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Following nearly a decade of unabated growth in the digitally disruptive short-term rental (STR) sector, the onset of the COVID-19 pandemic in 2020 caused a relatively abrupt two-year industry contraction characterised by substantial reductions in participating properties and booking activities. Given the sudden opportunity to rethink how STR markets fit alongside more traditional accommodation arrangements, this unanticipated Black Swan event catalysed much debate about the role that STRs play, and how they are regulated and managed. As the STR market rebounds toward a ‘new normal’, a timely opportunity exists to understand and theorise how and where the industry evolves. In this paper, we apply regression modelling and descriptive statistics to compare STR property utilisation across four regional markets in Australia both before and after the pandemic. We observe a sharp reduction yet strong intensification in available properties after the COVID-19 pandemic, with 27% fewer properties that were booked 26% more often across the nation. This was led by a significant geographical shift from capital cities to high-amenity, tourist-focused regions. Our findings suggest that this pivot is driven by spatial market segmentation, in which urban STRs are operated professionally, whereas regional markets are distinguished by their amenity value, both at the regional and property scales. Combined, the results of this study point to a more professionalised STR hosting landscape in which properties are more intensively used. Any potential policy reform should consider this evolution in the STR accommodation sector.

Abstract As Japan faces a growing crisis of vacant housing, this study investigates the underexplored relationship between vacant house distribution and neighbourhood characteristics in Toda City, a satellite city of the Tokyo metropolis located in Saitama Prefecture. We selected 10 community features, including living convenience, housing supply, and policy support, to explore their influence on vacancy rates. Using a comprehensive dataset primarily derived from the PLATEAU data, we employed a two-step method combining global and local Moran’s I analyses to assess the spatial distribution normality of vacant houses. We then compared traditional statistical data analysis with spatial data analysis through multiscale geographically weighted regression to evaluate their effectiveness and identify the factors most closely associated with vacancy rates. Our findings reveal that spatial data analysis provides superior insights compared to traditional statistical methods. Both approaches consistently indicate a significant positive correlation between the supermarket area ratio and vacancy rates, and a significant negative correlation between road adjacency and vacancy rates. This study demonstrates the feasibility of using spatial data to analyse neighbourhood characteristics in the context of declining vacancy rates in satellite cities. The insights gained offer theoretical support for selecting data and methodologies in future research and policymaking that are aimed at mitigating the growth of vacant houses.

Worldwide, the gender gap in urban cycling is considerable, with most cyclists being young to middle-aged men. In the current study, we first capture the suite of cycling barriers facing women before empirically investigating whether and how much three natural barriers (inclement weather, hilliness, and darkness) impact female users of bikesharing systems. For the analysis, we spatially integrate gender for more than 200 million bikesharing trips with fine-grained weather, gradient, and sunset/sunrise data. Computing a suite of the generalized additive models for ten cities worldwide covering a period of 14 years, we find that wind and precipitation disincentivise cycling, and more so for women than for men. Similarly, steeper gradients are a significant barrier for female bikeshare users for many cities. In every city, women make fewer trips in the dark (i.e., before sunrise and after sunset) compared to men. In higher-cycling cities, regardless of natural barriers, cycling declines less with age for women compared to other cities. To overcome the barriers presented by inclement weather, hilliness, and darkness we recommend (a) partial electrification of bikesharing fleets, (b) reduced exposure along bicycle paths (through manufactured shelters or tree canopies), and (c) adequate nighttime lighting along cycling paths. In the spirit of open science, all data and code on which this paper is based have been provided on Mendeley: https://data.mendeley.com/datasets/vmy42hywwx/1.

Our understanding of non-linear and time varying effects on shared e-scooter ridership dynamics is limited. Consequently, both operators and city councils supporting shared e-scooter schemes do not have the requisite information to help optimise infrastructure planning and operation management. Focussing on subtropical Brisbane, Australia, the current study examines time varying and non-linear effects of weather and built environment factors on shared e-scooter ridership. Results from XGBoost models reveal threshold relationships with both the availability of cycling infrastructure and the presence of park and commercial land uses. Additionally, we show how hot weather increases ridership especially around large parks and in commercial areas on both weekdays and weekends. Understanding the intricate (non-linear) interplay (interaction) between weather and built environment factors and their variation over time on shared e-scooter ridership have important implications for policymakers, transportation planners, and environmental advocates in providing the requisite evidence for data driven decision making.

Over the past century, the dramatic increase in private vehicles has resulted in a growing demand for parking spaces. Governments have sought to develop and adopt various parking policies to manage and regulate parking, but the results are mixed. Current research studies on parking policies often focus on a single aspect of parking. A comprehensive review and comparison of on-street and off-street parking policies (existing, proposed, and recently adopted) has been missing. This study addresses this gap by surveying 57 parking experts in 18 countries across six continents. In addition to reviewing parking policy measures, we consider implementation outcomes and barriers to parking reform. The results show that the countries have responded to parking innovations differently, and not all novel policies have been successful. Social, practical, institutional, and legal barriers are found to exist throughout the policymaking and implementation stages. Ultimately, there is no one-size-fits-all parking solution. Findings suggest that to achieve success, it is important to include all stakeholders in discussions around parking reform and accompany any parking restrictions by public and active transport improvements. In the medium-term future, it is likely that new technological developments will shift the direction of parking policy.

Climate change caused by rapid urbanization in the Guanzhong region of China is becoming an increasingly significant problem. Previous empirical studies have confirmed that landscape patterns inextricably linked with the thermal environment, but static results based on a single temporal cross section of image data provide only a partial understanding. In this paper, we constructed a dynamic framework using Weather Research and Forecasting Model (WRF) for temperature simulation and Geodetector to study the landscape factors and their interactions that influence near-surface temperature (NST) changes in the Guanzhong Plain Urban Agglomeration (GPUA) between 2000 and 2020. Results showed that the GPUA average NST increased by 0.012 °C and 0.053 °C in January and July from 2000 to 2020, respectively. In terms of the dynamic correlation between landscape patterns and NST, cropland (CPL) was negative, urban land (UBL) was positive, and the remainder of the landscapes differed in winter and summer. Furthermore, results from the Geodetector showed that UBL embodied a stronger influence in summer than during winter months. This finding helps to explain why the average NST increase is higher in summer than during winter. The Dynamic Q values (DQ) of the area-based landscape metrics were generally larger than those of other spatial configuration metrics, and the interaction results showed that the landscape metrics of various land-cover classifications were enhanced, indicating that the superposition effect among landscape metrics needs to be taken into account in landscape planning in addition to area factors. The study of the relationship between landscape patterns and thermal environment considering dynamic perspective using WRF offers an important theoretical reference allied with practical guidance for understanding and adapting to forthcoming change in our climate through which we can help drive sustainable development decisions of the GPUA.

Abstract The weather-crime association has intrigued scholars for more than 150 years. While there is a long-standing history of scholarly interest in the weather-crime association, the last decade has evidenced a marked increase in the volume and diversity of empirical studies concerned with weather and its social implications including crime. In this paper we conduct a review of the empirical literature on weather and crime. This is the first review on the topic for more than 30 years. We highlight the theoretic and empirical challenges that persist and act to impede our understanding and chart four interrelated thematic areas that require concerted attention. These are to: (1) strengthen and integrate theoretical explanations of the weather-crime association; (2) model multi-dimensional processes of the weather-crime association; (3) broaden the situational, climatic, and cultural contexts of study; and (4) to translate research into policy and practice. We conclude by advocating for scholarship to advance the four thematic areas such that we progress towards a more complete and robust understanding of the weather-crime association.

Abstract Assessing vulnerability to natural hazards is at the heart of hazard risk reduction. However, many countries such as Australia lack measuring systems to quantity vulnerability for hazard risk evaluation. Drawing on 41 indicators from multiple data sources at the finest spatial unit of the Australian census, we re-forged the Cutter’s classic vulnerability measuring framework by involving the ‘4D’ quantification of built environment (diversity, design, density and distance), and constructed the first nationwide fine-grained measures of vulnerability for urban and rural locales, respectively. Our measures of vulnerability include five themes—(1) socioeconomic status; (2) demographics and disability; (3) minority and languages; (4) housing characteristics; and (5) built environment—that were further used to assess the inequality of vulnerability to three widely affected natural hazards in Australia (wildfires, floods, and earthquakes). We found the inequality of vulnerability in the affected areas of the three hazards in eight capital cities are more significant than that of their rural counterparts. The most vulnerable areas in capital cities were peri-urban locales which must be prioritised for hazard adaptation. Our findings contribute to the risk profiling and sustainable urban–rural development in Australia, and the broad understanding of place-based risk reduction in South Hemisphere.

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation’s income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public’s real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

As climate change produces more extreme weather, it is increasingly important to understand the impacts of these changes on social behaviour. The relationship between weather and crime has been studied across numerous contexts. However, few studies examine the correlation between weather and violence in southern, non-temperate climates. In addition, the literature lacks longitudinal research that controls for international changes in crime trends. In this study, we examine over 12 years of assault-related incidents in the state of Queensland, Australia. Controlling for deviations in trend for temperature and rainfall, we explore the relationship between violent crime and weather across Köppen climate classifications. Findings provide important insight into the impact of weather on violence across temperate, tropical, and arid climate regions.

Abstract Background COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. Results The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion. Conclusions Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries’ settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense.

Abstract Background The existing smartphones’ technology allows for the objective measurement of a person’s movements at a fine-grained level of geographic and temporal detail, and in doing so, it mitigates the issues associated with self-report biases and lack of spatial details. This study proposes and evaluates the advantages of using a smartphone app for collecting accurate, fine-grained, and objective data on people’s transport-related walking. Methods A sample of 142 participants (mostly young adults) was recruited in a large Australian university, for whom the app recorded all their travel activities over two weekdays during August–September 2014. We identified eight main activity nodes which operate as transport-related walking generators. We explored the participants’ transport-related walking patterns around and between these activity nodes through the use of di-graphs to better understand patterns of incidental physical activity and opportunities for intervention to increase incidental walking. Results We found that the educational node — in other samples may be represented by the workplace — is as important as the residential node for generating walking trips. We also found that the likelihood of transport-related walking trips is larger during the daytime, whereas at night time walking trips tend to be longer. We also showed that patterns of transport-related walking relate to the presence of ‘chaining’ trips in the afternoon period. Conclusions The findings of this study show how the proposed data collection and analytic approach can inform urban design to enhance walkability at locations that are likely to generate walking trips. This study’s insights can help to shape public education and awareness campaigns that aim to encourage walking trips throughout the day by suggesting locations and times of the day when engaging in these forms of exercise is easiest and least intrusive.

This study establishes a new method for estimating the monthly Average Population Present (APP) in Australian regions. Conventional population statistics, which enumerate people where they usually live, ignore the significant spatial mobility driving short term shifts in population numbers. Estimates of the temporary or ambient population of a region have several important applications including the provision of goods and services, emergency preparedness and serve as more appropriate denominators for a range of social statistics. This paper develops a flexible modelling framework to generate APP estimates from an integrated suite of conventional and novel data sources. The resultant APP estimates reveal the considerable seasonality in small area populations across Australia’s regions alongside the contribution of domestic and international visitors as well as absent residents to the observed monthly variations. The modelling framework developed in the paper is conceived in a manner such that it can be adapted and re-deployed both for use with alternative data sources as well as other situational contexts for the estimation of temporary populations.

Understanding public transport passengers' loyalty has received growing research attention, given its potential to inform the long-term market management of a public transport service. However, the nature of public transport passengers' loyalty is arguably yet to be fully understood concerning its relationship to passengers' captivity and attitudes towards private car use, and their intentions to change their modal use. Hence questions remain largely unexplored that: Is public transport passengers' loyalty a result of a preferred or constrained choice? And does their loyalty have the potential to encourage more sustainable transport modal use patterns? Drawing on the busway network in Brisbane, Australia, as the case study, this paper aims to broaden the research on public transport passengers' loyalty by addressing these questions. Through developing a series of linear regression models, our findings highlight: (1) busway passengers were influenced by preferential (the experience of riding the busway service), moral (pro-environmental concerns) and constraint (cost of busway and car use) considerations; and (2) the potential of loyalty to alleviate busway passengers' intentions to shift to private car use for their trip-making. Through these findings, a series of implications are developed with the potential for the improvement of busway service and travel demand management to encourage more sustainable transport especially within a highly motorised context. Future research is encouraged to provide more evidence concerning passenger loyalty and their public transport use.

Weather influences our daily travel decisions in a number of important ways. At the individual scale, weather has been shown to influence trip-making behaviours where inclement conditions can induce re-scheduling, re-routing and cancellation of a planned journey. Furthermore at the transit system-level, poor weather can increase traffic congestion and reduce operational efficiencies. While some research has examined the weather–transit relationship, focus on the spatial dimension remain in their infancy. In this paper we adopt a visual analytic approach to spatially explore the complex weather–transit relationship at a micro geographical scale. We demonstrate that through spatially integrating a large disaggregate smart card database of bus ridership with hourly local weather measurements we can reveal how ‘heat stress’ changes the way in which passengers use the public transit system in sub-tropical Brisbane, Australia. Our approach has the potential for broader application across other public and private transport and climatic contexts to unveil the way in which weather influences our daily travel behaviour.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

In the provision of urban residential areas, private land developers play critical roles in nearly all stages of the land development process. Despite their important role little is known about how the spatial decisions of individual developers collectively influence urban growth. This paper employs an agent-based modelling approach to capture the spatial decisions of private land developers in shaping new urban forms. By drawing on microeconomic theory, the model simulates urban growth in the Jakarta Metropolitan Area, Indonesia, under different scenarios that reflect the decision behaviours of different types of developers. Results reveal that larger developers favour sites that are more proximate to the city centre whilst smaller developers prefer sites that are located further away from the city, that drive a more sprawled urban form. Our findings show that new urban areas are generated by different developers through different processes. The profit maximisation behaviour by developers with large capital reserves is more predictable than those with small capital funds. The imbalance in capital holdings by different types of developers interacts with one another to exert adverse impacts on the urban development process. Our study provides supporting evidence highlighting the need for urban policy to regulate urban expansion and achieve more sustainable urban development outcomes in a developing world context.

Employing the theory of planned behavior for guidance, this study explores the similarities and differences in beliefs about the decisions to commute by bicycle to work in three small, cyclingoriented cities: Gouda (The Netherlands), Shkodra (Albania), and Peja (Kosovo). The setting in the Balkan Peninsula, a less developed region of Europe which has been more rarely the subject of scientific inquiry, has the potential to offer applicability to (smaller) developing cities. The study identifies the following themes or beliefs related to cycling: (1) health and exercise (2) environment (3) safety (4) enjoyment (5) convenience and practicality (6) financial savings (7) pride and tradition (8) status and image and (9) female independence. The findings suggest that, in developing cities changes to the physical environment alone – although crucial - are likely to be insufficient if travel modes are to shift toward active transport. To this end attitudes and perceptions need to be tackled as well. In promoting cycling, policy makers need to strike a fine balance between the concept of the bicycle as an economical mode and as a “trendy” one. The most promising way forward appears to be a combination of public infrastructure investments, cycling tracks in particular, and social marketing strategies to alter travel behavior.

The local neighborhood forms an integral part of our lives. It provides the context through which social networks are nurtured and the foundation from which a sense of attachment and cohesion with fellow residents can be established. Whereas much of the previous research has examined the role of social and demographic characteristic in relation to the level of neighboring and cohesion, this paper explores whether particular environmental features in the neighborhood affect social porosity. We define social porosity as the degree to which social ties flow over the surface of a neighborhood. The focus of our paper is to examine the extent to which a neighborhood's environmental features impede the level of social porosity present among residents. To do this, we integrate data from the census, topographic databases and a 2010 survey of 4,351 residents from 146 neighborhoods in Australia. The study introduces the concepts of wedges and social holes. The presence of two sources of wedges is measured: rivers and highways. The presence of two sources of social holes is measured: parks and industrial areas. Borrowing from the geography literature, several measures are constructed to capture how these features collectively carve up the physical environment of neighborhoods. We then consider how this influences residents' neighboring behavior, their level of attachment to the neighborhood and their sense of neighborhood cohesion. We find that the distance of a neighborhood to one form of social hole-industrial areas-has a particularly strong negative effect on all three dependent variables. The presence of the other form of social hole-parks-has a weaker negative effect. Neighborhood wedges also impact social interaction. Both the length of a river and the number of highway fragments in a neighborhood has a consistent negative effect on neighboring, attachment and cohesion.