在台灣的總體經濟指標中，國內生產總額為每季更新，我們無法利用國內生產總額來進行短時間內的經濟分析，且各縣市也缺少了可以反映出經濟情況的指標，使我們鮮少對縣市進行經濟分析，整體來說，台灣缺乏了一種高頻率且高解析度的數據來代表經濟活動。
遙測數據擁有高頻率以及高解析度的特性，在眾多的遙測數據中，夜間燈光可以反映出公共照明以及商業照明，在夜晚的生活中，燈光與人們的各項活動息息相關，因此本研究使用了VIIRS夜間燈光數據在2012年4月到2020年11月的數據，並藉由遙測資料的可切割性取得台灣以及各縣市的夜間燈光資料，對台灣以及各縣市的變數進行迴歸分析，希望能了解夜間燈光與經濟活動是否有相關性，並更進一步的利用夜間燈光去分析各縣市的經濟情況。
本研究共得到四個結論：(一)VIIRS夜間燈光數據與台灣以及各縣市的經濟活動皆有顯著的正相關。(二)VIIRS燈光數據擁有相當彈性的資料型態，提供我們進行各種經濟分析。(三)燈光成長率與經濟成長率沒有顯著關係。(四)隨著經濟的進步，治安案件數有顯著的下降。

The gross domestic product in Taiwan’s macroeconomics indicator update every quarter of the year, we cannot use it to economic analyze in a short time period, counties and cities are lacked of the way in indicator in economic circumstances, so that we rarely conduct economic analysis of counties and cities. As a whole, Taiwan needs a high frequency and resolution to represent economic activities.
The remote sensing has high frequency and resolution feature, in most remote sensing data, nighttime lights can show public lighting and commercial lighting. People’s activity is connected with lights in the night. From April, 2012 to November, 2020, we used VIIRS nighttime lights data to get the divisibility nighttime lights data in Taiwan include every county and city from remote sensing, and use it for regression analysis. Hopefully to understand the correlation of nighttime lights and economic activity, and use it to analyze the economics in every county and city.

This study has four conclusions. First, VIIRS nighttime lights data has positive correlation with Taiwan include counties and cities in economic activities.
Second, VIIRS nighttime lights data has flexibility data type, provide us to processing every economic analyze. Third, there is not correlation between light growth rate and economic growth rate. Finally, the crime case goes down when economic rises.

Chen, X., & Nordhaus, W. (2015). A test of the new VIIRS lights data set: Population and economic output in Africa. Remote Sensing, 7(4), 4937-4947.

Chen, X., & Nordhaus, W. D. (2019). VIIRS nighttime lights in the estimation of cross-sectional and time-series GDP. Remote Sensing, 11(9), 1057.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.

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.

Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276.

Elvidge, C. D., Baugh, K. E., Zhizhin, M., & Hsu, F. C. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights. Proceedings of the Asia-Pacific Advanced Network, 35(0), 62.

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.

Granger, C. W., Newbold, P., & Econom, J. (1974). Spurious regressions in econometrics. Baltagi, Badi H. A Companion of Theoretical Econometrics, 557-61.

Gibson, J., Olivia, S., & Boe‐Gibson, G. (2020). Night lights in economics sources and uses 1. Journal of Economic Surveys, 34(5), 955-980.

Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. American economic review, 102(2), 994-1028.

Lo, C. P. (2002). Urban indicators of China from radiance‐calibrated digital DMSP‐OLS nighttime images. Annals of the Association of American Geographers, 92(2), 225-240.

Li, X., Xu, H., Chen, X., & Li, C. (2013). Potential of NPP-VIIRS nighttime light imagery for modeling the regional economy of China. Remote Sensing, 5(6), 3057-3081.

Lopez-Ruiz, H., Blazquez, J., & Hasanov, F. (2019). Estimating Saudi Arabia’s regional GDP using satellite nighttime light images (No. ks--2019-dp80).

Mellander, C., Lobo, J., Stolarick, K., & Matheson, Z. (2015). Night-time light data: A good proxy measure for economic activity?. PloS one, 10(10), e0139779.

Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.

Shi, K., Yu, B., Huang, Y., Hu, Y., Yin, B., Chen, Z., ... & Wu, J. (2014). Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS data. Remote Sensing, 6(2), 1705-1724.