Author Correction: Networks and identity drive the spatial diffusion of linguistic innovation in urban and rural areas
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Networks and identity drive the spatial diffusion of linguistic innovation in urban and rural areas
Cultural innovation (e.g., music, beliefs, language) tends to be adopted regionally. The geographic area where innovation is adopted is often attributed to one of two factors: (i) speakers adopting new behaviors that signal their demographic identities (i.e., an identity effect), or (ii) these behaviors spreading through homophilous networks (i.e., a network effect). In this study, we show that network and identity play complementary roles in determining where new language is adopted; thus, modeling the diffusion of lexical innovation requires incorporating both network and identity. We develop an agent-based model of cultural adoption, and validate geographic properties in our simulations against a dataset of innovative words that we identify from a 10% sample of Twitter (e.g., fleeky, birbs, ubering). Using our model, we are able to directly test the roles of network and identity by comparing a model that combines network and identity against simulated network-only and identity-only counterfactuals. We show that both effects influence different mechanisms of diffusion. Specifically, network principally drives spread among urban counties via weak-tie diffusion, while identity plays a disproportionate role in transmission among rural counties via strong-tie diffusion. Diffusion between urban and rural areas, a key component in innovation spreading nationally, requires both network and identity. Our work suggests that models must integrate both factors in order to understand and reproduce the adoption of innovation.
The spatial coupling and its influencing mechanism between rural human-habitat heritage and key rural tourism villages in China
Exploring the influencing factors and its influencing mechanism of the spatial coupling between rural human-habitat heritage (RHH) and key rural tourism villages (RTV) at county scale from the perspective of space can expand the theoretical research on the spatial coupling mechanism between RHH and RTV, and further provide theoretical reference and data support for the coordinated development and high-quality development of RHH and RTV in China. At the same time, previous studies have failed to systematically analyze the influencing factors and influencing mechanisms of the spatial coupling between RHH and RTV at the county scale, which restricted decision makers from formulating coordinated development measures between RHH and RTV at the macro level. In this study, bivariate spatial autocorrelation model and spatial coupling coordination model were used to quantitatively analyze the spatial coupling level between RHH and RTV at the county scale in China. Then, the linear regression (OLS) model, geographically weighted regression (GWR) model, and optimal parameter GeoDetector (OPGD) model were integrated to systematically analyze the linear influencing, spatial heterogeneity effect and interactive effect of natural environment and socioeconomic factors on the spatial coupling level between RHH and RTV in China, and explore the interactive influencing mechanism. The results show that the spatial coupling level of RHH and RTV in China show a significant east-west differentiation. There were 2024, 473, 293, 55 and 6 areas of severe, moderate, mild, basic and moderate coordination between RHH and RTV, respectively. Among them, severe and moderate discoordination areas are mainly distributed in Northeast China, arid and semi-arid areas in Western China, plateau areas in Southwest China, densely populated urban agglomerations and plains agricultural areas in the Middle East China. Mild discoordination areas and basic and moderate coordination areas are mainly located in transition zones in mountainous and plain areas, economically developed mountainous and hilly counties along the southeastern coast, and coastal tourist cities. Economic and population factors are the fundamental factors that affect the spatial coupling between RHH and RTV. Rural tourism facilities and rural public service facilities are important external driving forces for the coupling development of RHH and RTV, and Sociocultural environment factors are the important internal driving forces. Different surface forms, different climate conditions and different ecological environment conditions can form different natural textures and spatial organizations. Suitable climate conditions, sufficient water sources and ecological environment conditions can form more suitable rural settlement construction conditions and production and living conditions, and ultimately affect the protection and activation of rural human settlement heritage and the development and layout of key tourist villages. The spatial coupling relationship between RHH and RTV is the result of the complex interaction between the natural directivity law caused by natural environmental factors and the humanistic directivity law caused by human social and economic activities.
Uneven diffusion: a multi-scale analysis of rural settlement evolution and its driving forces in China from 2000–2020
In recent years, the spatial and temporal patterns of rural settlement expansion in China have shifted significantly due to rapid urbanization and industrialization. This study examines rural settlement expansion in China from 2000 to 2020, using the Landscape Expansion Index (LEI) and GIS spatial analysis to assess changes in land use scale and related factors. The findings reveal that: (1) From 2000 to 2020, China saw a rapid and large-scale expansion of rural settlements, with the total area increasing by 40,322.74 km², 87.42% of which resulted from outlying expansion, indicating a clear diffusion trend. (2) The movement of rural settlements has predominantly followed a southeast–northwest axis, focusing on the middle reaches of the Yangtze River, with a clockwise rotation shift. (3) Settlement expansion has been primarily concentrated in low-elevation, waterfront, and road-adjacent areas, where GDP per capita and population density significantly influence settlement patterns. These results offer valuable insights for optimizing the spatial distribution and industrial restructuring of rural settlements, as well as for guiding rural spatial planning and industrial policy development.
The decreasing housing utilization efficiency in China’s cities
‘Ghost cities’ are a well-known phenomenon of (almost) complete vacancy of urban living space in China. Underutilization of urban living space, however, is far more common than complete vacancy. Here we propose the concept of housing utilization efficiency (HUE) and present the following findings: (1) the overall HUE in China’s highly urbanized areas decreased from 84% in 2010 to 78% in 2020, (2) the HUE in central, old urban areas was generally lower than that in the outer layers of urban areas and declined more from 2010 to 2020 and (3) four development types are found to represent different patterns of urban population movement, urban housing growth and HUE change at the intraurban level. These findings provide comprehensive insight into the discrepancies between urban housing supply and demand in China and highlight their connections to the country’s particular urbanization characteristics and policies, which are crucial for future housing development and planning.
Urban growth strategy in Greater Sydney leads to unintended social and environmental challenges
Cities have advanced in terms of economic and social status over the past five decades, improving the living conditions of hundreds of millions of people. However, population growth and urban expansion have put pressure on social and environmental conditions. This study examines urban policymakers’ perceptions about causal relationships in the urban system as revealed in urban planning reports. Here we analyzed 500 pages from published urban plans of Greater Sydney between 1968 and 2018 and coded the text into causal maps. The findings show that policymakers adopted a dominant urban development strategy over the past 50 years to pursue economic and public infrastructure growth. Over time, this growth strategy resulted in a number of social and environmental challenges that negatively impacted societal well-being. Although policymakers eventually recognized the seriousness of social and environmental challenges, they never attempted to fundamentally change the dominant growth strategy. Instead, policymakers sought to address the challenges (that is, symptoms) by responding to each issue piecemeal.
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