Search results

With access to the large-scale search data from Rightmove plc, the paper firstly indicated the possibility of using user-generated data from online property portals to predict housing market activities and secondly embraced a GIS approach to explore what people search for housing and what they chose and investigated the issue of mismatch between search patterns and revealed patterns. Based on the analysis, the paper contributes a visual GIS-based approach which may help planners and designers to make more informed decisions related to new housing supply, particularly where to build, what to build and how many to build.

The paper used the 2013 housing search data from Rightmove and the 2013 price data from Land Registry with transactions made after the search period and embraced a GIS approach to explore the potential housing demand patterns and the mismatch between searches and sales. In the analysis, the paper employed the K-means approach to group prices into five levels and used GIS software to draw maps based on these price levels. The paper also employed a simple analysis of linear regression based on the coefficient of determination to investigate the relationship between online property views and values of house sales.

The result indicated the strong relationship between online property views and the values of house sales, implying the possibility of using search data from online property portals to predict housing market activities. It then explore the spatial housing demand patterns based on searches and showed a mismatch between the spatial patterns of housing search and actual moves across submarkets. The findings may not be very surprising but the main objective of the paper is to open up a potentially useful methodological approach which could be extended in future research.

It is important to identify search patterns from people who search with the intention to buy houses and from people who search with no intention to purchase properties. Rightmove data do not adequately represent housing search activity, and therefore more attention should be paid to this issue. The analysis of housing search helps us have a better understanding of households' preferences to better estimate housing demand and develop search-based prediction models. It also helps us identify spatial and structural submarkets and examine the mismatches between current housing stock and housing demand in submarkets.

The GIS approach in this paper may help planners and designers better allocate land resources for new housing supply based on households' spatial and structural preferences by identifying high and low demand areas with high searches relative to low housing stocks. Furthermore, the analysis of housing search patterns helps identify areas with latent demand, and when combined with the analysis of transaction patterns, it is possible to realise the areas with a lack of housing supply relative to excess demand or a lack of latent demand relative to the housing stock.

The paper proves the usefulness of a GIS approach to investigate households' preferences and aspirations through search data from online property portals. The contribution of the paper is the visual GIS-based approach, and based on this approach the paper fills the international knowledge gap in exploring effective approaches to analysing user-generated search data and market outcome data in combination.

This study aims to identify comfort food (CF) consumption and its associated factors during the pandemic period. The study also involves an online survey conducted five months after the quarantine started in Brazil.

Data on lifestyle, eating habits and anthropometric data were collected before and during the pandemic, and the differences in these habits were analyzed. Univariate and multivariate logistic regression models were performed to identify predictors of CF consumption by gender.

A total of 1,363 individuals were included in the sample, with a median age of 31 years old, of whom 80.3% were women. Since individuals were free to respond about the food consumed without predetermined categories, it was possible to carry out a faithful assessment of the occurrence of this behavior. At the same time, allowing the subjectivity and symbolism inherent to the concept of CF to be embraced. CF consumption was present for 54%, with “sweets” being the most mentioned group by both genders. The factors associated with CF consumption in women during the pandemic were increased snacking, increased bread, candies and alcoholic beverage intake, increased time spent at work, worsened sleep quality, reduced meals, perceived stress (PS), emotional eating (EE), age and increased frequency of meat intake. In men, the predictors for CF consumption were remote full-time work/study, PS, EE and early waking time. For both genders, CF consumption during the pandemic period was associated with PS and EE.

This study provides an important overview of the possible contributions of the pandemic on behaviors and food choices related to the consumption of CF in Brazilians. This information is valuable to support further studies to investigate and treat the impacts of the pandemic on lifestyle, eating habits and behavior, mental health and other factors in the postpandemic period.

This study aims to determine and analyze the predictors of food waste reduction intention and behavior among higher education institutions (HEIs) using an extended version of the theory of planned behavior (TPB).

This study empirically tested an extended TPB model through regression analyses using data obtained through an online survey.

Attitude toward food waste reduction, subjective norms, perceived behavioral control and food waste knowledge predicted intention to reduce food waste. Furthermore, the intention to reduce food waste predicted food waste reduction behavior.

The results imply that extending the TPB by adding food waste knowledge significantly predicted food waste reduction intention and behavior.

The study identified factors that predict food waste reduction behavior and suggested ways to influence Filipino students in HEIs to reduce food waste.

The findings support the inclusion of food waste knowledge to the TPB in predicting food waste reduction intention and behavior among students in HEIs.