How to estimate regional economic development level is important for solving regional inequality problems. Most of previous studies on regional economic development are based on the statistics collected typically in administrative units. This paper has analyzed the defects of traditional studies, and attempted to research regional economic development problems with 10-year DMSP/OLS nighttime light satellite imagery as a new data source. For exploring the relationship between DMSP/OLS nighttime light data and GDP, different types of curve fitting regression models have been tried, the Cubic model has shown the best performance with a coefficient of determination (R2) equal to 0.803. Based on this positive correlation, we have estimated provincial economic development level of China using DMSP/OLS nighttime light data. The research results have indicated that the DMSP/OLS nighttime light data can well reveal provincial economic development levels.

Abstract:The Chinese real estate industry has experienced rapid growth since China's economic reform. Along with a booming industry, a third of purchased lands were left undeveloped in the last decade. Knowledge of real estate development time lags between land being purchased and property being occupied can enable policymakers to produce more effective policies and regulations to guide the real estate industry and sustain economic development and social welfare. This paper presents an innovative method to estimate provincial land development time lags in China using DMSP/OLS NTL imagery and real estate statistical data. The results showed that real estate development time lag was common in China during 2000-2010. More than half of the study sites showed development time lags of three years or longer. An Increment of Developed Pixels (IDP) index was established to outline yearly land development completions in China between 2000 and 2010. A Comprehensive Real Estate Price Index (CREPI) was created to explore the causes of the time lags. A strong and positive correlation was found between the real estate development time lags and CREPI values (with r = 0.619, n = 31, p < 0.0005). The results indicated that the land development time lag during the study period was positively correlated to the activity of the local real estate market, the price trend of land and housing properties, and the local economic situation. The results also proved that with the support of statistical data the