Facility Location Research Articles

Geospatially explicit technoeconomic assessment of sustainable aviation fuel production: A regional case study in Virginia

There is strong interest in sustainable aviation fuels (SAF) to decarbonize aviation; however, local decision-makers will need to consider what additional incentives could stimulate SAF commercialization within their own jurisdictions. This study analyzed SAF production in Virginia, evaluating two biomass-to-energy platforms (gasification Fischer Tropsch [GFT] and pyrolysis) and two regionally abundant feedstocks (woody wastes and municipal solid wastes). A suite of open-access modeling tools were applied to possible SAF supply chains encompassing feedstock collection and transportation, conversion, and fuel upgrading and transport. Key modeling outputs were minimum product selling price (MPSP) ($/gallon) and life-cycle global warming potential (GWP) (g CO2eq/MJ). Results suggest that early SAF production via GFT will require local incentives of approximately $3.61 per gallon compared to $0.75 per gallon for pilot-scale pyrolysis. Location of production facility (by county) influences economic and environmental metrics but is not nearly as important as facility size (tonnes/year). Different formats of financial incentives (i.e., tax credits, loan forgiveness, etc.) offer markedly different reductions in SAF MPSP. Finally, under current federal incentives in the US, it is still more economically efficient to use pyrolysis (with higher GWP) than GFT (with lower GWP). Therefore, regional stakeholders will need to navigate the tradeoff between economic and environmental performances of these platforms. Though Virginia was used as a case study, the methodology is replicable for other jurisdictions, insofar it can be adapted for use in other locations without decision-makers having to completely build their own TEA models.

Geospatial evaluation of radiologic access in Rwanda

BackgroundRwanda has aimed to rebuild its health care system since the Rwandan genocide against the Tutsis in 1994, though one of the challenges has been a scarcity of radiologic resources.ObjectiveTo assess the location and accessibility of radiologic facilities in Rwanda using geospatial mapping and population-based data.MethodsA cross-sectional study was conducted in May 2023 using location and radiologic modality data provided by the Department of Radiology at the University Teaching Hospital of Kigali and the WorldPop database, a publicly available database providing open-access geospatial population data. Radiologic equipment included magnetic resonance (MR), computed tomography (CT), positron emission tomography (PET), radiotherapy, X-ray, mammography, and fluoroscopy machines. Geospatial analysis was performed using ArcGIS Pro 2.8.6 software.ResultsFifty-six radiologic facilities were identified, including 5 MR, 7 CT, 1 radiotherapy, 52 X-ray, 5 mammography, 5 fluoroscopy, and 0 PET machines. There were 0.4 MR, 0.5 CT, 0 PET, 0.1 radiotherapy, 3.9 X-ray, 0.4 mammography, and 0.4 fluoroscopy units per 1 million people.ConclusionRwanda is one of the countries with the lowest radiologic access in East Africa; however, there is evidence of progress, particularly in more advanced diagnostic imaging techniques such as computed tomography and magnetic resonance imaging.Critical relevance statementThis study provides a 10-year update on current radiologic resources and access in Rwanda, identifying areas of progress and ongoing scarcity, serving as a guide for future direction of growth.Key points• As Rwanda works on rebuilding its health care system, this study provides an assessment of the current radiologic resources within the country.• There is less than one radiologic unit for every million of the Rwandan population for every imaging modality other than X-ray.• While radiologic access in Rwanda lags behind that of its neighbors, there has been growth focused on advanced imaging modalities and the training of human resources.Graphical

LNG Logistics Model to Meet Demand for Bunker Fuel

The main objective of this manuscript is to build a model for the distribution of LNG as a marine fuel in the southern Baltic Sea based on a genetic algorithm in terms of cost. In order to achieve this, it was necessary to develop, in detail, research sub-objectives like analysis of the intensity of ship traffic in the indicated area and analysis of LNG demand in maritime transport. In the first part of this study, the authors use data from the IALA IWRAP Mk2 and the Statistical Office in Szczecin to analyse the marine traffic density (by type of vessel) in the southern part of the Baltic Sea. LNG used as marine fuel reduces toxic emissions into the atmosphere. The authors specify the LNG fleet size and locations of LNG storage facilities in a way to ensure that the defined LNG bunker vessels can supply fuel to LNG-powered vessels within the shortest possible time period. The database contains a set of traits necessary to determine the optimal demand for LNG. The traits were developed based on an existing LNG fleet and appropriately selected infrastructure, and they represent existing LNG-powered vessels as well as LNG bunker vessels and their specifications. Based on the created LNG distribution model, were performed in Matlab R2019a software. An LNG distribution model was developed, which uses a genetic algorithm to solve the task. The demand for LNG for the sea area under analysis was determined based on data on the capacity of LNG-powered vessels (by type of vessel) and their distance from the specified port.

Comparative study of robust optimization model of multimaterial emergency logistics in an uncertain environment

AbstractGiven the significance of objective functions in alleviating human suffering within the humanitarian relief supply chain, it is imperative to consider both logistics costs and the cost of deprivation as ideal targets. This study investigates a multimaterial single‐cycle stochastic mixed integer model, integrating uncertainties about post‐disaster demand, supply survival, and road networks. An innovative heuristic approach is used to solve the stochastic facility location problem, which minimizes unfulfilled requests and response times in lexicographic order. This research also compares the cost‐minimization model of the pre‐positioning problem to an alternative model that maximizes demand satisfaction under resource constraints. Empirical validation through numerous case studies and solutions confirms the effectiveness and feasibility of the alternative model. The results of our experiments consistently demonstrate that the best solution provided by the alternative model not only satisfies the minimum cost model but also surpasses the conventional problem, without compromising performance. Consequently, it is crucial to avoid the cost‐minimization approach at the expense of human suffering.

Resource allocation and route planning under the collection price uncertainty for the biomass supply chain

The escalating severity of global warming has drawn worldwide attention to ecological problems. Nevertheless, with the introduction of environmental policies, biomass resources have been effectively developed as a renewable energy source. This paper investigates an advanced biomass supply chain design wherein biomass resources are initially converted into bio-oil through widely distributed fast pyrolysis facilities, and are subsequently transported to a centralised biorefinery for further refining into biofuels. This novel biomass supply chain addressed three key issues: (1) The number of fast pyrolysis facilities, (2) The allocation of resources, and (3) The routes of resources transport. In respect of these problems, a two-stage stochastic mixed integer programming model is established to minimise the total cost of the biomass supply chain considering the uncertainty collection price of fast pyrolysis facilities. A hybrid simulated annealing algorithm which incorporates the sample average approximation method is proposed to solve the stochastic model and is effectiveness for large-scale examples. Finally, a sensitivity analysis is performed using the proposed algorithm and the results show that the proposed stochastic model outperforms the deterministic model under uncertain collection price. The model allows optimising the biomass supply chain economic performances and minimise financial risk on investment by determining the fast pyrolysis facility locations, reasonable resource allocation and optimal transport routes under uncertain collection price.

An Evaluation Study on Tourists’ Environmental Satisfaction after Re-Use of Industrial Heritage Buildings

As a witness to history, industrial heritage embodies the cultural, technological, and economic values of a particular era. Transforming it into a cultural and creative park can imbue new functions and vitality, supporting and promoting sustainable urban development. This paper focuses on the Mili Cultural and Creative Park in Quanzhou as its research subject. Through interviews and questionnaire analyses from the perspective of tourists, five evaluation factors are extracted: spatial environmental elements, social cultural elements, landscape and greening elements, supporting facilities elements, and transportation and location elements. An analysis of tourist satisfaction using the semantic differential method reveals a higher satisfaction with spatial environmental elements, social cultural elements, and landscape and greening elements, while satisfaction with supporting facilities elements and transportation location elements is low. Additionally, the modified IPA method is employed for the analysis, revealing poor overall performance in supporting facilities elements and transportation and location elements. In subsequent efforts, priority should be given to improving the quantity and reasonable distribution of garbage cans, organization of events in the park, traffic conditions outside the park, parking around the park, and park accessibility. The aim is to further optimize the environment after the reuse of industrial heritage, enhance tourist satisfaction, and promote urban sustainability.

An approximation algorithm for k-level squared metric facility location problem with outliers

An approximation algorithm for k-level squared metric facility location problem with outliers

OBABC: A one-dimensional binary artificial bee colony algorithm for binary optimization

Artificial bee colony (ABC) algorithm is a widely utilized swarm intelligence (SI) algorithm for addressing continuous optimization problems. However, most binary variants of ABC (BABC) algorithms may suffer from issues such as invalid searches and high complexity when applied to binary problems. To address these challenges, we first establish a set of criteria for developing a BABC algorithm. Following these criteria, we propose a novel BABC algorithm, denoted as oBABC, which not only adheres to the defined criteria but also successfully inherits the advantages of original ABC algorithm. To evaluate the performance of oBABC and verify its effectiveness, experiments are conducted on two typical binary problems: uncapacitated facility location problem (UFLP) and maximum cut problem (Max-Cut). The experimental results reveal the following findings: 1) The validity of the criteria and the accuracy of the theoretical analysis are confirmed. oBABC exhibits high search efficiency with an invalid learning rate (ILR) of 0 %, while the ILRs of other BABC algorithms almost exceeds 20 %. 2) In terms of search efficiency and capability, oBABC exhibits a significant improvement in search efficiency and consistently ranks at the top in terms of optimization capability. These results suggest that oBABC may be a highly efficient and effective tool for solving binary problems.

Area Deprivation and Postpartum Readmission Facility Location and Timing

This cohort study evaluates the role that community-level socioeconomic status plays in hypertension-related hospital readmission within 12 weeks after delivery.

‘Nowhere and no one is safe’: spatial analysis of damage to critical civilian infrastructure in the Gaza Strip during the first phase of the Israeli military campaign, 7 October to 22 November 2023

BackgroundSince the Hamas attacks in Israel on 7 October 2023, the Israeli military has launched an assault in the Gaza Strip, which included over 12,000 targets struck and over 25,000 tons of incendiary munitions used by 2 November 2023. The objectives of this study include: (1) the descriptive and inferential spatial analysis of damage to critical civilian infrastructure (health, education, and water facilities) across the Gaza Strip during the first phase of the military campaign, defined as 7 October to 22 November 2023 and (2) the analysis of damage clustering around critical civilian infrastructure to explore broader questions about Israel’s adherence to International Humanitarian Law (IHL).MethodsWe applied multi-temporal coherent change detection on Copernicus Sentinel 1-A Synthetic Aperture Radar (SAR) imagery to detect signals indicative of damage to the built environment through 22 November 2023. Specific locations of health, education, and water facilities were delineated using open-source building footprint and cross-checked with geocoded data from OCHA, OpenStreetMap, and Humanitarian OpenStreetMap Team. We then assessed the retrieval of damage at and with close proximity to sites of health, education, and water infrastructure in addition to designated evacuation corridors and civilian protection zones. The Global Moran’s I autocorrelation inference statistic was used to determine whether health, education, and water facility infrastructure damage was spatially random or clustered.ResultsDuring the period under investigation, in the entire Gaza Strip, 60.8% (n = 59) of health, 68.2% (n = 324) of education, and 42.1% (n = 64) of water facilities sustained infrastructure damage. Furthermore, 35.1% (n = 34) of health, 40.2% (n = 191) of education, and 36.8% (n = 56) of water facilities were functionally destroyed. Applying the Global Moran’s I spatial inference statistic to facilities demonstrated a high degree of damage clustering for all three types of critical civilian infrastructure, with Z-scores indicating < 1% likelihood of cluster damage occurring by random chance.ConclusionSpatial statistical analysis suggests widespread damage to critical civilian infrastructure that should have been provided protection under IHL. These findings raise serious allegations about the violation of IHL, especially in light of Israeli officials’ statements explicitly inciting violence and displacement and multiple widely reported acts of collective punishment.

Wireless sensor networks-based adaptive differential evolution for multimodal optimization problems

In wireless sensor networks (WSN), we often detect the monitoring areas among different sensors so that the sensors can be switched on and off adaptively to save energy and extend their lifetime. Inspired by the principle of WSN, a WSN-based adaptive differential evolution (WSNADE) algorithm is proposed in this paper, together with a WSN-based adaptive niching technique (WANT) and two novel strategies called protection-based dual-scale mutation (PDM) strategy and multi-level reset (MLR) strategy, for solving multimodal optimization problems (MMOPs). In WANT, each individual is considered as a sensor with its monitoring area. If the monitoring areas of two individuals intersect, which means these two individuals monitor the similar area and should be partitioned into the same niche. In this way, WANT can adaptively form a niche for each individual, avoiding the sensitivity of niching parameters. Based on WANT, the PDM strategy is designed to select the appropriate mutation strategy for each individual. Besides, to save fitness evaluations (FEs) for exploring more promising areas, the MLR strategy is developed to store the promising individuals and reset the stagnant individuals. The experimental results on 20 multimodal benchmark test functions in CEC2015 multimodal competition show that the proposed WSNADE algorithm generally performs better than or at least comparable with other state-of-the-art multimodal algorithms, including the winner of the CEC2015 competition. Finally, WSNADE is applied to a real-world multimodal application in multiple competitive facilities location design (MCFLD) problem to illustrate its practical applicability.

Increase in Hypofractionated Radiation Therapy Among Patients with Invasive Breast Cancer or Ductal Carcinoma In Situ: Who is Left Behind?

We aimed to update the trend of hypofractionated whole-breast irradiation (HF-WBI) use over time in the US and examine factors associated with lack of HF-WBI adoption for patients with early-stage invasive breast cancer (IBC) or ductal carcinoma in situ (DCIS) undergoing a lumpectomy. Among patients who underwent a lumpectomy, we identified 928,034 patients with early-stage IBC and 330,964 patients with DCIS in the 2004 to 2020 National Cancer Database. We defined HF-WBI as 2.5-3.33 Gy/fraction to the breast and conventionally fractionated WBI as 1.8-2.0 Gy/fraction. We evaluated the trend of HF-WBI utilization using a generalized linear model with the log link and binomial distribution. Factors associated with HF-WBI utilization were assessed using multivariable logistic regression in patients diagnosed between 2018 and 2020. Among patients with IBC, HF-WBI use has significantly increased from 0.7% in 2004 to 63.9% in 2020. Similarly, HF-WBI usage among patients with DCIS has also increased significantly from 0.4% in 2004 to 56.6% in 2020. Black patients with IBC were less likely than White patients to receive HF-WBI (adjusted odds ratio [AOR] 0.81; 95% CI, 0.77-0.85). Community cancer programs were less likely to administer HF-WBI to patients with IBC (AOR, 0.80; 95% CI, 0.77-0.84) and to those with DCIS (AOR, 0.87; 95% CI, 0.79-0.96) than academic/research programs. Younger age, positive nodes, larger tumor size, low volume programs, and facility location were also associated with lack of HF-WBI adoption in both patient cohorts. HF-WBI utilization among postlumpectomy patients has significantly increased from 2004 to 2020 and can finally be considered standard of care in the US. We found substantial disparities in adoption within patient and facility subgroups. Reducing disparities in HF-WBI adoption has the potential to further alleviate health care costs while improving patients' quality of life.

Awareness and Incorporation of the Dental Home Concept Among General Dentists and Dental Therapists in Malaysia.

Background The dental home concept (DHC) refers to an approach in oral healthcare that emphasizes establishing a long-term, comprehensive, and family-centered relationship between a patient and their primary dental care. This study determined the awareness and incorporation of the DHC among general dentists and dental therapists in Malaysia. Methodology A total of 154 general dentists and 137 dental therapists providing oral healthcare services at the Ministry of Health (MOH) primary care facilities throughout Malaysia participated in this cross-sectional study. A self-administered questionnaire was used to measure the respondents' awareness of the DHC and incorporation of the DHC characteristics into their practice. Results Most dentists and dental therapists (61.7% and 67.2%, respectively) had not heard of the term DHC and were unaware of the concept. The respondents' awareness was not associated with their age, sex, years of service, facility location, and percentage of treatment given to children aged five years and below. However, most dentists and dental therapists responded positively about incorporating most DHC characteristics into their current practice. Conclusions Most dentists and dental therapists serving the MOH primary oral healthcare facilities were unaware of the DHC, although most DHC characteristics have already been incorporated into their practice. This study provides evidence of the incorporation of the DHC into the MOH primary oral healthcare services and suggests an effort to increase the awareness of the workforce regarding the concept and its implementation.

Evaluating health workers attitude towards implementation of maternal and perinatal deaths surveillance and response system in Morogoro region; analytical cross-sectional study.

Maternal and Perinatal Deaths Review and Surveillance (MPDSR) is a technical system which was issued by the World Health Organization in 2013 to help developing countries improve maternal health. The major purpose of the system was to reduce the ongoing high numbers of maternal deaths and perinatal deaths from avertable causes. Tanzania adopted MPDSR system in 2015. The study aimed to assess health workers attitude towards implementation of MPDSR system in Morogoro Region. This analytical cross-sectional study was conducted in three districts of Morogoro region from April 27, 2020 to May 29, 2020 involving 360 health workers from 38 health facilities. A semi-structured questionnaire was used for data collection. SPSS software version 25 was used to analyze the obtained data. Descriptive analysis was done to describe the characteristics of study participants. Binary logistic regression analysis was used to assess predictors of health workers attitude towards the MPDSR system. A total of 255(70.8%) of respondents had positive attitude towards MPDSR system. After controlling of confounders predictor of positive attitude were location of health facility [rural (AOR = 0.216 at 95% CI = 0.121-0.387, p = <0.001)], Age group [Below 30(AOR = 0.459 at 95%CI = 0.264-0.796, p = 0.006)] and status of training on MPDSR [Yes (AOR = 4.892 at 95%CI = 2.187-10.942, P = <0.001)]. Substantial number of health workers had positive attitude towards the MPDSR system. Health workers who were residing in rural settings and younger than 30 years were less likely to have positive attitude towards the system. Health workers who had access to be trained about the system were more likely to have positive attitude towards MPDSR system. The study recommends the training of health workers about the system so as to increase their attitude and hence the use of the system.

Solving the uncapacitated facility location problem under uncertainty: a hybrid tabu search with path-relinking simheuristic approach

The uncapacitated facility location problem (UFLP) is a well-known combinatorial optimization problem that finds practical applications in several fields, such as logistics and telecommunication networks. While the existing literature primarily focuses on the deterministic version of the problem, real-life scenarios often involve uncertainties like fluctuating customer demands or service costs. This paper presents a novel algorithm for addressing the UFLP under uncertainty. Our approach combines a tabu search metaheuristic with path-relinking to obtain near-optimal solutions in short computational times for the determinisitic version of the problem. The algorithm is further enhanced by integrating it with simulation techniques to solve the UFLP with random service costs. A set of computational experiments is run to illustrate the effectiveness of the solving method.

Tailoring Evolutionary Algorithms to Solve the Multiobjective Location-Routing Problem for Biomass Waste Collection

Location-routing problems widely exist in logistics activities. For the biomass waste collection, there is a recognized need for novel models to locate the collection facilities and plan the vehicle routes. So far most location-routing models fall into the cost-driven-only category. However, comprehensive objectives are required in the specific context, such as time-dependent pollution and speed-and load-related emission. Furthermore, location-routing problems are hierarchical by nature, containing the facility location problems (strategic level) and the vehicle routing problems (tactical level). Existing studies in this field usually adopt computational intelligence methods directly without decomposing the problem. This can be inefficient especially when multiple objectives are applied. Motivated by these, we develop a novel multi-objective optimization model for the location-routing problem for biomass waste collection. To solve this model, we explore the way to tailor evolutionary algorithms to the hierarchical structure. We develop adapted versions of two commonly used evolutionary algorithms: the genetic algorithm and the ant colony optimization algorithm. For the genetic algorithm, we divide the population by the strategic level decisions, so that each subpopulation has a fixed location plan, breaking the location-routing problem down into many multi-depot vehicle routing problems. For the ant colony optimization, we use an additional pheromone vector to track the good decisions on the location level, and segregate the pheromones related to different satellite depots to avoid misleading information. Thus, the problem degenerates into vehicle routing problem. Experimental results show that our proposed methods have better performances on the location routing problem for biomass waste collection.

Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning

The transition to sustainable transportation is imperative in mitigating environmental impacts, with electric vehicles (EVs) at the forefront of this shift. Despite their environmental benefits, the global adoption of EVs is curtailed by challenges such as nascent battery technology, high costs, and insufficient charging infrastructure. This study addresses the optimizing electric vehicle charging station (EVCS) locations as a critical step toward enhancing EV adoption rates. Thus, establishing efficient charging stations is critical to meet the increasing demand. By integrating location modeling with demand forecasts and market penetration, we propose a comprehensive approach to determine optimal locations and capacities for EVCS. Firstly, review existing literature, highlighting the significance of facility location models in optimizing EV charging infrastructure and identifying gaps in addressing demand and market penetration. Our methodology uses a genetic algorithm to solve the p-median problem for location selection and Arena 14 simulation software to model station traffic and optimize charging unit types and quantities. The model prioritizes public areas, considering potential demand points and station locations to propose optimal charging areas. Results indicate that our model minimizes travel distances and waiting times, offering a scalable solution adaptable to future EV market growth. This study contributes to the field by presenting a sustainable and economical model for EVCS placement and capacity planning, underlining the importance of a robust charging network in the broader adoption of electric transportation. The findings suggest that proactive infrastructure development, guided by accurate demand predictions and optimized location strategies, can significantly enhance the feasibility and attractiveness of EVs, supporting global efforts towards a cleaner, more sustainable transportation system.

Computing Metric Dimension of Two Types of Claw-free Cubic Graphs with Applications

Consider the simple connected graph G with vertex set V(G) and edge set E(G). A graph \(G\) can be resolved by \(R\) if each vertex’s representation of distances to the other vertices in \(R\) uniquely identifies it. The minimum cardinality of the set \(R\) is the metric dimension of \(G\). The length of the shortest path between any two vertices, x, y in V(G), is signified by the distance symbol d(x, y). An ordered k-tuple \(r(x/R)=(d(x,z_1),d(x,\ z_2),…,d(x,z_k))\) represents representation of \(x\) with respect to \(R\) for an ordered subset \(R={\{z}_1,z_2,z_3…,z_k\}\) of vertices and vertex \(x\) in a connected graph. Metric dimension is used in a wide range of contexts where connection, distance, and connectedness are essential factors. It facilitates understanding the structure and dynamics of complex networks and problems relating to robotics network design, navigation, optimization, and facility location. Robots can optimize their localization and navigation methods using a small number of reference sites due to the pertinent idea of metric dimension. As a result, many robotic applications, such as collaborative robotics, autonomous navigation, and environment mapping, are more accurate, efficient, and resilient. A claw-free cubic graph (CCG) is one in which no induced subgraph is a claw. CCG proves helpful in various fields, including optimization, network design, and algorithm development. They offer intriguing structural and algorithmic properties. Developing algorithms and results for claw-free graphs frequently has applications in solving of challenging real-world situations. The metric dimension of a couple of claw-free cubic graphs (CCG), a string of diamonds (SOD), and a ring of diamonds (ROD) will be determined in this work.

Study of the Development Potential of Geospatial-Based PSEL (Waste Processing into Electrical Energy) Areas

Since 1989, most of the waste processing in DKI Jakarta Province has ended at the Bantar Gebang TPST, causing the available capacity to accommodate waste to approach the maximum limit. One alternative processing that has a high level of efficiency in reducing the volume of waste is the PSEL (Waste to Electrical Energy Management) facility. Planning PSEL facilities in urban areas requires consideration from all aspects. While the space utilization in DKI Jakarta is quite dense and there are no practical guidelines in determining the location of PSEL facilities, it is necessary to conduct a comprehensive study, namely a study of the potential development of PSEL areas. The purpose of this study is to obtain the location and spatial pattern of the potential development of the PSEL area and obtain the characteristics of the area with the 'Recommended' category as the location of PSEL facility development. This study uses geospatial analysis with the quantitative Weighted Overlay method, where geospatial modeling is carried out by inputting the weight of each criterion (aspects that have an influence on determining the location of PSEL facilities) obtained from the results of the Fuzzy Analytic Hierarchy Process (FAHP) analysis assisted by a questionnaire tool. The results of the study obtained 2 (two) areas that have High Category status with a total area of 1.52 ha located in Cilincing District and Kelapa Gading District, North Jakarta with the condition that the first area occupies an improper designation zone (Industrial and Warehousing Zone) while the second area has a radius distance of less than 500 m to the nearest settlement and tourist location.

상업시설 입지요인의 풍수적 해석

A Study on the Interpretation of Feng shui Factors in the Commercial Facility Location Analysis - Focused on the Multiple Auctions in Jung-gu, Seoul -Using the multiple auctions in Jung-gu, Seoul as a focal point, this study aims to analyze the interpretation of environmental factors in the location analysis of commercial facilities. The research focuses on the study of commercial facility locations, specifically in terms of their relationship with environmental factors.The research methodology includes the following steps: 1. Data Collection - Collect data related to commercial facility locations in Jung-gu, Seoul, particularly those obtained through multiple auctions. - Gather data on environmental factors such as geographic characteristics, natural environment (landscape, water, mountains, etc.), and geographical location. 2. Data Preprocessing - Preprocess the collected data to transform it into a suitable format for analysis - Cleanse the data and extract relevant variables for the study. 3. Environmental Factors Analysis: - Utilize Geographic Information Systems (GIS) to analyze the environmental factors related to commercial facility locations. - Conduct spatial analysis to identify patterns and relationships between commercial facilities and environmental factors. 4. Interpretation and Evaluation - Interpret the results of the analysis to gain insights into the interpretation of environmental factors in cmmercial facility location analysis - Evaluate the significance and impact of environmental factors on commercial facility locations in Jung-gu, Seoul. 5. Conclusion and Recommendations:Summarize the findings and draw conclusions based on the interpretation of environmental factors in commercial facility location analysis - Provide recommendations for future decision-making processes regarding commercial facility locations, taking into account the identified environmental factors. By conducting this study, we aim to contribute to the understanding and interpretation of environmental factors in the location analysis of commercial facilities, specifically focusing on the multiple auctions in Jung-gu, Seoul.