Restaurant Location Research Articles

UTILIZING GOOGLE EARTH FOR ENVIRONMENTAL PLANNING: EXTRACTION OF POINT SOURCE LOCATIONS AT THE UPSTREAM OF SUNGAI SKUDAI CATCHMENT, JOHOR MALAYSIA

This study explores the application of geospatial technologies, specifically focusing on Google Earth (GE), to extract and mapping point source pollution in the upstream of Sungai Skudai Catchment (SRC). A thorough analysis of the locations of restaurants, launderettes, car washes, and workshops was made practical by the combination of Geographic Information Systems (GIS) tools with satellite images, which offers important data for environmental planning and water quality management. The research area, located in Johor, Malaysia, offers an example of the difficulties driven by rapid urban growth. This study provides comprehensive directions for extracting point sources, with a focus on verifying data via field surveys and Google searches. The upstream region was found to have 373 restaurants, 62 launderettes, 73 car washes, and 80 workshops. The dataset was further improved by the validation process, which determined any inconsistencies and added additional data. This study highlights the importance of field verification for improving point source pollution data accuracy. It also provides a base for comprehensive strategies for pollution management, land-use planning, and preservation efforts in rapidly changing environments, such as the Sungai Skudai Catchment.

A Decision-Making Framework for Sustainable Highway Restaurant Site Selection: AHP-TOPSIS Approach based on the Fuzzy Numbers

This research work indicates the site selection problem for a restaurant beside the highway. It is really challenging as well as demanding work. It mainly depends on the cost of the selected location, transportation, parking facility, hygienic atmosphere, distance from the city, etc. In this paper, the proposed site selection problem is constructed and solved by using a fuzzy MUlti-Criteria Decision Making (MCDM) process. The two methods of MCDM, that we use here namely the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for calculate criteria weight and rank the proposed restaurant locations, respectively. Multiple decision makers (DMs) opinions are considered for data collection and Trapezoidal Fuzzy Numbers (TrFNs) are taken as uncertainty tools to deal with uncertainty. Finally, we perform the sensitivity and comparative analysis performed to verify the system's vagueness and flexibility.

Impact of sudden public crises on spatial distribution patterns and driving factors of the urban catering industry: a case study of Shanghai’s catering POI data before and after COVID-19

ABSTRACT This study uses Shanghai to explore the effects of the COVID-19 pandemic on the spatial distribution patterns and driving factors of the urban catering industry. Through quantitative analysis of restaurant point-of-interest data and influencing factors in Shanghai’s main urban area from 2016 to 2022 using geographic information technology, machine learning, and spatial econometric models, this study predicts spatial changes in the catering industry. The number of restaurants in Shanghai’s main urban area continuously decreased from 2016 to 2022, with a significant pre-pandemic decline. The catering industry in this area has a significant spatial correlation in the spatial distribution, showing a pattern of “one core dominance with multiple cores coexisting,” with Jing’an and Huangpu Districts having the highest densities, which is expected to continue. Target markets remain a decisive factor in restaurant location selection post-COVID. Restaurant aggregation is most favorable when the population density is between 0 –50,000 people/km2, GDP is between 0–20 billion yuan/km2, housing prices are between 20,000 –130,000 yuan/m2, school density is between 0–20/km2, hospital density is between 0–10/km2, and attraction density is between 0–40/km2. Transportation conditions have no apparent threshold; the more developed the transportation, the more favorable the conditions for the catering industry. Post-COVID-19, areas with high housing prices and around tourist attractions are less likely to attract many restaurants. Areas around hospitals maintain restaurant aggregation, although this will decrease. Areas around schools will become popular for restaurants in the future.

Local restrictions, population movement, and spillovers during the pandemic: Evidence from Japan's restaurant restriction.

We investigate how a local restaurant restriction aimed at containing the COVID-19 pandemic influenced population movement and COVID-19 prevalence within and outside the restricted districts. Using data on restaurant location and hourly population at the 500-m-mesh level and on COVID-19 prevalence at both prefecture and municipality level in Japan, we employ a triple-difference approach and a difference-in-differences approach with fixed effects. While the policy decreased population movement to restaurant areas in the restricted districts, it caused spillovers of increasing population movement to restaurant areas in the neighboring nonrestricted districts. Consequently, COVID-19 prevalence worsened in the neighboring nonrestricted districts but improved in the restricted districts. Our findings suggest that imposing such local restrictions in the context of the pandemic may contain the pandemic only in the restricted districts while sacrificing economic activities within these districts and public health in neighboring nonrestricted districts.

Identifying a good business location using prescriptive analytics: Restaurant location recommendation based on spatial data mining

This study proposes a new prescriptive analytics method that aims to provide decision-makers with a systematic and objective approach to identify suitable locations, considering the spatial distribution of different types of restaurants. The method comprises of two main components: spatial co-location pattern mining and locationGCN, where locationGCN is based on graph convolutional network (GCN). The spatial co-location pattern mining is utilized to capture the spatial correlation of specific restaurant to determine the candidate location selection range. The locationGCN is designed to further screen out final suitable location ranges for the specific restaurant type. A case study using restaurant data from Xiamen Island collected from Dianping.com is conducted. The empirical results demonstrate that the algorithm achieves an accuracy of 74.88%, precision of 63.59%, and recall of 77.48%. Results indicate that the proposed approach can provide suitable location recommendations for specific types of restaurants based on existing restaurant distribution information.

Developing a restaurant recommended system via the Vietnamese food image classification

A recommendation system is a system that recommends products and services to users based on daily online searching habits. The recommender system is applied in many fields such as job searching, health care, education, music, and tourism. However, few studies have combined computer vision and collaborative filtering to build a restaurant recommendation system in the tourism sector. In this study, we presented a solution to build a restaurant recommendation system through Vietnamese food image classification. First, we used ResNet-34 which is a variant of the convolutional neural network to classify Vietnamese food images. Then, the system applied the alternative least square technique in matrix factorization and Apache Spark in distributed computing to train the restaurant location dataset. The output was the most relevant restaurant places list to show many choices to users. The experimental datasets included the Vietnamese image and the restaurant location datasets that were collected from kaggle.com and foody.vn websites. For image classification task evaluation, we compared ResNet-34 to variants of ResNet. For the restaurant recommendation task evaluation, we compared alternative least squares with k-nearest neighbor. The comparison results show that the proposed solution is better than traditional popular models.

Qualitative benefits of MBESST in the selection of a restaurant location: Touristic restaurant Palencia de Lara, Toledo, Spain

Selecting the optimal location for a restaurant is a process that demands crucial and comparable data. The Model for the Best Site Selection for a Restaurant (MBESST) is a flexible model that combines quantitative and qualitative elements in assessing any restaurant site. This research provides the qualitative results derived from evaluating the location of an empirical case, Palencia de Lara Restaurant in Toledo, Spain, a tourist city. The design consisted of a non-experimental, transversal, and qualitative approach, using non-participant observation and interviews to gain a desirable appreciation of the issue. The global assessment's locations were 7.26, indicating the collection of essential components to maximize its appeal to guests. Palencia de Lara Restaurant should optimize its entire operational capacity more effectively. Flow pattern, visibility, and massive traffic generators were the three most essential factors in selecting this location. A significant contribution of the research is that using the definitions of the factors and sub-factors and the scale of specific quality levels allows for focusing on standard terms during the location assessment process. The designed instruments in the MBESST facilitate users to get an integral assessment of any locations of nine factors, fifty-one sub-factors, and individual quality levels of the sub-factors, enabling an in-depth analysis of each assessed element and comparing each other under the same parameters, gathering quantitative and qualitative data.

Locating Senior-Friendly Restaurants in a Community: A Bi-Objective Optimization Approach for Enhanced Equality and Convenience

Senior-friendly restaurants are dining establishments that cater specifically to the needs and preferences of older adults in a community. As the physical capabilities of seniors progressively decline and their activity spaces contract over time, determining optimal locations for such restaurants to ensure their accessibility becomes crucial. Nevertheless, the criteria for the location selection of senior-friendly restaurants are multifaceted, necessitating the consideration of both equality and convenience. First, these restaurants often receive government funding, which means that equitable access should be guaranteed for all community residents. Second, the daily activity patterns of seniors should be accounted for. Therefore, these restaurants should be situated in close proximity to other essential facilities utilized by seniors, such as recreational facilities that accommodate routine postmeal activities. Despite the long-standing application of spatial optimization approaches to facility location issues, no existing models directly address the location selection of senior-friendly restaurants. This study introduces a bi-objective optimization model, the Community Senior-Friendly Restaurants Location Problem (CSRLP), designed to determine optimal locations for senior-friendly restaurants, taking into consideration both service coverage and proximity to recreational facilities simultaneously. We formulated the CSRLP as an integer linear programming model. Simulation tests indicate that the CSRLP can be solved both effectively and efficiently. Applying the CSRLP model to two communities in Dongcheng District, Beijing, China, we explored Pareto optimal solutions, facilitating the selection of senior-friendly restaurant locations under diverse scenarios. The results highlight the significant value of spatial optimization in aiding senior-friendly restaurant location planning and underscore key policy implications.

Proximity Analysis using Weighted Product Method: Case Study for Fast-Food Restaurants

This study aims to analyze the proximity between fast-food restaurant locations and several factors to consider in opening outlets, including public facilities, roads, transportation facilities, community mobility, and distance to industrial areas. The method used in this study is the Weighted Product Method. The Geographic Information System tools are used to obtain location data and perform spatial data settings based on the reference system, as well as calculate the distance matrix between locations. The Weighted Product method is used to provide an assessment analysis regarding the factors to be considered in opening an outlet at a location.

Managing the efficiency and competitiveness of hotel, restaurant and tourism enterprises in Ukraine during the war

In each case, the tactics and strategy of management are complemented by other tools and mechanisms, depending on the conditions of accommodation, specialisation, and life cycle of a particular enterprise. However, the war has also led to the emergence of new circumstances for the functioning of hotel, restaurant and tourism enterprises. The purpose of the article is to substantiate and develop new and improve the existing directions, mechanisms and tools for managing the efficiency and competitiveness of hotel, restaurant and tourism enterprises in the context of war, taking into account military challenges and threats, possible reserves for relocation and spatial displacement of the tourism services market. In the process of writing the article, the following methods were used: historical and systemic approach; systemic and structural analysis and synthesis; statistical and economic, economic and mathematical, and ranking. As a result, new factors have been identified and the set of existing factors influencing the competitiveness of hotel, restaurant and tourism enterprises has been supplemented. It is found that these factors are both common and different. Thus, the growth of competitive advantages, efficiency and competitiveness is facilitated to a greater extent by: integration processes; quality of products and services; qualification of staff and management; use of digitalisation and marketing measures in the organisation and management of activities. Therefore, focusing on their support constitutes the strategic objectives and directions of efficiency and competitiveness management. At the same time, it is necessary to take into account the differences relating to the factors of location of hotels and restaurants (restaurants are preferable in very busy places, hotels – in quiet and peaceful places); hotel, restaurant and tourism enterprises – inclusive or exclusive services, authentic offers; seasonal demand, specially allocated time for rest, etc. The article emphasises that the continuation of Russia’s missile and bomb attacks on peaceful cities of Ukraine requires a corresponding intensification of security measures for the hotel, restaurant and tourism business. The results of the study can be used in managing the efficiency and competitiveness of hotel, restaurant and tourism enterprises, local governments, in the educational process of universities and colleges

An Adaptive Sequential Decision-Making Approach for Perishable Food Procurement, Storage and Distribution Using Hyperconnected Logistics

The fast-food industry currently relies on frozen ingredients to reduce the cost of procurement of raw materials. In recent years, consumers have started to curb their habit of eating out from fast-food chain restaurants due to the growing concerns for unhealthy menu choices made primarily from highly processed and/or frozen food ingredients. To address these issues, some organizations in the fast-food industry have started to offer menus with fresh unfrozen ingredients sourced locally or regionally. This paper addresses the problem of integrating sourcing, storage, and distribution strategies for a fast-food restaurant chain at the regional level. We present an adaptive sequential optimization decision-making approach for procurement, storage, and distribution of perishable food products to multi-unit restaurants at the regional level. This solution approach uses shelf-life considerations in developing a procurement and distribution strategy for fresh produce in the era of hyperconnected logistics. Three models are developed using Mixed Integer Linear Programming (MILP). First, a procurement model is developed to find the cost-effective supplier for each produce category based on shelf life. Then, a distribution model is developed to find the cost-optimal solution for distributing produce to multiple restaurant locations considering weight, volume, and operation hours. Finally, an integrated model is developed to optimally combine procurement and distribution options generated by the first two models to minimize costs while respecting total shelf-life constraints. Numerical experiments based on realistic data are carried out to show that the proposed sequential approach yields valid decisions and presents the effects of price, shelf-life, and demand changes on the supply chain.

Not about the Food: Social Aspect in Restaurant Dining Experience

Previous research has identified three social aspects that influence the dining experience: local food, foodscape, and the customer's lifestyle. However, little is known about the relationship between these aspects. This study seeks to address that gap by examining how the foodscape of a restaurant, the customer's lifestyle, and the presence of local food shape the dining experience. The study utilized qualitative research methods and conducted a case study on a restaurant located in a weekend tourist area in Bogor Regency. The case study focused on a cafe that received significant media coverage from 2021 to 2023. Through interviews and observations, it was found that the foodscape of a restaurant and the customer's lifestyle are interdependent in shaping the dining experience. The dining experience was seen as both a vacation and a working activity for customers. However, the study found that the foodscape did not have any relationship with the culture of the restaurant location and the type of food. Foodscape only dealt with the restaurant's ambience. This study suggests that further research is needed to reposition foodscape destinations in a locality context. The foodscape restaurant has developed an atmosphere separate from the local cultural context regarding the local food presentation.

Exploring memorable gastronomic experiences: Automatic topic modelling of TripAdvisor reviews

The article aims to identify memorable gastronomic experiences reported online and verify their relationships with the type of cuisine served and restaurant location. This study used text mining, LDA, Pearson’s chi-squaredtest and sentiment analysis. All 48,378 English reviews posted by TripAdvisor users concerning 155 restaurants in Krakow were scraped. Eight features that characterise MGEs were identified (service/staff, atmosphere,cuisine/food (taste), drinks, local specialities, location/setting, price & value and table booking). There are statistically significant differences in the frequency of the topic experiences depending on the location of restaurants in the city.

Managing the efficiency and competitiveness of hotel, restaurant and tourism enterprises in Ukraine during the war

In each case, the tactics and strategy of management are complemented by other tools and mechanisms, depending on the conditions of accommodation, specialisation, and life cycle of a particular enterprise. However, the war has also led to the emergence of new circumstances for the functioning of hotel, restaurant and tourism enterprises. The purpose of the article is to substantiate and develop new and improve the existing directions, mechanisms and tools for managing the efficiency and competitiveness of hotel, restaurant and tourism enterprises in the context of war, taking into account military challenges and threats, possible reserves for relocation and spatial displacement of the tourism services market. In the process of writing the article, the following methods were used: historical and systemic approach; systemic and structural analysis and synthesis; statistical and economic, economic and mathematical, and ranking. As a result, new factors have been identified and the set of existing factors influencing the competitiveness of hotel, restaurant and tourism enterprises has been supplemented. It is found that these factors are both common and different. Thus, the growth of competitive advantages, efficiency and competitiveness is facilitated to a greater extent by: integration processes; quality of products and services; qualification of staff and management; use of digitalisation and marketing measures in the organisation and management of activities. Therefore, focusing on their support constitutes the strategic objectives and directions of efficiency and competitiveness management. At the same time, it is necessary to take into account the differences relating to the factors of location of hotels and restaurants (restaurants are preferable in very busy places, hotels – in quiet and peaceful places); hotel, restaurant and tourism enterprises – inclusive or exclusive services, authentic offers; seasonal demand, specially allocated time for rest, etc. The article emphasises that the continuation of Russia's missile and bomb attacks on peaceful cities of Ukraine requires a corresponding intensification of security measures for the hotel, restaurant and tourism business. The results of the study can be used in managing the efficiency and competitiveness of hotel, restaurant and tourism enterprises, local governments, in the educational process of universities and colleges

Novel CNN-Based Approach for Reading Urban Form Data in 2D Images: An Application for Predicting Restaurant Location in Seoul, Korea

Artificial intelligence (AI) has demonstrated its ability to complete complex tasks in various fields. In urban studies, AI technology has been utilized in some limited domains, such as control of traffic and air quality. This study uses AI to better understand diverse urban studies data through a novel approach that uses a convolutional neural network (CNN). In this study, a building outline in the form of a two-dimensional image is used with its corresponding metadata to test the applicability of CNN in reading urban data. MobileNet, a high-efficiency CNN model, is trained to predict the location of restaurants in each building in Seoul, Korea. Consequently, using only 2D image data, the model satisfactorily predicts the locations of restaurants (AUC = 0.732); the model with 2D images and their metadata has higher performance but has an overfitting problem. In addition, the model using only 2D image data accurately predicts the regional distribution of restaurants and shows some typical urban forms with restaurants. The proposed model has several technical limitations but shows the potential to provide a further understanding of urban settings.

Availability of calorie information on online food delivery service platforms among major chain restaurants in Canadian provinces with different calorie labellingpolicies.

This study aimed to characterize the availability of calorie labelling on major online food delivery (OFD) platforms for the largest restaurant brands in Canada to examine differences in provinces with and thosewithout mandatory calorie labelling regulations. Data were collected for the 13 largest restaurant brands with locations in Ontario (with mandatory menu labelling) and Alberta and Quebec (with no mandatory menu labelling) from the web applications of the three largest OFD platforms in Canada. Data were sampled from three selected restaurant locations within each province (n = 117 locations across all provinces) on each platform. Univariate logistic regression models were used to test differences in the presence and amount of calorie labelling and other nutritional information across provinces and platforms. The analytical sample included 48,857 food and beverage items (n = 16,011 in Alberta, n = 16,683 in Ontario, and n = 16,163 in Quebec). Items were more likely to have menu labelling in Ontario (68.7%) than in Alberta (44.4%, OR = 2.75, 95% CI 2.63-2.88) or Quebec (39.1%, OR = 3.42, 95% CI 3.27-3.58). In Ontario, 53.8% of restaurant brands had calorie labelling for > 90% of items, compared to 23.0% in Quebec and 15.4% in Alberta. The presence of calorie labelling also differed across platforms. Nutrition information from OFD services differed across provinces with and thosewithout mandatory calorie labelling. Chain restaurants on OFD service platforms were more likely to provide calorie information in Ontario, where calorie labelling is mandatory, compared to elsewhere where no such policy exists. In all provinces, calorie labelling was inconsistently implemented across OFD service platforms.

Urban restaurants and online food delivery during the COVID-19 pandemic: a spatial and socio-demographic analysis

ABSTRACT In this research, we analyzed the delivery service areas of restaurants, customer satisfaction, and restaurant sales of urban restaurants during the COVID-19 pandemic. We obtained the datasets on food ordering options and restaurant rankings based on Google Maps, Open Street Map, and widely known online food order applications in Iran. Based on this analysis we further modeled suitable areas for future extension of restaurants. We analyzed the online food order data of restaurants’ sales and food delivery reports for 1050 restaurants in the city of Tabriz. We collected and analyzed data on the restaurant locations, the number of food orders for each restaurant, and the number of customers and their locations. Our results revealed that the spatial dimension of the newly emerging food delivery areas is of utmost importance for the success of restaurants. This indicates that an optimal location is not longer only dependent on factors like population density and competitors in the direct vicinity but on the services density even from more distant competitors. The results indicate that an optimized spatial distribution of the restaurants together with efficient quality in services can contribute to optimistic urban development.

A new synergistic strategy for ranking restaurant locations: A decision-making approach based on the hexagonal fuzzy numbers

This research addresses the problem of restaurant locations ranking with applications for a cosmopolitan big city like Kolkata, India. A restaurant selection is based on occasions, spending capability, environment, location, comfort, quality of the food etc. In this research paper an exhaustive set of factors and sub-factors is taken into consideration to select and rank restaurants situated at different locations in the city of Kolkata with a population of around fifteen million. The ranking of restaurants depends on complex, conflicting qualitative attributes. In the paper hexagonal fuzzy numbers (HFN) have been used to suitably depict the imprecise uncertain environment. HFN, its distance measure and defuzzification have been applied to deal with the hesitancy and impreciseness of the decision makers. Analytic hierarchy process (AHP) has been used as a Multi Criteria Decision Making (MCDM) tool to obtain factors and sub-factors weights. TOPSIS and COPRAS methods were used for ranking different restaurant locations. Using comparative analysis it is shown that HFN with the TOPSIS and COPRAS method gives better result than other fuzzy numbers. The sensitivity analysis portion also gives a direction for taking a suitable decision in different possible scenario.

Small sample field study:The effects of team-based recognition on employee engagement and effort

Firms have long used recognition programs to engage and motivate employees. However, minimal research has examined the effectiveness of these programs in actual organizational settings. We predict that the adoption of a team-based recognition program will be associated with improvements in employee engagement and effort. We test our predictions using a pre-post research design administered at six fast-food franchise restaurant locations for a 12-week period. Employees at each location were eligible to receive non-monetary recognition (i.e., thank-you card, token gift) as a team every two weeks. The results support both predictions. We also find evidence of indirect effects of team recognition on effort through employee engagement. Our findings should be of interest to compensation system designers in settings where employee motivation is low and individual performance is costly or difficult to measure. We show that team-based recognition can be effective in such settings. In so doing, we also extend the academic literature that has primarily focused on the behavioral effects of individual-level recognition.

The Development Of Halal Tourism in Muslim Minority Countries: Case Study in South Korea

This study aims to find the development of halal tourism in South Korea. The development of the number of tourists and the Muslim population there is increasing from year to year, so the need for halal food consumption is also increasing. The South Korean government implements the development of the halal food industry through MAFRA and Korea Muslim Federation (KMF) to obtain halal legality. This study is categorized qualitative research with phenomenology approach in South Korea. The techniques of data collection are dept interview, observation, and documentation. While the data analysis technique includes data reduction, data display, and conclusion. The research results show that the Korea Tourism Organization (KTO) issued a guidebook for halal food in Korea. In addition, They make the application is called Halal Korea which will provide information about the location of the mosque, Qibla direction, prayer times, the location of halal restaurants and the Qur'an. In 2016, the South Korean government collaborated with KMF to formulate a halal certification system. This is attempted as a follow-up to support for restaurants that have declared their place as halal restaurants.