Comma-separated Values Research Articles

Exploring the Landscape of Type 2 Diabetes Mellitus Research: A Comprehensive Review of 670 Scopus-Enrolled Journal Articles (2003-2022) by Pakistani authors

Background: Academics develop ideas, technologies, and creative techniques to promote economic, social, and cultural development through interdisciplinary collaborations and cutting-edge research; Scholarly research shapes educational curriculum. Aim: To associate the authors' connection with various institutes and count the availability of institutes in the study; the research intends to provide insights into the contribution of Pakistani scholars and institutions in this specific area of DM type 2 study. Methods: A retrospective method was utilized for this meta-analysis. "Diabetes mellitus type 2" is written down in the Scopus database's menu bar with "Pakistan' in the following search query as affiliation. Results: Comma separated value (CSV) file was downloaded with 861 documents on the term diabetes mellitus type 2. Seven hundred nineteen articles, 85 review articles, 39 letters, 8 editorials, 3 book chapters, 3 conference papers, 3 notes, and 1 erratum were downloaded. Seven hundred nineteen articles were further analyzed, and 670 were selected for meta-analysis for this study. A total of 2794 authors from 1457 institutes participated in writing 670 articles. 55 authors write 588 articles, 117 authors write 45 articles, 238 authors write 17 articles, and 2384 authors write 20 articles. The extensive participation of authors from a diverse range of institutes (1457 in total) underscores the collaborative nature of diabetes mellitus type 2 researches in Pakistan. Practical Implication: This study can help us learn more about the research done by Pakistani authors and institutions on type 2 diabetes. It also shows us where more research and collaboration are potentially needed to improve Pakistan's scientific efforts in this field Conclusion: The results indicate a strong and cooperative research environment for type 2 diabetes in Pakistani institutions. While the involvement of numerous writers demonstrates the comprehensive and multidisciplinary nature of the research, the concentration of papers among particular authors may indicate specialty or leadership in the topic. The distribution of documents among the various author groups points to broad contributions from a bigger pool of researchers and focused efforts from a smaller group of authors. Keywords: Type 2 Diabetes mellitus, landscape, comprehensive review

Evaluation of the District Health Information System in District Kotli, Azad Jammu and Kashmir: A Retrospective Analysis.

It is essential to implement a high-quality electronic database for keeping important information. The District Health Information System (DHIS) is an active data-keeping system in Pakistan. This study aimed to evaluate the patients' data from the DHIS dashboard for the District Headquarters Hospital, Kotli, Azad Jammu and Kashmir (AJK). The data was requested from the hospital administration at District Headquarters Hospital, Kotli, AJK, and the data was analyzed after permission was granted. The data was given in two forms; one was a hard copy of the data for August and September and the other was a comma-separated values file for October and November, 2023. The highest frequency of patients was received in the department of emergency and trauma and the patient's median age was between 15 and 49 years. The second department was medicine with the >50 years of age. Common conditions that needed more attention were chronic obstructive pulmonary disease, acute respiratory infection, diarrhea, pneumonia, diabetes mellitus, hypertension, and ischemic heart disease. For nations with constrained healthcare systems and funds, primary health care (PHC) is the only viable approach for managing non-communicable diseases (NCDs). However, PHC systems intended for infectious diseases have not sufficiently adapted to the growing requirement of chronic care for NCD. Research using health information databases offers numerous benefits, such as the evaluation of large data sets and unexpected prevalence of disease in certain populations, such as a higher prevalence of disease in one gender or age group. Health information system-based data analysis or studies are less expensive and faster but lack scientific control over data collection.

Assessing the reliability of paleomagnetic datasets using the R package PmagDiR

Paleomagnetism is the most important source of information for determining the position of tectonic plates in the geological past. Over the last few decades significant advancements have been made in improving the reliability of paleomagnetic data, ranging from analytical methods to statistical assessments. Here I present the first version of PmagDiR, an R-based open-source package which displays, assesses the reliability, and, when possible, corrects given paleomagnetic directions distributions. The main functions of the package are to: plot paleomagnetic directions, their averages and confidence boundaries; apply different direction cut-offs to identify and filter outliers; perform a test for antipodality; compare the distribution shape with the one predicted by a widely accepted paleosecular variation model; correct flawed distributions for paleomagnetic inclination flattening; and correct for strain-derived paleomagnetic directions deviations when the strain fabric is known. Furthermore, directions can be converted in virtual geomagnetic poles and plotted on a spherical projection for comparison with reference apparent polar wander paths. All main functions of the PmagDiR package return results as comma separated value text files as well as vector graphic files (pdf), optimized for publication purpose with minimal manipulation.

Plant macrofossil data for 48-0 ka in the USGS North American Packrat Midden Database, version 5.0

Plant macrofossils from packrat (Neotoma spp.) middens provide direct evidence of past vegetation changes in arid regions of North America. Here we describe the newest version (version 5.0) of the U.S. Geological Survey (USGS) North American Packrat Midden Database. The database contains published and contributed data from 3,331 midden samples collected in southwest Canada, the western United States, and northern Mexico, with samples ranging in age from 48 ka to the present. The database includes original midden-sample macrofossil counts and relative-abundance data along with a standardized relative-abundance scheme that makes it easier to compare macrofossil data across midden-sample sites. In addition to the midden-sample data, this version of the midden database includes calibrated radiocarbon (14C) ages for the midden samples and plant functional type (PFT) assignments for the midden taxa. We also provide World Wildlife Fund ecoregion assignments and climate and bioclimate data for each midden-sample site location. The data are provided in tabular (.xlsx), comma-separated values (.csv), and relational database (.mdb) files.

Untapped Location Discovery on Social Media by Combining Geospatial Clustering with Natural Language Processing

This methodology combines geospatial clustering and Natural Language Processing (NLP) to create a framework for discovering unexplored geotags in social media. The framework contains the collection of data from social media platforms, the preprocessing of data with Pandas, Natural Language Toolkit (NLTK) and SpaCy libraries for the NLP analysis as well as for sentiment analysis and named entity recognition, followed by spatial clustering with Density-based Space Clustering of Noise Applications (DBSCAN), K-Means and HDBSCAN algorithms, then visualising with Matplotlib and Folium libraries. The data analysis and statistics were done using Pandas and NumPy libraries, and exploration through the selection and collection of more data based on the previous step. In addition, a prediction model has been developed to predict a location cluster using its name by comparing it to the preprocessed comma-separated values data file. Currently, there are certain locations like small-scale hospitals or unknown tourist places which are not currently tagged on available maps applications. This framework can be useful for researchers and policy makers to identify those locations and gain insights from social media data and find its potential for decision-making in various fields.

An Efficient Summarisation and Search Tool for Research Articles

Building an efficient summarization and search tool for research articles is a complex task that involves interdisciplinary expertise in NLP, database management, web development, and user experience design. With the rapid growth of the scientific content, manually reading and selecting important content of research articles became challenging. Thus, there is a need for a summarization tool to help scholars reading their content fast along with a search tool that will find important contents and keep them in organized way. To summarize the contents of different articles a summarization tool is proposed in this work that generates extractive and abstractive summaries. Along with summarization a search engine also been proposed in this work that save the searched results in a comma-separated value (CSV) format including the search queries and meta information of articles such as keyword, title, author name, URL, year of publication, abstracts and summaries. These CSVs help users to get idea about article contents in offline mode without reading or searching the whole text. The efficiency of the summarizer tool is evaluated in terms of precision(pr), recall(re) and F-measure(F-m) of Rouge-1(R1), Rouge-2(R2), Rouge_sum(R_sum) and Bertscore(BS) measures for ten research articles. The average pr, re and F-m obtained from BS are 42%, 42% and 42% for extractive summarization and 41%, 41% and 41% for abstractive summarization. This tool will be helpful to research scholars in the collection of literature and the preparation of related work for their research.

OntoDin: An Islamic Ontology of Quran and Hadith

In the religion of Islam, the Quran and Hadith are considered the principal sources of legislation, and they comprise a large amount of knowledge in an unstructured textual form. Islamic texts are organized in such a way that automatic exploitation is both challenging and difficult to achieve. Lately, researchers have been interested in developing a formal description of Qur’an and Hadith semantics based on ontologies to allow computer systems to leverage their knowledge. The main problem in this field is the lack of a complete ontology that allows for a thorough understanding of Islamic texts. In this article, we attempted to solve this problem by developing an ontology covering the maximum amount of data possible. For the Hadith ontology, we opted for a method using only protégé plugins, where we based our work on Comma-Separated Values (CSV) files containing Hadith texts in Arabic and English with all the related data. This method allowed us to ensure the correctness of the results. The final result of this research is a complete Hadith ontology containing a total of 6 classes and 58 487 individuals, of which 34 373 are Hadiths in Arabic and English, with all the related data such as the list of narrators, the book, the chapter mentioned, and the topic it discusses. To further extend the ontology, we merged the Hadith ontology with full pre-existing Qur’an ontologies to build the OntoDin ontology, representing the Islamic texts with 51 classes and 168 122 individuals in Arabic and English.

Design and Implementation of an Interactive Visualization Dashboard for Monitoring the Flood Vulnerability and Mapping

This study aims to build a web-based interactive visualization dashboard from granular flood vulnerability index estimation maps using data from satellite imagery. The approach used to build this visualization dashboard is a two-dimensional (2D) approach created with the qgis2web python plugin facilitated with a JavaScript leaflet. Raw data from satellite imagery consisting of indicators of the causes of flooding are extracted in comma-separated value (CSV) format. Furthermore, the data is integrated based on its spatial attributes and stored in Geographic JavaScript Object Notation (GeoJSON) format to produce a visualization of the flood vulnerability index map. In web views, dashboards are built by utilizing hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). This interactive dashboard has several useful features in helping the process of monitoring the flood vulnerability of an area such as zoom, "show me where I am", measure distance, search, legend, and change year. Thus, the flood vulnerability estimation map dashboard is expected to assist the government in monitoring areas with extreme flood vulnerability and support the decision-making process related to mitigation of areas that have high flood vulnerability.

A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study.

In recent years, many researchers have focused on the use of legacy data, such as pooled analyses that collect and reanalyze data from multiple studies. However, the methodology for the integration of preexisting databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM. This study aimed to design a practical model for integrating preexisting databases using the CDISC SDTM. Data integration was performed in three phases: (1) the confirmation of the variables, (2) SDTM mapping, and (3) the generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the data sets were converted to a vertical structure. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the Research Electronic Data Capture (REDCap) field annotation. In phase 3, the data dictionary, including the SDTM metadata, was outputted in the Operational Data Model (ODM) format. Finally, the mapped SDTM data were generated using REDCap2SDTM version 2. SDTM data were generated as a comma-separated values file for each of the 7 domains defined in the metadata. A total of 17 items were commonly mapped to 3 databases. Because the SDTM data were set in each database correctly, we were able to integrate 3 independently preexisting databases into 1 database in the CDISC SDTM format. Our project suggests that the CDISC SDTM is useful for integrating multiple preexisting databases.

Do neutrophil extracellular traps implicate in atheromatous plaques from carotid endarterectomy? Re-analyzes of cDNA microarray data by surgeons.

Carotid artery stenosis is the cause of 15% of strokes. Neutrophil extracellular traps (NETs) and peptidyl arginine deiminase 4 (PAD4) are believed to be involved in thrombosis. This pilot study described the differential expression profile of NETs between atheromatous plaques and surrounding tissues. Microarray datasets of carotid plaques were obtained from Gene Expression Omnibus. The normalized data were processed into comma-separated value matrix files using spreadsheet software. Analyzes of microarray data were conducted using integrated differential expression and pathway analysis. The clustering results illustrated that the classifications of plaque and control had reasonable biological validity. Pathway analysis revealed the relevance of immune response, cell signaling, and other pathways. Differentially expressed genes were detected between carotid plaques and control specimens. However, enrichment analyzes did not reveal a difference in PAD4 expression between the groups and that NET implication was only found in one cDNA microarray dataset. This pilot study does not necessarily dismiss the possibility of a relationship between NETs and atherothrombotic stroke. Gene expression could differ between endothelial cells and atheromas, and further studies are needed.

LAMIS-DMDB: A new full field digital mammography database for breast cancer AI-CAD researches

The current state of machine learning (ML) and deep learning (DL) shows that they are powerful tools that can be used to glean knowledge from a huge amount of data. While a great deal of research in this area has centered around improving the accuracy and efficiency of training and inference algorithms, there has been relatively little attention paid to the equally important problem of monitoring the quality of data. To tackle this challenge, we have created a cutting-edge breast database capable of employing ML and DL algorithms to detect and classify breast cancer. It is noteworthy that compared to other digital mammogram databases (DMDB), the Laboratory of Mathematics, Informatics and System (LAMIS) images have better resolution and are in full-field digital mammography (FFDM) format. Furthermore, the images can be accessed according to their abnormality (normal, benign, malignant), lesions classification (mass, calcification, architectural distortion, multiple finding), American College of Radiology (ACR) density classification (ACR1, ACR2, ACR3, ACR4) and Breast Imaging Reporting and Data System (BI-RADS). Specifically, the database is designed to provide a variety of metadata as well as clinical data in simple comma separated values (CSV) format that can be really beneficial to the breast cancer research community and to the development of computer-aided diagnosis (CADx) tools that rely on artificial intelligence (AI) algorithms, such as those based on DL models.

Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods

This Research aims to review the machine learning methods used for classifying or predicting rainfall, using various features from existing data. The study used a bibliometric approach to search for metadata related to rainfall classification and prediction studies using machine learning keywords in Scopus journals. There found 94 metadata articles stored in a Comma Separated Values (CSV) file. The data in this article used published articles from 2014 to 2023 with relevant topics. The study provides information on the latest machine learning methods used for classifying or predicting rainfall. The findings of the study include an increase in published articles by 221.43% from 2018 to 2022. The article titled "An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives" by Cramer S., Kampouridis M, Freitas A.A, and Alexandridis A.K received the highest citation count of 129. The study also classified 15 keywords into 3 clusters, with common and fewer keywords. India emerged as the country with the most publications on classifying or predictiong rainfall, and the subject areas of computer science and engineering dominated the distribution of articles. Developing the use of deep learning methods and adding feature extraction algorithms in selecting features used to model data can improve the efficiency and accuracy of the rainfall classification - prediction process. The development of research data using radar images with the type of image processing research can also be maximised for research related to classification - prediction of rainfall using machine learning methods.

Portable BLAST-like algorithm libraryand its implementations for command line, Python, and R.

Basic local-alignment search tool (BLAST) is a versatile and commonly used sequence analysis tool in bioinformatics. BLAST permits fast and flexible sequence similarity searches across nucleotide and amino acid sequences, leading to diverse applications such as protein domain identification, orthology searches, and phylogenetic annotation. Most BLAST implementations are command line tools which produce output as comma-separated values files. However, a portable, modular and embeddable implementation of a BLAST-like algorithm, is still missing from our toolbox. Here we present nsearch, a command line tool and C++11 library which provides BLAST-like functionality that can easily be embedded in any application. As an example of this portability we present Blaster which leverages nsearch to provide native BLAST-like functionality for the R programming language, as well as npysearch which provides similar functionality for Python. These packages permit embedding BLAST-like functionality into larger frameworks such as Shiny or Django applications. Benchmarks show that nsearch, npysearch, and Blaster are comparable in speed and accuracy to other commonly used modern BLAST implementations such as VSEARCH and BLAST+. We envision similar implementations of nsearch for other languages commonly used in data science such as Julia to facilitate sequence similarity comparisons. Nsearch, Blaster and npysearch are free to use under the BSD 3.0 license and available on Github Conda, CRAN (Blaster) and PyPi (npysearch).

Computable bibliography: Using data analysis and data visualization to characterize a bibliography

The Scopus database, which includes many open-access items, conference papers, funding details, and patent linkages, has developed as a vital resource within the dynamic social and economic environment. Gaining popularity in several fields, systematic reviews synthesize the relevant research literature in order to guide deliberative judgments. However, researchers require assistance in keeping up with the ever-increasing multidisciplinary nature of work and the ever-changing nature of information. Researchers need efficient methods to navigate and leverage the wealth of available knowledge for their systematic review processes as the number of scholarly production grows tremendously. This study employs descriptive statistics to examine and graphically present the bibliography (the list of sources cited in the text). This study was conducted in Dr. Jodi Schneider's lab and aims to identify trends in scholarly publishing and evaluate the overall content of scholarly works. Publication dates, item types, author lists, titles, and keywords are examined in the analysis, which takes CSV(Comma Separated Values), BibTeX, or RIS formats as input. Emerging research fields and patterns of collaboration can be better understood with the help of the descriptive statistics generated. Word clouds also help readers evaluate the quality and topic focus of the papers by providing a visual assessment of the paper's composition.

Relational Reprojection Platform: Non-linear distance transformations of spatial data in R

When mapping relationships across multiple spatial scales, prevailing visualization techniques treat every mile of distance equally, which may not be appropriate for studying phenomena with long-tail distributions of distances from a common point of reference (e.g., retail customer locations, remittance flows, and migration data). While quantitative geography has long acknowledged that non-Cartesian spaces and distances are often more appropriate for analyzing and visualizing real-world data and complex spatial phenomena, commonly available GIS software solutions make working with non-linear distances extremely difficult. Our Relational Reprojection Platform (RRP) fills this gap with a simple stereographic projection engine centering any given data point to the rest of the set, and transforming great circle distances from this point to the other locations using a set of broadly applicable non-linear functions as options. This method of reprojecting data allows users to quickly and easily explore how non-linear distance transformations (including square root and logarithmic reprojections) reveal more complex spatial patterns within datasets than standard projections allow. Our initial release allows users to upload comma separated value (CSV) files with geographic coordinates and data columns and minimal cleaning and explore a variety of spatial transformations of their data. We hope this heuristic tool will enhance the exploratory stages of social research using spatial data.

Georectified polygon database of ground-mounted large-scale solar photovoltaic sites in the United States

Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MWdc) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON.

BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems.

Computer-aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI-RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above-mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CADsystems. The BUS dataset comprises1875 anonymized images from1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy-proven tumors divided into722 benign and342 malignant cases. Besides, a senior ultrasonographer performed a BI-RADS assessment in categories2 to5. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5- and 10-fold cross-validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breastlesions. The BUS dataset is publicly available for academic and research purposes through an open-access repository under the name BUS-BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma-Separated Value (CSV)files. The BUS-BRA dataset can be used to develop and assess artificial intelligence-based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI-RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for imagedescription.

A community convention for ecological forecasting: Output files and metadata version 1.0

AbstractThis paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma‐separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross‐forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short‐term dissemination and long‐term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.

Inglämnlagare: a tool for restructuring Swedish HER record site data for statistical analysis.

Background: This paper presents a new software tool, Inglämnlagare, developed to be open-source, that restructures information about ancient remains in Sweden for analysis. The background is a new version of the ancient sites database, the Historic Environment Record, curated by the Swedish National Heritage Board, that was launched in 2018 with a new database model that structures the information differently compared to previous versions. Methods: The program, written in Python programming language, has multicore support in order to improve performance for large files and uses regular expressions to extract information about individual features of composite sites. Such features, together with their summed amount, are written as new individual fields to a comma-separated value file. The program is delivered as a source script file that can be executed in any Python environment. Use cases: As an example of use, a case study of exploring graves of rectangular shape found within Sweden is provided. The use case also describes the different steps involved in preparing the data in QGIS to run the program, as well as some methods to efficiently analyse and visualize the output. Conclusions: Inglämnlagare will make more information from the Swedish record of ancient sites accessible for research and can be used to explore different content of the record more efficiently than previously possible. While the tool is written specifically for this dataset it also provides an example of how open-source tools can be used for data wrangling making information designed for a specific purpose, such as online dissemination, appropriate for analysis.

VISUALISATION OF URBAN SPRAWL WITHIN THE CITY USING TABLEAU

The urban sprawl still happening today is causing an imbalance in development between urban and rural areas. Issues such as non-optimal land use, land use development dispersal, land use conversion to municipal use, and reducing forest and agricultural areas need help in effective governance and enforcement. Furthermore, the condition of the village-in-the-city is increasingly threatened due to the urban sprawl. Currently, the existing visualisations are limited to only categorising the city and village boundaries. It cannot show the boundaries of a village-in-the-city area. The vague boundary categorisation caused by the village boundary area in the city cannot be determined clearly when the existing data provide only the city boundary and the village boundary. This study introduced a structured visualisation of village boundaries in the city using TABLEAU to assist agencies in making decisions in formulating development plans, especially for rural areas in the city. Focus Group Discussions (FGD) among the stakeholders are then used to evaluate the effectiveness of the developed visualisation. The visualisation will be based on dashboard development using Tableau software, including statistical information and urban, rural and village-in-the-city boundary information. The existing raw data in shapefiles, excel, and comma-separated values (CSV) formats are processed and integrated before the dashboard can be developed. This paper presented a pilot study for only two states: Negeri Sembilan and Melaka. The results of this study have been able to prove the existence of village-in-the-city visually, which previously could not be confirmed in the form of graphic maps. In addition, the results from the FGD can be concluded that the developed visualisations are promising and could provide the necessary information. Besides helping the agency management make better and more accurate decisions, this study will also benefit the public living within the city's village boundary area. On the other hand, from a scientific point of view, this study can be further explored to visualise the urban sprawl and apply predictive analysis to determine the needs of educational and health facilities in the area.