Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling

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(AUlevel)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.

Text GCN-SW-KNN:a novel collaborative training multi-label classification method for WMS application themes by considering geographic semantics

Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.