Environment and Planning B: Urban Analytics and City Science

In this study, we examine how different features of the built environment—density, diversity of land uses, and design—have consequences for personal networks. We also consider whether different features of the built environment have consequences for the spatial location of persons to whom one is tied by considering their distribution in local area, broader city region, and a more macro spatial scale. We test these ideas with a large sample of the Western United States for three different types of ties. Our findings suggest that the built environment is crucial for personal network structure, both in the number of social ties and where they are located.

In this paper, we demonstrate an integrated spatio-social network analysis to measure the degree of segregation within a Chinese urban neighborhood in terms of the everyday activities by rural migrants and local urban residents at such routine venues as restaurants, grocery stores, barber shops, etc. Our data were collected in May 2014, through an integrated geographical and social survey conducted within an inner-city neighborhood around Kecun in south China’s Guangzhou Municipality. Although Kecun features a highly condensed and mixed dwelling pattern between the rural migrants and indigenous urban population, we find that within our sample of 110 local and 132 migrant residents, the former tend to socialize more inwardly with their peer locals and visit neighborhood amenities more often such as pubs, stadia, and public kindergartens. In contrast, the migrants tend to attend local roadside food stalls, outdoor recreation facilities, small clinic shops, grocery malls, and private (minban) kindergartens more often. Overall, only a modest degree of social interaction between the locals and migrants appears to exist in Kecun. On top of the methodological implications of our study, we argue that urban segregation in China is both socially and spatially different from its Anglo-American counterpart. More empirical research is needed to understand and assess social segregation underlying the everyday urban life in Chinese cities.

Studies on the jobs–housing balance and self-containment of employment are mainly focused on observed journey-to-work trips using travel survey data. This study examines the relationship between the jobs–housing balance and the self-containment of employment through the use of mobile phone location data in Shenzhen, a megacity in southern China. Individual-level journey-to-work trips are explored based on mobile phone location big data. Self-containment of employment in the suburban districts is higher than that in the central districts. The effect of the jobs–housing balance on self-containment of employment is examined at a 2 km grid level. Jobs–housing balance policies positively affect the self-containment of employment in the suburban districts, but its effect is limited in the central districts. Two extreme commuting spectrum measures are used to analyze self-containment of employment in different journey-to-work scenarios with the same jobs–housing distribution. Workers are disaggregated into secondary and tertiary sector workers according to job types. The self-containment of employment is found to be mainly affected by the local jobs–housing balance for secondary-sector workers and the regional city level job distribution for tertiary-sector workers. The extreme scenarios of commuting behavior using the commuting spectrum method can provide benchmarks that can help to understand the observed self-containment of employment better.

While urban planners and transportation geographers have long emphasized the importance of social influences on individual travel behavior, many challenges remain to bridge the gap between complex conceptual frameworks and operational behavioral models. Improving the ability of models to forecast activity-travel behavior can provide greater insights into urban planning issues. This paper proposes a new model framework by evaluating how individual travel behavior is influenced by inter- and intra-household interactions. The built environment, land-use mix, and social interactions influence household member choices among different transport modes. We propose a spatial multivariate Tobit specification that allows each individual to face a set of potential destinations and transport modes and takes into consideration the travel behavior of other household members and nearby neighbors. Using the Greater Cincinnati Household Travel Survey, we analyzed more than 37,000 trips made by 1968 individuals located in Hamilton County in Cincinnati, Ohio. Results reveal that social influences and the built environment have a strong impact on the willingness to walk and to cycle.

Gentrification, the rise of affluent socioeconomic populations in economically depressed urban neighborhoods, has been accused of disrupting community in these neighborhoods. Social media networks meanwhile have been recognized not only to create new communities in neighborhoods, but are also associated with gentrification. What relation then does gentrification and social media networks have to urban communities? To explore this question, this study uses social media networks found on Twitter to identify communities in Washington, DC. With space-time analysis of 821,095 geo-tagged tweets generated by 77,528 users captured from 15 October 2015 to 18 July 2016, we create a location-based interaction measure of tweets which overlays the social networks of the comprising users based on their followers and followees. We identify gentrifying neighborhoods with the 2000 Census and the 2010–2014 American Community Survey at the block group level. We then compare the density of location-based interactions between gentrifying and nongentrifying neighborhoods. We find that gentrification is significantly related to these location-based interactions. This suggests that gentrification indeed is associated with some communities in neighborhoods, though questions remain as to who has access. Making novel use of big data, these results demonstrate the important role built environment has on social connections forged “online.”

The increasing availability of urban trajectory data from the GPS-enabled devices has provided scholars with opportunities to study urban dynamics at a finer spatiotemporal scale. Yet given the multi-dimensionality of urban trajectory dynamics, current research faces challenges of systematically uncovering spatiotemporal and societal implications of human movement patterns. Particularly, a data-driven policy-making process may need to use data from various sources with varying resolutions, analyze data at different levels, and compare the results with different scenarios. As such, a synthesis of varying spatiotemporal and network methods is needed to provide researchers and planning specialists a foundation for studying complex social and spatial processes. In this paper, we propose a framework that combines various spatiotemporal and network analysis units. By customizing the combination of analysis units, the researcher can employ trajectory data to evaluate urban built environment dynamically and comparatively. Two case studies of Chinese cities are carried out to evaluate the usefulness of proposed conceptual framework. Our results suggest that the proposed framework can comprehensively quantify the variation of urban trajectory across various scales and dimensions.

We introduce a version of the Huff retail expenditure model, where retail demand depends on households’ access to retail centers. Household-level survey data suggest that total retail visits in a system of retail centers depends on the relative location pattern of stores and customers. This dependence opens up an important question—could overall visits to retail centers be increased with a more efficient spatial configuration of centers in planned new towns? To answer this question, we implement the model as an Urban Network Analysis tool in Rhinoceros 3D, where facility patronage can be analyzed along spatial networks and apply it in the context of the Punggol New Town in Singapore. Using fixed household locations, we first test how estimated store visits are affected by the assumption of whether shoppers come from homes or visit shops en route to local public transit stations. We then explore how adjusting both the locations and sizes of commercial centers can maximize overall visits, using automated simulations to test a large number of scenarios. The results show that location and size adjustments to already planned retail centers in a town can yield a 10% increase in estimated store visits. The methodology and tools developed for this analysis can be extended to other context for planning and right-sizing retail developments and other public facilities so as to maximize both user access and facilities usage.

The rapid development of information and communication technology has led to the Internet and social media becoming a vital platform for public participation in China. The present research sought to understand the complexity of participation in the network society by taking the cancellation of the number 55 bus route in Shanghai as a case study. Both qualitative and quantitative research methods were used to analyze data from a leading social networking site in China. An analysis of participation patterns led to an understanding of the main characteristics of public participation in the network society, and a statistical analysis of the network revealed the features of elite participants in the planning adjustment. A qualitative approach was also used to explore the communication process, which was influenced by Chinese social capital—guanxi. The case study revealed an uneven pattern of public participation in the network society, and suggestions are provided to enhance fairness and effectiveness in this process.

Development of urban networks of cities and towns has received attention including discussions of tensions between population concentrations and overlaps with environmentally sensitive and disaster-prone areas. Moreover, certain development in broad regions of China, such as its deltas, has become a subject of debate. Contrary to some assumptions, this development within places like the Changjiang Delta (also known as the Yangtze River Delta) has proceeded in a relatively incremental manner. However, at this juncture, controlled development of larger cities, like Shanghai, has shifted to more conventional urbanization pathways forward involving larger city expansions. Nevertheless, further urban growth management appears to depend on development and maintenance of a well-balanced network of large, medium, and small-scaled cities and towns. An important aspect of this development involves definition of the Changjiang Delta region itself, and in particular, alongside its likely further economic performance. To these ends, a scenario-based Cellular Automata model of spatial distribution is deployed, reflecting separate thematic projections. A baseline for economic performance is developed, incorporating measures of fixed-asset investment in urban service, revenue from urban maintenance, and Gross Domestic Product. Revelation of a well-performing network involves spatial distribution of development at various scales, and in various concentrations within the region, moreover, location of this development, largely perpendicular to well-travelled corridors, appears as a preferable outcome, contrary to earlier depictions along the major transportation corridors.

For centuries, philosophers, policy-makers and urban planners have debated whether aesthetically pleasing surroundings can improve our wellbeing. To date, quantifying how scenic an area is has proved challenging, due to the difficulty of gathering large-scale measurements of scenicness. In this study we ask whether images uploaded to the website Flickr, combined with crowdsourced geographic data from OpenStreetMap, can help us estimate how scenic people consider an area to be. We validate our findings using crowdsourced data from Scenic-Or-Not, a website where users rate the scenicness of photos from all around Great Britain. We find that models including crowdsourced data from Flickr and OpenStreetMap can generate more accurate estimates of scenicness than models that consider only basic census measurements such as population density or whether an area is urban or rural. Our results provide evidence that by exploiting the vast quantity of data generated on the Internet, scientists and policy-makers may be able to develop a better understanding of people's subjective experience of the environment in which they live.