The rapid urbanization in China has corresponded with rapid economic development and beneficial transformations of society that have also exacerbated many sensitive urban problems. Several “ghost cities” have emerged due to the mismatch between the speed of expansion and the rationality of urban planning, especially when the extensive city construction goes far beyond the actual peoples’ demands. This paper presents a novel method for investigating “ghost city” phenomenon based on multisource remotely sensed data. Three components are included. First, coarse-to-fine built-up area extraction and multiscale integration based on nighttime light Defense Meteorological Satellite Program operational linescan system imagery, Chinese land cover/use datasets and maximum green vegetation fraction product. Second, improved “ghost city” index (IGCI) construction and discrimination of “ghost city” phenomenon comprising the rate of land urbanization, the rate of population urbanization, and the intensity of nighttime light. Third, comparison and validation of the IGCI with the traditional “ghost city” index with respect to spatio-temporal distribution and the landscape pattern of “ghost city.” The proposed method was validated using entire districts in Ordos, China. Experimental results demonstrated that the IGCI takes advantages of the spatio-temporal variations of cities and the characteristics of “ghost city” phenomenon. This is also validated by means of the per capita housing construction area and the landscape expansion index, and showed that the urban construction land grows much faster than the population of Ordos. With respect to the accuracy and distribution of spatial patterns, the obtained experimental results exhibit good consistency with results from previous statistical studies.

Secure and efficient spatial range query over encrypted outsourced vector data remains a challenging issue, particularly for polyline and polygon data. Existing schemes primarily focus on point data and have not been properly extended to more complex spatial objects. Therefore, we propose a secure and efficient spatial range query scheme that supports multi-type encrypted vector data. The data owner first outsources encrypted vector data and the encrypted R-tree index to a cloud server, and then the cloud server retrieves matching data identifiers using an encrypted query rectangle and encrypted index. After that, the cloud server accesses the spatial database to obtain the corresponding encrypted vector data. Finally, the query user decrypts the encrypted vector data and filters with a query rectangle to get actual query results. Experiments show that our scheme achieves query efficiency comparable to that of query based on a plaintext index, while providing enhanced query security at the cost of only 3.97% performance overhead. We also compared our point data query with existing scheme. And the results indicate that our scheme effectively reduces false positives and improves query efficiency by enabling intersection tests between the query rectangle and the minimum bounding rectangles of spatial objects in the encrypted domain. Overall, our scheme can support spatial range query over multi-type encrypted vector data, achieving a balance between security and efficiency.

Under the condition of limited resources, when the GIS service platform is faced with high concurrent service requests, the high concurrent processing capacity of a single server is the main bottleneck affecting the quality of service (QoS) of the GIS service platform. Traditional traffic shaping methods, such as token bucket algorithms, alleviate the pressure in high-concurrency situations to a certain extent, but due to their fixed-rate settings, it is difficult to cope with complex and variable task requests, leading to unstable system performance. This paper proposes an adaptive task traffic shaping method to improve the utilization efficiency of server resources, reduce task processing delay, and improve service stability and response speed by monitoring the system load in real time and dynamically adjusting the processing rate of GIS task traffic. The relationship between the task arrival rate and server load is established based on the queueing theory model, and the token fill rate of the token bucket is adjusted adaptively according to resource load evaluation, so as to optimize the task processing flow. The experimental results show that under different GIS task scenarios, the processing performance of this paper’s method is better than or close to that of the traditional method under the optimal fill rate condition, which can significantly reduce the task latency and improve the concurrent processing capability of the GIS service platform.