# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 3 State of the Art and Related Products
### Peer Review Literature
#### 3.1.2 Urbanization: Effects on Greece and Solutions Provided by Satellite Imagery
VIIRS showcased better results when predicting GDP for MSAS, suggesting a higher correlation with urban sectors than rural ones. This is in accordance with the results of previous studies for DMSP OLS nighttime lights. Additionally, VIIRS lights predict metropolitan statistical areas' GDP with higher accuracy compared with state GDP, suggesting that nighttime lights may be related to a bigger extent to urban sectors than rural ones. It is important to take into consideration possible biases that can impact hypothesis testing when trying to understand socioeconomic phenomena based on nighttime lights. Apart from the possible existence of bias in economic prediction through nighttime lights, there are also potential nonlinearity and measurement errors in the light production function. Ref. [32] studies DMSP nighttime lights for the economic evaluation of small geographies across six counties with high statistical capacity, i.e., their ability to gather, examine, and share high-quality information on their people and economy. Their results indicate the inability of nighttime lights to respond to higher baseline GDP changes, higher population densities, or agricultural GDP. While changes in night luminosity correlate with GDP changes, even in small geographical spaces, the documented nonlinearity implies that some studies may be unable to identify policy-relevant effects or misinterpret this as the treatment effect of their model's variables in areas where lights do not react much to economic activity. Urban areas can reflect the spatial distribution of commercial activities via nighttime lights, but data collection difficulties make traditional methods unable to easily detect them. Ref. [33] proposes a method for urban commercial area detection through the use of NTL satellite imagery. First, they preprocessed the images by setting the brightness value range between 0 and 255, a step necessary for improved cluster analysis efficiency. Then, they performed an exploratory data analysis where spatial patterns and optimal distribution characteristics were identified. Finally, after discerning hotspots through clustering analysis, they constructed standard deviation ellipses to detect the direction/trend of the development of commercial areas. Comparing the results of their study with ground-truth data, nighttime lights can indeed identify urban commercial areas, but the accuracy can be hindered by various factors, including weather conditions and vegetation coverage. Gross Domain Product enables policymakers and organizations to identify the state of the economy, i.e., if it is contracting or expanding. Satellite imagery offers the ability to estimate the Gross Domain Product almost in real time and even in small geographical areas compared with traditional statistical analyses, thus allowing businesses and economists to analyze the impact of changes (e.g., taxes, economic shocks, etc.) with relatively high precision.