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Explainable second-dimension spatial association between park sentiment and built-social environments
Authors: Xiaojun Wang, Yilong Wu Tingyu Shi, Rongyu Zhang, Yanxi Chen, Yongze Song
Cities • July 2026.
Understanding human–environment interactions is crucial for sustainable urban development and well-being. Urban parks are key urban environments affecting residential emotions. However, existing studies with fixed neighborhood ranges and mean-based indicators limit the examination of nonlinear effects across spatial scales and value ranges. This study develops an explainable second dimension of spatial association (ESDA) model to identify spatially nonlinear drivers of park sentiment. The sentiment is quantified using a RoBERTa model with volunteered geographic information for parks in Xiamen, China. ESDA constructs second dimension variables by searching ranges (b) and probability parameters (τ), and fits an SDA-based random forest with SHapley Additive exPlanations (SHAP) for interpretation. Leave-one-out cross-validation shows that SDA (R2 = 0.459) improved modeling accuracy compared with the fixed-neighborhood, mean-based baseline (R2 = 0.357). SHAP indicates nonlinear, scale-dependent, and sign-reversing effects on sentiment. For instance, high near-neighborhood road density and baseline population are associated with lower sentiment, whereas street-scale service aggregation is associated with higher sentiment, and the effects of building height and nighttime light vary with scale and background conditions. The findings suggest prioritizing parks in macro-scale areas with high accessibility and activity levels, while avoiding parcels with overly dense nearby roads and high existing light levels or population. In conclusion, the spatial nonlinearity is diagnosed through the ESDA model that constructs critical b–τ combinations and integrates SHAP for layered park siting and surrounding-environment optimization.
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Focal-Feature Regression Kriging
Authors: Peng Luo, Yilong Wu, Yongze Song
Geographical Analysis • 11 March 2026.
Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models-such as Ordinary Kriging (OK)-assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables. A common solution is trend surface modeling (e.g., Regression Kriging, RK), which relies on external explanatory variables to model the trend and then applies geostatistical interpolation to the residuals. However, this approach requires high-quality and readily available explanatory variables, which are often lacking in many spatial interpolation scenarios-such as estimating heavy metal concentrations underground. This study proposes a Focal Feature Regression Kriging (FFRK) method, which automatically extracts geospatial features to construct a regression-based trend surface without requiring external explanatory variables. We conducted experiments on the spatial prediction of three heavy metals in a mining area in Australia. In comparison with 17 classical interpolation methods, the results indicate that FFRK, which relies solely on extracted geospatial features, consistently outperforms both conventional Kriging techniques and machine learning models that depend on explanatory variables. This approach effectively addresses spatial nonstationarity while reducing the cost of acquiring explanatory variables, improving both prediction accuracy and generalization ability.
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Impact of impervious surface spatial morphologies on urban waterlogging: insights from a cascade modeling chain at catchment scale
Authors: Xiaochen Qin, Yilong Wu, Haocheng Huang, Xiaoliu Yang, Lu Gao
Sustainable Cities and Society • 24 October 2025.
The spatial morphology of impervious surfaces plays a critical role in urban flood dynamics, yet its underlying mechanisms remain underexplored. Previous studies often linked impervious patterns to flood occurrence but did not resolve the complex hydrological functions embedded within urban form. To address this gap, we developed a cascade modeling chain integrating hydrodynamic simulation with explainable machine learning and probabilistic networks. Our analysis revealed that road morphologies function as dynamic hydrological regulators with distinct archetypes: risk-amplifying “islets” and “branches” that fragment flow; tradeoff-dependent “cores” and “edges” whose performance hinges on topographic stability; and synergistic “bridges” that leverage elevation gradients. In contrast, building morphologies primarily act as static obstructions, with mitigation potential constrained by the high socio-economic costs of redevelopment. Our probabilistic framework quantifies these mechanisms and establishes an evidence-based intervention hierarchy. This hierarchy prioritizes cost-effective road modifications, aligned with urban rehabilitation and conservation, over disruptive architectural redevelopment. This study reframes urban flood resilience, advocating a paradigm shift from managing imperviousness extent to designing morphological function.
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A Spatial Information Extraction Method Based on Multi-Modal Social Media Data: A Case Study on Urban Inundation
Authors: Yilong Wu, Yingjie Chen, Rongyu Zhang, Zhenfei Cui, Xinyi Liu, Jiayi Zhang, Meizhen Wang, Yong Wu
ISPRS International Journal of Geo-Information • 5 September 2023.
With the proliferation and development of social media platforms, social media data have become an important source for acquiring spatiotemporal information on various urban events. Providing accurate spatiotemporal information for events contributes to enhancing the capabilities of urban management and emergency responses. However, existing research regarding mining spatiotemporal information of events often solely focuses on textual content and neglects data from other modalities such as images and videos. Therefore, this study proposes an innovative spatiotemporal information extraction method, which extracts the spatiotemporal information of events from multimodal data on Weibo at coarse- and fine-grained hierarchical levels and serves as a beneficial supplement to existing urban event monitoring methods. This paper utilizes the “20 July 2021 Zhengzhou Heavy Rainfall” incident as an example to evaluate and analyze the effectiveness of the proposed method. Results indicate that in coarse-grained spatial information extraction using only textual data, our method achieved a spatial precision of 87.54% within a 60 m range and reached 100% spatial precision for ranges beyond 200 m. For fine-grained spatial information extraction, the introduction of other modal data, such as images and videos, resulted in a significant improvement in spatial error. These results demonstrate the ability of the MIST-SMMD (Method of Identifying Spatiotemporal Information of Social Media Multimodal Data) to extract spatiotemporal information from urban events at both coarse and fine levels and confirm the significant advantages of multimodal data in enhancing the precision of spatial information extraction.
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Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China
Authors: Xiaochen Qin, Yilong Wu, Tianshu Lin, Lu Gao
Remote Sensing • 14 June 2023
Flood disasters caused by typhoon rainfall seriously threaten regional social and economic development. Accurately assessing the risk of typhoons and their secondary disasters is a great challenge in disaster prevention and reduction. To address this, the city of Fuzhou, Fujian Province, which was severely affected by Typhoon “Lupit” (2109), was selected as a case study. A typhoon rainfall flood disaster system including four components (the disaster-causing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention and reduction capacity) was constructed. A typhoon-rainfall process comprehensive intensity index (TPCI) based on different time scales within the typhoon process was developed to accurately evaluate the flood risk. The TPCI represented the disaster-causing factors of rainfall intensity, duration, and concentration features. Geographical similarity and random forest (RF) were applied to screen 23 typical indices for an urban flood disaster risk assessment model. The results indicated that the TPCI based on a 6 h precipitation simulation at a 24 h time scale was highly effective in highlighting the role of short-term precipitation in the typhoon process. A total of 66.5% of the floodplain area had a medium-grade or higher TPCI value, while 32.5% of the area had a low-grade TPCI. Only 1% of the flooded areas were not identified, which indicated that the TPCI could accurately capture the risk of typhoon rainfall. The urban flood disaster risk assessment model comprehensively considered socioeconomic and natural environment conditions. High-risk areas were identified as regions with extreme precipitation and dense populations. The dynamic evaluation results accurately described the spatiotemporal differences in the flood disaster risk. A period of extreme precipitation lagged the landfall time of Typhoon “Lupit”, causing the proportion of areas above the medium–high-risk threshold of flood disasters to rapidly increase from 8.29% before the landfall of the typhoon to 23.57% before its demise. The high-risk areas of flood disasters were mainly distributed in the towns of Shangjie, Nanyu, and Gaishan, which was consistent with the observed disasters. These study findings could contribute to the development of effective measures for disaster prevention and reduction, and improve the resilience of urban areas to typhoon disasters.
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Research on Urban Flood Risk Assessment Integrating Multi-Source Heterogeneous Data
Authors: Yilong Wu, Shan Yu, Yingjie Chen, Xinyi Liu, Zhenfei Cui
Geomatics & Spatial Information Technology • 2023
With the economic development of Tong’an District in Xiamen, the impact of urban waterlogging has become increasingly significant and cannot be ignored. Based on the HEV framework and using the AHP method, this study focuses on integrating multi-source heterogeneous big data as assessment factors to conduct a weighted comprehensive evaluation of urban waterlogging risk at a fine spatial scale in Tong’an District. The results indicate that, when the selected factors can accurately represent the varying conditions of the study area, the urban waterlogging risk assessment model that integrates multi-source heterogeneous big data achieves relatively high accuracy. This approach also provides meaningful insights into diversifying the selection of data factors for urban waterlogging risk assessment.