Estimating the housing vacancy rate (HVR) has always been a hard-to-break point in the study of housing vacancy. This paper used nighttime light and POI (point of interest) data to estimate the HVR in the main urban area of Xi’an city based on extracting built-up areas. The built-up area was extracted using the threshold method, and the spatial resolution of the results was 130 m (same as Luojia-1). Meanwhile, after removing the non-residential areas from the images, the HVRs for the period 2018–2019 from four nighttime light images were calculated, and the HVR of the main urban area of Xi’an city was estimated using the average method and its spatial patterns were analyzed. The results show that: (1) Luojia-1 has great advantages in estimating urban HVRs. The HVRs calculated by Luojia-1 were characterized by a high resolution and a short calculation time. (2) After estimating the results of the four scenes’ remote sensing images, it was found that the results obtained using the average were closest to the actual vacancy situation, and the spatial distribution of the vacancy could be seen using the minimum values. (3) The overall housing occupancy in Xi’an city was good, and the HVRs were low, but the overall vacancy rate for the edge of the built-up area was high. The government should devote more attention to places with high HVRs.

The rapid development of Internet of Things (IoT) technology and the widespread deployment of various sensors around the world have produced a large number of data streams. Thus, current computing systems face the challenge of quickly receiving and managing these large-scale streaming data. This study builds an efficient distributed database based on Greenplum (GP) and focuses on solving the problem of the low efficiency of structured data queries for observed ecological data collected from fragile areas in Northwest China’s desert oasis. First, a distributed database is designed and deployed at the physical storage structure level. A database table structure is then established based on the characteristics of the streaming data. On this basis, the data storage strategy is optimized at the data table level. Additionally, the query efficiency of the distributed database is compared with the query efficiency of traditional standalone databases. The results show that the distributed database significantly improves the data query efficiency. The greater the amount of data stored, the better the improvement in efficiency. Finally, based on the optimized distributed database, we develop a data sharing system for streaming data from ecologically fragile areas in the desert oasis in Northwest China, which provides a new approach for the efficient sharing of massive amounts of IoT streaming data for ecological monitoring. Our storage system is still currently working normally, which is highly important to both data managers and users.