (Last 6 weeks)17

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Koirala AR nair STai X(2024)Mapping Opium Poppy Cultivation: Socioeconomic Insights from Satellite ImageryACM Journal on Computing and Sustainable Societies10.1145/36484352:2(1-29)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3648435
Mehta SAshish Solanki MSeth A(2024)A Characterization of Land-use Changes in the Proximity of Mining Sites in IndiaACM Journal on Computing and Sustainable Societies10.1145/36247742:1(1-23)Online publication date: 13-Jan-2024
https://doi.org/10.1145/3624774

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents