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Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution – commonly known as filters or kernels – in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI. [ABSTRACT FROM AUTHOR]

The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

Microvibration induced by natural disturbance and human activities has an adverse effect on the operation of the large-scale and ultraprecise facilities in the world. Under such circumstances, a passive vibration control method is generally deployed for such vibration-sensitive facilities, taking the High Energy Photo Source (HEPS) in Beijing as an example, a 3 m-thick mass concrete layer forming a ring foundation was cast at the facility, where a 1 m-thick reinforced concrete slab (RC slab) lies. Since microvibration control plays a crucial role in the operation of such large-scale scientific and ultraprecise facilities and few studies have been reported for large-scale concrete layer as antimicrovibration devices, this paper presents four field tests in Beijing, China, to evaluate the vibration control capacity of a mass concrete layer. Based on a large number of field tests, the effect of applying the concrete layer is discussed, and a reference is provided for the construction of similar facilities. The vibration signals, generated by shock excitation and ambient excitation, are measured through a highly sensitive and high-accuracy vibration acquisition system. It is concluded that the existence of the 1 m-thick RC slab has little influence on the microvibration signal frequency distribution in the vertical direction and that the signals from the concrete layer and subsoil differ by approximately 10 Hz in the vertical direction while differing by approximately 5 Hz in the horizontal direction. The microvibration control ability of the concrete layer is favorable in a higher frequency band over 20 ~ 30 Hz and more than 50% attenuation can be gained through the concrete layer; however, the microvibration control ability is not significant below 20 ~ 30 Hz. The vibration levels across different heights of the concrete layer section are the same. To prevent adverse vibration disturbance below 20 ~ 30 Hz, it is suggested that the traffic and road surface conditions should be taken into consideration when choosing the construction location. In addition, a long-term monitoring shows that 75% vibration energy at the site is firmly related to the construction activities which are approximately 1.4 km from the site. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

Environment model is an important component of an artificial society. To level up the resolution of residence-building environment models in artificial Beijing, we divide residential buildings in Beijing into two categories, which are noncommunity residential buildings and community residential buildings, and statistically analyze the numbers of floors and areas of the real residential buildings. In this paper, we have exhibited the distribution of numbers of floors in noncommunity residential buildings and community residential buildings. Furthermore, we analyze the distribution of areas of noncommunity residential buildings and residence-use proportions of community zones by Nonlinear Regression. Results indicate that areas of noncommunity residential buildings in Beijing obey the log-normal distribution and residence-use proportions of community zones obey normal distribution. According to the statistical models, the virtual residential buildings in artificial Beijing can be endowed with rational attribute values. [ABSTRACT FROM AUTHOR]

Based on the stochastic concept for freeway capacity, the procedure of capacity estimation is developed. Due to the fact that it is impossible to observe the value of the capacity and to obtain the probability distribution of the capacity, the product-limit method is used in this paper to estimate the capacity. In order to implement estimation of capacity using this technology, the lifetime table based on statistical methods for lifetime data analysis is introduced and the corresponding procedure is developed. Simulated data based on freeway sections in Beijing, China, were analyzed and the results indicate that the methodology and procedure are applicable and validated. [ABSTRACT FROM AUTHOR]

This paper studies the location–allocation problem of distribution centers (DCs) for fresh products, in which it requires to determine the number and locations of DCs and design the allocation scheme for fresh products. The main challenge in optimizing this problem is to handle the uncertain demands with only partially known probability distribution information. To address this challenge, this paper first characterizes the ambiguous probability distributions of fresh products demands by an ambiguity set. Then, from different perspectives, this paper proposes two distributionally robust (DR) location–allocation models to find the optimal decisions that perform best in view of the worst-case distribution within the ambiguity set. The first model is with ambiguous expectation, which is transformed into a mixed-integer linear programming (MILP) model via employing the linear decision rule technique. The second model is with ambiguous chance constraints. This model is a semi-infinite chance constraint programming problem, which is transformed into a mixed-integer second-order cone programming (MISOCP) model by deriving safe approximations of the ambiguous chance constraints. To verify the feasibility and effectiveness of the proposed models, a case study about Hema fresh chain stores in Beijing is addressed. The experimental results demonstrate that the optimal decision provided by the proposed optimization methods can immunize against ambiguous probability distributions of demands with a small cost price. • Two DR location–allocation models of DCs for fresh products are raised. • The distribution information of uncertain demands is only partially known. • The DR model with ambiguous expectation is transformed into an MILP model. • The DR model with ambiguous chance constraints is transformed into an MISOCP model. • A case study shows the feasibility and effectiveness of the proposed DR models. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei region, with the capital Beijing as the core, carries a profound geo-culture and gathers numerous intangible cultural heritages. Studying the spatial distribution characteristics and influencing factors of intangible cultural heritage can help to propose positive suggestions for protection and development from the perspective of sustainable development. Taking 329 national intangible cultural heritages in Beijing-Tianjin—Hebei as the research objects, and using ArcGIS spatial analysis technology, combined with methods such as kernel density analysis, standard deviation ellipse and geographical detector, to visualize the spatial distribution of intangible cultural heritages and explore the influencing factors of their distribution. The results show that: (1) the distribution of intangible cultural heritage in Beijing-Tianjin-Hebei is not balanced, mainly concentrated in the central and southern regions, and three high-density core circles are formed in Beijing, Tianjin, and Handan-Xingtai in Hebei Province, showing large differences in spatial distribution density; (2) Traditional techniques, traditional arts, dramatic balladry, sports, music, and medicine show the characteristics of aggregated distribution. folkways, dance, and drama show the characteristics of random distribution. Folk literature shows the characteristics of uniform distribution; (3) The Cultural Development Period is an important evolutionary turning point in the spatial and temporal distribution of intangible cultural heritage in Beijing-Tianjin-Hebei, as well as an accelerating period in the development of intangible cultural heritage, and completes the transformation from decentralized distribution to localized aggregation distribution. In The Period of Regional Cultural Integration, the peak value is reached; (4) Economic development and social culture have the greatest influence on the spatial distribution of intangible cultural heritage in Beijing-Tianjin-Hebei, while the influence of physical environment is relatively small. [ABSTRACT FROM AUTHOR]

We explore the relationship between the melanophilin (MLPH) gene and quail plumage color and provide a reference for subsequent quail plumage color breeding. In this experiment, real-time quantitative PCR (RT-qPCR) technology was used to analyze the relative mRNA expression levels of Korean quail (maroon) and Beijing white quail embryos at different developmental stages. Two single-nucleotide polymorphisms (SNPs) in the MLPH gene were screened based on the RNA-sequencing (RNA-Seq) data of skin tissues of Korean quail and Beijing white quail during the embryonic stage. Kompetitive allele-specific PCR (KASP) technology was used for genotyping in the resource population, and correlation analysis was carried out with the plumage color traits of quail. Finally, bioinformatics was used to predict the effects of these two SNPs on the structure and function of the encoded protein. The results showed that the expression level of the MLPH gene during embryonic development of Beijing white quail was significantly higher than that of Korean quail (P<0.01). The frequency distribution of the three genotypes (CC, CA and AA) of the Beijing white quail at the c.1807C > A mutation site was significantly different from that of the Korean quail (P<0.01). The frequency distribution of the three genotypes (GG, GA and AA) of the Beijing white quail at the c.2129G > A mutation site was significantly different from that of the Korean quail (P<0.01). And there was a significant correlation between the c.1807C > A mutation site and the white plumage phenotype. Bioinformatics showed that SNP1 (c.1807C > A) was a neutral mutation and that SNP2 (c.2129G > A) was a deleterious mutation. The prediction of protein conservation showed that the mutation sites of coding proteins R603S and G710D caused by SNP1 (c.1807C > A) and SNP2 (c.2129G > A) were highly conserved. [ABSTRACT FROM AUTHOR]

The decomposition of a point process is useful for the analysis of spatial patterns and in the discovery of potential mechanisms of geographic phenomena. However, when a local repulsive cluster is present in a complex heterogeneous point process, the traditional solution, which is based on clustering, may be invalid for decomposition because a repulsive pattern is not subject to a specific probability distribution function and the effects of aggregative and repulsive components may be counterbalanced. To solve this problem, this paper proposes a method of decomposing repulsive clusters in complex point processes with multiple heterogeneous components. A repulsive cluster is defined as a set of repulsive density-connected points that are separated by a certain distance at a small scale and aggregated at a large scale simultaneously. The H-function is used to identify repulsive clusters by determining the repulsive distance and extracting repulsive points for further clustering. Through simulation experiments based on three datasets, the proposed method has been shown to effectively perform repulsive cluster decomposition in heterogeneous point processes. A case study of the point of interest (POI) dataset in Beijing also indicates that the method can identify meaningful repulsive clusters from types of POIs that represent different service characteristics of shops in different local regions. [ABSTRACT FROM AUTHOR]

Abstract: GPR (Ground penetrating radar) is one kind of the detection technologies with rapid, non-destruction, uninterrupted detection, and high-resolution. GPR can be applied to detect underground holes and loose soil layer and the other baneful anomalies, and therefore is widely applied to hole survey of the subway project in recent years. Based on briefly describing engineering geological background between F station and L station of a subway project in Beijing, this paper gives the arrangement method of GPR survey line in the construction area of shield method, analyzes detection results, verifies an anomaly by spading, determines the distribution range and its characteristic of the hole anomalies. The results showed that three obvious anomalies were detected in the area between F station and L station, and seriously loose soil layer was the cause of No. 1 and No. 3 anomalies, and hole was the cause of No. 2 anomaly. Thereby the anomalies of loose soil layer and hole could be detected accurately by the GPR technology, and then basic data of underground holes could be obtained. On the base of analyzing these data, effective measures could be adopted to avoid the hidden danger, then the safety of subway construction and surrounding environment of the subway project could be ensured. [Copyright &y& Elsevier]

In this paper, we applied a fractal approach to analyze the traffic data collected from the Beijing Yuquanying. The power spectrum, the empirical probability distribution function, the statistical moment scaling function and the autocorrelation function are used as indicators to investigate the presence of the fractal. The results from the fractal identification methods indicate that these data exhibit fractal behavior. A fractal framework seemed well suited for description of the data observed here, but its suitability for general traffic systems was not clear. [Copyright &y& Elsevier]

Typical urban and rural temperature records are essential for the estimation and comparison of urban heat island effects in different regions, and one of the key issues is how to identify the typical urban and rural stations. This study tried to analyze the similarity of air temperature sequences by using dynamic time warping algorithm (DTW) to improve the selection of typical stations. We examined the similarity of temperature sequences of 20 stations in Beijing, and the results indicated that DTW algorithm could identify the difference of temperature sequence, and clearly divide them into different groups according to their probability distributions. DTW algorithm could create more rigorous and reasonable classification for typical urban and rural stations in Beijing by comparing with remote sensing and cluster analysis method. We also found that some traditional rural stations did not represent temperature variation in rural areas because their surrounding environments have been highly modified by urbanization process in last decades, and this might lead to an underestimate of the urban climate effect by 1.24℃. The study suggests that DTW algorithm is a simple and efficient method for selecting appropriate temperature records and analyzing temperature sequences with a good potential in improving urban heat island estimation. [ABSTRACT FROM AUTHOR]

The seasonal variations of raindrop size distribution (DSD) and rainfall are investigated using three-year (2016–2018) observations from a two-dimensional video disdrometer (2DVD) located at a suburban station (40.13° N, 116.62° E, ~30 m AMSL) in Beijing, China. The annual distribution of rainfall presents a unimodal distribution with a peak in summer with total rainfall of 966.6 mm, followed by fall. Rain rate (R), mass-weighted mean diameter (Dm), and raindrop concentration (Nt) are stratified into six regimes to study their seasonal variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/light rain (R2: 0.2~2.5 mm h−1) has the maximum occurrence frequency throughout the year, while the total rainfall in summer is primarily from heavy rain (R4: 10~50 mm h−1). The rainfall for all seasons is contributed primarily from small raindrops (Dm2: 1.0~2.0 mm). The distribution of occurrence frequency of Nt and the relative rainfall contribution exhibit similar behavior during four seasons with Nt of 10~1000 m−3 registering the maximum occurrence and rainfall contributions. Rainfall in Beijing is dominated by stratiform rain (SR) throughout the year. There is no convective rainfall (CR) in winter, i.e., it occurs most often during summer. DSD of SR has minor seasonal differences, but varies significantly in CR. The mean values of log10Nw (Nw: mm−1m−3, the generalized intercept parameter) and Dm of CR indicate that the CR during spring and fall in Beijing is neither continental nor maritime, at the same time, the CR in summer is close to the maritime-like cluster. The radar reflectivity (Z) and rain rate () relationship (Z = R) showed seasonal differences, but were close to the standard NEXRAD Z-R relationship in summer. The shape of raindrops observed from 2DVD was more spherical than the shape obtained from previous experiments, and the effect of different axis ratio relations on polarimetric radar measurements was investigated through T-matrix-based scattering simulations. [ABSTRACT FROM AUTHOR]

Urban areas involve different functions that attract individuals and fit personal needs. Understanding the distribution and combination of these functions in a specific district is significant for urban development in cities. Many researchers have already studied the methods of identifying the dominant functions in a district. However, the degree of collection and the representativeness of a function in a district are controlled not only by its number in the district but also by the number outside this district and a number of other functions. Thus, this study proposed a quantitative method to identify urban functions, using Fisher's exact test and point of interest (POI) data, applied in determining the urban districts within the Sixth Ring Road in Beijing. To begin with, we defined a functional score based on three statistical features: the p-value, odds-ratio, and the frequency of each POI tag. The p-value and odds-ratio resulted from a statistical significance test, the Fisher's exact test. Next, we ran a k-modes clustering algorithm to classify all urban districts in accordance with the score of each function and their combination in one district, and then we detected four different groups, namely, Work and Tourism Mixed-developed district, Mixed-developed Residential district, Developing Greenland district, and Mixed Recreation district. Compared with the other identifying methods, our method had good performance in identifying functions, except for transportation. In addition, the Coincidence Degree was used to evaluate the accuracy of classification. In our study, the total accuracy of identifying urban districts was 83.7%. Overall, the proposed identifying method provides an additional method to the various methods used to identify functions. Additionally, analyzing urban spatial structure can be simpler, which has certain theoretical and practical value for urban geospatial planning. [ABSTRACT FROM AUTHOR]