Population spatialization is widely used for spatially downscaling census population data to finer-scale. The core idea of modern population spatialization is to establish the association between ancillary data and population at the administrative-unit-level (AU-level) and transfer it to generate the gridded population. However, the statistical characteristic of attributes at the pixel-level differs from that at the AU-level, thus leading to prediction bias via the cross-scale modeling (i.e. scale mismatch problem). In addition, integrating multi-source data simply as covariates may underutilize spatial semantics, and lead to incorrect population disaggregation; while neglecting the spatial autocorrelation of population generates excessively heterogeneous population distribution that contradicts to real-world situation. To address the scale mismatch in downscaling, this paper proposes a Cross-Scale Feature Construction (CSFC) method. More specifically, by grading pixel-level attributes, we construct the feature vector of pixel grade proportions to narrow the scale differences in feature representation between AU-level and pixel-level. Meanwhile, fine-grained building patch and mobile positioning data are utilized to adjust the population weighting layer generated from POI-density-based regression modeling. Spatial filtering is furtherly adopted to model the spatial autocorrelation effect of population and reduce the heterogeneity in population caused by pixel-level attribute discretization. Through the comparison with traditional feature construction method and the ablation experiments, the results demonstrate significant accuracy improvements in population spatialization and verify the effectiveness of weight correction steps. Furthermore, accuracy comparisons with WorldPop and GPW datasets quantitatively illustrate the advantages of the proposed method in fine-scale population spatialization.

Geospatial tile popularity reflects the general characteristics of user preferences in tile access. However, tile access has both long-term popularity features (characterized as stable) and short-term popularity features (characterized as explosive). Specific features of tile popularity are an important theoretical basis for improving the accuracy of caching and prefetching. This article considers both long-term and short-term popularity features for tile access and presents a Markov prefetching model in a cluster-based caching system based on a Zipf distribution. First, it describes the navigation path and the transition probability path for tile access based on the global features of tile access to find a way to estimate the transition tile access probabilities based on the access pattern, which satisfies Zipf's law. Then, based on temporal and spatial local changes in tile access patterns, the basic Markov model is used to prefetch tiles with the highest probability in the follow-up state for current hot tiles and these tiles are labeled as the set of prefetched objects. Finally, based on the access probability for prefetched tiles, they are evenly distributed in a cluster-based caching system. This method takes into account both global and local space–time changes in tile access patterns. This method not only makes the set of cached objects relatively stable but also adapts to changes in access distribution. Experimental results reveal that this method has a higher prefetch hit rate and a shorter average response time for tile requests and thus can improve the efficiency and stability of cluster-based caching systems.

The population analysis unit (PAU) is the basic unit employed in studies of urban populations. The commonly used PAUs are mostly administrative divisions, regular geographic grids. However, these units are different from urban forms, and cannot be used to consider the characteristics of population distributions and flow changes. In this study, we proposed a method for constructing a fine population analysis zone (FPAZ) based on the population aggregation pattern and urban form elements. First, considering the spatial structure of a city and the fine-grained demands of population analysis, the basic analysis unit was divided according to the functional heterogeneity of the population activity region at the micro-scale by combining urban form elements. Next, a population aggregation preference model was established by considering the spatial distribution characteristics of the local aggregation of the urban population flow and long-term stability characteristics depending on the dynamic changes in entrances and exits. Finally, we divided the FPAZ combined with the microstructural elements. Experimental results showed that compared with other types of PAUs, the FPAZ was more consistent with the urban morphology and was an appropriate and general spatial unit for expressing the accurate characteristics of population distributions and changes at the micro-scale.