Geographic Visualization Research Articles

Enhancing GeoHealth: A step-by-step procedure for spatiotemporal disease mapping.

Cartography, or geographical visualization of disease is an essential aspect of the field of GeoHealth, yet there is limited guidance on the visualization of spatiotemporal disease maps. In order to adequately contribute to understanding disease outbreaks, disease maps should be crafted carefully and according to relevant cartographic guidelines. This article aims to increase the understanding of space-time visualization techniques that are relevant to the field of GeoHealth, by providing a step-by-step framework for the creation of space-time disease visualizations. This study introduces a systematic approach to spatiotemporal disease mapping by integrating operations from the Generalized Space Time Cube (GSTC) Framework with established cartographic symbology guidelines. This resulted in an overview table that contains both the relevant GSTC operations and cartographic guidelines, as well as a step-by-step procedure that guides users through the process of creating informative spatiotemporal disease maps. The practical application of this step-by-step procedure is demonstrated with an example using Dutch COVID-19 data. By providing a clear, practical step by step procedure, this study enhances the capacity of public health professionals, policymakers, and researchers to monitor, understand, and respond to the spatial and temporal dynamics of diseases.

Design and Implementation of a Geographic Scenario Computation System Based on Knowledge Base

Abstract. The promotion and expansion of Geographic Information System (GIS) applications have increased the complexity of geocomputation. The concept of a geographic scene, which encompasses both geographic entities and complex spatial interaction relationships, serves as a means of organizing and expressing geographic information. Existing methods of organizing and computing geographic scenes often focus on single geometric elements or single-category objects, neglecting the complexity of the geocomputation process and leading to biased results. To address this issue, this study designs and implements a geographic scenario computation system based on WebGIS to overcome the lack of integration of knowledge elements in traditional geographic scenario computation methods. The system consists of a data management module, a geographic scenario visualization module, and a geographic scenario computation module. It not only realizes the visualization of geographic scenarios but also introduces a knowledge base into the geographic scenario computation process, enabling accurate management of geographic scenarios. This paper uses a farmland scenario in Shandong Province, China, as a case study to verify the effectiveness and feasibility of the system. The application of this system provides new technical support for the computation process of geographic scenarios, promoting the refinement and intelligent development of geographic scenario management. This method can also be applied to similar applications of intelligent sensors and the Internet of Things in the organization and management of sensing information in the future.

A bibliometric analysis and typology of drug pricing policies across the globe.

Limited research exists on pricing policies from a bibliometric and visualization perspective, and there is a lack of understanding of their typology. This study aims to address these gaps in knowledge and provide a deeper understanding of the research topics and development trends in this field. A bibliometric study was conducted on drug pricing approaches in healthcare literature, published between 2000 and June 2023. The literature was identified through an extensive search of healthcare databases and was then classified based on the year of publication, research topics, corresponding authors, location of corresponding authors, and journal titles. The citation data analysis was conducted using Bibliometrix, which consisted of descriptive, geographical, and time-series analyses and visualization. Between 2000 and June 2023, 173 articles were disseminated across 98 distinct publication sources. During the analysis, we observed a significant and consistent rise in literature reports on drug pricing approaches in healthcare, especially in 2010. The research topics were distributed almost equally, discussing improvement or issues with drug pricing models and addressing drug pricing applications. Our analysis revealed that the top ten corresponding authors were responsible for 19% of the total articles, with those based in the United States being the most productive. Furthermore, the "Health Economics" journal ranked first among the top ten journals. These findings align with the overall publication trends of drug pricing methods reported in other fields. The current study offers a comprehensive overview of drug pricing techniques utilized in medicine through visualization and bibliometric techniques. Analysis of authors, journals, institutions, and countries could serve as a reference for new researchers and guide them differently. Researchers can also consider emerging trends when determining the focus of their studies.

Variability and sustainability in sisal production: a study in the Eastern Seridó Micro-Region of Paraibano

This study analyzes the production of sisal (Agave sisalana) in the Eastern Seridó Microregion of Paraíba from 2012 to 2022, considering variables such as planted area, harvested area, quantity produced, average yield and production value. Using data from the Brazilian Institute of Geography and Statistics (IBGE) and the Municipal Agricultural Production (PAM), the statistical analysis revealed significant variability in sisal production over the years. The planted area ranged from 3.500 to 4.200 hectares, while the harvested area varied between 3.300 and 4.000 hectares. The quantity produced ranged from 2.500 to 3.200 tons, with an average yield of approximately 794 kg/ha. The value of production varied significantly, with an increase of 5,92% in 2018, reflecting the sensitivity of the local economy to climatic, economic and technological factors. The methodology involved descriptive and inferential data analysis, using RStudio and Excel for data processing and MapChart for geographical visualization. The results suggest that improvements in management techniques and agricultural practices, as well as public policies and investments in technology, are essential to stabilize sisal production and promote economic and environmental sustainability in the region. This study provides a solid basis for future research and policy formulation that can improve sisal production and contribute to local development.

Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis

Foreign direct investment (FDI) by transnational companies (TNCs) is the primary indicator of urban globalization. The initial publication on the topic of remote sensing and geographic information system-based urban globalization research was published in 1981. However, the number of publications on this topic remains relatively limited. Despite some advances in the field in recent decades, there is currently no comprehensive review of related research, and it is not clear how the different perspectives and views have been developed. Furthermore, previous literature reviews on the utilization of remote sensing and GIS technology in urban development have predominantly employed quantitative methodologies, which has resulted in a paucity of qualitative analysis. In order to address these shortcomings, this paper employs a mixed-methods approach, integrating quantitative and qualitative analyses. This entails the utilization of a combination of the scientometric method and a qualitative literature review method. The findings are as follows: (1) The number of publications is still relatively limited, and research in this area is still in its infancy. (2) Some of the articles are evidently interdisciplinary in nature. (3) Progress has been made in terms of geographic visualization of FDI, macro-environmental research at different scales, global value chains, the micro-geography of TNCs, and globalization of the geo-information industry. (4) The spatial and temporal development pattern, location, and accessibility of FDI have constituted a significant area of research interest in the past. Similarly, the relationships between FDI and regional development, urban growth, land use, and environmental change have emerged as prominent research directions. China’s Belt and Road Initiative is an emerging popular topic. (5) In recent years, there has been a notable increase in the number of papers employing multi-source data and multi-method approaches. (6) The extent of research collaborations between countries is relatively limited, with the majority of such collaborations occurring within the past five years. Finally, based on these research findings, this paper suggests future research directions.

Tensor Decomposition for Spatiotemporal Mobility Pattern Learning with Mobile Phone Data

Detecting urban mobility patterns is crucial for policymakers in urban and transport planning. Mobile phone data have been increasingly deployed to measure the spatiotemporal variations in human mobility. This work applied non-negative Tucker decomposition (NTD) to mobile phone-based origin–destination (O-D) matrices to explore mobility patterns’ latent spatial and temporal relationships in the province of Liège, Belgium. Four [Formula: see text] traffic tensors have been built for one regular weekday, one regular weekend day, one holiday weekday, and one holiday weekend day, respectively. The proposed method inferred spatial clusters and temporal patterns while interpreting the correlation between spatial clusters and temporal patterns through geographical visualization. As a result, we found the similarity of O-D and destination–origin (D-O) patterns and the symmetry for the trips of the temporal patterns with evening peak and morning peaks on the weekday. Moreover, we investigated the attraction of different spatial clusters with two temporal patterns on a regular weekday and validated the reconstructed demand using population counts and commuting matrices. Finally, the differences in spatial and temporal interactions have been addressed in detail.

Visual analysis of oceanic data for marine ecosystems

We present a data visualization approach that allows users to interactively analyse sequential movement patterns and automatically extract rules for marine animal behaviour. This enhances the ability of oceanic experts to understand marine ecosystems. Traditional time-series analysis, basic statistical methods, and single-view visualizations often fail to capture the intricate spatial and temporal dynamics of marine ecosystems. Our visual analytics approach, that leverages sequential pattern mining and rule mining techniques, integrates SPMF's Vertical Mining of Maximal Sequential Patterns (VMSP) algorithm and T-Rule Growth algorithm to efficiently extract and visualize movement patterns and behavioural rules of marine animals. The system also incorporates automated pre-processing to clean and prepare large datasets, reducing the time and effort required for manual handling. The visualization presents oceanic data from detection stations, allowing researchers to discover patterns in the movements of marine species. Our interface also provides geographical visualization of selected patterns or rules and helps users to navigate and find the relevant information. Key principles of our model include multi-view visualizations that provide comprehensive insights by displaying data from various perspectives simultaneously, and geo-spatial time-varying data visualization that captures and represents the dynamic movements of marine species. This approach allows domain experts to interactively explore and analyse sequential patterns and rules, enhancing their ability to make informed decisions. Additionally, feedback received, and insights gained from specialized professionals was used to determine if this approach would be a beneficial complement to existing practice.

Space Analysis in Palaeolithic Cave Art: Towards a Multidisciplinary and Integrated Approach

Abstract We present a revision of the concept of space in Palaeolithic cave art. Previous research attempting to approach this notion encounters several gaps, which surface on multiple levels: subjectivity, vagueness, restrictions on its conceptualisation, its illustration (two-dimensional description and representation), among others. We reassess the key elements at play, interpretative attempts, and theoretical concepts to gain a better understanding of the notion of space as applied to cave art. This is achieved by examining the historiography not only in prehistory but also in related disciplines, integrating new theoretical approaches and insights from ethnoarchaeology, linguistics, cognitive science, and analytic philosophy. Following this analysis of the existing research, we propose a new method to address the aforementioned issues. This method relies on digital quantitative tools for spatial analysis and three-dimensional representation, such as geographic information systems (GIS) and 3D visualisation. Through the application of these advanced technological tools, we aim to offer a more comprehensive and precise representation of the spatial aspects in Palaeolithic cave art, enabling researchers to analyse and comprehend the complexities of spatial distribution in a systematic and quantitative manner.

Traffic modeling and accidental data analysis using GIS: A Review

Nowadays, congestion and accidents are creating major risks to cities, including delays, higher fuel usage, and compromised safety. Effective traffic modelling and accident data analysis are critical for identifying high risk identifying accident-prone locations, understanding the causes of accidents and creating focused actions to enhance traffic flow and safety. GIS is an effective tool for integrating, analysing and visualizing different geographical data relevant to transportation networks such as, traffic flow, infrastructure, and safety. It enables geographical analysis and visualization of accident hotspots by integrating accident data, road conditions, traffic numbers, and environmental factors. The use of GIS in traffic modelling and accident data analysis provides considerable benefits in urban transportation planning and management. The aim of the paper is to provide an overview of the application of GIS in traffic modelling and accidental data analysis, highlighting the methodologies, advancements, and challenges in this field. The review shall provide a comprehensive assessment of the current state of traffic modelling and accidental data analysis using GIS. It will highlight the significant contributions of GIS technology, identify key research gaps, and offer insights into future directions for enhancing transportation planning and decision-making processes.

An MPI-based parallel genetic algorithm for multiple geographical feature label placement based on the hybrid of fixed-sliding models

ABSTRACT Multiple Geographical Feature Label Placement (MGFLP) has been a fundamental problem in geographic information visualization for decades. Moreover, the nature of label positioning has proven to be an Nondeterministic polynomial-time hard (NP-hard) problem. Although advances in computer technology and robust approaches have addressed the problem of label positioning, the lengthy running time of MGFLP has not been a major focus of recent studies. Based on a hybrid of the fixed-position and sliding models, a Message Passing Interface (MPI) parallel genetic algorithm is proposed in the present study for MGFLP to label mixed types of geographical features. To evaluate the quality of label placement, a quality function is defined based on four quality metrics: label-feature conflict; label-label conflict; label association with the corresponding feature; label position priority for all three types of features. The experimental results show that the proposed algorithm outperforms the DDEGA, DDEGA-NM, and Parallel-MS in both label placement quality and computation time efficiency. Across three datasets, compared to Parallel-MS, running times decreased from 118.45 to 8.34, 45.98 to 3.51, and 20.01 to 0.43 min, with further reductions in label-label and label-feature conflicts.

A dataset of recorded electricity outages by United States county 2014–2022

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018–2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

Understanding Everyday Mobility of Skilled U.S. Migrants in the Pearl River Delta Region of China

Everyday mobility is critical to understand migrants’ accessibility and inclusions to the destination cities. Existing research was insufficient to explain the everyday movement of relatively privileged skilled migrants from the Global North to the Global South, nor did it distinguish the role of one’s capability and intentionality in shaping mobility. To fill in the research gap, we employ the capital-mobility framework to investigate how skilled U.S. migrants’ capital accumulation and intentionality jointly affect their everyday mobility in Chinese cities, and the implications for their social inclusion in the local society. We use sketch mapping with semistructured interviews to conduct this study in the Pearl River Delta region, a pioneer of economic development in China. We find that although migrants’ capital accumulation facilitated their everyday movement, COVID-19 disrupted such mobility. Whether migrants had intentions to move varied, shaped by geopolitical relations, urban environment, and migrants’ own capital. The voluntary or involuntary nature of their everyday mobility also suggests whether their social inclusion or exclusion in the local society is by choice or constraint. Our study integrates geographic information systems visualization with qualitative data to highlight the spatial mobility and immobility at the individual level and generates policy implications for retaining global talent at the city level.

Visual attention-guided augmented representation of geographic scenes: a case of bridge stress visualization

Efficient geovisualization is beneficial for understanding geospatial phenomena, an important research direction for GISers and Cartographers. However, the current research on geovisualization overemphasizes the visual effects while neglecting the prominent representation of crucial information and failing to consider the user’s cognitive workload of information processing. Following the laws of visual perception of the human eyes, this article proposes a visual attention-guided augmented representation approach of geographic scenes that involves area of interest computation, background simplification, and compound graphic variables. Finally, we select bridge stress visualization as a case study for experimental analysis. The experimental results of eye-tracking show that augmented representation could draw the participants’ attention to areas of interest in a short time, increasing their duration of fixations and the accuracy of completing given tasks. These findings suggest that our approach can enhance geographic scenes’ cognitive efficiency, offers a new idea for the theoretical studies of geovisualization, and holds promising potential for broader application in various geographical phenomena visualization.

Geographic information system and information visualization capacity building: Successful polio eradication and current and future challenges in the COVID-19 era for the World Health Organization's African region.

Despite a half-century-long global eradication effort, polio continues to have a devastating impact on individuals and communities worldwide, especially in low-income countries affected by conflict or geographic barriers to immunization programs. In response, the World Health Organization (WHO) Global Polio Eradication Initiative (GPEI) employs disease surveillance and vaccination campaigns coordinated through the WHO Regional Office for Africa (AFRO) Geographic Information System (GIS) Centre. Established in 2017, the AFRO GIS Centre played a key role in the eradication of wild-type polioviruses (WPVs) in 2020, but the COVID-19 pandemic, emergence of circulating vaccine-derived polioviruses, and transmission of WPV1 from Central Asia have led to a resurgence of polio in Sub-Saharan Africa. The AFRO GIS comprises a set of mobile device or cloud-based tools for geospatial data collection, analysis, and visualization. Using tools such as Auto-Visual Acute Flaccid Paralysis Detection and Reporting, electronic surveillance, and Integrated Supportive Supervision, GIS personnel collect polio case numbers and locations, track field worker activities, follow the movements of nomadic populations vulnerable to polio and other diseases, and determine needs for further healthcare deployments. The system is location specific and operates in real time, enabling the AFRO GIS to promptly target its responses to polio, COVID-19, Ebola virus disease, and other public health crises and natural disasters. The present review describes the components of the AFRO GIS and how the AFRO GIS Centre coordinated on-the-ground polio eradication efforts to help secure Africa's certification as WPV free. It also examines current and prospective challenges regarding other disease outbreaks in the COVID-19 era and how the AFRO GIS Centre is addressing these ongoing public health needs.

Geovisualization: Current Trends, Challenges, and Applications

Geovisualization (or Geographic Visualization) represents an interdisciplinary scientific field spanning cartography, geographic information science (GIScience) and technology, computer science and human–computer interaction (HCI), psychology, and cognitive science [...]

Web-based agricultural infrastructure digital twin system integrated with GIS and BIM concepts

Geographic Information Systems (GIS) are currently used in agriculture to represent various farming-related geographical information. While prior research has highlighted the potential of GIS in visualizing agricultural data, it still faces limitations in terms of model quality, interaction capabilities, and representing detailed topographic information, particularly in hydrological structures like culverts, irrigation systems, and drainage facilities. A method integrating Building Information Modeling (BIM) concepts with GIS is proposed to overcome these limitations. To achieve this, information on agricultural infrastructure in beneficiary areas was collected through field surveys and photos captured by Unmanned Aerial Vehicle (UAV). Subsequently, a digital twin model was created with various characteristics and information about agricultural hydraulic structures such as land shape, sluices, and channels of the entire beneficiary area of the test bed (Heungeop reservoir in the City of Wonju, Gangwon Province, South Korea) based on this information. A web-based geographic information visualization platform (Cesium) was integrated with the concepts of BIM to check the information associated with the selected 3D agricultural infrastructure models in a pop-up window. The proposed method overcomes the limitations of existing GIS information representation by enabling a detailed depiction of the agricultural environment. It allows for an intuitive understanding of information such as structural features, details of the material, history of maintenance, year of completion, etc., on agricultural infrastructure through a pop-up window, providing a foundation for research on efficient management and planning.

Study on smart grid fault detection based on ZigBee and MapX technologies

As the complexity of the power system and the increasing demand for electricity continue to rise, ensuring the reliability and stability of the power system becomes critically important. This study utilizes ZigBee communication modules to establish a sensor network for real-time monitoring of the power system's status and employs MapX technology to achieve geographic information visualization of the power system. Through an improved RBF-PCA-WFCM algorithm, grid data can be dimensionally reduced, enabling simulation training tailored to grid loads, resulting in more accurate algorithm analysis and optimization. This paper introduces fault localization and diagnostic methods, as well as clustering optimization algorithms, to achieve fault monitoring, analysis, and identification in the smart power grid. Research findings demonstrate that the intelligent power grid data analysis and fault detection system based on ZigBee and MapX technologies can enhance the reliability of the power system and better support the increasing demands of the evolving field of electricity.

Loitering behavior detection by spatiotemporal characteristics quantification based on the dynamic features of Automatic Identification System (AIS) messages.

The capability of the Automatic Identification System (AIS) to provide real-time worldwide coverage of ship tracks has made it possible for maritime authorities to utilize AIS as a means of surveillance to identify anomalies. Anomaly detection in maritime traffic is crucial as anomalous behavior may be a sign of either emergencies or illegal activities. Anomalous ships are recognized based on their behavior by manual examination. Such work requires extensive effort, especially for nationwide surveillance. To deal with this, researchers proposed computational methods to analyze vessel behavior. However, most approaches are region-dependent and require a profile of normality to detect anomalies, and amongst the six types of anomaly, loitering is the least explored. Loitering is not necessarily anomalous behavior as it is common for certain types of ships, such as pilot boats and research vessels. However, tankers and cargo ships normally do not engage in loitering. Based on 12-month manually examined data, nearly 60% of the identified anomalies were loitering, particularly for those of types cargo and tanker. Although manual identification is inefficient, automatically identifying abnormal vessels by merely implementing computing algorithms is not yet feasible. It still needs subject matter experts' assessments. This study proposes a region-independent method to automatically detect loitering without training normal instances and produces a ranked list of loitering vessels to facilitate further anomaly investigation. First, the loitering spatiotemporal characteristics are defined: (1) movement of frequent course change, with a certain speed, within a certain spatial range, (2) movement of frequent course change within traversed geodetic distance, (3) might demonstrate frequent extreme turning, and (4) extreme turning produces a significant discrepancy between the course over ground and the heading of the ship. Then, the characteristics are quantified by manipulating the dynamic information of AIS messages. Finally, the parameters to determine a loitering trajectory are formulated by comparing the rate of course change, speed, and the discrepancy between heading and course with the area of spatial range enclosing the trajectory and the geodetic distance between the start and end point. The loitering score of each trajectory is calculated with the parameters, and the Isolation Forest algorithm is employed to establish a threshold and rank. Then, geographic visualization is created for intuitive evaluation. An experiment was conducted on a real-world dataset covering a sea area of 610,116.37 km2. The results prove the efficacy of the proposed method. It remarkably outperforms the existing approach with 97% accuracy and 92% F-score. The experiment produces a ranked list of loitering vessels and an intuitive visualization in the relevant geographic area. In the realworld scenario, they are practical means to support further examination by human operators.

The geographic visualization of financial inclusion and exclusion in Mexican municipalities

In this paper, we present a gridded population cartogram illustrating a recently constructed financial inclusion index for Mexico 2020 (Dircio-Palacios-Macedo et al., 2023). Its importance relies in that it allows us to identify the municipalities with different levels of financial inclusion in a geographical disaggregated manner, which can be used for the ultimate goal of relevant policy analysis.

IDENTIFICATION OF MESOSCALE CONVECTIVE COMPLEX (MCCS) AT SOUTHERN PAPUA FOR THE PERIOD OF DECEMBER 2017 – NOVEMBER 2022

One factor contributing to rainfall in a certain region is the presence of convective cloud growth. Extensive convective clouds can trigger extreme weather conditions and the occurrence of Mesoscale Convective Complex (MCC) phenomena. MCCs phenomena are characterized by circular cloud cover with eccentricity ≥ 0.7, core cloud area ≥ 50,000 km², cloud cover area ≥ 100,000 km², and cloud top temperature ≤ -52 °C or 221 K. This study aims to identify the spatial and diurnal of MCC in the Southern Papua and for the period of December 2017 to November 2022, as well as to assess its impacts on rainfall during MCC events. Satellite data from Himawari-8 in IR channel was used to analyze MCCs criteria, which were then plotted using a geographic visualization software. Rainfall data in GSMaP was processed using GrADS to display rainfall values. The data processing procedure is carried out using an algorithm based on the study conducted by Maddox. The results of the spatial distribution analysis showed that there were 20 MCC events that occurred during the 5-year period, with dominant growth in inland areas near mountainous and high-topography regions. Seasonally, MCC events predominantly occurred during the MAM period and were least frequent in the JJA period. The diurnal distribution revealed that MCC events had a nocturnal life cycle, forming during the nighttime until early morning. Analysis of the GSMaP rainfall data indicated that the dominant rainfall intensity caused by MCC events was heavy rain (20 – 50 mm/hr).