Geospatial Representations Research Articles

Open-Source Solutions for Real-Time 3D Geospatial Web Integration

Abstract. The recent development of Urban Digital Twin (UDT) structures and the spread of Internet of Things (IoT) technology have extended the concept of Digital Twin (DT) to urban environment representation, enabling real-time connections between geospatial representations and their real-world counterparts. Concurrently, advancements in surveying technologies and web geospatial services have facilitated the acquisition of extensive 2D and 3D geospatial data, making their spatial integration essential. While some proprietary software solutions have recently enabled initial examples of UDT by integrating various 3D geospatial data and real-time sensor acquisition, the development of open-source solutions for real-time web-based geospatial management remains a challenge. This work demonstrates a possible open-source solution for developing a UDT accessible via the web. The case study involves the web visualization platform of the new Faculty of ITC building at the University of Twente in Enschede (The Netherlands), which is connected in real-time with weather data services provided by Visual Crossing (1). The platform is based on an HTML5 framework and employs WebGL JavaScript graphic libraries to visualize different modules of 3D web visualization that integrate various local and remote 3D datasets. The framework adopted in this experiment can be used in the future for developing low-cost UDT web platforms beneficial for researchers, municipalities, and private companies interested in the real-time geospatial management of urban environments.

Geospatial Representations of COVID-19: Evidential Relevance of Medical Geography for Health and Wellness

The significant role of geospatial techniques in the evaluation of COVID-19 dynamics in the last year is reviewed in this work. The application of geographical concepts, quantitative techniques and geographical methods is not a new approach but a well-established branch of geography called medical geography. This contribution presents how implementation of geospatial representation and geographical analysis has been used to understand the spread and patterns of COVID-19. It emphasises the need for the relevance of this branch of geography in our society and argues for the rejuvenation or introduction of medical geography in countries or institutions where the discipline is lacking. The principles and concepts of medical geography have served the medical field and society well during this pandemic. While there may have been limited interest among researchers and academia in geography on this area of specialisation, recent attention given to its principles in addressing the issue of COVID-19 should serve as strong motivation to revitalise this sub-field where needed. Development of medical and/or health geography has the potential to contribute to a healthy society and nation.

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (SAR,RSC,Na%,MH,and PI), as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca2+, Mg2+, Na+, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region.

Inequity in Washington state covid-19-related crowdfunding

The first seven months of the US COVID-19 pandemic saw a massive increase in COVID-19-related crowdfunding campaigns. Despite their popularity, these campaigns were rarely successful in reaching their monetary goals, with nearly 40% of them not receiving a single donation. Previous research has indicated that crowdfunding has increased inequities and disparities in wealth, and this study set out to examine the situation in Washington State, an area greatly divided socio-economically, culturally, and geographically. Using GIS-based Multi-Criteria Evaluation (MCE) models with Quantile classification, we created geospatial representations of composite independent variables representing the impacts of COVID-19 and social marginalization on Washington state counties. We then examined the relationships between these variables with the dependent variables, campaign prevalence and outcome (campaign median amount raised), and assessed for relationships through ANOVA tests. These representations allowed us to assess the possibility that both COVID-19 impact and social marginalization may be amplifying already present inequities in Washington state by influencing crowdfunding monetary outcomes and prevalence. Our research indicates that county-wide social marginalization is associated with COVID-19-related crowdfunding campaign prevalence, generally pointing towards counties with lower social marginalization producing more campaigns. We also found that COVID-19 impact is, surprisingly, not associated with campaign prevalence, and that there was no relationship between campaign outcome and marginalization or COVID-19 impact. These findings indicate that inequitable access to, and use of, technologies like crowdfunding may be further contributing to COVID-19 inequities within Washington state. Policy-makers should be advised to use crowdfunding information with extreme caution when considering it as a useful tool in assessing community needs within counties and allocating resources, because those with the highest need likely are not being represented in this data due to lack of access.

Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs.

The Suitability of Stool Antigen Testing in the Detection of Helicobacter pylori in a Regional and Rural Area of Australia

Background. Helicobacter pylori is considered the most widespread bacterial pathogen worldwide. Successful eradication protocols are well established, highlighting the importance of appropriate infection detection. Noninvasive testing (NIT) methods are commonly used to detect infection, with test selection dependent on access and previous infection. This study examined trends in NIT by age group and test selection for eradication screening as well as examining H. pylori area prevalence by socioeconomic status (SES) in the Illawarra Shoalhaven and surrounding region. Materials and Methods. This retrospective cohort quantitative study is based on 20,998 NIT including stool antigen test (SAT), urea breath test (UBT), or H. pylori serology via Southern.IML Pathology between 2018 and 2020. Test percentage positives per and total test percentages within age groups were calculated for each NIT. Positive sample postcode data was assigned to socioeconomic percentiles. Total test utilisation and prevalence were calculated and depicted as geospatial representations. Results. Overall: 58.5% UBT, 31% serology, and 10.5% SAT were performed, with 14.7% positive for any NIT. Highest percent positive age group: SAT 80-89yo (18.6%), UBT 0-9yo (20.8%), and serology 90–99yo (32.6%). Test majority per age group: SAT 0-9yo (67.4%), UBT 10-89yo (59.4%), and serology 90-99yo (48.3%). A trend was seen between increasing infection prevalence and increasing socioeconomic disadvantage ( p = 0.161 , R 2 = 0.0361 ). Prevalence rates visually correlated with total test utilisation. Conclusions. SAT was underutilised compared to UBT or serology. Serology was inappropriately used in older age groups, and the result validity was questioned following confirmed infection. SAT is a viable alternative for use in these settings. No significant correlation was seen between lower SES areas and higher H. pylori infection prevalence, but low-test utilisation suggests likely prevalence underestimation within the studied area and may indicate reduced accessibility to healthcare.

How can voting mechanisms improve the robustness and generalizability of toponym disambiguation?

Natural language texts, such as tweets and news, contain a vast amount of geospatial information, which can be extracted by first recognizing toponyms in texts (toponym recognition) and then identifying their geospatial representations (toponym disambiguation). This paper focuses on toponym disambiguation, which can be approached by toponym resolution and entity linking. Recently, many novel approaches, especially deep learning-based, have been proposed, such as CamCoder, GENRE, and BLINK. However, these approaches were not compared on the same and large datasets. Moreover, there is still a need and space to improve their robustness and generalizability further. To mitigate the two research gaps, in this paper, we propose a spatial clustering-based voting approach combining several individual approaches and compare a voting ensemble with 20 latest and commonly-used approaches based on 12 public datasets, including several highly challenging datasets (e.g., WikToR). They are in six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing 98,300 toponyms. Experimental results show that the voting ensemble performs the best on all the datasets, achieving an average Accuracy@161km of 0.86, proving its generalizability and robustness. It also drastically improves the performance of resolving fine-grained places, i.e., POIs, natural features, and traffic ways. The detailed evaluation results can inform future methodological developments and guide the selection of proper approaches based on application needs.

Visual exploration of historical maps

AbstractHistorical maps tell rich stories, and they are also valuable data objects. They vary greatly in size, type, and context, as well as the kinds and density of information they contain. Historical maps are indeed objects that invite close reading, interpretation, and debate. Whereas a variety of environments exist for the annotation, manipulation, and representation of digital maps, or map-derived data, workflows in the spatial digital humanities can be complex and those environments are not often well integrated. In this article, we describe a prototype named ‘MapFolder’ for studying maps, its algorithms for calculating the areal distortion, its visual design for communicating that distortion, along with a scholarly workflow. We blend annotation practices common in the spatial humanities with the workflows of georeferencing in order to be able to visualize how historic cartographic documents compare with the geospatial representations we are familiar with today. The case studies we use to demonstrate ‘MapFolder’ are maps of the medieval period, a body of maps that are less often studied algorithmically and that are usually avoided in typical workflows of georeferencing. MapFolder is by no means a prototype designed to work exclusively with medieval maps, but since maps of this period are only partially geographic in their design, they offer a particularly fruitful opportunity to rethink the algorithmic manipulation of historical depictions of the world. Working with this complex data from the humanities allows us, as well, to propose the use of visualization for critical, comparative spatial analysis in pre-modern studies and beyond.

An Ecological Approach for Mapping Socio-Economic Data in Support of Ecosystems Analysis: Examples in Mapping Canada’s Forest Ecumene

Integrating socio-economic dimensions in ecosystems analysis and management is becoming increasingly important, particularly from a mapping standpoint. A key challenge with such integration is reconciling different geospatial representations based on census and administrative frameworks with natural ecosystems boundaries.This article presents one method for addressing this challenge by mapping an information rich 'ecumene'. In this approach, communities are mapped as human habitats using natural boundaries as opposed to administrative-type boundaries, integrated with authoritative socio-economic data. To illustrate the benefits of this approach, two example applications are provided that: 1) map and estimate the population of the 'forest ecumene' of Canada, and 2) map labour force distribution patterns associated with the forest sector and its relation to forest areas in Canada. Benefits and limitations of this approach are discussed, from which a number of priority areas for future research are identified.

A knowledge-data framework and geospatial fuzzy logic-based approach to model and predict structural complexity

Prediction of structural complexity for geohazard and subsurface resource applications requires constraining and interpreting data that are often ambiguous or lack key information. Moreover, structural complexity is a subjective term, requiring context for quantification. Recognizing this, a new knowledge-data framework and a geospatial fuzzy logic method is developed to represent and predict structural complexity in the subsurface. A conceptual model for known structural complexity serves as a basis for associating geospatial representations with types of damage zones. A second conceptual model for zones of structural complexity facilitates its prediction, notably in areas with limited explicit structural data. For each conceptual model, a fuzzy logic inference model is developed to incorporate geospatial data and estimate structural complexity potential. This approach is demonstrated using several public geospatial datasets within the state of Oklahoma. Explicit fault and earthquake location data were integrated using a fuzzy model of known structural complexity to train topographic, lithologic, and geophysical proxy datasets, applied to a fuzzy model to predict structural complexity, and evaluated with Receiver Operating Characteristic analyses and error classification. The final model output, displayed in maps and cross sections, offers comparison with interpreted structural data for validation. Together, these results demonstrate the effectiveness and limitations of the new approach as a screening tool for predicting structurally complex areas.

Mapping groundwater suitability for irrigation using GIS: A Case study of Ilorin, Kwara State, Southwestern Nigeria

Agricultural water in the right quantity and desired quality is needed to drive agrarian revolution in Nigeria. Groundwater, among the forms of water in nature is strategic for Nigeria has it is easy to exploit, readily available not affected by seasonality and the largest natural storage of available freshwater on the planet. The study was aimed at evaluating and mapping irrigation water quality of groundwater system within Ilorin metropolis. Forty-four well samples were collected in triplicates over the dry and wet seasons with their locations georeferenced. The water samples were sent to the laboratory and the results were incorporated into a GIS database in the development of a water map. Six indices, Sodium Adsorption Ratio (SAR), Magnesium Adsorption Ratio (MAR), Permeability Index (PI), Residual Sodium Carbonate (RSC), Kelly’s Ratio (KR) and Soluble Sodium Percentage (SSP), were used to evaluate suitability for irrigation. The geo-spatial representation is displayed using Surfer 9 and ARC Map software. The sampling points were concentrated in the southern portion of the study area, with dense settlements and expected anthropogenic activities. The western portion of the study area within the vicinity of Moro river basin reflected general good irrigation water quality and low settlement is prime for irrigated agriculture. The geospatial representations of irrigation water quality developed guides decision makers on the use of various ground water sources within the area. In the same light, it demonstrates high efficiency of GIS in elucidating complex geospatial data through the development of quality maps.

Розвиток елементів порівняльного підходу в античній природничій географії

At the current stage of scientific research development, the comparative approach, an efficient logical method of cognition of the Earth’s nature, does not lose its significance in the light of new and classic scientific methods, particularly in geography. The article presents the in-depth analysis of the origins and development of comparative approach in natural geography on the basis of national geographical sources. The researchers who applied comparative approach to their studies are also presented. The time intervals – stages of development of comparative approach in geography are defined. Two of them are considered – pre-antique and antique. Such periodization is based on time, because this approach has been developed and improved along with the development of physical-geographical ideas and knowledge. The stages of its development were defined on the basis of different, characteristic for them, logical features of comparative approach. Considering the various aspects, one can learn about various significant events during the various stages of developing of geographical sciences. The pre-antique stage of origin and the enriched development of the components of the comparative approach (stage 1) were characterized by all significant comparisons, and therefore comparisons, the content of which, in particular, even reached the idea of possible distant lands similar to the known ones. The beginnings of geospatial representations in our ancestors began to take shape at the dawn of human culture. On the background of the limited cognitive tools of ancient science, the comparative approach was distinguished by its capabilities and applications as one of the most effective in the then scientific and practical knowledge of the world.

Tinker, Mapper, Soldier, Spy – Captain Henry Elsdale’s Pioneering 1882–83 Experiment with Small Balloons and Vertical Aerial Photography in Halifax, Canada

The first aerial photography trials in Canada took place at Halifax, Nova Scotia, in 1882 and 1883. They are attributed to an Englishman, Captain Henry Elsdale (1842–1900) of the Royal Engineers Corps. He experimented with a lightweight camera and a small, tethered balloon to successfully acquire a series of vertical aerial photographs of the Citadel Hill area of Halifax. The concept of miniaturization of his apparatus and its skillful airborne deployment is echoed some 130 years later in the proliferation of unassisted aerial vehicles and aerial imaging. This paper addresses three aspects. The first includes an appreciation of Elsdale as an early leader of aerial photography and related experimental activities involving a semi-autonomous airborne device. His original documentation of the effort, including scope, technical challenge and outcome, establish him as a remote-sensing protagonist who clearly anticipated the utility of vertical aerial imaging. The second aspect focuses on the examination of locale and content of the 1883 aerial photography and a comparison with contemporary geo-spatial representations of Halifax. Finally, documentation and outcome of those air photo experiments are reviewed in the context of similar efforts in Europe and North America, ascertaining the strikingly modern vision of Henry Elsdale to innovate surveillance practices for military and civilian application.

Geoliteracy, cartology, and a mobile serious game

Geoliteracy, cartology, and a mobile serious game

Undertriage in trauma: Does an organized trauma network capture the major trauma victim? A statewide analysis.

Proper triage of critically injured trauma patients to accredited trauma centers (TCs) is essential for survival and patient outcomes. We sought to determine the percentage of patients meeting trauma criteria who received care at non-TCs (NTCs) within the statewide trauma system that exists in the state of Pennsylvania. We hypothesized that a substantial proportion of the trauma population would be undertriaged to NTCs with undertriage rates (UTR) decreasing with increasing severity of injury. All adult (age ≥15) hospital admissions meeting trauma criteria (ICD-9, 800-959; Injury Severity Score [ISS], > 9 or > 15) from 2003 to 2015 were extracted from the Pennsylvania Health Care Cost Containment Council (PHC4) database, and compared with the corresponding trauma population within the Pennsylvania Trauma Systems Foundation (PTSF) registry. PHC4 contains all hospital admissions within PA while PTSF collects data on all trauma cases managed at designated TCs (Level I-IV). The percentage of patients meeting trauma criteria who are undertriaged to NTCs was determined and Network Analyst Location-Allocation function in ArcGIS Desktop was used to generate geospatial representations of undertriage based on ISSs throughout the state. For ISS > 9, 173,022 cases were identified from 2003 to 2015 in PTSF, while 255,263 cases meeting trauma criteria were found in the PHC4 database over the same timeframe suggesting UTR of 32.2%. For ISS > 15, UTR was determined to be 33.6%. Visual geospatial analysis suggests regions with limited access to TCs comprise the highest proportion of undertriaged trauma patients. Despite the existence of a statewide trauma framework for over 30 years, approximately, a third of severely injured trauma patients are managed at hospitals outside of the trauma system in PA. Intelligent trauma system design should include an objective process like geospatial mapping rather than the current system which is driven by competitive models of financial and health care system imperatives. Epidemiological study, level III; Therapeutic, level IV.

Qualitative spatial reasoning methodology to determine the particular domain of a set of geographic objects

Nowadays, there are many geospatial information from different sources such as satellite images, aerial photographs, maps, databases and others. They provide a comprehensive description of geographic objects. However, the task to identify the geographic domain is not an easy task, because it involves a semantic processing related to inference approaches that are based on the conceptualization of a domain. These approaches allow us to understand in a similar way that human beings recognize the geographic entities and help us to avoid vagueness and uncertainty. In this paper, a methodology to perform a qualitative spatial reasoning in geospatial representations is proposed. It is based on a priori knowledge, which is explicitly formalized by means of an application ontology. The knowledge described in the ontology is assessed according to a set of labels, belonging to any geographical domain for semantic analysis and mapping those labels to matching concepts defined in the ontology. As result, a set of geographic domains ordered by their relevance is obtained, providing a general concept directly related to the input labels, simulating the way that we perceive cognitively any geographic domain in the real world.

GeoSRM – Online geospatial safety risk model for the GB rail network

RSSB and the University of Southampton's GeoData Institute have collaborated to research and develop a toolkit for managing large volumes of rail risk data. The pilot system encompasses concepts of highly complex geospatial 'big data', open standards, open source development tools and methodologies, and enables stakeholders to filter, analyse and visualise risk across the rail network, for a range of risk models. These include train derailments, suicides and passenger slip, trips and falls, and feature a wide range of spatially dependent parameters that affect the causal, escalation and consequence mechanisms. The risk has been calculated to a high resolution, splitting 2,100,000 m of track typically into 10 m sections. By creating geospatial representations of risk, the tool can help to identify risk hotspots and in this way contribute to the improvement of rail safety. Once scaled up to a National level and full range of risk models, the tool will deliver a powerful capability, unique across Europe. Further research is extending the prototype to incorporate live and historic environmental and related rail incident data to augment and improve the risk model.

Maps, Space-time Cubes, and Meta-Information for Understanding Path Information

Two experiments were conducted to investigate the usefulness of different geospatial representations in interpreting paths and activities of individuals moving through a complex spatial environment. Three-dimensional “space-time cubes” were compared to more traditional two-dimensional map displays both with and without animation/playback of the movements. Additionally, we investigated whether or not providing overlaid meta-information about individuals’ possible locations in between known (“sensed”) locations would improve performance. Results indicated that there were no advantages to, and some indications of increased workload, due to the 3D representation, perhaps because of challenges in interpreting the third (time) dimension. However, providing meta-information about possible locations supported performance when it was necessary for participants to recognize potential meetings or other events that did not occur at sensed locations. Geospatial displays used to support interpretation of movement tracking (e.g., for use in intelligence, surveillance, and reconnaissance applications) should compute and provide meta-information about possible paths between sensed locations. Additionally, if a third (time) dimension is included, additional training and support may be required.

User Assessment as Input for Useful Geospatial Representations within Mobile Location-Based Services

Mobile Location-Based Services (mLBS) are an increasingly consumer-based concept borne from, and continually driven by, technology-centred development; as opposed to the needs of end users. Where users have been made a focus, the research generally concerns issues of overall system appearance, functionality, information content and interaction methods, with little emphasis on the component geospatial representations. This paper describes the initial stages of a research project aimed at filling this void through the application of a qualitative User-Centred Design (UCD) methodology for optimising geospatial representations within mLBS applications, in order to support a selected user group: Australian ‘leisure-based travellers’. Presented in this paper is an account of two UCD activities adopted for the research. The first, user profiling, served to define the target user population in terms of their technological, geospatial and travel experiences, using an online questionnaire. The second, user task analysis, involved in-depth interviews with a subset of users in order to obtain a deeper understanding of the geospatial goals, tasks, needs and preferences within the population, as well as the range of user differences and variability in tasks present. An overall user assessment, through combined analysis of the two result sets, highlighted considerations for the ongoing research, including a set of specific implications for the design of alternative models for geospatial services, representations and interactions. The themes described in this paper represent an initial and necessary component of UCD, which has been largely overlooked in research relating to mLBS. Whilst the focus here is on a specific user group and context of use, it is envisaged that many of the concepts tested and ratified by the resulting models will be relevant to mLBS applications in general.

The Spatiograph: A Classification Framework for Geospatial Representations

Geospatial Information Technology (GIT) is being used in an increasing number of application fields and is relevant for more users than ever before. This diversity of applications and users raise questions about the relevance of different types of georepresentation (such as, maps, aerial photos, satellite imagery, 3D models) and their congruency with different user profiles. Studies have shown that the convergence and divergence of georepresentations between individuals are potential sources of alliance and conflict in organizations, especially during GIT development projects. For a better understanding of the interactions between users, technologies and georepresentations, we need to reach a deeper understanding of each of these elements. More specifically, it remains difficult to understand and describe geo-representations, both cognitive and physical, in a synthetic and practical manner. Many studies suggested classification frameworks of georepresentations, but to our knowledge, none of them have been designed to present the different geo-representations used in an organization. Based on previous studies, we conducted this research in order to build a global and practical classification framework of georepresentations. We attained this goal through a survey, as well as quantitative and qualitative analyses. This paper presents the methodology and the results of this research project: a practical classification framework called Spatiograph. This quantitative study made it possible to conduct exploratory analyses that will define, in a later stage, the Spatiograph as a classification tool on a large scale and to validate it in various domains. The paper concludes with a few examples of visual analyses that may be conducted with the Spatiograph.