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Indoor evacuation efficiency heavily relies on the connectivity status of navigation networks. During disastrous situations, the spreading of hazards (e.g., fires, plumes) significantly influences indoor navigation networks’ status. Nevertheless, current research concentrates on utilizing classical statistical methods to analyze this status and lacks the flexibility to evaluate the increasingly disastrous scope’s influence. We propose an evaluation method combining 3D spatial geometric distance and topology for emergency evacuations to address this issue. Within this method, we offer a set of indices to describe the nodes’ status and the entire network under emergencies. These indices can help emergency responders quickly identify vulnerable nodes and areas in the network, facilitating the generation of evacuation plans and improving evacuation efficiency. We apply this method to analyze the fire evacuation efficiency and resilience of two experiment buildings’ indoor networks. Experimental results show a strong influence on the network’s spatial connectivity on the evacuation efficiency under disaster situations.
Lei Niu; Zhiyong Wang; Yiquan Song; Yi Li. An Evaluation Model for Analyzing Robustness and Spatial Closeness of 3D Indoor Evacuation Networks. ISPRS International Journal of Geo-Information 2021, 10, 331 .
AMA StyleLei Niu, Zhiyong Wang, Yiquan Song, Yi Li. An Evaluation Model for Analyzing Robustness and Spatial Closeness of 3D Indoor Evacuation Networks. ISPRS International Journal of Geo-Information. 2021; 10 (5):331.
Chicago/Turabian StyleLei Niu; Zhiyong Wang; Yiquan Song; Yi Li. 2021. "An Evaluation Model for Analyzing Robustness and Spatial Closeness of 3D Indoor Evacuation Networks." ISPRS International Journal of Geo-Information 10, no. 5: 331.
In this work, a synthesis of the Bayesian maximum entropy (BME) and the Kalman filter (KF) methods, which enhances their individual strengths and overcomes certain of their weaknesses for spatiotemporal mapping purposes, is proposed in a spatiotemporal disease mapping context. The proposed BME-Kalman synthesis allows BME to use information from both parametric regression modeling and KF estimation leading to enhanced knowledge bases. The BME-Kalman synthetic approach is used to study the space-time incidence mapping of the hand, foot and mouth disease (HFMD) in Shandong province (China) during the period May 1st, 2008 to March 19th, 2009. The results showed that the BME-Kalman approach exhibited very good regressive and predictive accuracies, maintained a very good performance even during low-incidence and extremely low-incidence periods, offered an improved description of hierarchical disease characteristics compared to traditional mapping techniques, and provided a clear explanation of the spatial stratified incidence heterogeneity at unsampled locations. The BME-Kalman approach is versatile and flexible so that it can be modified and adjusted according to the needs of the application.
Bisong Hu; Pan Ning; Yi Li; Chengdong Xu; George Christakos; Jinfeng Wang. Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach. International Journal of Geographical Information Science 2020, 35, 466 -489.
AMA StyleBisong Hu, Pan Ning, Yi Li, Chengdong Xu, George Christakos, Jinfeng Wang. Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach. International Journal of Geographical Information Science. 2020; 35 (3):466-489.
Chicago/Turabian StyleBisong Hu; Pan Ning; Yi Li; Chengdong Xu; George Christakos; Jinfeng Wang. 2020. "Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach." International Journal of Geographical Information Science 35, no. 3: 466-489.
Flood visualization is an effective and intuitive tool for representing flood information from abstract spatiotemporal data. With the growing demand for flood disaster visualizations and mitigation, augmented flood visualizations that support decision makers’ perspectives are needed, which can be enhanced by emerging augmented reality (AR) and 3D printing technologies. This paper proposes an innovative flood AR visualization method based on a 3D-printed terrain model and investigates essential techniques, such as the suitable size calculation of the terrain models, the adaptive processing of flood data, and hybridizing virtual flood and terrain models. A prototype experimental system (PES) based on the proposed method and a comparison experimental system (CES) based on a virtual terrain are developed to conduct comparative experiments, which combine the system performance and questionnaire method to evaluate the efficiency and usability of the proposed method. The statistical results indicate that the method is useful for assisting participants in understanding the flood hazard and providing a more intuitive and realistic visual experience compared with that of the traditional AR flood visualization method. The frame rate is stable at 60 frames per second (FPS), which means that the proposed method is more efficient than the traditional AR flood visualization method.
Guoyong Zhang; Jianhua Gong; Yi Li; Jun Sun; BingLi Xu; Dong Zhang; Jieping Zhou; Ling Guo; Shen Shen; Bingxiao Yin. An efficient flood dynamic visualization approach based on 3D printing and augmented reality. International Journal of Digital Earth 2020, 13, 1302 -1320.
AMA StyleGuoyong Zhang, Jianhua Gong, Yi Li, Jun Sun, BingLi Xu, Dong Zhang, Jieping Zhou, Ling Guo, Shen Shen, Bingxiao Yin. An efficient flood dynamic visualization approach based on 3D printing and augmented reality. International Journal of Digital Earth. 2020; 13 (11):1302-1320.
Chicago/Turabian StyleGuoyong Zhang; Jianhua Gong; Yi Li; Jun Sun; BingLi Xu; Dong Zhang; Jieping Zhou; Ling Guo; Shen Shen; Bingxiao Yin. 2020. "An efficient flood dynamic visualization approach based on 3D printing and augmented reality." International Journal of Digital Earth 13, no. 11: 1302-1320.
Building fire is a complex geographic process related to the indoor spatial environment, a smart spatial data model can accurately describe the spatial-temporal information of a building fire scene, which is important for modeling a fire process. With the development of fire dynamics and computer science, many building fire models have been proposed and widely used. However, the spatial representation of these models is relatively weak. In this study, a fire process modeled via the Fire Dynamics Simulator (FDS) and the requirements of a spatial data model are initially analyzed. Then, a new spatial data model named the Combinatorial Spatial Data Model (CSDM) is combined with Geographic Information System (GIS). The key features of the CSDM, which include spatial, semantic, topological, event and state representations of a building fire scene modeled via the CSDM are subsequently presented. In addition, the Unified Modeling Language (UML) class diagram of the CSDM is also presented, and then experiments with a simplified building are conducted as a CSDM implementation case. A method of transferring data from the CSDM to FDS and a building fire analysis approach using the CSDM are subsequently proposed.
Yiquan Song; Lei Niu; Yi Li; Song; Niu; Li. Combinatorial Spatial Data Model for Building Fire Simulation and Analysis. ISPRS International Journal of Geo-Information 2019, 8, 408 .
AMA StyleYiquan Song, Lei Niu, Yi Li, Song, Niu, Li. Combinatorial Spatial Data Model for Building Fire Simulation and Analysis. ISPRS International Journal of Geo-Information. 2019; 8 (9):408.
Chicago/Turabian StyleYiquan Song; Lei Niu; Yi Li; Song; Niu; Li. 2019. "Combinatorial Spatial Data Model for Building Fire Simulation and Analysis." ISPRS International Journal of Geo-Information 8, no. 9: 408.
For the simplicity of spatial modeling in Cellular Automaton (CA) and the complexity of vector spatial expression in the Multi-Agent System (MAS), the concept of grid object as the spatial model of individual behavior simulation was proposed with spatial information, semantic information, and connection relationship of geographic entity. Then, by incorporating the MAS, the method for individual behavior simulation with the Grid Object and Agent Model (GOAM) was demonstrated. Meanwhile, a prototype system including the three subsystems was developed based on the GOAM, and experiments were conducted for two cases in different spatial environments. The prototype system can be used to obtain grid object data with 3D model data, to compute and simulate the behavior of individuals, and to render individuals. The two cases involve goal-driven behavior in both indoor and outdoor environments as examples to evaluate the validity of the GOAM and to provide a reference for building individual behavior simulation with the GOAM in other scenarios.
Yiquan Song; Lei Niu; Yi Li; Song; Niu; Li. Individual Behavior Simulation Based on Grid Object and Agent Model. ISPRS International Journal of Geo-Information 2019, 8, 388 .
AMA StyleYiquan Song, Lei Niu, Yi Li, Song, Niu, Li. Individual Behavior Simulation Based on Grid Object and Agent Model. ISPRS International Journal of Geo-Information. 2019; 8 (9):388.
Chicago/Turabian StyleYiquan Song; Lei Niu; Yi Li; Song; Niu; Li. 2019. "Individual Behavior Simulation Based on Grid Object and Agent Model." ISPRS International Journal of Geo-Information 8, no. 9: 388.
The Tibetan Plateau is one of the most vulnerable areas to extreme precipitation. In recent decades, water cycles have accelerated, and the temporal and spatial characteristics of extreme precipitation have undergone dramatic changes across the Tibetan Plateau, especially in its various ecosystems. However, there are few studies that considered the variation of extreme precipitation in various ecosystems, and the impact of El Niño-Southern Oscillation (ENSO), and few researchers have made a quantitative analysis between them. In this study, we analyzed the spatial and temporal pattern of 10 extreme precipitation indices across the Tibetan Plateau (including its four main ecosystems: Forest, alpine meadow, alpine steppe, and desert steppe) based on daily precipitation from 76 meteorological stations over the past 30 years. We used the linear least squares method and Pearson correlation coefficient to examine variation magnitudes of 10 extreme precipitation indices and correlation. Temporal pattern indicated that consecutive wet days (CWD) had a slightly decreasing trend (slope = −0.006), consecutive dry days (CDD), simple daily intensity (SDII), and extreme wet day precipitation (R99) displayed significant increasing trends, while the trends of other indices were not significant. For spatial patterns, the increasing trends of nine extreme precipitation indices (excluding CDD) occurred in the southwestern, middle and northern regions of the Tibetan Plateau; decreasing trends were distributed in the southeastern region, while the spatial pattern of CDD showed the opposite distribution. As to the four different ecosystems, the number of moderate precipitation days (R10mm), number of heavy precipitation days (R20mm), wet day precipitation (PRCPTOT), and very wet day precipitation (R95) in forest ecosystems showed decreasing trends, but CDD exhibited a significant increasing trend (slope = 0.625, P < 0.05). In the other three ecosystems, all extreme precipitation indices generally exhibited increasing trends, except for CWD in alpine meadow (slope = −0.001) and desert steppe (slope = −0.005). Furthermore, the crossover wavelet transform indicated that the ENSO had a 4-year resonance cycle with R95, SDII, R20mm, and CWD. These results provided additional evidence that ENSO play an important remote driver for extreme precipitation variation in the Tibetan Plateau.
Junnan Xiong; Zhiwei Yong; Zegen Wang; Weiming Cheng; Yi Li; Hao Zhang; Chongchong Ye; Yanmei Yang. Spatial and Temporal Patterns of the Extreme Precipitation across the Tibetan Plateau (1986–2015). Water 2019, 11, 1453 .
AMA StyleJunnan Xiong, Zhiwei Yong, Zegen Wang, Weiming Cheng, Yi Li, Hao Zhang, Chongchong Ye, Yanmei Yang. Spatial and Temporal Patterns of the Extreme Precipitation across the Tibetan Plateau (1986–2015). Water. 2019; 11 (7):1453.
Chicago/Turabian StyleJunnan Xiong; Zhiwei Yong; Zegen Wang; Weiming Cheng; Yi Li; Hao Zhang; Chongchong Ye; Yanmei Yang. 2019. "Spatial and Temporal Patterns of the Extreme Precipitation across the Tibetan Plateau (1986–2015)." Water 11, no. 7: 1453.
Emergency risk assessment of debris flows in residential areas is of great significance for disaster prevention and reduction, but the assessment has disadvantages, such as a low numerical simulation efficiency and poor capabilities of risk assessment and geographic knowledge sharing. Thus, this paper focuses on the construction of a VGE (virtual geographic environment) system that provides an efficient tool to support the rapid risk analysis of debris flow disasters. The numerical simulation, risk analysis, and 3D (three-dimensional) dynamic visualization of debris flow disasters were tightly integrated into the VGE system. Key technologies, including quantitative risk assessment, multiscale parallel optimization, and visual representation of disaster information, were discussed in detail. The Qipan gully in Wenchuan County, Sichuan Province, China, was selected as the case area, and a prototype system was developed. According to the multiscale parallel optimization experiments, a suitable scale was chosen for the numerical simulation of debris flow disasters. The computational efficiency of one simulation step was 5 ms (milliseconds), and the rendering efficiency was approximately 40 fps (frames per second). Information about the risk area, risk population, and risk roads under different conditions can be quickly obtained. The experimental results show that our approach can support real-time interactive analyses and can be used to share and publish geographic knowledge.
Lingzhi Yin; Jun Zhu; Yi Li; Chao Zeng; Qing Zhu; Hua Qi; Mingwei Liu; Weilian Li; Zhenyu Cao; Weijun Yang; Pengcheng Zhang. A Virtual Geographic Environment for Debris Flow Risk Analysis in Residential Areas. ISPRS International Journal of Geo-Information 2017, 6, 377 .
AMA StyleLingzhi Yin, Jun Zhu, Yi Li, Chao Zeng, Qing Zhu, Hua Qi, Mingwei Liu, Weilian Li, Zhenyu Cao, Weijun Yang, Pengcheng Zhang. A Virtual Geographic Environment for Debris Flow Risk Analysis in Residential Areas. ISPRS International Journal of Geo-Information. 2017; 6 (11):377.
Chicago/Turabian StyleLingzhi Yin; Jun Zhu; Yi Li; Chao Zeng; Qing Zhu; Hua Qi; Mingwei Liu; Weilian Li; Zhenyu Cao; Weijun Yang; Pengcheng Zhang. 2017. "A Virtual Geographic Environment for Debris Flow Risk Analysis in Residential Areas." ISPRS International Journal of Geo-Information 6, no. 11: 377.
Natural deltas can provide human beings with flat and fertile land to be cultivated. It is important to monitor cropland dynamics to provide policy-relevant information for regional sustainable development. This paper utilized Landsat imagery to study the cropland dynamics of the Yellow River Delta during the last three decades. Multi-temporal Landsat data were used to account for the phenological variations of different plants. Several spectral and textural features were adopted to increase the between-class separability. The robust random forest classifier was used to generate the land cover maps of the Yellow River Delta for 1986, 1995, 2005 and 2015. Experimental results indicated that the proposed methodology showed good performance with an average classification accuracy of 89.44%. The spatial-temporal analysis indicated that the cropland area increased from 467.6 km2 in 1986 to 718.5 km2 in 2015 with an average growth rate of 8.65 km2/year. The newly created croplands were mainly due to the reclamation of grassland and bare soil while the losses of croplands were due to abandoned cultivation and urban sprawl. The results demonstrate that a sustainable perspective should be adopted by the decision makers in order to simultaneously maintain food security, industrial development and ecosystem safety.
Quanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015. Sustainability 2015, 7, 14834 -14858.
AMA StyleQuanlong Feng, Jianhua Gong, Jiantao Liu, Yi Li. Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015. Sustainability. 2015; 7 (11):14834-14858.
Chicago/Turabian StyleQuanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. 2015. "Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015." Sustainability 7, no. 11: 14834-14858.
Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery.
Quanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China. Remote Sensing 2015, 7, 12539 -12562.
AMA StyleQuanlong Feng, Jianhua Gong, Jiantao Liu, Yi Li. Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China. Remote Sensing. 2015; 7 (9):12539-12562.
Chicago/Turabian StyleQuanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. 2015. "Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China." Remote Sensing 7, no. 9: 12539-12562.