Geospatial and Simulation a Point of Convergence

Background: A key strength of population sciences and epidemiology is the breadth and depth of the data these studies, especially prospective cohorts, collect over time. However, publications from and websites of these studies tend to present collapsed or aggregate data in traditional tables, often underestimating the true complexity of information in those data. One goal of the NIH Strategic Plan for Data Science is to “support the development and dissemination of advanced data management, analytics, and visualization tools.” We recently developed a robust, innovative, and scalable data visualization environment for the California Teachers Study (CTS), a prospective cancer epidemiology cohort. Methods: We identified the need to eliminate the practice of storing CTS data in a collection of disparate data silos and constructed a data warehouse to compile two decades of study data, eliminating additional harmonization steps. We then developed a data commons, a remotely accessible environment where cohort data could be utilized by our researchers and study collaborators. This allowed questionnaire-based data, cancer registry information, and hospitalization and mortality data from our study participants to be easily accessed in a secure environment. Serving as a single unified source of information, the data warehouse, coupled with the industry-leading data visualization tool Tableau, was configured using the following key epidemiologic cohort activities: reports, cohort selection, calculated values, and exploring and transforming data variables for analysis. Results: With the ability to sort and filter on specific covariates we can define subsets of populations for cohort selection and endpoint-specific analysis. Using interactive reports with drags and drops, and creating calculated variables within Tableau, gives us the ability to visualize time between event data, such as time from study entry to a cancer diagnosis, multiple cancer diagnoses, hospitalizations, or mortality without requiring programmatic back-end coding. Conclusion: The creation of a data warehouse and data commons has given the CTS the ability to visualize information about the demographics and health-related patterns of our study participants, leading to faster answers to complex questions about our data. The interactive data visualizations that we have created allow users the ability to identify new patterns easily, and drill down into the data for a more detailed look. Defining analytic populations through cohort selection and adding geospatial data to our data warehouse has already resulted in the discovery of new patterns that had been unidentified before—simply by providing the same information but utilizing a more accessible and innovative approach to presenting it. As epidemiology uses and shares more of its data, it’s essential to help the widest possible community understand those data in detail. Data visualization can provide specific benefits to key, essential population sciences research activities. Citation Format: Nadia T. Chung, James V. Lacey Jr., Emma Spielfogel, Paul Hughes, Sandeep Chandra, Elena Martinez, Jennifer Benbow. Opportunities for and benefits of incorporating data visualization in population sciences [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A01.

Chapter 2 - Information Visualization: Scope, Techniques and Opportunities for Geovisualization

Abstract. In a transformation process to become a climate-neutral city campus, universities have to deal with the sustainable concept. Since “human factor” plays a significant role in the transformation process, providing easy access to environmental data to influence building occupants’ behavior is essential. By utilizing energy-related data without spatial attribute and existing building geospatial data, data visualization in a web browser can be established for both 2D and 3D platforms. Our implementation presents a visualization of indoor sensor measurement data, where the same geospatial data can be used for both 2D and 3D visualizations even though the 3D platform needs an adjustment. Our approach results in a monitoring tool prototype based on visualization of indoor sensors measurement data, which can be accessed easily in a web browser by all building occupants.

This article addresses the urgency of improving hydrological monitoring and effective water resource management, which is particularly crucial for flood prevention and rapid emergency response.The problem lies in the need to develop a tool for efficient collection, processing, and visualization of water level data in the rivers of the Lviv region. The research objective is to develop and implement an interactive web application based on ArcGIS for visualizing and analyzing water level dynamics. This tool is designed to display the location of hydrological posts, provide access to their current status information, and analyze time series of hydrological data. The key feature of this approach is the integration and visualization of data from both automated and manual (gauge) posts, significantly expanding the cover-age of hydrological information.Based on the stated objectives, the geoinformation approach has been implemented for collecting and visualizing hydro-logical post data in the Lviv region using ArcGIS tools. The data source for this system comprises observations provided by the Basin Water Resources Management of the Western Bug and Sian Rivers. As a result, the interactive ArcGIS monitoring dash-board has been developed. It visualizes spatial and temporal water level data, integrating a web map with tabular time series data. This ensures automatic synchronization between selecting a post on the map and displaying dynamic charts and analytical tables. The web application facilitates comprehensive analysis of hydrological data.The proposed solution significantly enhances hydrological monitoring and supports managerial decision-making regarding flood prevention and emergency response. Its practical value lies in its application for water resource management and flood risk assessment, including the visualization of indicators and access to operational information for specialists and au-thorities. Future research will focus on real-time data updates, integration with meteorological indicators, and development of models for forecasting water levels and potential flood zones.

Prototyping the visualization of geographic and sensor data for agriculture

Open data published by various organizations is intended to make the data available to the public. All over the world, numerous organizations maintain a considerable number of open databases containing a lot of facts and numbers. However, most of them do not offer a concise and insightful data interpretation or visualization tool, which can help users to process all of the information in a consistently comparable way. Canadian Federal and Provincial Elections is an example of these databases. This information exists in numerous websites, as separate tables so that the user needs to traverse through a tree structure of scattered information on the site, and the user is left with the comparison, without providing proper tools, data-interpretation or visualizations. In this paper, we provide technical details of addressing this problem, by using the Canadian Elections data (since 1867) as a specific case study as it has numerous technical challenges. We hope that the methodology used here can help in developing similar tools to achieve some of the goals of publicly available datasets. The developed tool contains data visualization, trend analysis, and prediction components. The visualization enables the users to interact with the data through various techniques, including Geospatial visualization. To reproduce the results, we have open-sourced the tool.

Abstract. With the wide application of Big Data, Artificial Intelligence and Internet of Things in geographic information technology and industry, geospatial big data arises at the historic moment. In addition to the traditional "5V" characteristics of big data, which are Volume, Velocity, Variety, Veracity and Valuable, geospatial big data also has the characteristics of "Location Attribute". At present, the study of geospatial big data are mainly concentrated in: knowledge mining and discovery of geospatial data, Spatiotemporal big data mining, the impact of geospatial big data on visualization, social perception and smart city, geospatial big data services for government decision-making support four aspects. Based on the connotation and extension of geospatial big data, this paper comprehensively defines geospatial big data comprehensively. The application of geospatial big data in location visualization, industrial thematic geographic information comprehensive service and geographic data science and knowledge service is introduced in detail. Furthermore, the key technologies and design indicators of the National Geospatial Big Data Platform are elaborated from the perspectives of infrastructure, functional requirements and non-functional requirements, and the design and application of the National Geospatial Public Service Big Data Platform are illustrated. The challenges and opportunities of geospatial big data are discussed from the perspectives of open resource sharing, management decision support and data security. Finally, the development trend and direction of geospatial big data are summarized and prospected, so as to build a high-quality geospatial big data platform and play a greater role in social public application services and administrative management decision-making.

Digital geoscientific and geospatial datasets are rapidly growing in both number and size. These data present powerful new resources for understanding the evolution of the earth, but working with them requires computational skills are not part of typical geoscience curricula at universities. To leverage the power of these growing geoscientific and geospatial data, students need targeted educational resources that provide basic computational skills.The new textbook Introduction to Python for Geographic Data Analysis provides a framework for learning to work with (geospatial) datasets of varying size from loading the data to producing interactive visualizations of processed data. Part 1 of the book covers the basics of programming using the Python language, introducing both programming concepts and their Python syntax. It also covers the analysis of tabular data using the pandas Python library and the basics of data visualization. Part 2 introduces working with geospatial data, including fundamental geospatial concepts, working with vector and raster data, geospatial data visualization, and loading data from online sources. Part 3 includes several case studies that build on things presented in the first two parts to demonstrate what can be done with the readers’ new skills. Finally, the appendices provide information about best practices in programing, version control with git and GitHub, and other practical coding tips that promote open, reproducible science.The book materials are freely available online at https://pythongis.org, and we anticipate that hard copies of the book will be available later in 2026. We hope the book will appeal to a broad range of “geo” scientists, including teachers who provide courses on introductory programming or data analysis for geology and geography students, those interested in learning to interact with and batch process large datasets, and those interested in finding open-source alternatives to commercial GIS software packages.

This paper presents a novel method for presenting the concrete bridge deck condition assessed by multiple nondestructive evaluation (NDE) technologies using a three-dimensional (3D) visualization program, NDEFuse. Six types of NDE data can be visualized: (1) impact-echo data for mapping and describing the depth and severity level of concrete delamination, (2) ultrasonic surface wave data for concrete quality (elastic modulus) assessment, (3) electrical resistivity for the description of corrosive environment and estimate of the corrosion rate of steel reinforcement, (4) mapping of top rebar layer and (5) ground penetrating radar condition assessment data, and (6) high-resolution deck surface image for documenting signs of deterioration, previous repairs, and surface wear. The 3D visualization platform integrates and visualizes the six NDE results in a very intuitive way. Correlations between the different types of NDE survey results were established visually in a 3D space utilizing the developed program.

Using SOA and RIAs for water data discovery and retrieval

The rise in important initiatives such as asset modelling & well integrity assessment in ADCO has clearly identified the need for effectively managing the down-hole completion data across drilling and assets. The down-hole completion data has become more strategic to ADCO. The completion schematic standards, data visualization, phase-out legacy systems for preparation of down-hole schematic, data ownership & data integration amongst other challenges which were addressed as part of this automation project. The previous/old workflow utilizes third-party visualization software to display downhole completions equipment. However, well schematics being generated by these workflows displayed are standardized images and are not data-driven. The engineer will essentially build up the image separately and then enter the data into well-schematic as primarily text-based data entry. This leads to creation and maintenance of multiple reports, spreadsheets and other sources of data to inform the engineer as to the current status of downhole equipment in the wellbore. Well integrity assessment (such as MAASP calculations…etc) and well performance modelling (Inflow performance calculation) has clearly impacted due to unavailability of completion data at right time. The new prescribed workflow acts as a bridge between the drilling and assets. This automated workflow enhanced the data quality by using standard data entry template and visualization solution using standard completion templates (single, dual, smart completion…etc) and completion symbols which involves managing the time required from the drilling engineers, completion engineers and drilling supervisors. There are guidelines for data quality checking through the use of data validation rules and verification using visualization has been established in this automated workflow. The workflow has identified the business and data interfaces required to be considered for automation. The petroleum engineers are notified in their real time optimization solution when a new well completion has been added and whenever there is a change in existing well configuration & completion design and prompt them to create or update the well performance models. The data is also retrieved for well integrity purposes as well with notifications. The Engineers are required to understand mechanism and workflows governing the usage of this automated workflow, so that they generate reliable and sustainable well-schematics templates in a self-sufficient process, certain tasks may be performed in advance to increase efficiency of the workflow and reduce overall time spent in data preparation and generating a complete well-schematic for the hand-over. The templates that satisfy business and technical criteria, an engineer can make huge time gains while generating well-schematics. This workflow automation further facilitated as common access point for all required completion data with clear data ownership, governance, standard visualizations & schematics & ensures the availability of the data at the right time

Big data analytics in health is an emerging area due to the urgent need to derive actionable intelligence from the large volumes of healthcare data to efficiently manage the healthcare system and to improve health outcomes. In this paper we present a ‘big’ healthcare data analytics platform—termed as Healthcare Analytics for Intelligence and Learning (HAIL)—that is an end-to-end healthcare data analytics solution to derive data-driven actionable intelligence and situational awareness to inform and transform health decision-making, systems management and policy development. The innovative aspects of HAIL are: (a) the integration of data-driven and knowledge-driven analytics approaches, (b) a sand-box environment for healthcare analysts to develop and test health policy/process models by exploiting a range of data preparation, analytical and visualization methods, (c) the incorporation of specialized healthcare data standards, terminologies and concept maps to support data analytics, and (d) text analytics to analyze unstructured healthcare data. The architecture of HAIL comprises the following four main modules (fig 1): (A) Health Data Integration module that entails a semantics-based metadata manager to synthesize health data originating from a range of healthcare institutions to formulate a rich contextualized data resource. The data integration is achieved through ETL workflows designed by health analysts and researchers, (B) Health Analytics Module provides a range of healthcare analytics capabilities including, (i) Exploratory Analytics using data mining to perform data clustering, classification and association tasks, (ii) Predictive Analytics to predict future trends/outcomes derived from past observations of the healthcare processes, (iii) Text Analytics to analyze unstructured texts (such as clinical notes, discharge summaries, referral notes, clinical guidelines, etc.), (iv) Simulation-based Analytics to simulate what-if questions based on simulation models, (v) Workflow analytics to interact with modeled clinical workflows to understand the affects of various confounding factors, (vi) Semantic Analytics to infer contextualized relationships, anomalies and deviations through reasoning over a semantic health data model, and (vii) Informational Analytics to present summaries, aggregations, charts and reports, (C) Data Visualization Module offers a range of interactive data visualizations, such as geospatial visualizations, causal networks, 2D and 3D graphs, pattern clusters and interactive visualizations to explore high dimensional data, (D) Data Analytics Workbench is an interactive workspace to enable data health analysts to specify and set-up their analytics process in terms of data preparation, selection and set-up of analytical methods and the selection of visualization methods. Using the workbench analysts can design sophisticated data analytics workflows/models using a range of data integration, analytical and visualization methods. The HAIL platform is available via a web portal and the desktop application, and it is deployed on a cloud infrastructure. HAIL can be connected with existing health data sources to provide front-end data analytics. Fig 2 shows the technical architecture. The HAIL platform has been applied to analyze a real-life clinical healthcare situations using actual data from the provincial clinical data warehouse. We will present two case studies of the use of HAIL. In conclusion, the HAIL platform addresses a critical need for healthcare analytics to impact health decision-making, systems management and policy development.

Business owners, managers, and policymakers are overwhelmed by the volume of data available in their company’s internal databases and external sources. The ability to represent meaningful and concise information that facilitates decision-making is challenging. Consider a technology firm such as Company A, where a large quantity of data is generated across numerous data sources covering all the North American locations. Traditionally, these data come in different formats such as email attachments (.pdf, .xlsx, .docx, etc.) and at different time intervals, e.g., daily, weekly, and some non-periodic. The data arrives at the team’s folder on the Microsoft SharePoint platform, where team members calculate key metrics. This existing process is tedious, manual, and prone to computation or latency errors due to database refresh. Data visualization design and automation serve as a solution to these issues by creating a method through which large volumes of data can be easily aggregated, represented, consumed, and understood. When data is visualized correctly, it is easy to draw simple, actionable conclusions. This study will explore how modern data visualization tools can be used to help policymakers and managers understand supply chain operations and performance in a relatively short time, as well as make strategic plans by interacting with the dashboard. This would be achieved by creating a visualization dashboard for the real-time monitoring of Company A North America Supply Chain data across all departments using the Microsoft Power BI tool. This would help to see the big picture all at once and enable business owners, key managers, and policymakers to make sound business choices and judgments. The overarching goal of this project is to automate the data collection and visualization process to help key business owners make informed decisions. This will allow the team managers to spend less time compiling large volumes of data and leverage the visualization tool to better identify and mitigate risk as well as proactively uncover valuable opportunities that lie in their databases.

Studies estimate a sea level rise range of 0.5 to greater than 2 meters by the year 2100. A rise of this magnitude will significantly impact the socio-economic dimensions of coastal communities, which contain approximately 40 percent of the U.S. population and account for close to 50 percent of the U.S. gross domestic product. Data visualizations represent a useful tool for communities preparing for this environmental change. The singular capabilities of GIS for assimilating data allow users to link science to society in a geographic context. The results provide valuable insight for community planning efforts. Increasing the data visualizations from two dimensional (2D) to three dimensional (3D) will help communities better understand the impacts of sea level rise. Charleston, South Carolina is uniquely situated for a case study of this approach due to its location at the confluence of environmental change and cultural, historic, and economic influence. Predicted levels of sea level rise will disrupt this community and result in wide ranging impacts to the region's economic, employment, and infrastructure sectors. In this study data visualizations and 3D mapping are used to get a holistic understanding of the nexus of a changing climate and its effect on Charleston. The lessons learned here are transferable to other coastal cities and to government officials and planners interested in using exploratory visualizations to address sea level rise impacts. The results of this case study indicate that despite limitations in display and analysis, visualizations and 3D mapping are an engaging medium for shaping interdisciplinary spatial planning and revealing important connections between the environment and the community.

Data-driven visual model development and 3D visual analytics framework for underground mining