Jiangping Zhou

Familiar strangers: Visualising potential metro encounters in Beijing

Why can cities in general be more productive and richer than villages? Some scholars believe that the main reason lies in economic agglomeration and spillover effects (e.g. Anas et al., 1998). But what exactly can trigger those effects? Glaeser (2012) regarded face-to-face interaction, along with other factors such as human capital, communication, built form, development density and professional network, as the premise and booster of the effects. Cities with tall buildings and high population/employer density may be a pain for those who prefer and enjoy a countryside lifestyle; however, such cities can better facilitate a variety of face-to-face interactions, with ‘familiar strangers’ as one of them. Familiar strangers are those urban residents or visitors who encounter one another at various locales in the city. Prior to the emergence of big data such as smartcard and cellular network data, it is mostly through discrete evidence, anecdotal personal experience and/or movie/novel episodes that we know the existence of the familiar-stranger phenomenon and perceive its magnitude. Nobody knows exactly where familiar strangers encounter the most, how many familiar strangers on average there are at a specific locale and which factors would affect the distribution and the number of familiar strangers at a locale. Using a week’s smartcard and cellular network data and the average number of points of interest (POIs) in a year of Beijing, the capital of China with a population over 20 million, we have created three visuals for us to fathom the distribution and the number of familiar strangers at different metro-served areas (MSAs) – metro stations and their respective surroundings – and two factors we speculated that influence the former: the active cellphone users, which represent the pool of people who can potentially encounter, and the POIs, which are opportunities/destinations that people would go for. Our smartcard and cellular network data are both for 10–14 August 2015. Based on these data, we derived on average 2,664,796 distinct smartcard holders (assuming each user has one distinct smartcard) and approximately 17 million distinct cellphone users (assuming each user has

Integrating road carrying capacity and traffic congestion into the excess commuting framework: The case of Los Angeles:

The excess commuting framework has advanced a series of metrics through which a city or a region’s jobs-housing balance and commuting efficiency can be measured. This study seeks to add to the conceptual development and extension of the excess commuting framework. Specifically, it considers the carrying capacity (of links) and related congestion issues in the excess commuting framework and demonstrates that overlooking these characteristics has important implications for excess commuting metrics. Drawing on an empirical case study, it shows that when carrying capacity and traffic congestion are accounted for, the observed commute is longer than otherwise. Excess commuting tends to be higher than its counterparts in previous excess commuting studies. The findings suggest that future excess commuting studies should take account of carrying capacity and congestion in determining excess commuting metrics. Moreover, high-quality connections (preferably via public transport) between jobs and housing allied with sufficient carrying capacity of popular links/routes for commuters are crucial preconditions for cities and regions to harvest the full benefits of jobs-housing balance policies targeted at the reduction of the average commute distance and vehicle miles travelled.

Someone like you: Visualising co-presences of metro riders in Beijing

Co-presence is a concept and a phenomenon of lasting interest among sociologists and the like (e.g. see Cooley, 1956; Mead, 1934; Zhao, 2003). It has two dimensions: co-presence as mode of being with others and co-presence as sense of being with others (Zhao, 2003). The former emphasizes the physical conditions that ‘structure human interaction’, whereas the latter stresses the ‘subjective experience of being with others’ (Zhao, 2003: 445). As cities become more and more populous and widespread, regardless of whether we like it or not, anonymity has become a defining characteristic of our daily lives (Milgram, 1977). Despite such anonymity, there are still recurrent physical co-presences of people in different locales and on different journeys that engender many ‘familiar strangers’ for us: we only know them by their respective faces and we never interact. They are treated as indispensable part of the environment or scenery (Milgram, 1977). We feel something missing if they are absent. They may look insignificant on the surface whereas are a precondition for more complex and meaningful social interactions that are crucial to a healthy community/city (Gehl, 2011). Relying on traditional methods such as photographs and interviews, Milgram (1977) quantified and visualised physical co-presences of a small group of train riders at the platform level. Can we replicate that for thousands even millions of urban residents or visitors? It seems to be a daunting task. We are, however, still eager to accomplish it, hoping to acquire such knowledge as where and when co-presences are most likely to occur and what are the characteristics of the “where” and “when”. Zhou et al. (2018) were able to quantify and

Mapping cities by transit riders’ trajectories: The case of Brisbane, Australia

Emerging non-traditional data (NTD) such as transit agencies smartcard data and Googles 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.