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Superspreading events underscore the uneven distribution of COVID-19 transmission among individuals and locations. These heterogenous transmission patterns could stem from human mobility, yet the underlying mechanisms are still not fully understood. Here, we employ an agent-based model incorporating urban scaling structure to simulate fine-grained mobility and the human-to-human transmission process. Our results reveal that not only the quantity but also the scaling structure of mobility profoundly influences local transmission risk. Urban scaling structure is characterized by a widely found power-law scaling distribution of mobility volumes across different locations. By integrating this structure, our model fits reasonably well with empirical Omicron data at various spatial scales in Hong Kong. Further analyses show a positive association between the scaling index, representing the location's importance within the structure, and local transmission risks among urban areas as well as the likelihood of becoming superspreaders among local visitors. This implies that urban scaling structure could offer the first-mover advantage to a minority of places and individuals to infect earlier and thus infect more. This study brings important insights for the transmission dynamics of COVID-19 and similar diseases, highlighting the role of urban scaling structure in influencing local transmission risks and superspreading events. [ABSTRACT FROM AUTHOR]

As urbanization accelerates, cities become more complex, coming along with more complex urban issues. Agent‐based model (ABM) is a traditional method to simulate activities in a complex system, which has been widely applied in urban studies. However, due to its rigid initial settings, ABM has been criticized for its lack of intelligence, especially in dealing with modern urban issues. With the success of artificial intelligence (AI) and complexity science, it is generally agreed that ABM can be enhanced with AI agents, a promising technology that can bridge the gaps. For that, this article provides a systematic review, in which 10 subsections correspond to 10 different ways that AI can work with ABM in the methodological framework. The sections include that (1) ABM is Al; (2) ABM provides training data for Al; (3) Al provides data for ABM; (4) ABM is a submodule in the ensemble Al; (5) Al leads an optimization framework with ABM participation; (6) Al tunes ABM initialization parameters; (7) Al provides the environment for ABM; (8) Al aids in choosing the agent's attributes; (9) Al provides behaviors for agents in ABM; (10) Al helps to evaluate the performance of ABM. For each case, some typical works are examined for illustration. Finally, we discuss some of the current limitations and prospects for future development. [ABSTRACT FROM AUTHOR]

• Providing a planning-scale flood risk assessment of Hong Kong from 2006 to 2021. • Demonstrating the uneven patterns of flood risk across different planning units. • Discovering coastal areas with denser buildings are more vulnerable to flooding. • Predicting spatial–temporal patterns of flood risk and impacts for the coming years. • Emphasizing the need to integrate flood risk management into urban planning. Climate change has significantly increased the risks associated with urban flooding. However, most research on flood risk assessment focuses on large-scale climate changes and impacts, leaving a research gap in the high spatial resolution of flood risk assessment in inter-urban areas. This gap makes it difficult to guide regional planning for the government. Therefore, this study aims to explore the risks of floods in ultra-high-density cities under climate change at the planning scale, using Hong Kong as a case study. We comprehensively assessed the flood risk index (FRI) in the built environment in 211 tertiary planning units (TPUs) from the three dimensions of vulnerability, exposure, and hazard from 2006 to 2021. We also employed a prediction model to forecast the spatial–temporal patterns of FRI in the next 5, 10, and 15 years and evaluated the uneven distribution of flood risks. The results show that the FRI of TPUs increased yearly, which poses higher threats to agglomerative areas of transportation and functional facilities. Additionally, future FRI will further impact coastal TPUs in western Hong Kong, resulting in more negative impacts on high-building areas. Therefore, urban planning should prioritize integrating flood management and risk mitigation measures. [ABSTRACT FROM AUTHOR]

Corynespora cassiicola is an important phytopathogenic fungus that severely impairs crop production. Here, we report the molecular characterization of a novel positive-sense single-stranded RNA (+ssRNA) mycovirus, Corynespora cassiicola fusarivirus 1 (CcFV1), isolated from C. cassiicola strain 20200826-3-1. Excluding the poly(A) tail, the genome of the virus is 6491 nt in length and contains three putative open reading frames (ORFs). The large ORF1 encodes a polypeptide of 1524 aa with a conserved RNA-dependent RNA polymerase (RdRp) domain and a helicase (Hel) domain. BLASTp analysis showed that CcFV1 ORF1 has the highest similarity to Setosphaeria turcica fusarivirus 1 (StFV1, 50.45% identity, E-value 0.0). ORF2 encodes a polypeptide with a conserved chromosome segregation ATPase (Smc) domain. The smaller ORF3 encodes a polypeptide with an unknown function. Phylogenetic analysis based on the ORF1- encoded polypeptide showed that CcFV1 is phylogenetically related to members of the newly proposed family "Fusariviridae". Thus, we suggest that CcFV1 might be a novel member of the family "Fusariviridae", and is the first to be discovered in C. cassiicola. [ABSTRACT FROM AUTHOR]

Most existing studies on public transit network (PTN) rely on either small-scale passenger flow data or small PTN, and only traditional network parameters are used to calculate the correlation coefficient. In this work, the real smart card data (SCD) (when passenger tap in and tap out a station) of over eight million users is used as a proxy of passenger flow to dynamically explore and evaluate the structure of large-scale PTNs with tens of thousands of stations in Beijing, China. Three types of large-scale PTNs are generated, and the overall network structure of PTNs are examined and found to follow heavy-tailed distributions (mostly power law). Further, three traditional centrality measures (i.e. degree, betweenness and closeness) are adopted and modified to dynamically explore the relationship between PTNs and passenger flow. Our findings show that, the modified centrality measures outperform the traditional centrality measures in estimating passenger flow. [ABSTRACT FROM AUTHOR]

Developing data‐driven approaches to understanding urban structures is important for urban planning. However, it is still challenging to combine different transport datasets into a unified framework and reveal the dynamics of urban structures with the emergence of shared mobility. In this study, we propose two empirical multilayer networks to infer and profile urban structures. First, a temporal network is constructed using traditional taxi data over years to reveal the urban structures. Second, a multimodal network is constructed using shared mobility and traditional taxi data over a year to reveal the urban structures. The proposed networks are tested in New York City using a large volume of shared bike, shared vehicle, and traditional taxi data. The multilayer network centralities and community detection enable us to profile the characteristics of the urban flows and urban structure. The analytical results allow us to acquire a better understanding of urban structures from a multilayer perspective, and also provide a geocomputation framework that is useful for urban and geographic researchers. [ABSTRACT FROM AUTHOR]

Urban agglomeration is an important strategy used to promote economic development and urbanization in China. Understanding the structure of urban agglomeration is therefore essential for policy‐makers and planners. In this study, the Beijing–Tianjin–Hebei urban agglomeration (BTHUG) is explored through a proposed spatial network analytical framework and a large mobile phone data set (over 20 million users). We first construct a weight‐directed spatial interaction network based on an origin–destination matrix derived from the data set. Several network metrics (i.e., degree, strength, the rich‐club coefficient, and the assortativity coefficient) and three selected community detection algorithms (i.e., Infomap, Louvain, and Regionalization) are applied and compared to reveal the structure of the BTHUG. A four‐level hierarchical structure is defined and observed: one global center, two local centers, major cities that have low mobility flow but strong linkages with the three centers, and peripheral cities that have low mobility flow and weak linkages with the three centers. In particular, the results imply that the spatial structure of the BTHUG is over‐dependent on the global center (i.e., Beijing and northern Langfang). Further, ignoring spatial interaction patterns in top‐down administrative planning for urban agglomeration may lead to ineffective integrated development. The implications for BTHUG planning are discussed. [ABSTRACT FROM AUTHOR]

Corynespora leaf spot (CLS) is one of the most important diseases in sesame and is caused by a fungal pathogen, Corynespora cassiicola. In this study, we investigated the role of cell‐wall‐degrading enzymes (CWDEs) in the process of pathogen infection and aimed to identify the enzyme that exerts positive effect. Corynespora cassiicola was inoculated in liquid culture and in sesame leaf. At 7 days postinoculation, CWDEs including polygalacturonase (PG), cellulase (CL), neutral xylanase (NEX) and laccase (LC) were detected in the supernatant and leaf extracts inoculated with Corynespora cassiicola. The result showed that the activities of LC and NEX were higher in liquid culture, while the activities of CL and PG were lower. In the sesame leaf infected by Corynespora cassiicola, the activity of NEX was the highest, those of CL and LC were the second highest and that of PG was the weakest, which indicated that NEX was the key enzyme in the process of sesame infection by Corynespora cassiicola. Secondly, the activities of the CWDEs secreted by Corynespora cassiicola were disparate in different sesame varieties. CWDEs activities of cultivar Jz were generally higher than cultivar Hn, and the activity change curve of Jz was also steeper than that of Hn. This indicates that the resistance mechanism of Hn may inhibit the activities of CWDEs and that the appearance and expansion of leaf necrosis spot is related to the comprehensive accumulation and action of various CWDEs. This is the first report investigating the association between CWDEs and pathogenesis of Corynespora cassiicola, causing Corynespora leaf spot in sesame. [ABSTRACT FROM AUTHOR]

A group event such as human and traffic congestion can be very roughly divided into three stages: converging stage before congestion, gathered stage when congestion happens, and dispersing stage that congestion disappears. It is of great interest in modeling and identifying converging behaviors before gathered events actually happen, which helps to proactively predict and handle potential public incidents such as serious stampedes. However, most of existing literature put too much emphasis on the second stage, only a few of them is dedicated to the first stage. In this paper, we propose a novel group pattern, namely converging, which refers to a group of moving objects converging from different directions during a certain period before gathered. To discover efficiently such converging patterns, we develop a framework for converging pattern mining (CPM) by examining how moving objects form clusters and the process of the "cluster containment". The framework consists of three phases: snapshot cluster discovery phase, cluster containment join phase, and converging detection phase. As cluster containment mining is the key step, we develop three algorithms to discover cluster containment matches: a containment-join-algorithm, called SSCCJ, by using spatial proximity; a signature tree-based cluster-containment-join-algorithm, called STCCJ, which takes advantage of the cluster containment relations and signature techniques to filter enormous unqualified candidates in an efficient and effective way; and third, to keep the advantages of the above algorithms while avoiding their flaws, we further propose a signature quad-tree based cluster-containment-join algorithm, called SQTCCJ, which can identify efficiently matches by considering cluster spatial proximity as well as containment relations simultaneously. To assess the proposed methods, we redefine two evaluation metrics based on the concept of "Precision and Recall" in the field of information retrieval and the characteristics of converging patterns. We also propose a new indicator for measuring the duration of the converging stage in a group event. Finally, the effectiveness of the CPM and the efficiency of the mining algorithms are evaluated using three types of trajectory datasets, and the results show that the SQTCCJ algorithm demonstrates a superior performance. [ABSTRACT FROM AUTHOR]

Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning. [ABSTRACT FROM AUTHOR]

The rapid development of information and communications technology (ICT) in recent years leads to the emergence of cyber space and results in the fragmentation of human activities to some extent. Nevertheless, few studies focused on the exploration of fragmentation, the new characteristic of human activities, partially due to the limited access to proper datasets. In this work, massive Call Detail Records (CDR) data and Uniform Resource Locator (URL) data from millions of mobile phone users are used to extract the activity fragmentation in both cyber and physical spaces. The relationships between activity fragmentation in the two spaces and built environment factors are further examined at different geographical scales. The results show that: 1) the spatial distributions of physical human activity fragmentation (PHAF) and cyber human activity fragmentation (CHAF) are inversely correlated; 2) compared with PHAF, CHAF are less associated with the built environment, probably because cyber activities are less restricted in time and space; 3) CHAF are more influenced by physical fragmentations and the relationships are stable across different cites, indicating that cyber space cannot be completely detached from physical space; and 4) the impact of built environment on CHAF and PHAF are different across different cities. The findings not only shed deeper light on activity fragmentation, but also enlighten the future exploration of its association with both cyber and physical spaces. [ABSTRACT FROM AUTHOR]

Clustering is an important approach to identifying hotspots with broad applications, ranging from crime area analysis to transport prediction and urban planning. As an on-demand transport service, taxis play an important role in urban systems, and the pick-up and drop-off locations in taxi GPS trajectory data have been widely used to detect urban hotspots for various purposes. In this work, taxi drop-off events are represented as linear features in the context of the road network space. Based on such representation, instead of the most frequently used Euclidian distance, Jaccard distance is calculated to measure the similarity of road segments for cluster analysis, and further, a network distance and graph-partitioning-based clustering method is proposed for improving the accuracy of urban hotspot detection. A case study is conducted using taxi trajectory data collected from over 6500 taxis during one week, and the results indicate that the proposed method can identify urban hotspots more precisely. [ABSTRACT FROM AUTHOR]

Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel analysis. Of interest is the need to allow someone to monitor travel momentum and associated congestion in any location in space in real time. However, despite an abundant literature in taxi data visualization and its applicability to travel analysis, no easy method exists. To measure taxi travel momentum at a location, current methods require filtering taxi trajectories that stop at a location at a particular time range, which is computationally expensive. We propose an alternative, computationally cheaper way based on preprocessing vector fields from the trajectories. Algorithms are formalized for generating vector kernel density to estimate a travel-model-free vector field-based representation of travel momentum in an urban space. The algorithms are shared online as an open source GIS 3D extension called VectorKD. Using 17 million daily taxi GPS points within Beijing over a 4-day period, we demonstrate how to generate in real time a series of projections from a continuously updated vector field of taxi travel momentum to query a point of interest anywhere in a city, such as the CBD or the airport. This method allows a policy-maker to automatically identify temporal net influxes of travel demand to a location. The proposed methodology is shown to be over twenty times faster than a conventional selection query of trajectories. We also demonstrate, using taxi data entering the Beijing Capital International Airport and the CBD, how we can quantify in nearly real time the occurrence and magnitude of inbound or outbound queueing and congestion periods due to taxis cruising or waiting for passengers, all without having to fit any mathematical queueing model to the data. [ABSTRACT FROM AUTHOR]

Resistivity is a key factor in the efficient and stable operation of electrostatic precipitators. Sixty types of typical industrial fly ashes were collected for this study. Subsequently, fly ash resistivity at the temperature range of 303–1073 K, and the chemical compositions, micrographs, and size distributions of the samples were measured. The joint influence of chemical composition and temperature on resistivity was also investigated. The main components of the fly ash samples were Fe (0.8–5.0%), K + Na + Li (0.3–5.1%), Ca + Mg (0.5–4.0%), and Al + Si (10–34%), respectively. Fe, K, and Na were highly sensitive to fly ash resistivity. Resistivity decreased with the increase in Fe, K, Na, and Li contents; by contrast, resistivity increased with Ca and Mg contents. The effects of Si and Al on fly ash resistivity were weak. Resistivity initially increased first and then decreased with the increase in temperature. Maximum resistivity was observed at 373–473 K. Based on the experimental data, a prediction model for fly ash resistivity over a wide range of temperature (303–1073 K) was established. The resistivity diagrams generated specifically for this study suggest that typical fly ash samples from different industries can be estimated using chemical composition and temperature data. [ABSTRACT FROM AUTHOR]

Due to climate change and global warming, sea ice studies have recently gained more and more attention from various fields. Despite previous work on sea ice data collection, digitalization and dissemination over the web, efficient sharing of ice data and information remains unsolved. Further, little has been done on analyzing specific sea ice data user requirements and developing flexible web-based processing and analytical services based on existing studies, which are useful for both sea ice and non-sea-ice data users. In this paper, we aim to develop a series of Google Maps-like sea ice web application programming interfaces (API) for JavaScript, which provide a flexible and easy-to-use way to enable online sea ice data sharing, visualization and spatio-temporal analyses. We identify sea ice data user requirements at different levels through investigating a group of interested Canadian northerners, and then we design and implement an online sea ice web APIs prototype. We also provide a web tool for registering and sharing other ice web services and an online web coding and debugging sandbox to facilitate and attract more sea ice users and developers. We believe this work will be a good example and potentially benefit the development of web APIs for sea ice data and information services as well as other relevant studies. [ABSTRACT FROM AUTHOR]

The rise of urban Big Data has made it possible to use demand data at an operational level, which is necessary to directly measure the economic welfare of operational strategies and events. GIS is the primary visualization tool in this regard, but most current methods are based on scalar objects that lack directionality and rate of change – key attributes of travel. The few studies that do consider field-based time geography have largely looked at vector fields for individuals, not populations. A population-based vector field is proposed for visualizing time-geographic demand momentum. The field is estimated using a vector kernel density generated from observed trajectories of a sample population. By representing transport systems as vector fields that share the same time–space domain, demand can be projected onto the systems to visualize relationships between them. This visualization tool offers a powerful approach to visually correlate changes in the systems with changes in demand, as demonstrated in a case study of the Greater Toronto Area using data from the 2006 and 2011 Transportation Tomorrow Surveys. As a result, it is now possible to measure in real time the effects of disasters on the economic welfare of a population, or quantify the effects of operational strategies and designs on the behavioural activity patterns of the population. [ABSTRACT FROM AUTHOR]

Environmental exposure of people plays an important role in assessing the quality of human life. The most existing methods that estimate the environmental exposure either focus on the individual level or do not consider human mobility. This paper adopts a vector field generated from the observed locations of human activities to model the environmental exposure at the population level. An improved vector-field-generation method was developed by considering people's decision-making factors, and we proposed two indicators, i.e., the total exposure indicator (TEI) and the average exposure indicator (AEI), to assess various social groups' environmental exposure. A case study about the risky environmental exposure of coronavirus disease 2019 (COVID-19) was conducted in Guangzhou, China. Over 900 participants with various socioeconomic backgrounds were involved in the questionnaire, and the survey-based activity locations were extracted to generate the vector field using the improved method. COVID-19 pandemic exposure (or risk) was estimated for different social groups. The findings show that people in the low-income group have an 8% to 10% higher risk than those in the high-income group. This new method of vector field may benefit geographers and urban researchers, as it provides opportunities to integrate human activities into the metrics of pandemic risk, spatial justice, and other environmental exposures. [ABSTRACT FROM AUTHOR]

Agglomeration economies have become commonplace globally as a means to promote productivity advantages in cities. Despite that previous studies have examined external determinants such as input resource, labor pooling, and knowledge spillovers, a small portion of literature aims at emphasizing the impacts from local contexts. This research therefore attempts to investigate the spatial variations of the sectoral agglomeration economies and their relationships to built environments and socio-demographics. By computing the location quotient (LQ) of sectoral employment in Hong Kong, the spatial distribution of agglomeration economies is measured at the level of District Council Constituency Area (DCCA). We then derive built environment and socio-demographic factors and evaluate their associations with the agglomeration economies using ordinary least squares regression (OLS) and geographically weighted regression (GWR) models. Our results exhibit a considerable spatial heterogeneity across economic sectors, highlighting that sectoral economies tend to be concentrated in a few particular regions. Further, the findings from regression analysis reveal that such clustering activities have distinct and close relationships with locational factors, suggesting that sector-specific agglomerations economies are preferential to various locational factors. Ultimately, data-driven geographical evidence is supplemented by this study to current literature for understanding local agglomeration economies in a metropolitan context. The implications from our research findings and policy recommendations for local and regional economic development are discussed. • Local agglomeration economies in manufacturing, service and trade and logistics sectors are respectively identified in Hong Kong. • Our findings indicate substantial heterogeneities among sectoral economies in terms of the spatial distribution and their associations with local characteristics. • Agglomerations are sector-specific and preferential to their locational factors, including natural attributes, markets, land use, and others. • Manufacturing activities tend to locate within the New Territories that are decentralized from downtown areas. • Geographic proximity to borders with mainland China plays an advantageous role in trade and logistics industries. [ABSTRACT FROM AUTHOR]

In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework is proposed that includes three methods. First, a rule-based approach is proposed to identify the home location of the elderly cardholders based on individual travel pattern. The result has strong correlation with the real elderly population. Second, the clustering method is adopted to group bus stops based on the elderly travel flow. The center points of clusters are utilized to construct a Voronoi diagram. Third, a quasi-gravity model is proposed to reveal the elderly mobility between regions, using the public facilities index. The model measures the elderly travel number between regions, according to public facilities index on the basis of the total number of point of interest (POI) data. Beijing is used as an example to demonstrate the applicability of the proposed methods, and the methods can be widely used for urban planning, design and management regarding the aging population. [ABSTRACT FROM AUTHOR]

Map generalization is a process of producing maps at different levels of detail by retaining essential properties of the underlying geographic space. In this article, we explore how the map generalization process can be guided by the underlying scaling of geographic space. The scaling of geographic space refers to the fact that in a geographic space, small things are far more common than large ones. In the corresponding rank-size distribution, this scaling property is characterized by a heavy-tailed plot such as a power law, lognormal, or exponential function. In essence, any heavy-tailed distribution consists of the head of the distribution (with a low percentage of vital or large things) and the tail of the distribution (with a high percentage of trivial or small things). Importantly, the low and high percentages constitute an imbalanced contrast, e.g., 20 versus 80. We suggest that the purpose of map generalization is to retain the objects in the head and to eliminate or aggregate those in the tail. We applied this selection rule to three generalization experiments and found that the scaling of geographic space indeed underlies map generalization. We further relate the universal rule to Töpfer's radical law (or trained cartographers' decision making in general) and illustrate several advantages of the universal rule. [ABSTRACT FROM AUTHOR]

Axial lines are defined as the longest visibility lines for representing individual linear spaces in urban environments. The least set of axial lines that cover the free space of an urban environment or the space between buildings constitute what is often called an axial map. This is a fundamental tool in space syntax, a theory developed by Bill Hillier and his colleagues for characterizing the underlying urban morphologies. For a long time, generating axial lines with the help of some graphic software has been a tedious manual process that is criticized for being time consuming, subjective, or even arbitrary. In this article, we redefine axial lines as the least set of individual straight line segments mutually intersected along natural streets that are generated from street center lines using the Gestalt principle of good continuity. Based on this new definition, we develop an automatic solution for generating the newly defined axial lines from street center lines. We apply this solution to six typical street networks (three from North America and three from Europe) and generate a new set of axial lines for analyzing the urban morphologies. Through a comparison study between the new axial lines and the conventional or old axial lines and between the new axial lines and natural streets, we demonstrate with empirical evidence that the newly defined axial lines are a better alternative for capturing the underlying urban structure. [ABSTRACT FROM PUBLISHER]

Scaling of geographic space refers to the fact that for a large geographic area its small constituents or units are much more common than the large ones. This article develops a novel perspective to the scaling of geographic space using large street networks involving both cities and countryside. Given a street network of an entire country, we decompose the street network into individual blocks, each of which forms a minimum ring or cycle such as city blocks and field blocks. The block sizes demonstrate the scaling property, that is, far more small blocks than large ones. Interestingly, we find that the mean of all the block sizes can easily separate between small and large blocks –a high percentage (e.g., 90%) of smaller ones and a low percentage (e.g., 10%) of larger ones. Based on this regularity, termed as the head/tail division rule, we propose an approach to delineating city boundaries by grouping the smaller blocks. The extracted city sizes for the three largest European countries (France, Germany, and United Kingdom) exhibit power law distributions. We further define the concept of border number as a topological distance of a block far from the outmost border to map the center(s) of the country and the city. We draw an analogy between a country (or a city or geographic space in general) and a complex organism like the human body or the human brain to further elaborate on the power of this block perspective in reflecting the structure or patterns of geographic space. [ABSTRACT FROM AUTHOR]

In this article, we introduce a novel approach to computing the fewest-turn map directions or routes based on the concept of natural roads. Natural roads are joined road segments that perceptually constitute good continuity. This approach relies on the connectivity of natural roads rather than that of road segments for computing routes or map directions. Because of this, the derived routes possess the fewest turns. However, what we intend to achieve are the routes that not only possess the fewest turns but are also as short as possible. This kind of map direction is more effective and favored by people because they bear less cognitive burden. Furthermore, the computation of the routes is more efficient because it is based on the graph encoding the connectivity of roads, which is substantially smaller than the graph of road segments. We experimented on eight urban street networks from North America and Europe to illustrate the above-stated advantages. The experimental results indicate that the fewest-turn routes possess fewer turns and shorter distances than the simplest paths and the routes provided by Google Maps. For example, the fewest-turn-and-shortest routes are on average 15% shorter than the routes suggested by Google Maps, whereas the number of turns is just half as much. This approach is a key technology behind FromToMap.org - a web mapping service using openstreetmap data. [ABSTRACT FROM AUTHOR]

Based on the concepts of isovists and medial axes, we developed a set of algorithms that can automatically generate axial lines for representing individual linearly stretched parts of open space of an urban environment. Open space is the space between buildings where people can freely move around. The generation of the axial lines has been a key aspect of space syntax research, conventionally relying on hand-drawn axial lines of an urban environment, often called axial map, for urban morphological analysis. Although various attempts have been made towards an automatic solution, few of them can produce the axial map that consists of the least number of longest visibility lines, and none of them really works for different urban environments. Our algorithms provide a better solution than existing ones. Throughout this article, we have also argued and demonstrated that the axial lines constitute a true skeleton, superior to medial axes, in capturing what we perceive about the urban environment. [ABSTRACT FROM AUTHOR]

• Isolate 47M-1 from rhizosphere soil of tobacco was identified as Penicillium bilaiae. • 47M-1 inhibited growth of F. oxysporum and showed broad-spectrum antibiotic activity. • 47M-1 reduced disease incidence of Fusarium wilt of sesame with 50% control efficacy. • 47M-1 induced the defense genes in sesame against Fusarium wilt. • 47M-1 promoted plant growth by producing IAA, siderophore and dissolving phosphorus. The purpose of the present study was to explore the control of Fusarium wilt of sesame caused by Fusarium oxysporum via Penicillium sp. 47M-1. Isolate 47M-1 was isolated from rhizosphere soil samples of tobacco and identified as Penicillium bilaiae based on morphological characteristics and ITS-rDNA, β-tubulin and calmodulin sequences. Isolate 47M-1 inhibited the mycelial growth of F. oxysporum by 81.3% and overgrew the colonies of F. oxysporum in co-cultures. In a potting test, the control efficiency of isolate 47M-1 against Fusarium wilt of sesame was 50%, which was superior to that of Bacillus subtilis (P = 0.022). There was no significant difference (P = 0.068) in the control of Fusarium wilt between treatment with the fungicide carbendazim and treatment with isolate 47M-1. The results also showed that isolate 47M-1 and its culture filtrate significantly promoted the growth of sesame. Quantitative real time-PCR detection showed that the response-related genes NPR1 , Coi1 , PR1 , PR2 and PR3 were significantly upregulated under the combined stimulation of isolate 47M-1 and F. oxysporum compared to their expression under F. oxysporum treatment alone. In conclusion, isolate 47M-1 can control Fusarium wilt of sesame via multiple mechanisms, including competition, production of inhibitory substances, promotion of plant growth and induction of disease resistance. [ABSTRACT FROM AUTHOR]

The development of Information and Communication Technology has shifted human activity from offline to online, and promoted digital economy. These changes challenge traditional methodologies relying on physical human activity as a micro-level reflection of the macro-level economy. To address this, a hierarchical framework is proposed to characterize cyber human activity, incorporating activity diversity, size, and preference. Then, Ordinary Least Squares and Geographically Weighted Regression models are used to examine the spatial interplay between cyber human activity and township-level economy. K-Medoids clustering is further applied to coefficients in cyber GWR model to reveal mechanisms influencing local economies. Taking diverse geospatial data in Jilin Province, China as an example, the result indicates that the significant extreme values of cyber human activity have divided Jilin Province into four subregions, closely aligning with local economic segmentation. Moreover, the local economy can be better reflected by cyber human activity rather than physical ones, especially in highly digitized regions. Furthermore, the regions with similar influence mechanisms cluster geographically. These clusters are categorized into three types, namely, dynamics, robust, and balanced, representing different mechanisms influencing the local economy. Practical and theoretical implications are discussed, including using cyber human activity in assessing the economy and implementing adaptable economic policies at township level. • The relationship between cyber human activity and economy is investigated. • A hierarchical framework including three dimensions is proposed to characterize cyber human activity. • The findings suggest that significant extreme values of cyber human activity divide Jilin Province into four subregions. • The results indicate that the local economy can be better reflected by cyber human activity rather than physical ones. • Practical and theoretical implications regarding using cyber human activity as substitute for local economy are discussed. [ABSTRACT FROM AUTHOR]

Insufficient urban parking in densely populated cities has led to challenges like traffic congestion and unauthorized parking. While existing literature extensively covers parking behaviors among private vehicles and taxis for commuters, limited research has been proposed centering on urban shared freight activities. It can be problematic to neglect shared freight activities because these logistics activities often require more flexible parking space for goods loading/unloading near urban destinations. This study therefore introduces a geospatial data-driven approach to investigate urban parking behaviors from shared freight activities using GPS trajectories collected in Hong Kong. The analysis involves three main folds: using rank-size distribution and log-odds ratio to comparatively examine spatial heterogeneity of parking, evaluating illegal on-street parking events via illegal parking index, and quantifying relationships between parking behaviors and urban functions using random forest (RF) and SHapley Additive exPlanations (SHAP). The findings reveal significant concentrations of parking events on weekdays/weekends and with different parking durations. Particularly, high illegal on-street parking concentration is reported in Tsuen Wan and Kwai Chung, where the port container terminals and industrial activities are located. We further discover that industrial and dining densities both indicate significantly positive impacts on the relationship to the increases of overall parking frequency and illegal parking index. This study can be of great interest to current parking management and enforcement strategies in cities and relevant practical implications are discussed. [ABSTRACT FROM AUTHOR]

Many studies have evaluated the influence of the built environment on public transport. Some studies assign subjective weights to environmental factors, which could oversimplify spatial heterogeneity and overlook the temporal dimension. On the other hand, the spatial-interaction network of public transport system is seldom considered. In this paper, we propose an improved framework to explore how individual factors unevenly affect public transport demand over space and time using a geographically and temporally weighted regression (GTWR) model. The proposed framework extends the local built environmental factors by including two network factors extracted from the spatial-interaction network of the public transport system. We conduct a case study in Beijing, China using 686 traffic analysis zones (TAZs). The actual usage of public transport, namely the public transport index (PTI), is estimated by passenger flow divided by the total amount of human flow in a given TAZ. The daily patterns of the spatial heterogeneity in some selected places in the study area is identified and analyzed. It is also found that the estimated coefficient of the variables of the spatial-interaction network is significantly larger than other static environmental factors, indicating that spatial-interaction network can more effectively reflect spatiotemporal heterogeneity in public transport demand. This study provides a better decision-making support for more accurately identifying which factors are most worthy of development, and when and where they can be implemented to improve public transit services. [ABSTRACT FROM AUTHOR]

Classification of land use and land cover from remote sensing images has been widely used in natural resources and urban information management. The variability and complex background of land use in high-resolution imagery poses greater challenges for remote sensing semantic segmentation. To obtain multi-scale semantic information and improve the classification accuracy of land-use types in remote sensing images, the deep learning models have been wildly focused on. Inspired by the idea of the atrous-spatial pyramid pooling (ASPP) framework, an improved deep learning model named RAANet (Residual ASPP with Attention Net) is constructed in this paper, which constructed a new residual ASPP by embedding the attention module and residual structure into the ASPP. There are 5 dilated attention convolution units and a residual unit in its encoder. The former is used to obtain important semantic information at more scales, and residual units are used to reduce the complexity of the network to prevent the disappearance of gradients. In practical applications, according to the characteristics of the data set, the attention unit can select different attention modules such as the convolutional block attention model (CBAM). The experimental results obtained from the land-cover domain adaptive semantic segmentation (LoveDA) and ISPRS Vaihingen datasets showed that this model can enhance the classification accuracy of semantic segmentation compared to the current deep learning models. [ABSTRACT FROM AUTHOR]

Urban functional area (UFA) recognition is one of the most important strategies for achieving sustainable city development. As remote-sensing and social-sensing data sources have increasingly become available, UFA recognition has received a significant amount of attention. Research on UFA recognition that uses a single dataset suffers from a low update frequency or low spatial resolution, while data fusion-based methods are limited in efficiency and accuracy. This paper proposes an integrated model to identify UFA using satellite images and taxi global positioning system (GPS) trajectories in four steps. First, blocks were generated as spatial units in the study area, and the spatiotemporal information entropy of the taxi GPS trajectory (STET) for each block was calculated. Second, a 24-hour time-frequency series was formed based on the pick-up and drop-off points extracted from taxi trajectories and used as the interpretation indicator of the blocks. The K-Means++ and k-Nearest Neighbor (kNN) algorithm were used to identify their social functions. Third, a multilabel classification method based on the residual neural network (MLC-ResNets) and "You Only Look Once" (YOLO) target detection algorithms were used to identify the features of the typical and atypical spatial textures, respectively, of the satellite images in the blocks. The confidence scores of the features of the blocks were categorized by the decision tree algorithm. Fourth, to find the best way to integrate the two sub-models for UFA identification, the 10-fold cross-validation method based on stratified random sampling was applied to determine the most optimal STET thresholds. The results showed that the average accuracy reached 82.0%, with an average kappa of 73.5%—significant improvements over most existing studies. This paper provides new insights into how the advantages of satellite images and taxi trajectories in UFA identification can be fully exploited to support sustainable city management. [ABSTRACT FROM AUTHOR]

The social function of areas of interest (AOIs) is crucial to the identification of urban functional zoning and land use classification, which has been a hot topic in various fields such as urban planning and smart city fields. Most existing studies on urban functional zoning and land use classification either largely rely on low-frequency remote sensing images, which are constrained to the block level due to their spatial scale limitation, or suffer from low accuracy and high uncertainty when using dynamic data, such as social media and traffic data. This paper proposes an hour-day-spectrum (HDS) approach for generating six types of distribution waveforms of taxi pick-up and drop-off points which serve as interpretation indicators of the social functions of AOIs. To achieve this goal, we first performed fine-grained cleaning of the drop-off points to eliminate the spatial errors caused by taxi drivers. Next, buffer and spatial clustering were integrated to explore the associations between travel behavior and AOIs. Third, the identification of AOI types was made by using the standard HDS method combined with the k-nearest neighbor (KNN) algorithm. Finally, some matching tests were carried out by similarity indexes of a standard HDS and sample HDS, i.e., the Gaussian kernel function and Pearson coefficient, to ensure matching accuracy. The experiment was conducted in the Chongchuan and Gangzha Districts, Nantong, Jiangsu Province, China. By training 50 AOI samples, six types of standard HDS of residential districts, schools, hospitals, and shopping malls were obtained. Then, 108 AOI samples were tested, and the overall accuracy was found to be 90.74%. This approach generates value-added services of the taxi trajectory and provides a continuous update and fine-grained supplementary method for the identification of land use types. In addition, the approach is object-oriented and based on AOIs, and can be combined with image interpretation and other methods to improve the identification effect. [ABSTRACT FROM AUTHOR]

Public transport plays an important role in developing sustainable cities. A better understanding of how different public transit modes (bus, metro, and taxi) interact with each other will provide better sustainable strategies to transport and urban planners. However, most existing studies are either limited to small-scale surveys or focused on the identification of general interaction patterns during times of regular traffic. Transient demographic changes in a city (i.e., many people moving out and in) can lead to significant changes in such interaction patterns and provide a useful context for better investigating the changes in these patterns. Despite that, little has been done to explore how such interaction patterns change and how they are linked to the built environment from the perspective of transient demographic changes using urban big data. In this paper, the tap-in-tap-out smart card data of bus/metro and taxi GPS trajectory data before and after the Chinese Spring Festival in Shenzhen, China, are used to explore such interaction patterns. A time-series clustering method and an elasticity change index (ECI) are adopted to detect the changing transit mode patterns and the underlying dynamics. The findings indicate that the interactions between different transit modes vary over space and time and are competitive or complementary in different parts of the city. Both ordinary least-squares (OLS) and geographically weighted regression (GWR) models with built environment variables are used to reveal the impact of changes in different transit modes on ECIs and their linkage with the built environment. The results of this study will contribute to the planning and design of multi-modal transport services. [ABSTRACT FROM AUTHOR]

Fusarium oxysporum is a widespread plant pathogen that causes fusarium wilt and fusarium root rot in many economically significant crops. Here, a novel dsRNA virus tentatively named "Fusarium oxysporum virus 1" (FoV1) was identified in F. oxysporum strain 3S-18. The genome of FoV1 is 2,944 nucleotides (nt) in length and contains two non-overlapping open reading frames (ORF1 and 2). The larger of these, ORF2, encodes an RNA-dependent RNA polymerase (RdRp) of 590 amino acids with a molecular mass of 67.52 kDa. ORF1 encodes a putative nucleocapsid protein consisting of 134 amino acids with a molecular mass of 34.25 kDa. The RdRp domain of FoV1 shares 60.00% to 84.24% sequence identity with non-segmented dsRNA viruses. Phylogenetic analysis further suggested that FoV1 is a new member of the proposed genus "Unirnavirus" accommodating unclassified monopartite dsRNA viruses. [ABSTRACT FROM AUTHOR]

Background: Sesame (Sesamum indicum) charcoal rot, a destructive fungal disease caused by Macrophomina phaseolina (Tassi) Goid (MP), is a great threat to the yield and quality of sesame. However, there is a lack of information about the gene-for-gene relationship between sesame and MP, and the molecular mechanism behind the interaction is not yet clear. The aim of this study was to interpret the molecular mechanism of sesame resistance against MP in disease-resistant (DR) and disease-susceptible (DS) genotypes based on transcriptomics. This is the first report of the interaction between sesame and MP using this method. Results: A set of core genes that response to MP were revealed by comparative transcriptomics and they were preferentially associated with GO terms such as ribosome-related processes, fruit ripening and regulation of jasmonic acid mediated signalling pathway. It is also exhibited that translational mechanism and transcriptional mechanism could co-activate in DR so that it can initiate the immunity to MP more rapidly. According to weighted gene co-expression network analysis (WGCNA) of differentially expressed gene sets between two genotypes, we found that leucine-rich repeat receptor-like kinase (LRR-RLK) proteins may assume an important job in sesame resistance against MP. Notably, compared with DS, most key genes were induced in DR such as pattern recognition receptors (PRRs) and resistance genes, indicating that DR initiated stronger pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). Finally, the study showed that JA/ET and SA signalling pathways all play an important role in sesame resistance to MP. Conclusions: The defence response to MP of sesame, a complex bioprocess involving many phytohormones and disease resistance-related genes, was illustrated at the transcriptional level in our investigation. The findings shed more light on further understanding of different responses to MP in resistant and susceptible sesame. [ABSTRACT FROM AUTHOR]

Weather radar data play an important role in meteorological analysis and forecasting. In particular, web‐based real‐time 3D visualization will enable and enhance various meteorological applications by avoiding the dissemination of a large amount of data over the internet. Despite that, most existing studies are either limited to 2D or small‐scale data analytics due to methodological limitations. This article proposes a new framework to enable web‐based real‐time 3D visualization of large‐scale weather radar data using 3D tiles and WebGIS technology. The 3D tiles technology is an open specification for online streaming massive heterogeneous 3D geospatial datasets, which is designed to improve rendering performance and reduce memory consumption. First, the weather radar data from multiple single‐radar sites across a large coverage area are organized into a spliced grid data (i.e., weather radar composing data, WRCD). Next, the WRCD is converted into a widely used 3D tile data structure in four steps: data preprocessing, data indexing, data transformation, and 3D tile generation. Last, to validate the feasibility of the proposed strategy, a prototype, namely Meteo3D at https://202.195.237.252:82, is implemented to accommodate the WRCD collected from all the weather radar sites over the whole of China. The results show that near real‐time and accurate visualization for the monitoring and early warning of strong convective weather can be achieved. [ABSTRACT FROM AUTHOR]

Background: Corynespora leaf spot is a common leaf disease occurring in sesame, and the disease causes leaf yellowing and even shedding, which affects the growth quality of sesame. At present, the mechanism of sesame resistance to this disease is still unclear. Understanding the resistance mechanism of sesame to Corynespora leaf spot is highly important for the control of infection. In this study, the leaves of the sesame resistant variety (R) and the sesame susceptible variety (S) were collected at 0–48 hpi for transcriptome sequencing, and used a combined third-generation long-read and next-generation short-read technology approach to identify some key genes and main pathways related to resistance. Results: The gene expression levels of the two sesame varieties were significantly different at 0, 6, 12, 24, 36 and 48 hpi, indicating that the up-regulation of differentially expressed genes in the R might enhanced the resistance. Moreover, combined with the phenotypic observations of sesame leaves inoculated at different time points, we found that 12 hpi was the key time point leading to the resistance difference between the two sesame varieties at the molecular level. The WGCNA identified two modules significantly associated with disease resistance, and screened out 10 key genes that were highly expressed in R but low expressed in S, which belonged to transcription factors (WRKY, AP2/ERF-ERF, and NAC types) and protein kinases (RLK-Pelle_DLSV, RLK-Pelle_SD-2b, and RLK-Pelle_WAK types). These genes could be the key response factors in the response of sesame to infection by Corynespora cassiicola. GO and KEGG enrichment analysis showed that specific modules could be enriched, which manifested as enrichment in biologically important pathways, such as plant signalling hormone transduction, plant-pathogen interaction, carbon metabolism, phenylpropanoid biosynthesis, glutathione metabolism, MAPK and other stress-related pathways. Conclusions: This study provides an important resource of genes contributing to disease resistance and will deepen our understanding of the regulation of disease resistance, paving the way for further molecular breeding of sesame. [ABSTRACT FROM AUTHOR]

Background: Sesame charcoal rot caused by Macrophomina phaseolina is one of the most serious fungal diseases in sesame production, and threatens the yield and quality of sesame. WAKL genes are important in the plant response to biotic stresses by sensing and transmitting external signals to the intracellular receptor. However, there is still a lack about the WAKL gene family and its function in sesame resistance to M. phaseolina. The aim of this study was to interpret the roles of WAKL genes in sesame resistance to M. phaseolina. Results: In this study, a comprehensive study of the WAKL gene family was conducted and 31 WAKL genes were identified in the sesame genome. Tandem duplication events were the main factor in expansion of the SiWAKL gene family. Phylogenetic analysis showed that the sesame SiWAKL gene family was divided into 4 groups. SiWAKL genes exhibited different expression patterns in diverse tissues. Under M. phaseolina stress, most SiWAKL genes were significantly induced. Notably, SiWAKL6 was strongly induced in the resistant variety "Zhengzhi 13". Functional analysis showed that SiWAKL6 was induced by salicylic acid but not methyl jasmonate in sesame. Overexpression of SiWAKL6 in transgenic Arabidopsis thaliana plants enhanced their resistance to M. phaseolina by inducing the expression of genes involved in the salicylic acid signaling pathway and reconstructing reactive oxygen species homeostasis. Conclusions: Taken together, the results provide a better understanding of functions about SiWAKL gene family and suggest that manipulation of these SiWAKL genes can improve plant resistance to M. phaseolina. The findings contributed to further understanding of functions of SiWAKL genes in plant immunity. [ABSTRACT FROM AUTHOR]

Destination, as a key concept in tourism geography, has largely determined the scale at which tourist activity space was modeled and studied. Existing studies usually focused on investigating tourists' activities and movements either at the intradestination (e.g., within a city) or interdestination scale. Although useful in numerous research contexts, these models based on fixed spatial scales are incapable of portraying the complex spatial structure of tourist activity spaces, which sometimes exhibit hierarchical structures, and could span across different spatial scales. In this study, we propose a new representation of tourist activity space to bridge these gaps. The representation takes tourists' accommodation locations as key reference points. At the macroscale, the sequence of accommodation locations forms the backbone of tourist activity space, denoted as itinerary type. At the microscale, we introduce the concept of territory to describe how individuals organize activities around these overnight "base camps" (i.e., accommodation locations). We apply this representation over a large-scale mobile phone data set of international travelers visiting South Korea to demonstrate its capability. Results show that four generic itinerary types capture the activity space structure of 89 percent of the tourists. The interrelationships of territories and their topological structures further categorize activity spaces into subtypes, leading to a new method of tourist classification based on their spatiotemporal activity patterns. We believe the proposed representation could enrich new perspectives and debates on how tourist activities can be studied. The representation can also be extended as a generic framework to delineate complex forms of human activity space. [ABSTRACT FROM AUTHOR]

Macrophomina phaseolina is an important phytopathogenic fungus with a broad host range. Here, the complete genome sequence of a novel victorivirus, tentatively named Macrophomina phaseolina victorivirus 1 (MpV1), was identified from strain 2012-019 of M. phaseolina. The MpV1 genome is 5,128 nucleotides long with a predicted GC content of 62%. Sequence analysis indicated that two open reading frames (ORF 1 and 2) overlap at a tetranucleotide AUGA sequence. Proteins encoded by ORF1 and ORF2 showed significant sequence similarity to coat proteins and the RNA-dependent RNA polymerases, respectively, of members of the family Totiviridae. Analysis of the genomic structure of MpV1, homolog searches of the deduced amino acid sequences, and phylogenetic analysis indicated that MpV1 is a new member of the genus Victorivirus. As far as we know, this is the first report of the full-length nucleotide sequence of the genome of a novel victorivirus that infects M. phaseolina. [ABSTRACT FROM AUTHOR]

Household size and its spatial distributions reflect not only the socioeconomic development in a city but also the rationality of urban resource allocation. Most existing studies rely heavily on census data to explore the potentially influential factors using methods such as macro-statistical analysis and socioeconomic analysis, of which the spatial resolutions and geographic scales are constrained. More importantly, the association between the household size distribution and the built environment is oversimplified or even neglected to some extent. In this work, we use massive mobile phone data combined with travel surveys of Beijing inhabitants' data (TSBI) to infer the household size and analyze the effect of spatial heterogeneity in a finer spatial resolution in Beijing, China. First, the machine learning method (i.e., support vector machine (SVM)) is applied to identify the household relationships of mobile users, and there are around 3.44 million households (families) with different sizes are obtained. Second, we analyze the spatial distribution patterns of household size and its association with built environmental indicators (e.g., public service density, public transportation density, etc.). The results exhibit a heterogeneous effect of the regional built environment on average household size (AHS). For instance, "commercial density" and "administrative density" show a negative impact on household size, while "public service density" and "public transportation density" show positive correlations with household size. As a complement to census data, mobile phone data can be used to obtain the household size in real-time. This paper provides quantified evidence for government departments to allocate facilities in a more targeted, balanced, and reasonable way according to the regional differences in household size, which would potentially support the sustainable urban development. • Using phone call characteristics between family members to infer family relationships • Family relationship was determined using SVM algorithm. • MGWR is applied to explore the effects of built environment factors on family size. • House prices and distance to city center have significant effects on household size. • Potential decision-making support is provided for the facility allocation for government. [ABSTRACT FROM AUTHOR]

This article presents a proof‐of‐concept designer‐in‐the‐loop schematic map drawing tool, based on the marriage of two approaches—manual and automated, which provides the technical interactivity of drawing tools between the user and the computer. We focus on concentric circle maps as opposed to the commonly used orthogonal mode representation, which is suggested by previous studies that it could promote better network learning. In comparison with existing methods, the proposed method is more compatible with the framework of effective map design from psychological and aesthetic perspectives, and a range of options can be provided in conjunction with users' preferences. We evaluated our approach on a set of iterations with case studies of Hong Kong metro with a group of three co‐authors from the fields of geography, transport engineering, and education. [ABSTRACT FROM AUTHOR]

Abstract: As an important topological relation model, the dimensionally extended 9‐intersection model (DE‐9IM) has been widely used as a basis for standards of queries in spatial databases. However, the negative conditions for the specification of the topological relations within the DE‐9IM have not been studied. The specification of the topological relations is closely related to the definition of the spatial objects and the topological relation models. The interior, boundary, and exterior of the spatial objects, including the point, line, and region, are defined. Within the framework of the DE‐9IM, 43 negative conditions are proposed to eliminate impossible topological relations. Configurations of region/region, region/line, line/line, region/point, line/point, and point/point relations are drawn. The mutual exclusion of the negative conditions is discussed, and the topological relations within the framework of 9IM and DE‐9IM are compared. The results show that: (1) impossible topological relations between spatial objects can be eliminated by the application of 43 negative conditions; and (2) 12 relations between two regions, 31 relations between a region and a line, 47 relations between two lines, three relations between a region and a point, three relations between a line and a point, and two relations between two points can be distinguished by the DE‐9IM. [ABSTRACT FROM AUTHOR]

Agglomeration economies have been an increasingly prevalent interest in research communities of regional and urban studies. Recent evidence reports the importance of agglomeration economies closely associated with the urban network. However, limited efforts have been proposed to emphasize the spatial-functional roles of regional agglomeration and their network behaviors. With the use of geospatial big data and network analytical approaches, this paper, therefore, presents a data-driven intraregional case study in China's Greater Bay Area (GBA) to investigate the curiosities related to a regional network of sectoral agglomeration economies. By extracting industry-related points of interest (POIs), industrial activities are derived by topic modeling in representing industrial functional topics. Bipartite network projection is implemented to construct a one-mode network based on the two-mode agglomeration network. Community detection is further applied over the agglomeration networks, for uncovering groups of agglomerations that are closely connected by their industrial functions. The results confirm substantial geographic heterogeneities of agglomeration networks related to locational advantages. A robust radiation effect on the north-western hinterland is observed regarding material and mechanical production industries. It is suggested that complementary relationships in regional cooperative activities have been partially established through intensive agglomeration networks. • Spatial-functional patterns of the regional network in China's Greater Bay Area (GBA) are investigated. • Industrial functions are extracted from Points of interest (POIs) in the manufacturing sector. • A robust radiation effect on the north-western hinterland is observed in material and mechanical production industries. • Spatial-functional heterogeneities of agglomeration networks are confirmed, probably related to locational advantages. • Practical implications associated with stakeholders' concerns are discussed to enhance regional economies' integration. [ABSTRACT FROM AUTHOR]

Urban life involves a large variety of urban functions and human activities in a dense context due to the inherent nature of cities. Although technical frameworks have been previously proposed to understand urban functions and activities, there are limited studies that concern the individual places within a city and their detailed characteristics at a local scale. Using points of interest (POIs), we present a data-driven analytical framework to explore urban space containing urban functions and relevant activities by focusing on particular urban places. Urban functions are first extracted and induced by leveraging a latent Dirichlet allocation (LDA) topic modeling technique. We next evaluate the thematic functional differences among the selected places using the location quotient (LQ). Furthermore, tourist functions are assumed to occur in places within a city and carry broadly identifiable information; thus, tourist places are studied by comparing their perceptual experiences using the high-frequency keywords retrieved from tourist reviews on TripAdvisor. By adopting Hong Kong as our case study, the findings reveal considerable diversity of urban functions across different places, while each place displays the distinctive trait of urban functions. Tourist impressions reflected online are primarily consistent with the corresponding functional identities of these places but exhibit additional details associated with emotional and temporal aspects. This study uses a bottom-up assessment of local functions, and discusses their practical implications as related to city branding strategies. • Urban functions are investigated through the POIs collected in Hong Kong. • The findings exhibit a heterogeneity of urban functions with spatial concentrations related to specific human activities. • Local places familiar to local citizens are explored and each of them displays a distinctive trait of urban functions. • Tourist impressions are mostly consistent with urban functions but exhibit supplemental emotional and temporal signatures. • Analytical results can be associated with the government's focus on formulating city branding strategies. [ABSTRACT FROM AUTHOR]

With the growth in the vehicle industry, autonomous driving has become a hot topic worldwide and has attracted increasing attention from both industrial and academic sectors. Maps, as pivotal geospatial information carriers, play a vital role in route planning and navigation service. Compared with conventional maps, high-definition (HD) maps possesses higher precision, richer information, and various services and are regarded as critical infrastructure for autonomous driving. However, heterogeneous HD map data standards and models have different characteristics and advantages, and thus they rarely meet all autonomous driving requirements for different driving objectives. This research presents an interoperable map data model, the Open HD Map Service Model (OHDMSM), to provide a reference for HD map development. The designed OHDMSM, which contains three data layers and a set of corresponding interfaces, demonstrates high interoperability for HD map data fusion and application. As a proof of concept, an HD map data system is implemented with all functions following the designed data model and interfaces of OHDMSM. The design and development of OHDMSM data structures, interfaces and systems will benefit data requesting, updating, and interoperation for HD map data worldwide, which can be helpful for developing autonomous driving and intelligent transportation in the Digital Earth. [ABSTRACT FROM AUTHOR]

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R2) derived from the data with and without implementing the activity change detection. The results show that theR2is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies. [ABSTRACT FROM PUBLISHER]

A novel victorivirus was detected in an isolate of Corynespora cassiicola strain 20180909-03 and was named "Corynespora cassiicola victorivirus 1" (CcVV1). The complete genome sequence of this virus is 5140 bp in length and contains 57% GC with two large open reading frames (ORFs) overlapping at the tetranucleotide AUGA. The ORFs were predicted to encode a coat protein (CP) and an RNA-dependent RNA polymerase (RdRp), respectively, which are conserved in dsRNA fungal viruses of the family Totiviridae. Comparison and phylogenetic analysis of the deduced amino acid sequences of RdRp and CP showed that CcVV1 is a new member of the genus Victorivirus. This is the first report of a genomic sequence of a victorivirus infecting Corynespora cassiicola. [ABSTRACT FROM AUTHOR]

Although e-scooter sharing has become increasingly attractive, little attention has been paid to a comprehensive comparison of e-scooter sharing mobility in multiple cities. To fill this gap, we conduct a comparative study to reveal the similarity and difference of e-scooter sharing mobility by collecting and analyzing vehicle availability data from 30 European cities during post COVID-19 pandemic. The comparisons are implemented from four perspectives, including temporal trip patterns, statistical characteristics (i.e., trip distance and duration), utilization efficiency, and wasted electricity during idle time. Results suggest that the similarity and difference co-exist between e-scooter sharing services in the cities, and utilization efficiency is significantly related with the number of e-scooters per person and per unit area. Surprisingly, on average nearly 33% of electricity are wasted during idle time in these cities. These research findings can be beneficial to further optimizing e-scooter sharing mobility services for transportation planners and micro-mobility operators. • Reveling the similarity and difference of e-scooter sharing patterns in 30 European cities. • Exploring the temporal and statistical distribution of e-scooter trips. • Quantifying the utilization efficiency and wasted electricity of e-scooters during idle time. • Improving utilization efficiency can help reduce the wasted electricity. [ABSTRACT FROM AUTHOR]

The strategies using transit-oriented development (TOD) to optimize transportation sustainability have been implemented in many metropolitan areas and extended beyond the role of exclusively offering transit services. Research findings from existing literature have largely shown that metro station catchment areas can attract a substantial number of urban functions and human activities that lead metro stations to be vital and vibrant places of urban daily life. In this work, we propose a data-driven semantic framework to characterize metro stations through points of interest (POIs) in Hong Kong. The analytical results reveal four thematic topics of urban functions that are closely related to commercial, residential, tourism, and industrial activities. Given the implementation of a hierarchical clustering approach on these thematic topics, the similarities among different stations are investigated. In particular, metro stations in the same thematic group tend to be spatially concentrated, suggesting an evident geographical proximity relating to similar urban functions. Plus, results from the Multinomial Logit Model (MNLM) confirm that the surrounding built environment of metro stations has close relationships with the heterogeneity of urban functions. Ultimately, this study introduces alternative insights into the urban functional heterogeneity exhibited by metro station areas, and the practical implications for more targeted TOD strategies are discussed. • Latent Dirichlet allocation (LDA) is used to identify the thematic topics of urban functions for each station catchment area based on their POI types. • Four thematic topics are uncovered that mainly relate to commercial, residential, tourism, and industrial functions and activities. • Stations containing similar urban functions and relevant activities tend to be spatially concentrated. • Urban functional patterns within station catchment areas indicate strong connections with surrounding built environments. • The analytical findings can be used for formulating more targeted TOD strategies [ABSTRACT FROM AUTHOR]