Geovisual Analytics Research Articles

A web-based geovisualization framework for exploratory analysis of individual VGI contributor’s participation characteristics

ABSTRACT Understanding the participation characteristics of VGI (volunteered geographic information) contributors is important as their active participation and consistent data contribution are key to success of any VGI project. Existing studies on this matter focused primarily on deriving and interpreting participation patterns of contributor groups. There is a lack of investigation into the participation pattern of individual contributors, which can be complementary to a comprehensive understanding of VGI contributors’ participation characteristics. Building and using a custom web-based geovisualization framework, this study explores the individual-level participation characteristics of VGI contributors from the perspectives of spatial, temporal, thematic, and social interaction patterns. I conducted geovisual exploratory data analysis on VGI datasets from the iNaturalist biodiversity citizen science project to gain intuitions on the clustering and variabilities of participation patterns in iNaturalist, detect participation pattern shifts over time and form explanation hypotheses, and assess and develop metrics to measure participation. The geovisualization framework is expected to be generally applicable to other VGI datasets for exploring individual-level contributor participation patterns. This work is among the first efforts to explore individual-level VGI participation characteristics through geovisualization and geovisual analytics.

Using geovisual analytics to enrich conservation science: a review of interactive visualization of wildlife and environmental spatial data across ecosystems

ABSTRACT The size and complexity of spatial data in conservation science has been increasing dramatically as new tracking and monitoring technology has become more advanced and accessible. While the resulting high-resolution datasets have the potential to advance conservation science, new approaches in data management, analysis, and visualization are needed to realize time-sensitive on the-ground applications. Geovisual analytics (GVA) presents a novel approach to supporting interdisciplinary groups of conservation scientists, land managers, public and private stakeholders, and policymakers in thoughtful, timely, and data driven decision-making. The use of GVA in conservation science has emerged in the past decade, however its potential applications are under-explored and existing research is dispersed. Here we present a cross-disciplinary review of literature on the use of GVA in conservation science. Three key themes emerged: GVA as a decision support tool for land and wildlife management, movement analysis and/or monitoring of species and their potential threats, and GVA for tracking environmental condition and progress toward conservation goals. We then categorize existing GVA applications across both landscape and wildlife applications. We found that GVA has clear value in the conservation science community, however much work is left to be done to improve big data management and visualization.

Legends of the dashboard: an empirical evaluation of split and joint layout designs for geovisual analytics interfaces

ABSTRACT Geodashboards are often designed with explanatory elements guiding users. These elements (e.g. legends or annotations) need to be carefully designed to mitigate split attention or information integration issues. In this paper, we report expert interviews followed by a controlled experiment where we compare two interface designs with a focus on the split attention effect: (1) a multiple-legend layout with explanatory elements located next to each view, and (2) a single-legend layout with all explanatory elements gathered in one place. Different legend layouts did not affect the performance, but affected user satisfaction. 75% of the participants preferred the multiple-legend layout, and rated it with a higher usability score, mainly attributing this preference to the proximity of legend elements to the view of interest. Eye tracking data strongly and clearly verifies that participants indeed make use of the proximity: With the single-legend, the majority of eye-movement transitions were between the single-legend and the closest view to the legend, whereas with multiple-legend participants have shorter and more frequent legend visits, as well as more transitions between legends and views. Taken together, the design lesson we learned from this experiment can be summarized as ‘split the legend elements, but make it close to the explained elements’.

Cartography & geovisual analytics in personal contexts: designing for the data creator

ABSTRACT With the rise of the Quantified-Self movement and widespread adoption of self tracking technologies, 'personal' has become a new dimension in the map and geovisualization design process. Despite a rich cartographic literature on how to map movement data, and an equally extensive geovisual analytics literature on how to make sense of complex visual representations of movement data at scale, opportunities exist to adapt and create new map and interaction design paradigms to meet the disparate needs of an ever-growing number of data creators. This paper presents a cross-disciplinary review of literature that reports on the design and evaluation of personal geovisualizations. Specifically, this review paper: contextualizes individual movement data types and provides a short synthesis of cartographic and geovisual analytics approaches that have been employed to explore such data in non-personal contexts; surveys a growing yet disconnected body of foundational work beyond the GIScience discipline; presents a detailed discussion on a collection of works that exemplifies the potential of applying a geographic perspective to personal visualization; and outlines key challenges and opportunities for advancing the state-of-the-art & science in the design of maps and geovisual analytics applications constructed from personal data and that benefit the data creator.

Geographic information science in the era of geospatial big data: A cyberspace perspective

Geographic information science in the era of geospatial big data: A cyberspace perspective

WITHDRAWN: Exploring descriptions of movement through geovisual analytics

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause.

GeoSparkViz: a cluster computing system for visualizing massive-scale geospatial data

In the last decade, geospatial data which is extracted from GPS traces and satellites image has become ubiquitous. GeoVisual analytics, abbr. GeoViz, is the science of analytical reasoning assisted by geospatial map interfaces. GeoViz involves two phases: (1) spatial data processing: that loads spatial data and executes spatial queries to return the set of spatial objects to be visualized. (2) Map visualization: that applies a map visualization effect, e.g., Heatmap, on the spatial objects produced in the first phase. Existing GeoViz system architectures decouple these two phases, which lose the opportunity to co-optimize the data processing and map visualization phases in the same cluster. To remedy this, the paper presents GeoSparkViz, a full-fledged system that allows the user to load, process, integrate and execute GeoViz tasks on spatial data at scale. GeoSparkViz extends a state-of-the-art distributed data management system to provide native support for general geospatial map visualization. The system encapsulates the main steps of the map visualization process, e.g., pixelize spatial objects, pixel aggregation, and map tile rendering into a set of massively parallelized map building operators. This allows the system to co-optimize the spatial query operators and map building operators side by side. GeoSparkViz is also equipped with a GeoViz-aware spatial partitioning operator that achieves load balancing for GeoViz workloads among all nodes in the cluster. Experiments based on an implementation in Spark show that GeoSparkViz achieves up to an order of magnitude less data-to-visualization time than its counterparts when running visual analytics tasks over large-scale spatial data extracted from the NYC taxi dataset and OpenStreetMaps.

User-centered Design and Evaluation of a Geovisualization Application Leveraging Aggregated QS Data

Individual movement traces recorded by users of activity tracking applications such as Strava provide opportunities that extend beyond delivering personal value or insight to the individual who engages in these “quantified-self” (QS) activities. The large volumes of data generated by these individuals, when aggregated and anonymized, can be used by city planners, Departments of Transportation, advocacy groups, and researchers to help make cities safer and more efficient. This opportunity, however, is constrained by the technical skills and resources available to those tasked with assessing bicycling behavior in urban centers. This paper aims to address the question of how to design cartographic interfaces to serve as mediated platforms for making large amounts of individual bicycling data more accessible, usable, and actionable. Principles of cartographic representation, geovisual analytics techniques, and best practices in user interface/experience design are employed to arrive at an effective visualization tool for a broad urban planning audience. We use scenario-based design methods to encapsulate knowledge of map use practice gleaned from the development process, and conduct a post-implementation two-part user study with seven domain experts to further assess the usability and utility of the interactive mapping tool.

Mobility Community Reports data: Geovisual analytics and cartographic synthesis of behaviour changes due to COVID-19 pandemic in Europe

Mobility Community Reports data: Geovisual analytics and cartographic synthesis of behaviour changes due to COVID-19 pandemic in Europe

A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics

In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making sense out of the combined data via sophisticated analysis methods. To address this challenge we rely on two well-established research areas: data integration and geovisual analytics, and propose to adopt an ontology-based approach to decouple the challenges of data access and analytics. Our framework consists of two modules centered around an ontology: (1) an ontology-based data integration (OBDI) module, in which mappings specify the relationship between the underlying data and a domain ontology; (2) a geovisual analytics (GeoVA) module, designed for the exploration of the integrated data, by explicitly making use of standard ontologies. In this framework, ontologies play a central role by providing a coherent view over the heterogeneous data, and by acting as a mediator for visual analysis tasks. We test our framework in a scenario for the investigation of the spatiotemporal patterns of meteorological and traffic data from several open data sources. Initial studies show that our approach is feasible for the exploration and understanding of heterogeneous geospatial data.

GeoVisual analytics for understanding the distribution of speeding patterns on arterial roads: assessing the traffic safety of vulnerable road users

ABSTRACT Arterial roads have operational significance and play a substantial role in the mobility of modern society. They make up the majority of road network in urban and rural areas and allow high-speed movement despite traffic-controlling elements. In densely populated areas where the presence of Vulnerable Road Users (VRUs) is high, high-speed movement is problematic, and speed calming measures are needed to improve traffic safety, since many VRUs do crossroads, regardless of the road network regulations. These aspects have been researched in the traffic domain in a small scale, and not much has been investigated from a visualisation perspective. To provide comprehensive insights on the movement characteristics of arterial roads, we propose a GeoVisual Analytics (GVA) approach. GVA techniques are suitable solutions to display and extract knowledge from large amounts of Floating Car Data (FCD) collected through on-board devices of vehicles. By cross-sector collaboration between cartographic and traffic experts, five arterial road segments in Aalborg City were selected to answer where and when in particular VRUs do cross streets by ignoring traffic rules. Based on clusters of large unexplainable deviations from driving speed in FCD, the results uncovered meaningful patterns from complex traffic movements. They also allowed for the provision of some recommendations that are critical for traffic safety in urban areas.

Interpreting rescue vehicle patterns using geovisual analytics for spatiotemporal resource allocation

This article presents a geovisual analytics approach to explore hidden travel patterns of emergency response vehicles in the city of Lahore, Pakistan. The Rescue 1122 is a department that provides emergency response services in Pakistan. These services are like the 911 working in the USA or the 999 emergency services in the UK. The Rescue 1122 emergency vehicles are categorized as fire, rescue, and ambulance services. Different categories of vehicles handle various types of requests depending upon the nature of the emergency. All vehicles are equipped with a Global Positioning System (GPS) tracker. Data obtained from these vehicles generate spatiotemporal sequences. These sequences—also known as trajectories—form the core component of moving object analysis. The aim of this study is to use visualization techniques and to assist government officials for efficient spatiotemporal allocation of the emergency vehicles. Several visualization techniques are applied in this study to get useful insight including clustering, mean time gaps in visits, hotspots, spatiotemporal aggregation and sub-setting, and caller data analysis. The results demonstrate some interesting patterns and highlight the areas that require allocation pre-planning. The findings are beneficial for effective resource planning and for understanding the complexities of a highly urbanized city of Lahore.

Analitis Geovisual Okupansi Halte dan Pergerakan Penumpang Transjakarta

Public eagerness to use Transjakarta as a transport mode has been increasing over the past few years as the ridership growth consistently. To keep the growth up, stakeholders need to understand the passenger behavior by creating an effective operational system so that the passenger satisfaction maintained. An understanding of passenger behavior can be obtained by identifying its pattern and trend which recorded as passenger transaction data. This project uses passenger transaction data which contains information about time and space to produce interactive visualization as a means to identify passenger behavior. Interactive visualization created in this project depicts the trend of bus stop occupancy and passenger movement from one stop to another. The visualization consists of a map and diagrams which relate to each other as one integrated analytical dashboard system. The visualization design process combines Tableau 2019.2 and Mapbox GL JS to create powerful visualization supported by a 3D map view that facilitates analytical reasoning work. Geovisual analytics dashboard as a result of this project is intended for PT. Transportasi Jakarta staff to make decisions. Some staff was asked to examine the usability of the dashboard regarding its effectiveness, efficiency, and accessibility aspects. The usability test pointed out that the analytical dashboard can generate new information about the patterns and trends of Transjakarta passengers.

VA‐TRAC: Geospatial Trajectory Analysis for Monitoring, Identification, and Verification in Fishing Vessel Operations

AbstractIn order to ensure sustainability, fishing operations are governed by many rules and regulations that restrict the use of certain techniques and equipment, specify the species and size offish that can be harvested, and regulate commercial activities based on licensing schemes. As the world's second largest exporter offish and seafood products, Norway invests a significant amount of effort into maintaining natural ecosystem dynamics by ensuring compliance with its constantly evolving science‐based regulatory body. This paper introduces VA‐TRAC, a geovisual analytics application developed in collaboration with the Norwegian Directorate of Fisheries in order to address this complex task. Our approach uses automatic methods to identify possible catch operations based on fishing vessel trajectories, embedded in an interactive web‐based visual interface used to explore the results, compare them with licensing information, and incorporate the analysts’ domain knowledge into the decision making process. We present a data and task analysis based on a close collaboration with domain experts, and the design and implementation of VA‐TRAC to address the identified requirements.

Exploring the Use of a Spatio-Temporal City Dashboard to Study Criminal Incidence: A Case Study for the Mexican State of Aguascalientes

By considering public safety as a relevant component of a smart city framework, the development and use of city dashboards that explore the spatio-temporal monitoring of crime incidence to help local governments base their decision-making process on evidence is becoming more relevant. This research deals with the case study of the state of Aguascalientes, Mexico, whose capital hosts the annual San Marcos Fair, considered the most important fair in the country. By developing an online dynamic platform consisting of several different modules that rely on the use of geovisual analytics for dynamic and interactive data display and exploration, authorities can gain insights about the times and locations of the impact of criminal incidence, detect patterns over space and time, and look into what actions could be put in place. This becomes useful in advancing a circular model of the smart city in which urban processes are observed, data is collected and analyzed, management and decision actions occur, and more data is collected to measure their effectiveness. By comparing statistics for the three year period of 2016–2018, it is found that the second year of the study had a significant decrease in pedestrian crime incidence during the Fair, supporting the use of city dashboards with geovisual analytics to help monitor urban processes and aid authorities in making decisions. Further research is needed to uncover more efficient practices to achieve inter-institutional collaboration and data sharing schemes that adhere to and boost the principles of the smart city.

Lateral Extrapolation of Coastal Catenary Sequences Using the Biophysical Cross-shore Classification System (BCCS) to Create Shore-parallel Situational Zonation Mapping Units

Finkl, C.W. and Makowski, C., 2020. Lateral extrapolation of coastal catenary sequences using the Biophysical Cross-shore Classification System (BCCS) to create shore-parallel situational zonation mapping units. Journal of Coastal Research, 36(3), 457–471. Coconut Creek (Florida), ISSN 0749-0208.The Biophysical Cross-shore Classification System (BCCS), which uses transects to assess shore-normal ecological and geomorphological successions from offshore to onshore within a coastal belt (Finkl and Makowski, 2020a), also provides a basis for extrapolating cross-shore catenas into shore-parallel units. This paper shows that three-dimensional transects can be parameterized in terms of alongshore breadth as well as cross-shore width and depth below or elevation above sea level. The codification of cross-shore environments and habitats in the framework of the BCCS provides an interpretative basis for determining the lateral extent of transect units (archetypes and sub archetypes) by lateral extrapolation to polygonal mapping units. The method discussed here is akin to geological cores or cross-sections that are used to laterally extrapolate units within vertical sequences. Repetitive successions of archetypes, based on cross-shore ecological interpretation of satellite imagery, results in a common master sequence referred to as a Dominant Catenary Sequence (DCS). The DCS is composed of generic archetypes, such as Barrier, Beach, Beach Ridge, Cliff, Coral Reef, Delta, Dune, Flat, Ice, Lagoon, Mountain, Rock, Till (Glacial Material), Upland, and Wetland. The more detailed Coastal Ecological Sequence (CES) of a coastal belt, which is defined by a discrete codification sequence built up from the DCS, is formulated by cognitive geovisual analytics to link the dominant catena with a numbered shore-parallel shape distinction and subscripted sub archetypes to refine the sequential composite archetypes in a DCS. Once the DCS- or CES-labeled transect has been plotted on a satellite image, the identified shore-normal units can be extrapolated into shore-parallel polygons by traditional (i.e. visual, cognitive) image interpretive and mapping techniques to show the spatial extent of classified archetypes and sub archetypes.

An interactive web-based geovisual analytics platform for co-clustering spatio-temporal data

Clustering methods are useful in analyzing patterns from big spatio-temporal data. However, previous studies typically rely on traditional clustering methods to explore spatial or temporal patterns. Co-clustering methods allow the concurrent analysis of spatial and temporal patterns by identifying location- and timestamp-clusters at the same time. By combining co-clustering with coordinated multiple views (CMV) in an interactive geovisual analytics platform, we facilitate the exploratory co-clustering analysis of spatio-temporal data and the results. Further enhanced by Web 2.0 standards, our geovisual analytics platform ease the access to co-clustering analysis from any web browser. More specifically, our platform allows users to upload data and to visually explore it using interactive CMV to help the selection of co-clustering parameters. Our platform also allows users to run co-clustering and to visually and interactively explore the results. To illustrate the use of our platform, we analyze Dutch annual average temperature for 28 stations from 1992 to 2011. Results show that our platform not only helps to get a better understanding of the dataset but also to choose the co-clustering parameters. Our platform helps to interpret the co-clustering results too, and it supports the extraction and exploration of complex patterns buried in the data. In the era of big data, our web-based platform enables the exploration of concurrent spatio-temporal patterns from large datasets by combing both computer power and human interpretative capabilities.

Urban lake spatial openness and relationship with neighboring land prices: Exploratory geovisual analytics for essential policy insights

Possessing ecological service functions, as well as social and economic values, urban lakes are an important part of urban open spaces. The improvement in the openness of urban lakes is crucial to enhancing the efficiency and fairness of urban land use. Accompanied by China's rapid urbanization, China's urban land prices continue to rise and the government relies on land finance. Meanwhile, the number of urban lakes is decreasing, the lake water surface area is shrinking, and water pollution is becoming more serious. By using exploratory indicators, this study quantifies the degree of openness of urban lakes, and focuses on the relationship between lake spatial openness and neighboring land prices. Taking Wuhan (China) as a study case, we proposed a conceptual framework to guide the analysis within the context of land use policy. Then, we created exploratory indicators to quantify the spatial openness of urban lakes and mapped the results. Afterward, we conducted an exploratory geovisual analytics of urban land prices and lake spatial openness. Finally, we used a regression model to examine the relationship between prices and openness. Our results show that our new indicators were able to reflect the openness of the lakes in the central urban area of Wuhan simply and effectively. The overall spatial openness is relatively high, the mean values of two main indicators reflecting the openness percentage of the lakes are 69.91 % and 67.57 % respectively. However, there are still obvious regional differences between the openness indicators. The relationship between openness and neighboring land prices is spatially heterogeneous. Based on our analysis, we finally propose policy suggestions for protecting urban lakes and rational land use in the rapid urban expansion occurring in developing countries.

COVID-19 data sources: evaluation of map applications and analysis of behavior changes in Europe’s population

Due to the current situation, and preventive measures taken to tackle COVID-19, it is crucial to keep society well-informed. Besides media and official news, that often include tabular data, it has also become a new standard for information sources to incorporate a map application or geovisualization. This paper offers a comprehensive and systematic overview describing the most prominent and useful map applications and map visualizations. News outlets should place the same importance on data analysis and interpretation as they place on data visualization. This paper emphasizes the role of geospatial data and analysis during the COVID-19 pandemic and aims to provide insights into the topic in order to better understand the consequences caused by the disease. Specifically, the paper deals with the COVID-19 Community Mobility Reports dataset, offering unique information about changes in human activity due to the pandemic. We show how this dataset can be utilized in terms of geovisual analytics and clustering in order to reveal the spatial pattern of such changes in human behavior.

GEOVISUAL ANALYTICS ON THE VERIFICATION OF THE PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST

Abstract. Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country’s weather pattern in each of its climate type. It also discusses improvements in model performance throughout the time-frame of analysis. Error propagation patterns were identified using Geovisual Analytics to allow comparison of verification scores among individual stations. The study concluded that a major update in the physics parameterization of the model in 2016 and continued minor updates in the following years, surface precipitation forecasts greatly improved from an average RMSE of 9.3, MAE of 3.2 and Bias of 1.36 in 2015 to an RMSE of 7.9, MAE of 2.5 and bias of −0.63 in 2018.