Data Collection Challenges Research Articles

Customer Churn Analysis in Telecom Industry using Logistics Regression in Machine Learning with Kaplan–Meier and Cox Proportional Hazards Model

Customer churn poses a significant challenge for companies in the telecom industry, impacting revenue and profitability. In this paper, we present a comprehensive survey of customer churn analysis methodologies, focusing on the application of logistic regression, Kaplan-Meier survival analysis, and the Cox Proportional Hazards Model. Logistic regression serves as a fundamental technique for binary classification, enabling the prediction of churn based on customer attributes and behaviors. Kaplan-Meier survival analysis offers insights into customer survival probabilities over time, facilitating the identification of high-risk cohorts. Additionally, the Cox Proportional Hazards Model provides a flexible framework for analyzing the impact of predictor variables on churn timing, accounting for censoring and time-varying covariates. Through a review of existing literature, case studies, and practical applications, we explore the strengths and limitations of these methodologies in customer churn prediction. We also discuss challenges in data collection, feature engineering, and model evaluation, along with future research directions to enhance churn analysis effectiveness. By leveraging logistic regression with Kaplan-Meier and the Cox model, telecom companies can develop robust churn prediction models to improve customer retention strategies and mitigate revenue loss. Keywords: Customer churn analysis, Telecom industry, Logistic regression, Kaplan-Meier survival analysis, Cox Proportional Hazards Model, Retention strategies.

HARNESSING DATA INSIGHTS FOR CRISIS MANAGEMENT IN U.S. PUBLIC HEALTH: LESSONS LEARNED AND FUTURE DIRECTIONS

The effective management of crises in U.S. public health relies heavily on harnessing insights from data. This paper examines the lessons learned from past crises and outlines future directions for leveraging data to enhance crisis management efforts. Drawing on case studies of significant events such as the COVID-19 pandemic and natural disasters, we analyze the role of data in informing decision-making, identifying challenges, and deriving best practices. Despite the advancements in data infrastructure, including real-time monitoring systems and predictive analytics, there remain significant challenges in data collection, integration, and analysis. The paper highlights the importance of improving data governance, investing in technology, and fostering interdisciplinary collaboration to address these challenges. Additionally, considerations such as equity and privacy are crucial in the development and implementation of data-driven strategies. Looking ahead, the paper provides recommendations for enhancing crisis management practices, including the adoption of standardized data protocols, the development of early warning systems, and the promotion of data-driven decision-making processes. By prioritizing data-driven approaches and embracing continuous learning, stakeholders can better prepare for and respond to future public health crises. This paper serves as a roadmap for policymakers, public health officials, and researchers to optimize data utilization in crisis management, ultimately safeguarding the well-being of communities across the United States.
 Keywords:  Data Insights, Crisis Management, in U.S. Public Health, Lessons Learned, Future Directions.

Mapping Seagrass Percent Cover And Biomass In Nusa Lembongan, Bali, Indonesia

Seagrass meadow is one of the blue-carbon ecosystems capable of absorbing and storing carbon more effectively in the bodies and sediments than terrestrial ecosystems. However, nationwide data on its carbon stock remains elusive due to limitations and challenges in data collection and mapping. Seagrass percent cover and biomass, which were closely related with above-ground carbon stock, can be effectively mapped and monitored using remote sensing techniques. Therefore, this study aimed to compare the accuracy of 4 scenarios as well as assess the performance of random forest and stepwise regression methods, for mapping seagrass percent cover and biomass in Nusa Lembongan, Bali, Indonesia. The scenarios were experimented using only atmospherically corrected images, sunglint, water, as well as sunglint and water column corrected images. Furthermore, WorldView-3 images and in-situ seagrass data were used, with the image corrected by applying the scenarios. Random forest and stepwise regression methods were adopted for mapping and modelling. The optimum mapping scenario and method were chosen based on R2, RMSE, and seagrass spatial distribution. The results show that the atmospherically corrected image produced the best seagrass percent cover and biomass map. Range of R2 using random forest and stepwise regression model was 0.49–0.64 and 0.50–0.58, with RMSE ranging from 18.50% to 21.41% and 19.36% to 20.72%, respectively. Based on R2, RMSE, and seagrass spatial distribution, it was concluded that the random forest model produced better mapping results, specifically for areas with high seagrass percent cover.

Building a maternal and child cohort amidst Lebanon’s socioeconomic collapse: preliminary results and navigating research challenges

The impact of conflict and crisis on maternal and child health underscores the need for reliable research in vulnerable populations. Lebanon, amidst ongoing economic collapse, political instability, and healthcare system strain, offers a case study for exploring these impacts, particularly on preterm babies and their development. This study aims to assess the feasibility of establishing a prospective cohort of mothers and their full-term and preterm babies in Lebanon, examining the association between social determinants, preterm birth, and developmental outcomes amidst the nation's multifaceted crises. The planned cohort involves 50 full-term and 50 preterm mother-baby pairs recruited at birth and followed up to 9–12 months post-birth. Data collection spans social determinants, perceived stress, social support, quality of life, and developmental assessments. Challenges in recruitment, follow-up, and data collection in the context of Lebanon's socio-political and economic turmoil are evaluated, alongside ethical considerations for research in vulnerable populations. Preliminary findings highlight substantial recruitment and follow-up challenges, notably due to population mobility, economic instability, and healthcare access issues. Despite these obstacles, 113 mother-baby pairs have been recruited. Early analysis reveals significant stress and reduced quality of life among mothers, particularly those with preterm infants, against a backdrop of declining birth rates and healthcare worker exodus. Conducting research in crisis settings like Lebanon presents unique methodological and ethical challenges but remains crucial for understanding and improving health outcomes in vulnerable populations. The study underscores the importance of adaptable research designs and ethical diligence in crisis research, highlighting the need for interventions tailored to these contexts. Establishing a mother and child cohort in Lebanon's crisis-ridden setting is faced with many challenges but is essential for guiding future interventions. Research in such contexts is needed to address health disparities and supporting vulnerable populations, emphasizing the need for dedicated funding and innovative research approaches in times of crisis.

Multitarget Device-Free Localization via Cross-Domain Wi-Fi RSS Training Data and Attentional Prior Fusion

Device-free localization (DFL) using easily-obtained Wi-Fi received signal strength (RSS) has wide real-world applications for not requiring people to carry trackable devices. However, accurate multitarget DFL remains challenging due to the unknown number of targets, multipath interference (MPI), especially between nearby targets, and limited real-world data. In this study, we pioneeringly propose a transformer-based learning method with Wi-Fi RSS as input, and an attentional prior fusion module, to simultaneously locate an unknown number of people at random positions. To overcome the multitarget data collection challenges, we contribute a large-scale cross-domain real-simulation-augmentation training dataset with one and two real-world nearby non-person objects at limited positions and up to five simulated and augmented randomly distributed targets. Experimental results demonstrate our method's improved accuracy, generalization ability, and robustness with fewer Wi-Fi nodes than previous methods.

A pilot conceptualisation of the data space ecosystems for cultural heritage

The digital heritage sector is among the nine common European data spaces out- lined in the EU Data Act: Health, Industrial, Agriculture, Finance, Mobility, Green Deal, Energy, Public Administration, and Skills. The data space concept is complex, and being linked with the data ecosystem brings together methodologies and techniques from numerous domains: artificial intelligence (AI), text/data mining, data visualisation, mapping, image analysis, audio analysis, network analysis, and rights management. There is a need for clarifying data terminology in use for the GLAM field and transferable methodology to strengthen its’ ongoing datafication and digital skill set. The paper offers a comprehensive literature review (incl. EU Data (space) policy documents) on data spaces and data ecosystems aimed at (1) formulating the main challenges in data collection, data processing, and data maturity for heritage-related information systems and (2) justification of the necessary mindset and digital skill set change for a mature museum. General conclusions are made as well as rec- ommendations for the Bulgarian GLAM sector.

Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems

Battery energy storage systems (BESS) play a pivotal role in energy management, and the precise estimation of battery capacity is crucial for optimizing their performance and ensuring reliable power supply. Deep learning methodologies applied to battery capacity estimation have exhibited exemplary performance. However, deep learning methods necessitate supervised training with a significant volume of labeled data, presenting challenges for data collection in industrial scenarios. Moreover, a diverse range of battery types in industrial settings makes it difficult to develop capacity estimation models for different types of batteries from scratch. To address these issues, a semi-supervised adversarial deep learning (SADL) method is proposed for lithium-ion battery capacity estimation. Initially, a subset of labeled lithium-ion battery data, coupled with a subset of unlabeled data, is collected. Voltage and current data are then transformed into capacity increment features. Subsequently, an adversarial training strategy is employed, subjecting labeled and unlabeled data to adversarial training to enhance the performance of SADL. Finally, the effectiveness of the SADL method in estimating the capacity of other lithium-ion batteries is analysed. Experimental results demonstrate that the SADL method accurately estimates the capacity of various battery types, showcasing an RMSE error of approximately 2%, surpassing the performance of other methods. The proposed SADL method emerges as a promising solution for the precise estimation of lithium-ion battery capacity in BESS.

Using the principles of practice development to address challenges in recruitment and data collection when face-to-face methods are unavailable.

Researchers conducting studies involving pregnant women often find recruitment challenging. The COVID-19 pandemic added further complexity to studies requiring face-to-face participation. To demonstrate how to maintain the principles of practice development (PD) when a study must switch from face-to-face to remote methods of collecting data. The number of participants in the authors' study increased when they moved from face-to-face to telephone engagement during the COVID-19 pandemic. They continued using PD principles when they changed method and the quality of the data they collected remained constant, even once lockdown restrictions were in place. PD principles can offer ways for nurse researchers to engage, collaborate with and reflect with people for research projects, including when constraints compete with participation. They can also assist researchers in optimising and maintaining recruitment and data collection when face-to-face research methods are impossible. The telephone can be a valuable alternative medium for recruiting participants and collecting data when face-to-face methods are impossible to use. PD principles can be maintained and response rates and participation may even be greater when using it.

The Compliance Risk Assessment of EU REACH SVHC in Articles of IC Package by Studies of Analysis Methodology

Companies may have legal obligations resulting from the inclusion of the Candidate List of substances of very high concern (SVHC) in a concentration above 0.1% w/w in an Article, as defined under EU REACH Regulation and Waste Framework Directive (SCIP Database) if they place articles on the EU market. Only a few studies have so far been made on how to determine the contents of the Chemical in Product (CiP). The approach to identifying the risk associated with the SVHC in the IC package is mainly to collect and review large information from the supplier chain or by chemical analysis. This article tries to discuss the current challenges for data collection and identifying risks with chemical substances in the IC package. Based on the chemical test results of 2-benzyl-2-dimethylamino-4’-morpholinobutyrophenone and bisphenol A, it was found that the test results of SVHC could be affected by numerous factors such as tested sample form (e.g., powder or flaky shaped), tested sample curing conditions, and chemical analytical methods. Although chemical analysis provides more accurate information for SVHC, when utilizing chemical analysis for checking legal compliance, we still need to compare and analyze the result of chemical substances analysis with the materials self-declaration provided by suppliers to judge whether the data are reasonable.

Unveiling the Obstacles: Understanding Data Collection Challenges for Researchers in Pakistani Universities

This study investigates the challenges researchers face in data collection within Pakistani universities, shedding light on the significant dynamics underlying this phenomenon. Through a qualitative approach employing semi-structured interviews, researchers explored the experiences and perspectives of faculty members and graduate students regarding data collection obstacles. Findings reveal common challenges, including difficulty accessing data sources, ensuring data quality and reliability, mitigating biases, managing logistical constraints, and addressing ethical considerations. Participants also expressed reluctance and resistance towards data collection, leading to incomplete and unreliable data. To address these challenges, recommendations include providing cultural sensitivity training, allocating institutional support, fostering community engagement, offering incentives, adopting flexible methods, prioritizing ethics, and promoting collaboration. The study underscores the significance of overcoming data collection challenges to enhance research integrity, facilitate evidence-based decision-making, foster collaboration, empower researchers, and contribute to methodological literature.

A Review of the Processes and Procedures of Road Traffic Accident Mortality Data Collection in Zambia

Road traffic crashes (RTCs) pose significant public health challenges globally, leading to millions of deaths, injuries and disabilities each year. Low- and middle-income countries, including Zambia, bear a disproportionate burden of RTC fatalities and injuries. However, the collection and accuracy of RTC data in Zambia are hampered by various challenges, including underreporting and inadequate data collection procedures. Strengthening RTC data collection systems is crucial to accurately understand the magnitude of the problem and implement effective measures to reduce RTC fatalities and injuries. This article presents a review of RTC mortality data collection processes and procedures in Zambia, focusing on the key stakeholders involved, namely the Zambia Police Service, Health Care Facilities, and the Department of National Registration, Passports and Citizenship. The study identifies key challenges such as paper-based records, inadequate training, resource limitations, varying case definitions, and low death registration rates. The research emphasizes the need to strengthen data systems and improve coordination among stakeholders. Recommendations include establishing a multidisciplinary road crash data analysis team, utilizing technology for data collection and analysis, providing training and capacity building, conducting public awareness campaigns, and enhancing collaboration between the police, hospitals and Civil Registration and Vital Statistics (CRVS). Implementing these recommendations is crucial to improve the accuracy and completeness of RTC mortality data, leading to evidence-based road safety interventions and policies. This research contributes to the understanding of RTC data collection practices in Zambia and provides valuable insights for policymakers, researchers, and practitioners in addressing the challenges in RTC mortality data collection.

Emphasizing Sexual Orientation and Gender Identity Data Capture for Improved Cardiovascular Care of the LGBTQ+ Population

ImportanceThe rising self-identifying lesbian, gay, bisexual, transgender, and queer (LGBTQ+) population makes understanding the unique health care needs of sexual and gender minoritized patients an urgent one. The interaction between minority stress and cardiovascular disease has been well described among underrepresented minoritized populations. The underrepresentation of minoritized populations in clinical research is partly responsible for worse cardiovascular outcomes in these populations. The absence of sexual orientation and gender identity and expression (SOGIE) data makes it difficult to understand the cardiovascular health of LGBTQ+ adults, thereby widening health care disparities in this population. Advancing cardiovascular health equity for LGBTQ+ patients must begin with careful and accurate SOGIE data collection.ObservationsCurrent SOGIE data capture remains inadequate despite federal mandates. Challenges in data collection include political and regulatory discrimination, patient/practitioner hesitancy, lack of supportive guidance on SOGIE data collection, improper terminology, regulatory inertia, and inadequate and often incorrect integration of SOGIE data into electronic health records (EHRs). Additional challenges include grouping participants as “others” for statistical significance. The inclusion of SOGIE data has demonstrated an impact in other fields like cancer survivorship and surgery. The same needs to be done for cardiology.Conclusions and RelevancePotential solutions for improving much-needed SOGIE data collection include (1) implementing LGBTQ+ inclusive policies, (2) integrating SOGIE data into the EHR, (3) educating health care professionals on the relevance of SOGIE to patient-centered care, and (4) creating a diverse cardiovascular workforce. These steps can substantially enhance the ability to collect SOGIE data to address LGBTQ+ cardiovascular health care disparities.

Experiential Learning for Circular Operations Management in Higher Education

This research-to-practice article delves into novel learning experiences for operations management education, involving the circular economy and experiential learning. Higher Education academics are required to develop effective learning that actively and impactfully helps nurture in students the essential competency to face sustainable development demands. In operations management education, one possibility is to integrate real-world circular economy challenges into learning activities that address issues concerning solid waste generation in business processes and operations. This type of innovative learning experience involves both conceptual understanding and practical implementation. Accordingly, experiential learning is considered a suitable pedagogy for this purpose in this work because of its hands-on applications, critical thinking, and active engagement. To illustrate this proposition, this paper presents a case study concerning an operations management undergraduate course at a Mexican university. The case study indicates how to translate a situation of solid waste generation in a business into relevant disciplinary experiential learning. The results show that students regarded the learning experience as motivating, interesting, and relevant while widely accomplishing their learning objectives. However, limitations did exist regarding experiential learning, the methodological approach, data collection, and implementation challenges. Future work points to the need for further learning experiences and to improve research reliability, transferability, and validity.

Proceedings from the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference

We summarize here the proceedings of the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference, where experts and advocates gathered to advance research into applications of Artificial Intelligence (AI), Machine Learning (ML) and other computational tools in the diagnosis and management of Inborn Errors of Immunity (IEI). The conference focused on key themes such as expediting IEI diagnoses, challenges in data collection, roles of natural language processing (NLP) and large language models (LLMs) in interpreting electronic health records (EHRs), and ethical considerations in implementation. Innovative AI-based tools trained on EHRs and claims databases have discovered new patterns of warning signs for IEIs, facilitating faster diagnoses, and enhancing patient outcomes. Challenges persist in training AIs due to data limitations especially in rare diseases, overlapping phenotypes, and biases inherent in current datasets. Furthermore, experts highlighted the significance of ethical considerations, data protection, and the necessity for open science principles. The conference delved into regulatory frameworks, equity in access, and the imperative for collaborative efforts to overcome these obstacles and harness AI's transformative potential. Concerted efforts are still needed to successfully integrate AI into daily clinical immunology practice.

Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery

The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies.

A Review of the Processes and Procedures of Road Traffic Accident Mortality Data Collection in Zambia

Road traffic crashes (RTCs) pose significant public health challenges globally, leading to millions of deaths, injuries and disabilities each year. Low- and middle-income countries, including Zambia, bear a disproportionate burden of RTC fatalities and injuries. However, the collection and accuracy of RTC data in Zambia are hampered by various challenges, including underreporting and inadequate data collection procedures. Strengthening RTC data collection systems is crucial to accurately understand the magnitude of the problem and implement effective measures to reduce RTC fatalities and injuries. This article presents a review of RTC mortality data collection processes and procedures in Zambia, focusing on the key stakeholders involved, namely the Zambia Police Service, Health Care Facilities, and the Department of National Registration, Passports and Citizenship. The study identifies key challenges such as paper-based records, inadequate training, resource limitations, varying case definitions, and low death registration rates. The research emphasizes the need to strengthen data systems and improve coordination among stakeholders. Recommendations include establishing a multidisciplinary road crash data analysis team, utilizing technology for data collection and analysis, providing training and capacity building, conducting public awareness campaigns, and enhancing collaboration between the police, hospitals and Civil Registration and Vital Statistics (CRVS). Implementing these recommendations is crucial to improve the accuracy and completeness of RTC mortality data, leading to evidence-based road safety interventions and policies. This research contributes to the understanding of RTC data collection practices in Zambia and provides valuable insights for policymakers, researchers, and practitioners in addressing the challenges in RTC mortality data collection.

Patterns of vehicle lights: Addressing complexities of camera-based vehicle light datasets and metrics

This paper explores the representation of vehicle lights in computer vision and its implications for various pattern recognition tasks in autonomous driving. Different representations for vehicle lights, including bounding boxes, center points, corner points, and segmentation masks, are discussed in terms of their strengths and weaknesses toward a variety of domain tasks, as well as associated data collection and annotation challenges. This leads to the introduction of the LISA Vehicle Lights Dataset, providing light annotations related to position, state, color, and signal, specifically designed for downstream applications in vehicle detection, intent and trajectory prediction, and safe path planning. A comparison of existing vehicle light datasets is provided, highlighting the unique features and limitations of each dataset. Because occlusions from vehicle pose and passing objects can limit camera observation, we introduce a group of Light Visibility neural networks, which take as input a detected vehicle image and return as output whether a corresponding vehicle light is present in the image. This is especially important for the evaluation of light localizations, states, and signals, as systems decisions should account for differences of lights being in unknown states due to occlusion versus model uncertainty. We show that our trained Light Visibility Models achieve over 90% accuracy on each of the four light classes. Our dataset and model are made available at https://cvrr.ucsd.edu/vehicle-lights-dataset.

Modelling the effect of macroeconomic factors on residential real estate investment returns in Abuja and Lagos, Nigeria

PurposeThis study evaluated the effect of macroeconomic factors on residential real estate (RE) investment returns in the cities of Abuja and Lagos, Nigeria, with a view to guiding RE investors and researchers.Design/methodology/approachA survey research design was employed using a questionnaire to collect RE transaction data from 2008 to 2022 from estate surveying and valuation firms in the study areas. Rental and capital value data collected were used to construct rental and capital value indices and total returns on investment. The macroeconomic data used were retrieved from the archives of the Central Bank of Nigeria (CBN). Granger causality (GC) and multiple regression models were adopted to evaluate the effect of selected macroeconomic variables on residential RE investment returns in the study areas.FindingsThe study found a progressive upward movement in rental and capital values of residential RE investment in the study areas within the study period. Total and risk-adjusted returns on investment were equally positive within the study period. Only the inflation rate, unemployment rate and real gross domestic product (GDP) per capita were found to be the major determinants of residential RE investment returns in the study areas within the study period.Research limitations/implicationsThe secrecy associated with property transaction information/data by RE practitioners in the study areas posed a challenge. Property transaction data were not adequately kept in a way for easier access and retrieval in many of the estate firms and agent offices. Consequently, there was a lack of data that spanned the study period in some of the sampled estate firms or agent offices. This data collection challenge was, however, overcome by the excess time spent retrieving the required data for this study to ensure that the findings appropriately answer the research questions. Practical implicationsInflation and GDP per capita have been found to be significant factors that influence residential RE investment performance in the study areas. Therefore, investors should pay attention to these identified macroeconomic factors for residential RE investment in the study areas whilst making investment decisions in order to mitigate a possible loss of income or return. The government should formulate and implement economic policies that would address the current high unemployment and inflation rates in Nigeria at large.Originality/valueThis study has extended and further enriched the existing body of knowledge in the field of RE investment analysis in Nigeria. To the best of the authors' knowledge, this study is the first to adopt the Cornish Fisher value-at-risk and modified Sharpe ratio models to analyse risk and risk-adjusted returns on residential RE investment, respectively, in Nigeria. It has therefore redirected the focus of RE researchers and practitioners to a more objective approach to RE investment performance analysis in Nigeria.

Current issues in doctoral supervision practice in the UK

ABSTRACT Doctoral study remains the most popular form of research degree globally. The student and supervisor relationship is an integral part of the postgraduate researcher (PGR) experience. Supervisory relationships can be complex and multi-faceted, with differing expectations from both supervisor and PGR. However, comparing different perspectives of the PGR supervisory journey is relatively unexplored. This study aimed to explore how both PGRs and supervisors perceive the evolution of supervisory relationships throughout the doctoral journey. Employing a mixed methods design, the findings indicate that there is a transition in the dynamics of the relationship between the supervisor and the student which changes during this process. The role of the supervisor moves from being a more knowledgeable other operating in an advisory capacity, described by one participant as a ‘balance of expertise’ that ‘swings’ as the relationship evolves. Drawing upon Jindal-Snape’s multiple and multi-dimensional transitional theory, the results suggest that managing expectations, supporting the transition to doctoral study, the role of peer support for PGRs and supervisors are fundamental to a positive supervisory experience. The global pandemic affected the supervisory relationship and research journey posing some challenges for data collection; however, it facilitated opportunities to adapt and evolve supervisory practice.

Enhancing Property Valuation in Ruwa, Harare, Zimbabwe, using Kobo Collect and GIS

The escalating urbanization of Ruwa, a burgeoning suburb on the outskirts of Harare, Zimbabwe, has catalyzed an exigent demand for precise property valuation. Traditional valuation methods have struggled amidst the challenges of manual data collection, data inaccuracies, and protracted analyses. To address these constraints, a pioneering solution emerged at the crossroads of modern data collection and geospatial analysis. The fusion of Kobo Collect and Geographic Information Systems (GIS) presents a transformative avenue to surmount these challenges. The conventional practices faced hurdles marked by manual data collection's limitations. Inherent subjectivity and potential errors from pen-and-paper surveys posed challenges to data integrity. Moreover, as Ruwa underwent rapid changes, the inability to update data in real-time diminished valuation accuracy. Slow analysis processes also constrained timely access to property valuation insights. The integration of Kobo Collect and GIS provides a solution aligned with Ruwa's urban evolution. Kobo Collect's agile data collection forms, accessible on mobile devices, amplify data accuracy and practicality. The assimilation of GIS adds a spatial layer, and geotagging properties, and facilitates spatial analyses, unraveling intricate interplays of property values and geographic context. The outcomes encompass heightened data accuracy, as digital data entry minimizes errors. Efficiency gains result from real-time data entry and expedited analyses. Spatial insights from GIS analysis divulge patterns influencing property values. Stakeholders, empowered with informed decision-making capabilities, envision strategic urban planning and property development. The endeavor was not without challenges. Technical training ensured optimal tool utilization, while data privacy measures secured sensitive information.