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Types of ideational metaphor in English considered in this research incorporate nominalization, verbalization, and adjectivization. Previous studies particularly highlighted functions of nominalization, leaving the other types of ideational metaphor unconsidered; in addition, expressions of ideational metaphor are not carefully selected while exploring the functions realized by these expressions. Against this backdrop, functions of ideational metaphor uncovered in previous studies are insufficient of coverage and typicality. Drawing on COCA in this research, a corpus of typical American English, expressions of the three types of ideational metaphor with significant attraction by means of the simple collexeme analysis are employed to analyze the functions that these metaphorical expressions actually realize in their linguistic contexts. The findings demonstrated that expressions of ideational metaphor could realize such interpersonal functions as authoritativeness and objectivity, such ideational functions as conciseness, abstractness, ambiguity, and semantic condensation, and such textual functions as formality and cohesion. Explanations to these realized functions of ideational metaphor are also provided. This research is of great significance in that the documented functions are typical and representative of ideational metaphor in English.

As an overarching policy and development model, transit-oriented development (TOD) is widely used to promote the integration of land use and transit services, which arguably brings about many benefits. TOD advocates have continuously paid much attention to metro station areas (MSAs), i.e., areas within a reasonable walking distance to a metro station. To TOD advocates, well-planned MSAs should have a sufficient supply of TOD-nesses encapsulating characteristics such as destinations, density, diversity, and design. In the existing scholarship, however, little has been done on (a) TOD-nesses' potential "consumers," the population that is only a short metro ride away from an MSA, i.e., the metro-based accessibility (MBA) to the consumers of the MSA; and (b) whether and how TOD-nesses of one MSA and several MSAs in proximity are affected by the MBA. With the help of big and/or open data, we examine whether and to what degree the MBA of an MSA cluster (MSAC), a set of MSAs within t minutes of a metro ride from a metro station is correlated to that MSAC's TOD-nesses in Shenzhen, China. We measure the MBA by the daytime/nighttime population in the same MSAC. We quantify TOD-nesses using the published indices or the averages of the indices in a refereed article. Coefficient of geography association, Gini index, pairwise correlation, and linear regression analyses are carried out. We find that the MBA in one period significantly predicts MSAC-level TOD-nesses in ensuing periods. However, the MBA's prediction power decreases or even disappears over time. Besides, metro station characteristics such as the jurisdiction membership significantly predict the overall TOD-nesses and individual aspects of the TOD-nesses after controlling for the MBA. Our study thus sheds new light on meso- or MSAC-level TOD-nesses and related policy and planning evaluation.

AbstractThis research scrutinizes expressions of modality at both word and clause ranks. Expressions at the former rank encompass modal verbal operators, modal adjuncts, and modal nominalizations, and expressions at the latter rank encompass 'explicit subjective' orientation such as I think and 'explicit objective' orientation, such as it is possible. These latter expressions are designated as interpersonal metaphors of modality within the framework proposed by Halliday. Previous studies delimited the distribution of expressions of modality either at word rank or at clause rank; in addition, they undertheorized the relationship between this distribution of modality and formality of registers because the register is testified to be an influential factor in the distribution of expressions of modality. This study, drawing on such statistical methods as variation analysis and correlation analysis, explored the distribution of expressions of modality at both word and clause ranks across registers with different degrees of formality by employing the Corpus of Contemporary American English (COCA). The findings are summarized as follows. First, registers in COCA employ expressions of modality with low and/or median values to judge the proposition at issue for the purpose of entertaining other varying voices. Second, in more formal registers, language users seem to prefer modal nominalizations to modal verbal operators and modal adjuncts at word rank, and 'explicit objective' orientation to 'explicit subjective' orientation at clause rank. In doing so, they could efficiently conceal the commentators' subjective meaning, and thus, the objectivity of propositions is highlighted. This study will shed light on the full understanding of the distribution of expressions of modality across registers and the application of such theory as interpersonal metaphor of modality in SFL.

Considered a total social phenomenon, mobility is at the center of intricate social dynamics in cities and serves as a reading lens to understand the whole society. With the advent of big data, the potential for using mobility as a key social analyzer was unleashed in the past decade. The purpose of this research is to systematically review the evolution of big data's role in revealing social dimensions of urban mobility and discuss how they have contributed to various research domains from early 2010s to now. Six major research topics are detected from the selected online academic corpuses by conducting keywords-driven topic modeling techniques, reflecting diverse research interests in networked mobilities, human dynamics in spaces, event modeling, spatial underpinnings, travel behaviors and mobility patterns, and sociodemographic heterogeneity. The six topics reveal a comprehensive, research-interests, evolution pattern, and present current trends on using big data to uncover social dimensions of human mobility activities. Given these observations, we contend that big data has two contributions to revealing social dimensions of urban mobility: as an efficiency advancement and as an equity lens. Furthermore, the possible limitations and potential opportunities of big data applications in the existing scholarship are discussed. The review is intended to serve as a timely retrospective of societal-focused mobility studies, as well as a starting point for various stakeholders to collectively contribute to a desirable future in terms of mobility.

By implementing such corpus approaches as collexeme analysis and hierarchical cluster analysis, this paper investigates the semantic categorizations of adjectives that are extremely significantly attracted to the it BE ADJ clause construction in English. The findings demonstrate that these adjectives could be at least subdivided into seven different semantic clusters that denote (1) importance, necessity, and possibility; (2) appropriateness, reasonability, and unreality; (3) impracticability and irrelevance; (4) undeniability and axiomaticity; (5) obviousness; (6) dubiety, desirability, and ease; (7) improbability and anomalousness.

The beginning of 2020 has seen coronavirus spreading to many countries and regions. To contain the virus, China adopted, arguably, the most stringent quarantine countermeasures in the country's history concerning restricting people flows, limiting outdoor activities, and extending the Spring Festival break. On the one hand, thousands of doctors and nurses directly fought and are still fighting coronavirus in various medical facilities; on the other hand, millions and even billions of residents and tourists self-quarantined and are still self-quarantining themselves in their homes (or temporary homes), fighting the virus in another manner. Across cities and regions, which have the highest percentage of "fighters" at home? To answer this, we downloaded and processed the publicly available Baidu Qianxi (migration) data for 11 consecutive days in 2019 and 2020. Then we geovisualized the answer. The visual indicates that several cities in Hubei, as expected, had the highest percentage, followed by several cities in Zhejiang Province and several cities in Guangxi Zhuang Autonomous Region and Hainan Province. In terms of percentage ranking across regions, East China is no.1.

Like pubs in Western countries, restaurants in China are not just places where people consume drinks and eat food. They are also venues where acquaintances, friends, colleagues and families meet, relax, entertain, talk and socialize. In the era of fast-paced life, Fordism, easy online ordering and increased metro mobility, what could happen to restaurants in cities? To answer this, we collected and processed two distinct years (2014 and 2017) of point of interest and metro network data in Shenzhen, China. The visuals based on the data indicate that most metro station areas saw the advent of new restaurants as Shenzhen expanded its metro network, shortening the average travel time by transit between the two officially designated central business districts (CBDs): Luohu and Futian. Several metro station areas in or around the CBDs enjoyed the most growth in new restaurants. In terms of the 10 metro station areas that experienced the greatest decline in the number of restaurants, five of them were in areas that were around 15 minutes' metro travel from the CBDs. The remaining five scattered within suburbs rather than exurbs.

When optimising the overall commuting pattern for a city or a region, there are often winners and losers among commuters at the subdivision level. Losers are those who are burdened with longer commutes than before the optimisation. Knowing who or where losers are is of interest to both researchers and policy-makers. The information would help them efficiently locate losers and compensate them. Few, however, pay attention to such losers. By revisiting 'excess commuting' in the economic framework, we show that optimising the commuting pattern is comparable to restoring Pareto optimality in commuting. Using Beijing as a case study, we identify and geo-visualise the losers when the city's bus commuting pattern is optimised. We examine the severity of the loss among the losers, the spatial pattern of the losers and their influencing factors. We find that most losers are located around the epicenter. The severity of the loss is independent of jobs/housing ratio but is associated with the commute distance before the optimisation. Workers whose commute distance is less than the global average are more likely to become losers. Places where losers reside have significantly lower employment density in a few industries than where non-losers reside. A low jobs/housing ratio in individual subareas does not necessarily increase the average trip length of commuters therein. A low jobs/housing ratio of one or several subareas, however, could influence the average trip length of all the commuters in the area. Locating diverse jobs and housing opportunities around or along transit corridors could compensate the losers and reduce the overall commuting cost.

A complete tour of metro users consists of their journeys inside the carriage and various activities outside the carriage, in particular, those in or around metro station areas (MSAs). To fathom out the spatiality and magnitude of those activities, which involve substantial interactions among people or with urban spaces, we assume that (a) metro users who spent 30 min or more together in or around the same MSA would physically interact with at least another person there; (b) the more an MSA sees metro riders co-presenting there the higher social interaction potential (SIP) there is; (c) SIP of an MSA is positively correlated with the number of distinct riders co-presenting in that MSA. By exploiting two-day metro smartcard data of Wuhan, China, we use the number of distinct riders co-presenting in that MSA to measure and visualize the MSA-level SIP in that city. Our visuals show the SIP varies across MSA and time of the day. Some MSAs have higher SIP in the daytime whereas other MSAs have higher SIP at the nighttime. Few MSAs continuously have high SIP. These results inform us where and when SIP would be the highest and the lowest across MSAs, which can facilitate metro operators' monitoring and management of MSAs on the one hand and help businessmen and officials decide where and when to provide services and/or sell products across MSAs.

As a sustainable mode of travel, walking for transportation has multiple environmental, social, and health-related benefits. In existing studies, however, such walking has rarely been differentiated between commuting and non-commuting trips. Using multilevel zero-inflated negative binomial regression and multilevel Tobit regression models, this study empirically examines the frequency and duration of commuting and non-commuting walking and their correlates in Xiamen, China. It finds that (1) non-commuting walking, on average, has a higher frequency and longer duration than commuting walking; (2) most socio-demographic variables are significant predictors, and age, occupation, and family size have opposite-direction effects on commuting and non-commuting walking; and (3) different sets of built environment variables are correlated with commuting and non-commuting walking, and the built environment collectively influences the latter more significantly than the former. The findings provide useful references for customized interventions concerning promoting commuting and non-commuting walking.

As more and more metro lines and stations have started serving our metropolises, they have (re)shaped our travels and lives, creating (new) venues and (additional) opportunities for serendipitous contacts. To know the odds and locales of serendipitous contacts among millions of metro riders, we employ the smartcard data of the metro riders in Beijing to visualize metro riders' identifical trip trajectories and to visualize/disclose connections between metro station pairs because of these trajectories. We find that the pairs that produce the largest and smallest numbers of identifical trip trajectories are not randomly distributed in the city. Rather, they concentrate in the northwest and in the central, respectively.

Excess commuting, which concerns the differences between the actual commute and the optimal (minimum) commute afforded by a given distribution of jobs and housing, i.e., urban form, has been extensively studied across disciplines. In the existing excess commuting framework, the optimal commute considers commuting efficiency but overlooks commuting equity, which is defined as the variation in commuting cost across workers before and after the optimisation. The framework also overlooks the variation in commuting frequency across workers for a period of interest, which also affects the overall commuting cost for the period. In this paper, we propose a novel excess commuting framework using a Greedy-Initialisation-based Genetic Algorithm, where the optimal commute accounts for commuting efficiency and equity and commuting-frequency variation simultaneously. We illustrate and calibrate the framework using one-month metro smart card data in Shanghai. Comparing with two other existing models, the Greedy-Initialisation-based Genetic Algorithm can generate a commuting pattern that balances commuting efficiency and commuting equity, which the existing commuting framework and corresponding algorithms cannot.