Search Results (95)

Urban supermarkets are increasingly challenged to design spatial strategies that align with consumers’ demand for convenience, accessibility, and local embeddedness. Despite the growing recognition of spatial behavior in retailing, limited research has examined how different forms of proximity jointly shape consumers’ perceptions of store attractiveness and their subsequent location choices, particularly in emerging urban contexts. This study investigates how four proximity dimensions—access, identity, relational, and process proximity—affect durable and situational attractiveness, which in turn drive consumers’ retail location choices. Data from 567 supermarket shoppers in Tangier, Morocco, were analyzed using a structural model integrating these spatial and behavioral constructs. Results reveal that proximity exerts a strong positive effect on store attractiveness, with access and identity dimensions emerging as the most influential drivers of consumer patronage. This study contributes to the geo-marketing and spatial consumer behavior literature by conceptualizing proximity as a multidimensional construct that bridges spatial accessibility, social attachment, and retail experience, offering new insights for localization strategies in emerging markets. Full article

People in midlife interact with several different environments during their daily life including employment, leisure, commuting, and various family responsibilities, a concept defined as activity space. However, little is known about how these activity spaces contribute to individuals’ daily health behavior choices. The Everyday Environments and Experiences (E3) study was conducted to explore these relationships. In this paper, we provide a reproducible GPS processing workflow to generate time-weighted exposure measures (activity spaces) inferred from 21 days of continuous GPS monitoring among 340 midlife adults in Cook County, Illinois (n = 340) from the E3 study. Data from waist-mounted GPS devices that recorded one-minute location epochs were aggregated after excluding time spent within an 800 m buffer around the home. For each epoch, we derived proximity and kernel density measures for eleven food and physical-activity-related location types (e.g., supermarkets, fitness facilities), along with twenty-six environmental context variables (e.g., land use, crime, population density). Time-weighted averages characterized each participant’s typical non-home environmental exposure. After adjustment for environmental context, age and gender were generally unrelated to activity-space measures. However, Black and Hispanic participants (as compared to White participants) spent less time near both food and physical-activity resources, suggesting systemic inequities in access beyond neighborhood composition. These findings highlight the need to move beyond static residential measures toward time-weighted, dynamic assessments of environmental exposure. They also indicate that racial and ethnic disparities in routine activity space may reflect structural inequities shaping daily physical activity and access to healthy food. Future research is needed to explore how these observed disparities translate into differences into disease risk, using longer exposure periods and different geographic settings to identify causal pathways and inform multi-level interventions. Full article

Urban commercial restructuring, driven by the closure of traditional supermarkets and the expansion of new-format superstores, creates a large-scale spatial reallocation challenge requiring scientific location-allocation methods. Traditional heuristic algorithms such as Genetic Algorithm (GA) struggle with discrete spatial optimization under 400+ candidate sites and complex geographic mask constraints: they converge slowly and easily fall into local optima. This study proposes a Deep Reinforcement Learning (DRL) framework named GeoPPO (Geospatial Proximal Policy Optimization) to address this gap. Using Xi’an’s retail restructuring as a case setting—427 candidate locations and multidimensional geographic features—the approach models spatial constraints via a gridded environment encoded as a five-channel state tensor. Key innovations include a dynamic action-constraint mechanism that masks invalid actions based on boundary rules and competition avoidance, and a curriculum learning strategy that enables stable convergence. The framework fills the need for methods that handle hard spatial constraints in large-scale location-allocation. Tests demonstrate rapid convergence within 1,000 epochs, achieving 75% average demand coverage—2.7% and 5.5% higher than GA and Particle Swarm Optimization (PSO), respectively. Ablation experiments confirm that Vanilla PPO without dynamic action masking fails to produce feasible solutions. The framework offers a feasible technical path for handling highly dynamic urban facility spatial configuration with geographic mask constraints. Full article

The objective of this study was to evaluate the impact of the material (plastic or glass) and color (green or colorless) of extra virgin olive oil (EVOO) and virgin olive oil (VOO) bottles on the acquisition of SORS spectra using portable equipment. Sixteen bottles of EVOO and three bottles of VOO were analyzed, including different volumes. A range of similarity indices was calculated between vial-reference (offline measurements) and bottles (online measurements), including R², COS θ, NEAR, and a new index called WSI (Weighted Similarity Index). WSI is calculated from the pondered linear combination of the previous three, and a threshold of >0.95 is established as high similarity. The results showed that plastic bottles, regardless of color and volume, and colorless glass bottles had WSI values > 0.95. In contrast, green glass bottles demonstrated a lower degree of similarity (WSI < 0.95), which impacted the reliability of their spectral fingerprints. A hierarchical cluster analysis (HCA) was performed by locating EVOO bottles according to their material in two clusters. A study of storage under optimal, non-optimal, and commercial conditions showed that both EVOO and VOO maintain highly similar spectral profiles for 10–18 days (WSI > 0.965), even in bottles purchased in supermarkets. These results demonstrate that the SORS technique is suitable for the direct analysis of olive oils in plastic and colorless glass containers, without the need to open the bottles. The SORS technique is a fast, reliable, non-invasive, and non-destructive tool for quality control of olive oil. Full article

This study introduces a mobile application designed to assist visually impaired persons (VIPs) in navigating complex environments, such as supermarkets. Recent assistive tools often identify objects in isolation without providing contextual awareness. In contrast, our proposed system uses seamless scene and object recognition to help users efficiently locate target items and understand their surroundings. Employing a “human-in-the-loop approach”, users control their smartphone camera direction to explore the space. Experiments conducted in a simulated shopping environment show that the system enhances object-finding efficiency and improves user orientation. This approach not only increases independence, but also promotes inclusivity by enabling VIPs to perform everyday tasks with greater confidence and autonomy. Full article

Background: Spatial navigation is one’s ability to travel through their environment to reach a goal location. Self-expansion is the motivation to increase one’s self-perception through engaging in novel activities. Our objective was to examine the relations among self-expansion, age, and navigation ability and investigate how one’s internal motivation may influence navigation performance across paradigms. Methods: In total, 33 younger adults (YAs; 19F, 14M, mean age = 25.0 ± 1.6) and 74 older adults (OAs; 52F, 22M, mean age = 69.5 ± 8.0) completed the following: Self-Expansion Preference Scale (SEPS), Wayfinding Questionnaire (WQ), Virtual Supermarket Task, Virtual Morris Water Maze (vMWM), and a Floor Maze Task (FMT). Mann–Whitney U tests and Spearman ρ correlations were used to examine differences in navigation performance between YAs vs. OAs and self-expanders vs. self-conservers, and relations among the measures, respectively. Results: YAs had lower vMWM completion times compared to OAs (p < 0.001). Self-expanders had better recall of the vMWM environment compared to self-conservers (p = 0.049), independent of age. Greater self-expansion in YAs was correlated with lower spatial anxiety (ρ = −0.356, p = 0.042) and faster completion of the FMT (ρ = −0.36, p = 0.042). Discussion: Our results build on established age-related deficits in navigation abilities to identify correlations of self-expansion and better performance in various navigation tasks. Independent of age, individuals with greater inclination towards self-expansion exhibit superior navigation abilities. Future research should explore underlying mechanisms driving these associations and investigate intervention strategies aimed at improving navigation skills in aging populations through increasing self-expansion. Full article

Currently, supermarket-run shopping centers, known as “Big Marts,” monitor sales information for every single item in order to predict potential customer demand and update inventory management. Anomalies and general trends are commonly discovered through data warehouse mining using a range of machine learning techniques, and businesses such as Big Marts can use the obtained data to forecast future sales volumes. Compared to other research publications, this one forecasted sales with higher accuracy using machine learning models including KNN (K Nearest Neighbors), Naïve Bayes, and Random Forest. To adapt the proposed business model to anticipated outcomes, the sales forecast is based on Big Mart sales for various stores. Using different machine learning methods, the data that is produced may then be used to predict potential sales volumes for retailers such as Big Marts. The projected cost of the suggested system includes the following identifiers: price, outlet, and outlet location. In order to facilitate data-driven decision-making in retail operations and help Big Marts optimize their business models and effectively satisfy anticipated demand, this study emphasizes the importance of incorporating cutting-edge machine learning approaches. Full article

E-commerce growth has reshaped consumer behavior and retail services, driving parcel demand and challenging last-mile logistics. Existing research predominantly relies on survey data and global regression models that overlook intra-urban spatial heterogeneity in shopping behaviors. This study bridges this gap by analyzing e-commerce demand’s spatial distribution from a retail service perspective, identifying key drivers, and evaluating implications for omnichannel strategies and logistics. Utilizing waybill big data, spatial analysis, and multiscale geographically weighted regression, we reveal: (1) High-density e-commerce demand areas are predominantly located in central districts, whereas peripheral regions exhibit statistically lower volumes. The spatial distribution pattern of e-commerce demand aligns with the urban development spatial structure. (2) Factors such as population density and education levels significantly influence e-commerce demand. (3) Convenience stores play a dual role as retail service providers and parcel collection points, reinforcing their importance in shaping consumer accessibility and service efficiency, particularly in underserved urban areas. (4) Supermarkets exert a substitution effect on online shopping by offering immediate product availability, highlighting their role in shaping consumer purchasing preferences and retail service strategies. These findings contribute to retail and consumer services research by demonstrating how spatial e-commerce demand patterns reflect consumer shopping preferences, the role of omnichannel retail strategies, and the competitive dynamics between e-commerce and physical retail formats. Full article

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

Background/Objectives: Poultry meat is a popular and nutritious food, valued for its high protein content and healthy fat profile. However, like other animal products, it can contain pharmaceutical residues, including coccidiostats, antimicrobials commonly used to prevent parasitic infections caused by Eimeria species. While most monitoring focuses on raw meat, it is important to understand how these compounds behave during cooking to assess potential health risks better and ensure food safety. Methods: This study examined how five different cooking methods (roasting, grilling, and microwaving, beer and wine marinating) affect the levels of eight coccidiostat residues in 45 samples of poultry muscle collected from a supermarket located in the center of mainland Portugal from May to July 2024. After applying different cooking procedures, ionophore and synthetic coccidiostat residue levels were measured using solid–liquid extraction followed by ultrahigh-performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS). Results are expressed as percentages of the original concentrations: 100% indicates stability, values above 100% suggest a relative increase (often due to moisture loss), and values below 100% reflect a decrease, likely from heat degradation. Results: Roasting, grilling, and microwaving all increased residue concentrations—up to 198.5%, 180.1%, and 158.4%, respectively. In contrast, marinating meat in wine or beer before cooking reduced residues to 73.1% and 72.0%, suggesting a mitigating effect. The initial concentration also influenced the outcome: samples fortified at the maximum residue limit (MRL) had an overall higher mean concentration after cooking (148.3%,) than those fortified at twice the MRL (2 MRL), which averaged 124.5%. Conclusions: These results show that cooking can significantly alter coccidiostat residue levels depending on the cooking procedures and initial concentration. Ongoing monitoring and further research are essential to better understand how cooking affects these residues and their by-products. This knowledge is key to improving food safety practices and refining consumer health risk assessments. Full article

This study investigates residential segregation and housing types in Craiova, Romania, with a particular focus on the disparities shaped by historical and contemporary urban developments. Using collected data from former hostels built for young workers during the communist era, this research maps and analyzes the spatial distribution and living conditions of these housing types at a neighborhood level. Key metrics such as the number of inhabitants, the surface area of rooms, the current occupancy rates, and the number of unoccupied rooms were collected. Additionally, residential segregation is measured using indices of dissimilarity, isolation, exposure, concentration, and centralization, providing a comprehensive view of the socio-spatial divides within the city. The findings indicate significant disparities between these buildings with unsuitable living conditions and the newer residential developments, revealing a clear urban divide. No differences have been identified in terms of access to urban services like education, health, green areas, banks, or supermarkets, despite the appropriate location differences being noted in access to water and gas supply, and internet services. This study contributes to the understanding of how housing types and access to services in Craiova shape patterns of residential segregation, and it suggests policy interventions aimed at mitigating the negative impacts of these urban divides. Full article

The subcooling potential of a novel R717-based waste heat-driven multi-ejector chiller (HEC) integrated with an R744 refrigeration system was evaluated for use in supermarkets. The performance was compared with an R744 refrigeration system coupled to R718- and R600a-based HECs, an R744 system equipped with parallel compression (PC), and a standard R744 booster system (CB) in various warm and hot climatic locations. Integration of the R717-based HEC was found to improve the coefficient of performance by 3.7% at 27 °C to 12.1% at 45 °C compared to the R718, and by 1.6% at 27 °C to 7.6% at 45 °C compared to the R600a-based system. The energy-saving potential of the R717 system (6.2% to 9.4%) was also found to be higher than that of the R718 (0.7% to 2.8%) and R600a systems (2.5% to 6.6%). The use of the existing high-pressure controllers of the CB system was found to impose a relatively lower penalty on the system performance compared to the controllers of the PC system. Although the integration of the R718 system incurred a significantly lower additional investment, the recovery time of the R600a-based HEC (2.3–4.8 years) was found to be the shortest. Full article

In current times, sustainability is paramount, and businesses are increasingly adopting renewable energy sources (RESs) and electric vehicle (EV) charging infrastructure to minimise their environmental impact and operational costs. Such a transition can prove challenging to multi-location businesses since each chain store functions under different constraints; therefore, the implementation of a corporate policy requires adaptations. The increased electricity demand associated with EV charging stations and their installation cost could prove to be a significant financial burden. Therefore, this study aims to investigate and develop strategies for effectively incorporating RES and EV charging stations into the operations of a supermarket chain in Crete. Monthly electricity consumption data, parking availability, and premise dimensions were collected for 20 supermarkets under the same brand. To achieve a more tailored approach to custom energy system sizing, the integration of energy storage coupled with a photovoltaic (PV) system was investigated, using the Moth–Flame Optimiser (MFO) to maximise the Net Present Value (NPV) of 20 years. The algorithm managed to locate optimal solutions that yield profitable installations for all supermarkets by installing the necessary number of PV units. Manual exploration around the solutions led to the optimal integration of energy storage systems with a total upfront cost of EUR 856,477.00 and a total profit for the entire brand equal to EUR 6,426,355.14. Full article

Reduced energy use and increased energy efficiency are essential for decarbonization and sustainable development. The food cold chain is a crucial infrastructure with high energy consumption and rising demand. A careful selection of technology used in food retail systems could result in long-term energy savings, functionality, ease of maintenance, and high energy efficiency. In this work, a comparison between the energy performance of three low-GWP refrigerants, namely, R-454C, R-449A, and R-744, is carried out with reference to an existing refrigerating system that serves a real supermarket. Energy usage and seasonal efficiency have been examined for three locations in Europe, representing mild and hot ambient temperatures. Results show that systems based on R-454C and R-449A perform significantly better than R-744 architectures, improving sCOP of up to +12% and +17%, respectively, with a simpler equipment design. This suggests that careful assessment is required to select the most appropriate refrigerant for each store size and climatic condition. Full article