Search Results (6)

Environmental exposures (EE) are increasingly recognised as important determinants of health and well-being. Understanding the influences of EE on health is critical for effective policymaking, but better-quality spatial data is needed. This article outlines the theoretical and technical foundations used for the construction of individual-level environmental exposure measurements for the population of a northern English city, Bradford. The work supports ‘Connected Bradford’, an entire population database linking health, education, social care, environmental and other local government data over a period of forty years. We argue that our current understanding of environmental effects on health outcomes is limited both by methodological shortcomings in the quantification of the environment and by a lack of consistency in the measurement of built environment features. To address these shortcomings, we measure the environmental exposure for a series of different domains including air quality, greenspace and greenness, public transport, walkability, traffic, buildings and the built form, street centrality, land-use intensity, and food environments as well as indoor dwelling qualities. We utilise general practitioners’ historical patient information to identify the precise geolocation and duration of a person’s residence. We model a person’s local neighbourhood, and the probable routes to key urban functions aggregated across the city. We outline the specific geospatial procedure used to quantify the environmental exposure for each domain and use the example of exposure to fast-food outlets to illustrate the methodological challenges in the creation of city and nationwide environmental exposure databases. The proposed EE measures will enable critical research into the relationship and causal links between the built environment and health, informing planning and policy-making. Full article

Commercial buildings in Malaysia contribute to 35% of the total electricity demand. During the recent COVID-19 pandemic, the global economy faced a challenging situation that forced many businesses to shut down. However, fast-food restaurants with drive-through features managed to get through this pandemic phase without much effect from the economic impact. Since COVID-19, the operational guidelines have changed for restaurants. However, from an energy perspective, fast–food restaurants are high energy consumers in the retail sector. This paper analyses the load profile of fast-food restaurants and the potential strategies that can be adopted in a free-standing fast-food restaurant. From analysis, it is calculated that a total of RM 97,365.9 of utility savings can be obtained in a year. A total of 91,392.1 kg CO₂, 881.8 kg SO₂ and 385.5 kg CO pollutant emissions can be reduced. The BEI for the restaurant was reduced to 856.4 kWh/m²/year. By converting to energy-saving strategies, the return on investment was 27.3% and 3.7 years, which is a very short period of time and is attractive for businesses of this nature. Full article

This research examines customers’ intention to buy depending on their use of nutrition labelling (NL) in fast food operations (FFOs) and their intention to visit and recommend these FFOs with nutrition-labelled menus. The research model draws on the theory of planned behaviour (TPB) to examine customers’ intentions to buy from nutrition-labelled menus and their behaviour of visiting and recommending to others FFOs with nutrition-labelled menus. To achieve this purpose, a self-administrated questionnaire was distributed to and collected from a random sample of customers at FFOs in Greater Cairo, Egypt, i.e., McDonald’s and Subway. The results from the structural equation modelling (SEM) using AMOS software indicated positive and direct significant paths from the constructs of the TPB, except for customers’ attitude, to customer intention to buy nutrition-labelled menu items. The results also showed a positive significant impact of customers’ intention on their behaviour of visiting and recommending FFOs featuring nutrition-labelled menus. The findings showed that there is an awaking of nutritional awareness among fast-food customers and that providing nutritional information on fast-food menus will affect their purchasing intention in the future by encouraging them to make healthy food choices. Theoretical implications for scholars and managerial implications for FFOs, especially in relation to public health in general and healthy food choices in particular, are explained and discussed. Full article

Food advertisement exposure is associated with increased caloric intake, but little is known about food/beverage placements in the digital media environment. We aimed to examine the correlation between the number of people who follow food and beverage brand social media accounts (i.e., user engagement) and state-level obesity rates; quantify social media followers’ use of “healthy” vs. “unhealthy” hashtags; and analyze the relationship between user engagement and hashtag usage. We identified the 26 fast-food and beverage brands with the highest advertising expenditures and used Demographics Pro to determine the characteristics of social media users amongst the 26 brands. A series of regression analyses were conducted that related the mean percentage of brand followers and state-level obesity rates. We then identified 733 hashtags on Instagram and 703 hashtags on Twitter, coding them as “healthy”, “unhealthy”, “neutral”, or “unrelated to health”. Intercoder reliability was established using ReCal2, which indicated a 90% agreement between coders. Finally, we conducted ANCOVA to examine the relationship between the mean percentage of brand followers and their hashtag usage. There was a significant, positive correlation between the state-level obesity rate and the mean percentage of followers of sugary drink or fast-food brands on Instagram and Twitter, but such a correlation between obesity and low-calorie drink brand followers was only found on Twitter. Our findings illustrate the relationship between the social media food environment and obesity rates in the United States. Given the high rates of engagement with food brands on social media, policies should limit digital advertisements featuring fast-food, sugary drink, and low-calorie drink brands. Full article

Static indicators may fail to capture spatiotemporal differences in the spatial influence of urban features on different crime types. In this study, with a base station analogy, we introduced crime risk stations that conceptualize the spatial influence of urban features as crime risk signals broadcasted throughout a coverage area. We operationalized these risk signals with two novel risk scores, risk strength and risk intensity, obtained from novel distance-aware risk signal functions. With a crime-specific spatiotemporal approach, through a spatiotemporal influence analysis we examined and compared these risk scores for different crime types across various spatiotemporal models. Using a correlation analysis, we examined their relationships with concentrated disadvantage. The results showed that bus stops had relatively lower risk intensity, but higher risk strength, while fast-food restaurants had a higher risk intensity, but a lower risk strength. The correlation analysis identified elevated risk intensity and strength around gas stations in disadvantaged areas during late-night hours and weekends. The results provided empirical evidence for a dynamic spatial influence that changes across space, time, and crime type. The proposed risk functions and risk scores could help in the creation of spatiotemporal crime hotspot maps across cities by accurately quantifying crime risk around urban features. Full article

We propose an incremental spectral clustering method for stream data clustering and apply it to stream image segmentation. The main idea in our work consists of generating the data points in the kernel space by Fastfood features and iteratively calculating the eigendecomposition of data. Compared with the popular Nyström-based approximation, our work accesses each data point only once while Nyström, in particular the sampling scheme, will go through the entire dataset first and calculate the embeddings of data points with a second visit. As a result, our method is able to learn data partitions incrementally and improve eigenvector approximation with more and more data seen from a stream. By contrast, the performance of the standard Nyström is fixed when the sample set is selected. Experimental results show the superiority of our method. Full article