Study of Small Area Estimation when Nighttime Lights as an Auxiliary Information is Measured with Error

Kajian Pendugaan Area Kecil dengan Kesalahan Pengukuran pada Peubah Penyerta Nighttime Lights

Authors

  • Ardi Surya Mahasiswa IPB University, Indonesia
  • Indahwati Department of Statistics, IPB University, Indonesia
  • Erfiani Department of Statistics, IPB University, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v8i1p47-57

Keywords:

eblup-fh, measurement error, nighttime lights, saeme

Abstract

The need for accelerated development requires rapid data collection. In today's increasingly advanced technological landscape, the utilization of big data emerges as a highly reliable solution for data collection. One exemplary form of big data is the daily capture of satellite imagery, particularly nighttime lights (NTL). NTL serves as a valuable product derived from satellite imagery and can be employed as an alternative dataset for analysis. This research utilizes Nighttime lights as an auxiliary variable to estimate the average household per capita expenditure in small areas, namely districts, employing the empirical best linear unbiased prediction Fay Herriot (EBLUP FH) method and small area estimation by incorporating measurement error effects on the covariate (SAE-ME). The study demonstrates that Nighttime lights can be employed as an alternative auxiliary variable for estimating the average per capita expenditure in districts, as evidenced by a lower RRMSE compared to direct estimation results. However, the measurement error effects on the NTL covariate should be considered by employing a model that takes into account measurement errors. The SAE-ME method provides estimated average expenditure values at the district level that closely align with BPS publications, with an average RRMSE per district of 7.5 percent.

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References

Beyer, R. C. M., Franco-Bedoya, S., & Galdo, V. (2021). Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity. World Development, 140, 105287.

Budiati I, Riyadi, Santoso DH, Yulianingsih E, Tusianti E. 2021. Indikator Kesejahteraan Rakyat 2021. Jakarta: BPS RI.

Bustamante-Calabria, M., Sánchez de Miguel, A., Martín-Ruiz, S., Ortiz, J.-L., Vílchez, J. M., Pelegrina, A., García, A., Zamorano, J., Bennie, J., & Gaston, K. J. (2021). Effects of the COVID-19 lockdown on urban light emissions: ground and satellite comparison. Remote Sensing, 13(2), 258.

Dai, Z., Hu, Y., & Zhao, G. (2017). The suitability of different nighttime light data for GDP estimation at different spatial scales and regional levels. Sustainability, 9(2), 305.

Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., & Ghosh, T. (2017). VIIRS night-time lights. International Journal of Remote Sensing, 38(21), 5860–5879.

Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C., & Taneja, J. (2021). Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sensing, 13(5), 922.

Feng, Z., Peng, J., & Wu, J. (2020). Using DMSP/OLS nighttime light data and K–means method to identify urban–rural fringe of megacities. Habitat International, 103, 102227.

Ghosh, T., Anderson, S. J., Elvidge, C. D., & Sutton, P. C. (2013). Using nighttime satellite imagery as a proxy measure of human well-being. Sustainability, 5(12), 4988–5019.

Kaban, P. A., Nasution, B. I., Caraka, R. E., & Kurniawan, R. (2024). Implementing night light data as auxiliary variable of small area estimation. Communications in Statistics-Theory and Methods, 53(1), 310–327.

Karimah, I. D., & Yudhistira, M. H. (2020). Does small-scale port investment affect local economic activity? Evidence from small-port development in Indonesia. Economics of Transportation, 23, 100180.

Kurnia, A. (2009). Prediksi Terbaik Empirik untuk Model Transformasi Logaritma di dalam Pendugaan Area Kecil dengan Penerapan pada Data Susenas.

Liu, Q., Sha, D., Liu, W., Houser, P., Zhang, L., Hou, R., Lan, H., Flynn, C., Lu, M., & Hu, T. (2020). Spatiotemporal patterns of COVID-19 impact on human activities and environment in mainland China using nighttime light and air quality data. Remote Sensing, 12(10), 1576.

Rao, J. N. K., & Molina, I. (2015). Small area estimation. John Wiley & Sons.

Tian, J., Zhao, N., Samson, E. L., & Wang, S. (2013). Brightness of nighttime lights as a proxy for freight traffic: A case study of China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1), 206–212.

Yu, B., Shi, K., Hu, Y., Huang, C., Chen, Z., & Wu, J. (2015). Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3), 1217–1229.

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Published

11-06-2024

How to Cite

Surya, A., Indahwati, & Erfiani. (2024). Study of Small Area Estimation when Nighttime Lights as an Auxiliary Information is Measured with Error: Kajian Pendugaan Area Kecil dengan Kesalahan Pengukuran pada Peubah Penyerta Nighttime Lights. Indonesian Journal of Statistics and Its Applications, 8(1), 47–57. https://doi.org/10.29244/ijsa.v8i1p47-57

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