The livability environment is an important aspect of urban sustainable development. The floating population refers to people without local hukou (also called ‘non-hukou migrants’). The floating population distribution is influenced by livability environment, but few studies have investigated this relationship. Especially, the influence of social environment on floating population distribution is rarely studied. Therefore, we study 1054 communities in Guangzhou’s urban district to explore the relationship between livability environment and floating population distribution. The purpose of this article is to study how livability environment affects floating population distribution. We develop a conceptual framework of livability environment, which consists of physical environment, social environment and life convenience. A cross-sectional dataset of the impact of livability environment on the floating population distribution is developed covering the proportion of floating population in the community as the dependent variable, eight factors of livability environment as the explanatory variables, and two factors of architectural characteristics and one factor of location characteristics as the control variables. We use spatial regression models to explore the degree of influence and direction of physical environment, social environment and life convenience on the floating population distribution in livability environment. The results show that the spatial error model is more effective than ordinary least squares and spatial lag model models. The five factors of the livability environment have statistical significance regarding floating population distribution, including four social environment factors (proportion of middle- and high-class occupation population, proportion of highly educated people in the population, proportion of rental households, and unemployment rate) and regarding life convenience factors (work and shopping convenience). The conclusion has value for understanding how the social environment affects the residential choice of the floating population. This study will help city administrators reasonably guide the residential pattern of the floating population and formulate reasonable management policies, thereby improving the city’s livability, attractiveness and sustainable development.

The residential location choice of the highly educated population is an important consideration to construct a livable city. While landscape and environment are important factors, few studies have deeply analyzed the spatial heterogeneity effects of landscape and environment on the residential location choices of a highly educated population. Taking Guangzhou as the sample, we built a livability-oriented conceptual framework of landscape and environment, and constructed datasets for highly educated population proportion, landscape, and environment factors, and other influencing factors for Guangzhou’s 1364 communities. Global regression and geographically weighted regression (GWR) models are used for analysis. The GWR model is more effective than the global regression model. We found spatial heterogeneity in the strength and direction of the relationship between the highly educated population proportion and landscape and environment. We find that landscape and environment exert spatial heterogeneity effects on the residential location choice of the highly educated population in Guangzhou. The conclusions will be of reference value to further understand how the spatial limitations of landscape and environment affect residential location choices. This study will help city managers formulate spatially differentiated environment improvement policies, thereby increasing the city’s sustainable development capabilities.

The unprecedented development of the internet has compelled a growing number of tourists to share their photographs on social media. These images convey valuable memories and points of interest. As photography and content sharing have become commonplace among visitors, pictorial digital footprints represent a prevalent topic in tourism research. Studies on tourists’ movement trajectories hold great importance for destination management, marketing, and services. Flickr is a popular source in photo-based tourism research given the digital footprints embedded in photos’ metadata; however, the site’s bottlenecks (e.g. declining user activity, overly professional photographs) raise concerns. Scholars have instead gradually shifted their attention to emerging photo platforms such as Instagram—yet these pictures do not contain geographical information. Taking Beijing as a focal location, we introduce an approach in which landmark recognition complements the geographical cues in Instagram photos. Instagram check-in data and data identified through landmark recognition are validated. Ultimately, the recognized landmark information appears highly correlated with check-in data. This study demonstrates the feasibility of landmark recognition for extracting tourists’ footprints from ordinary content in user-generated photos. Findings also confirm that many photos from general social media platforms can serve as alternative and representative data sources in photo-based tourism research.

Hotel chains shape the industry’s development and competition by locating multiple business units in multiple regions. However, how chain hotels are located and regionalized is severely under-researched. A better understanding of hotel chains’ location and regionalization patterns is key to the studies of the hotel industry, which has been increasingly dominated by chains. Six chains from the emerging economy hotel segment in China were selected, and their location patterns from 2004–2019 were explored through the hotspot detection method. Subsequently, a heuristic method for geographic regionalization based on machine learning was applied to explore the spatial-temporal heterogeneity in the six chains’ dominant markets. The results showed that (1) hotel chains tended to form different spatial distribution patterns, although they adopted the same location strategy; (2) each chain, regardless of size, had one or more dominant market(s); and (3) most chains’ location patterns remained relatively stable during the study period. Our exploratory study contributes to the literature by illustrating hotel chains’ location patterns and their evolution, alongside the development and evolution of their dominant-market regions. It also offers practical contributions by recommending that chain operators develop their location patterns based on their resources and capabilities and try to foster dominant market(s) in which they are absolute leaders.