Utility Of Data Research Articles

Navigating business transformation and strategic decision-making in multinational energy corporations with geodata

In the dynamic landscape of multinational energy corporations, navigating business transformation and strategic decision-making requires innovative approaches. Geodata, encompassing spatial and location-based information, emerges as a pivotal asset in this endeavor. This paper outlines the integration of geodata into the fabric of energy corporations, facilitating informed decisions and operational excellence. The introduction elucidates the significance of geodata amidst evolving business paradigms within the energy sector. It explores the transformative potential of geospatial intelligence in fostering agility and resilience, crucial attributes for multinational corporations. Understanding business transformation delves into the fundamental concepts and drivers reshaping the energy industry. Drawing from case studies, it illustrates how corporations leverage strategic shifts to adapt to market dynamics and emerging technologies. The subsequent section emphasizes the pivotal role of geodata in strategic decision-making processes. It elucidates the diverse applications of geospatial information in optimizing operations, mitigating risks, and capitalizing on market opportunities. Integrating geodata into business processes outlines the methodologies and technologies underpinning effective utilization of spatial data. It underscores the importance of robust data management and geospatial analysis techniques for deriving actionable insights. Leveraging geodata for operational excellence explores how energy corporations optimize asset management, streamline supply chains, and enhance risk assessment through geospatial analytics. Case studies and best practices offer real-world examples of successful geodata integration, highlighting key lessons and future trends. Challenges and considerations encompass the complexities of data quality, regulatory compliance, and organizational change management. In conclusion, the paper underscores the transformative potential of geodata in shaping the future trajectory of multinational energy corporations, advocating for a strategic embrace of geospatial intelligence in decision-making processes. Keywords: Business Transformation, Strategic Decision-Making, Multinational Energy Corporations,  Geodata.

A Comprehensive Dataset of Four Provincial Legislative Assembly Members

Abstract This research note reports on a new dataset about legislators in four Canadian provinces since the establishment of their colonial assemblies in the eighteenth century. Over 7,000 legislators from Ontario, Quebec, New Brunswick, and Nova Scotia are included, with consolidated information drawn from multiple sources about parliamentarians’ years of birth and death, religion, electoral performance, kinship, and several other biographical indicators. We also illustrate the utility of such data with the help of a few descriptive examples drawn from the four provinces. We believe this consolidated dataset offers several opportunities for future research on representation, legislative activities and party politics.

Uncertainty-Aware Contrastive Learning for semi-supervised named entity recognition

Named entity recognition (NER) based on deep neural networks has shown competitive performance when trained on large-scale human-annotated data. However, they face challenges in low-resource settings, where the available labeled data are scarce. A typical solution is pseudo-labeling which assigns pseudo-labels to the certain (i.e., high confidence) tokens of unlabeled sentences while discards the uncertain (i.e., low confidence) ones. But there still have two potential challenges: (1) discarding the uncertain tokens leads to low utilization of unlabeled data; (2) the intrinsic quality-quantity trade-off issue of pseudo-labeling with confidence threshold. In this work, we propose an innovative method named Uncertainty-Aware Contrastive Learning (UACL) for semi-supervised named entity recognition. Specifically, UACL first utilizes a Gaussian-based class-wise token separation mechanism to dynamically distinguish certain and uncertain tokens, which can self-adaptively adjust the confidence threshold to balance the quantity and quality of pseudo-labeled certain tokens. Then we perform pseudo-supervised learning based on certain tokens and contrastive learning based on uncertain ones, which not only improves the utilization of unlabeled data, but also provides uncertainty-aware guidance information for model training. Furthermore, our method leverages uncertain tokens to optimize token representation, leading to improving performance. The extensive experimental results on four benchmarks demonstrate that the performance of our proposed approach surpasses that of previously leading low-resource baselines.

Concise Review on Scientific Approaches to Burns and Scars.

Burns are large open surgical lesions bathed in virulent pus that result in rupturing of the cutaneous membrane, which has serious consequences such as an extensive loss of proteins, and body fluids, increased chances of infections, and sometimes death. These can be classified based on their penetration levels, i.e., first-degree burns penetrating the epidermis, second-degree burns including both epidermis and dermis, third-degree burns to both layers including the hair follicular cells, sweat glands and various core tissues, fourth-degree burns to adipose tissue, fifth stage burns to muscles, and sixth stage burns to bones. Wound healing/wound repair is a very perplexing process in which the tissues of the affected/burnt area repairs themselves to attain their original form and functionality but develop a scar at the wound site. This article mainly focuses on the algorithms to differentiate various degrees of burns, general first aid approaches to burns and scars, the rationale of treatment of burns, basic mechanisms highlighting the healing processes in humans in terms of free from scar formation as well as with scar formation at their elementary levels including cellular as well as biochemical levels, utility, and progression of pre-clinical data to humans and finally approaches for the improvement of scar formation in man.

Positive Bacterial Cultures on Spinal Wound Closure Do Not Predict Postoperative Outcomes.

Complex surgical back wounds represent significant morbidity in patients who have undergone spinal procedures requiring closure or revision by plastic surgeons. This study aimed to assess the utility of bacterial wound culture data for predicting surgical outcomes of wound management. This study is a single-institution retrospective review of consecutive patients who required plastic surgery intervention for wound infection following spinal procedures between the years 2010 and 2021 (n = 70). Statistical analysis was performed for demographics, comorbidities, perioperative laboratory studies, and treatment methods. The primary outcomes of interest were rate of postoperative complications after soft tissue reconstruction and reconstructive failure. The secondary outcome of interest was time to healing in number of days. The overall complication rate after wound closure was 31.4%, with wound infection in 12.9%, seroma in 10%, dehiscence in 12.9%, and hematoma in 1.4%. Increasing number of debridements before wound closure increased the likelihood of a surgical complication of any kind (odds ratio [OR], 1.772; 95% confidence interval [CI], 1.045-3.002). Positive wound cultures before reconstruction were associated with development of seroma only (OR, 0.265; 95% CI, 0.078-0.893). Use of incisional vacuum-assisted closure devices significantly decreased the odds of postoperative wound dehiscence (OR, 0.179; 95% CI, 0.034-0.904) and increased odds of healing (hazard ratio, 3.638; 95% CI, 1.547-8.613). Positive wound cultures were not significantly associated with negative outcomes after complex closure or reconstruction of infected spinal surgical wounds. This finding emphasizes the importance of clinical judgment with a multidisciplinary approach to complex surgical back wounds over culture data for wound closure timing.

Location Based Privacy Preserving using Machine Learning

Location-based privacy preservation is crucial in the digital age due to the widespread use of location-based services and growing concerns about individual privacy. Despite the use of k-anonymity measures, current systems face challenges, particularly the risk of re-identification, especially when attackers have additional contextual information. These systems also suffer from information loss, leading to a significant decrease in data utility. Striking the right balance between privacy and data utility remains a prominent challenge in the field of location-based privacy preservation. To address the existing gaps and challenges in privacy-preserving location data publishing, a robust framework termed Location anonymization framework is being introduced which consists of three methods Generalization, Sequence alignment and Clustering. DGH Trees are being used to minimize the information loss and also maintains a balance between privacy and utility. To enhance privacy preservation in overly sensitive datasets, a modified version of the k-means algorithm is being put forth. Moreover, to improve the alignment process, the more efficient iterative multisequence alignment is being opted for over the progressive counterpart within this framework. The aim is to achieve a more balanced trade-off between privacy and utility in location data publishing.

The Digital Transformation Model for Innovative Marketing Maturity in the Oil Companies

Purpose: The manufacturing industry has been at the cutting-edge of technology advancements, which has Objectives: The objective of this research is to examine digital transformation within the oil and gas industry, focusing on the transition to renewable energy sources and the role of innovative technologies. It aims to explore how digital technology transforms operational strategies and the importance of data utilization, brand recognition, and ethical data practices in this transformation process.   Methods: This study employs a qualitative investigation methodology to delve into digital transformation in the oil and gas sector. Through interviews with industry decision-makers, the research gathers insights into the challenges and opportunities faced by firms adopting digital solutions and strategies.   Results: The findings of the research highlight the transformative nature of digital technology within the oil and gas industry. They underscore the significance of managing internal and external boundaries during the transformation process. Key factors such as data utilization, brand recognition, and ethical data practices emerge as crucial for successful digital transformation. The study provides empirically grounded directions for developing frameworks to guide digital transformation practices in the industry.   Conclusion: In conclusion, this research contributes to the understanding of digital transformation in the oil sector. By shedding light on the challenges and opportunities inherent in adopting digital solutions, the study aims to facilitate sustainable development and enhance innovation potential in the energy landscape. The findings offer valuable insights for firms navigating the complexities of digital transformation and pave the way for future research in this area.

Spatial Identification and Distribution Pattern of the Complexity of Rural Poverty in China Using Multisource Spatial Data

Regional poverty is one of the most serious challenges facing the world today. Poverty, antipoverty, and poverty alleviation are the focus of the attention of scholars and the public. This paper takes China’s counties as the research unit, selects the influencing factors of poverty from natural and socio-economic factors, establishes an evaluation index system, simulates the natural poverty index and socio-economic poverty eradication index of each county, and clarifies the distribution characteristics of spatial poverty using GIS spatial analysis and BP artificial neural network. The results indicate that natural factors are the main cause of poverty in Chinese counties, with 710 counties having a high natural poverty index, accounting for nearly 30% of the total number of counties in the country. The national county-level natural poverty index shows a clear strip distribution pattern along latitude and longitude, with a strip distribution from north to south and from west to east; socio-economic factors have played a certain role in poverty alleviation, with as many as 1521 counties with low socio-economic poverty alleviation indices, accounting for approximately 64% of the total number of counties in the country. The spatial distribution of the county-level socio-economic poverty alleviation index is relatively fragmented. Through spatial scanning statistics, a total of 44 county poverty pressure index risk clusters reached a statistical significance level, involving 243 counties and districts. In poverty reduction practice, the internal counties and districts of contiguous poverty-stricken areas should strengthen cooperation and exchange. In the process of poverty alleviation and development, targeted poverty alleviation and economic development should be carried out based on the poverty-dominant type and self-development ability of the county, in order to improve efficiency. Regions that are relatively prosperous and have taken the lead in poverty reduction should play a leading and exemplary role in strengthening the radiation power of regional central cities. The prominent feature of this study is the comprehensive utilization of multisource data and the use of new spatial analysis methods (flexible spatial scanning method is widely used in the field of infectious disease prevention and control research). By constructing a multidimensional poverty measurement system that includes natural and social factors, it distinguishes the differences between the factors that cause poverty and the factors that eliminate poverty in regional poverty. At the same time, the flexible spatial scanning detection method was used to detect the differentiation mechanism of poverty spatial patterns.

Potential Landslide Identification in Baihetan Reservoir Area Based on C-/L-Band Synthetic Aperture Radar Data and Applicability Analysis

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results.

Handling Unstructured Image using Generative AI and Dev-Ops

Abstract: This study explores the application of generative artificial intelligence (AI) to convert unstructured medical image data into structured formats. Medical imaging is vital for diagnosis and treatment, yet handling the large volume of unstructured data presents challenges. Utilizing generative AI, particularly convolutional neural networks (CNNs), enables the transformation of raw medical images into structured data representations. By integrating AI techniques, this approach enhances the accessibility and interoperability of medical imaging data, providing valuable insights for healthcare professionals. The proposed methodology aims to streamline the process, optimizing scalability and reproducibility without extensive reliance on DevOps practices. This research signifies a significant step towards leveraging AI for efficient utilization of medical imaging data in healthcare applications

Fundamental of Design Thinking and its Phases

Abstract: The practice of solving issues by putting the requirements of the customer first is known as "design thinking." Its foundation lies in the compassionate observation of people's interactions with their surroundings and the use of an experiential, iterative process to generate creative solutions. The "human-centered" nature of design thinking refers to the utilization of data showing how customers interact with a good or service, as opposed to how a company or other party anticipates such interactions. A product or service must be continuously improved by designers seeing how users interact with it in order to be really human-centered. In design thinking, this is the "iterative" phase. In place of incessant study or ruminating, it encourages moving fast to test prototypes. Unlike traditional problem solving, which is a linear process of identifying a problem and then finding solutions, design thinking only works if it is iterative. It's less of a way to arrive at a single solution, and more of a way to continually evolve your way of thinking and respond to consumer needs.

Data Resource Profile: The Cancer Public Library Database in South Korea.

This paper provides a comprehensive overview of the Cancer Public Library Database (CPLD), established under the Korean Clinical Data Utilization for Research Excellence project (K-CURE). The CPLD links data from four major population-based public sources: the Korea National Cancer Incidence Database in the Korea Central Cancer Registry, cause-of-death data in Statistics Korea, the National Health Information Database in the National Health Insurance Service, and the National Health Insurance Research Database in the Health Insurance Review & Assessment Service. These databases are linked using an encrypted resident registration number. The CPLD, established in 2022 and updated annually, comprises 1,983,499 men and women newly diagnosed with cancer between 2012 and 2019. It contains data on cancer registration and death, demographics, medical claims, general health checkups, and national cancer screening. The most common cancers among men in the CPLD were stomach (16.1%), lung (14.0%), colorectal (13.3%), prostate (9.6%), and liver (9.3%) cancers. The most common cancers among women were thyroid (20.4%), breast (16.6%), colorectal (9.0%), stomach (7.8%), and lung (6.2%) cancers. Among them, 571,285 died between 2012 and 2020 owing to cancer (89.2%) or other causes (10.8%). Upon approval, the CPLD is accessible to researchers through the K-CURE portal. The CPLD is a unique resource for diverse cancer research to investigate medical use before a cancer diagnosis, during initial diagnosis and treatment, and long-term follow-up. This offers expanded insight into healthcare delivery across the cancer continuum, from screening to end-of-life care.

Keywords-based conditional image transformation

In recent years, Generative Adversarial Networks (GANs) and their variants, such as pix2pix, have occupied a significant position in the field of image generation. Despite the impressive performance of the pix2pix model in image-to-image transformation tasks, its reliance on a large amount of paired training data and computational resources has posed a crucial constraint to its broader application. To address these issues, this paper introduces a novel algorithm, Keywords-Based Conditional Image Transformation (KB-CIT). KB-CIT dynamically extracts keywords from the input grayscale images to acquire and generate training data, thus avoiding the need for a large amount of paired data and significantly improving the efficiency of image transformation. Experimental results demonstrate that KB-CIT performs remarkably well in image colorization tasks and can generate high-quality colored images even with limited training data. This algorithm not only simplifies the data collection process but also exhibits significant advantages in terms of computational resource requirements, data utilization efficiency, and personalized real-time training of the model, thereby providing new possibilities for the widespread application of the pix2pix model.

Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks

This paper proposes a reinforcement learning method using a deep residual shrinkage network based on multi-reward priority experience playback for high-frequency and high-dimensional continuous vibration control. Firstly, we keep the underlying equipment unchanged and construct a vibration system simulator using FIR filters to ensure the complete fidelity of the physical model. Then, by interacting with the simulator using our proposed algorithm, we identify the optimal control strategy, which is directly applied to real-world scenarios in the form of a neural network. A multi-reward mechanism is proposed to assist the lightweight network to find a near-optimal control strategy, and a priority experience playback mechanism is used to prioritize the data to accelerate the convergence speed of the neural network and improve the data utilization efficiency. At the same time, the deep residual shrinkage network is introduced to realize adaptive denoising and lightweightness of the neural network. The experimental results indicate that under narrowband white-noise excitation ranging from 0 to 100 Hz, the DDPG algorithm achieved a vibration reduction effect of 12.728 dB, while our algorithm achieved a vibration reduction effect of 20.240 dB. Meanwhile, the network parameters were reduced by more than 7.5 times.

Research on the Driving Factors and Prediction Model of Urban Underground Space Demand in China

The development and utilization of urban underground space (UUS) have emerged as critical strategies to address the challenges posed by urban population growth and land resource depletion. Accurate prediction of UUS demand serves as the cornerstone for scientifically planning underground space and promoting sustainable urban development. In this study, statistical analysis methods were used to investigate the relationship between potential driving factors and UUS demand based on collected data from 16 cities in China. The identification of primary driving factors involves correlation, path, and determination coefficient analyses. Subsequently, univariate regression, multiple linear regression, and LASSO regression methods are employed to construct prediction models for UUS demand. Additionally, the link between historical data and UUS demand in each city was studied separately. The findings reveal that GDP per km2 and GDP per capita comprehensively capture the influence of urban population, economy, and transportation on UUS demand. Notably, GDP per km2 makes the most significant contribution to the proposed regression models, followed by GDP per capita. The application of LASSO regression proves effective in selecting potential factors while maximizing data utilization, presenting itself as a valuable auxiliary tool for UUS planning.

Improving Risk and Customer Management Information System - A Case of Vietnam Commercial Bank

Purpose: The purpose of this paper is to delineate strategies for enhancing the risk and customer management information system (MIS) within Asia Commercial Bank (ACB), a prominent Vietnam-based commercial bank. Through a blend of qualitative methods involving synthesis, analysis, and inductive reasoning alongside quantitative approaches such as econometric modeling employing Ordinary Least Squares (OLS) regression, this study aims to offer actionable insights. It underscores the necessity for ACB to adopt a flexible approach in implementing the proposed methodologies.   Methods: The study employs a combination of qualitative and quantitative methodologies. Qualitative techniques involve synthesis, analysis, and inductive reasoning to discern patterns and extract insights. Quantitative methods entail the development of an econometric model employing OLS regression to analyze systematic factors influencing stock returns within the observed period. The synthesis of these methodologies enables a comprehensive understanding of the factors affecting the efficacy of the MIS within ACB.   Results and Discussion: The intelligent implementation of the data collection, management, exploitation, and utilization processes within the 4th Industrial Revolution technology platform promises enhanced convenience in data analysis and processing for banks, including ACB. Furthermore, the recognition of systematic factors influencing stock returns over the observation period underscores the significance of refining the organization of the MIS system within ACB. The results obtained from both qualitative and quantitative analyses serve as a foundation for informed discussions regarding the optimization of risk and customer management within ACB's operational framework.   Implications of the Research: The findings of this research carry significant implications for the strategic direction of ACB's risk and customer management practices. By embracing flexible implementation strategies and leveraging advanced technologies inherent to the 4th Industrial Revolution, ACB stands poised to streamline its data processing capabilities and enhance decision-making processes. Moreover, the recognition of systematic factors influencing stock returns underscores the importance of fine-tuning the organization of ACB's MIS system to accommodate dynamic market conditions.   Originality/Value: This study contributes to the existing body of knowledge by offering novel insights into the optimization of risk and customer management information systems within the banking sector, with a specific focus on ACB. By synthesizing qualitative and quantitative methodologies, the research provides a nuanced understanding of the challenges and opportunities inherent to MIS enhancement. The practical implications derived from this study serve as a valuable resource for banking institutions seeking to augment their operational efficiency and strategic effectiveness in an increasingly complex financial landscape.

A Literature Survey on Demographics and Topic Impacts on the co-spread of Healthcare prediction based on Twitter(X) Data

Big data is being used much more frequently to monitor trends in business, economics, society, and health as a result of more individuals utilizing Twitter and other social media. Businesses utilize sophisticated statistical techniques to forecast and monitor events such as disease outbreaks and trends in mental health by combining a wide range of data sets from social media. This kind of data utilization has benefits, but there are drawbacks as well, such as the rapidity with which false information can proliferate on Twitter. The approach that is being suggested emphasizes the necessity of improving fact-checking procedures, especially in the healthcare industry where false information can have detrimental effects. This underscores the growing importance of reliable data, information, and knowledge flows in shaping economies while acknowledging the imperative to address challenges associated with the information revolution.

NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data

Recent advances in protecting node privacy on graph data and attacking graph neural networks (GNNs) gain much attention. The eye does not bring these two essential tasks together yet. Imagine an adversary can utilize the powerful GNNs to inferusers’ private labels in a social network. How can we adversarial defend against such privacy attacks while maintaining the utility of perturbed graphs? In this work, we propose a novel research task, adversarial defenses against GNN-based privacy attacks, and present a graph perturbation-based approach, NetFense, to achieve the goal. NetFense can simultaneously keep graph data unnoticed ability (i.e., having limited changes on the graph structure), maintain the prediction confidence of targeted label classification (i.e., preserving data utility), and reduce the prediction confidence of private label classification (i.e., protecting the privacy of nodes). Experiments conducted on ingle- and multiple-target perturbations using three real graph data exhibit that the perturbed graphs by NetFense can effectively maintain data utility (i.e., model unnoticed ability) on targeted label classification and significantly decrease the prediction confidence of private label classification (i.e., privacy protection). Extensive studies also bring several insights, such as the flexibility of NetFense, preserving local neighbourhoods in data unnoticed ability, and better privacy protection for high-degree nodes.

Antibiotic prescribing practices in community and clinical settings during the COVID-19 pandemic in Nairobi, Kenya.

The COVID-19 pandemic has significantly impacted healthcare systems, including antibiotic use practices. We present data on patterns of antibiotic dispensing and use in community and hospital settings respectively in Nairobi, Kenya during the pandemic. We conducted interviews with 243 pharmacies in Nairobi using a standardised questionnaire from November to December 2021. The data collected included demographic characteristics, antibiotic customers, types of antibiotics sold, and antibiotic prescribing practices. Additionally, we retrospectively reviewed health records for 992 and 738 patients admitted in COVID-19 and general wards at two large inpatient hospitals between April 2020 and May 2021, and January 2019 to October 2021, respectively. Demographic, utilisation of laboratory services, treatment, clinical, and outcome data were collected using a modified Global WHO Point Prevalence Surveys (Global-PPS) tool. Almost all pharmacies (91.4%) served customers suspected of having COVID-19 with a mean weekly number of 15.6 customers. All pharmacies dispensed antibiotics, mainly azithromycin and beta lactams to suspected COVID-19 infected customers. 83.4% of hospitalised COVID-19 patients received at least one antibiotic at some point during their hospitalisation, which was significantly higher than the 53.8% in general ward patients (p<0.001). Similarly, the average number of antibiotics administered to COVID-19 patients was higher than that of patients in the general ward (1.74 vs 0.9). Azithromycin and ceftriaxone were the most commonly used antibiotics in COVID-19 patients compared to ceftriaxone and metronidazole in the general wards. Only 2% of antibiotic prescriptions for COVID-19 patients were supported by microbiological investigations, which was consistent with the proportion of 6.8% among the general ward population. Antibiotics were commonly prescribed to customers and patients suspected of having COVID-19 either in community pharmacies or in hospital, without a prescription or laboratory diagnosis. These findings emphasize the crucial role of antibiotic stewardship, particularly in community pharmacies, in the context of COVID-19.

ENHANCEMENT OF THE FIELD ASSESSMENT PROTOCOLS AND SUITABILITY MAPS FOR COCONUT

Abstract. The crop suitability assessment particularly in coconut planting has not been revisited in over two decades. An in-depth analysis of the various factors affecting coconut farming was conducted. This involved the utilization of local remotely-sensed data on elevation, slope, soil type, and land cover. Global gridded data on rainfall and land surface temperature were acquired to supplement the local data. Each factor was given its corresponding weight assignments upon consultation with the experts from the Philippine Coconut Authority. Variable factors such as rainfall (15%) and temperature (10%) were given with lower scores than the permanent or nearly unchanging factors on elevation (25%), slope (25%), and soil type (25%). These individual factors were processed for the final multi-criteria weighted overlay analysis. Finally, the land cover map was used to remove areas deemed unsuitable for coconut planting. Results showed that about 20 million hectares of the country’s total land area are suitable for coconut planting with varying levels of suitability. Of the total suitable areas, only 3.64 million hectares (18%) were planted with coconuts based on the PSA data for 2021. One important factor to consider is that most of the areas not planted with coconut are devoted to other important land cover types like forests, perennials, and annual crops. The utilization of the open source Quantum GIS (QGIS) software proves to be essential in this kind of enormous data processing and analysis, coupled with the generation of very detailed coconut suitability data and maps on a nationwide scale.