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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. [ABSTRACT FROM AUTHOR]

The impacts of coronavirus disease 2019 (COVID-19) on society and economy are wide-ranging, long-lasting, and global. The experience of multiple countries or regions in fighting the pandemic indicates that there could be multiple COVID-19 surges, where a growing number of cases can be observed in the more recent surge(s). Were COVID-19 cases and clusters of cases (across surges) randomly distributed in spaces? Did population density and activity centres influence clusters of cases and associated venues? Based on information on the associated venues of the four surges of COVID-19 cases between January 2020 and February 2021 as well as population density, visuals were made to distinguish the relationships between population density, activity centres, and clusters of cases in Hong Kong. Different spatial patterns were observed across the four surges: fewer cases were observed in the first surge with a more evenly distributed pattern of clusters; the second surge as compared to the first surge saw a wider distribution and an increase in the number/layer of clusters; compared to the second surge, the third surge suffered from many more cases but saw a decrease in the general number of clusters; and compared to the previous three surges, the fourth surge had the largest number of cases, yet even fewer clusters were observed, where several clusters are again concentrated in specific areas similar to the previous surge. Across the four surges, a few locales could see recurrent clusters of cases and a few communities were without cases. [ABSTRACT FROM AUTHOR]

China is generally considered a safe place: among the safest for foreigners to visit. For local (long-term) residents and for Chinese criminology scholars, China as a country and its individual cities might be safer compared to many of their foreign peers; however, they might not be as safe as some travel agents claim. To show why this is the case, we crawled and geovisualized the 2015–2016 crime records (n = 24,803) available to the public online. The geovisualization shows that the seven crimes (n = 12,516) that were most likely to be influenced by space in Beijing were aggravated assault, blackmail, cheating and bluffing, dangerous driving, picking quarrels and provoking trouble, robbery and theft. It also shows a Beijing that many might not have known before. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article discusses the co-presence which is a concept and a phenomenon of lasting interest among sociologists that has two dimensions such as co-presence as mode of being with others and co-presence as sense of being with others. It states that smartcard swipe data were used to quantify and visualise physical co-presences of metro riders along certain routes of the local metro system in Beijing, China.

The article offers information on big data such as smartcard and cellular network data have great potential in visualising and explaining such complex urban phenomena as encounter. It mentions information on smartcard and cellular network data and the average number of points of interest (POIs) in a year of Beijing, the capital of China.

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. [ABSTRACT FROM AUTHOR]

In contemporary cities, the catering sector is much more than only food and/or eating. On the one hand, it is an important source of income, employment and waste; on the other hand, it provides many opportunities for eating out and social interaction. For a given city, where does the catering sector concentrate the most and how far are people, on average, from this location across hours of a day? Are people, on average, closer to the concentration around the prime dinner time? Taking advantage of open and big data, we geovisualised the answers in the context of Beijing, the capital of China, with a population of 22+ million people. Specifically, the data we used were the 2015 points of interest of Baidu Map (the Chinese counterpart of Google Maps) and 10–16 August 2015 cellular network data for Beijing. The former provide us with the number and location of most if not all restaurants around the local metro-served area (MSA) – that is, areas that are within an 800-metre radius of a metro station. The latter allow us to derive the average number of cellular phone users (as a proxy for the general population, residents and/or employees) by MSA across hours of a day. We depict the top five population/employment/residential centres (quantified by the average number of cellular phone users) at three different times of the day, and the top 10 food centres (measured by the number of restaurants) at the MSA level in Beijing. [ABSTRACT FROM AUTHOR]

Emerging non-traditional data (NTD) such as transit agencies' smartcard data and Google's General Transit Feed Specifications (GTFS) have made it easier to unveil the way in which public transit remains relevant, reveal how it facilitates daily mobility, and highlight the way in which different locales across a metropolitan area are connected by public transit. Based on a 24-h period of smartcard data for Brisbane (4 March 2014) allied with GTFS data, we retrieved 205,560 distinct transit riders' trip trajectories by direction (AM/inbound vs. PM/outbound) in Brisbane, Australia. It visualises the trajectories using a waterpark metaphor, in that, like water, people flow downhill. [ABSTRACT FROM AUTHOR]

The article focuses on the visualization of the employment density in Shanghai, China through three-dimensional (3D) format considering the country's economic census data. Topics discussed include the significant of the visualized data on analyzing the impact of distribution, the application of quadrivariate normal distribution model, and the impact of metro lines on the city's employment distribution.

The article focuses on the dual concept of urban form with compact development with concentration of employment, clusering of housing and mixing of land uses in Shanghai, China. It mentions that causes, consequences and cures of urban sprawl for urban planners with emerging big data such as cellular network data. It also mentions that exurbs was chosen and the dispersal of residences or workplaces.

The article focuses on a study aimed at mapping the commuting with cellular network data for solving problem of traffic jam in Shanghai, China. It mentions need for quantifying and visualizing the location of commuters where they reside and work. It states various solutions for dealing with traffic jam such as increasing the carrying capacity of the transportation system, reducing number of commuting trips and distributing commuting trips across modes of travel, space and time.