Geographic Data Sources Research Articles

Improving the performance of evidential belief function model using five machine learning models in earth fissure susceptibility mapping

The frequency and occurrence of earth fissures threaten human settlements, infrastructure, and agricultural lands in many parts of the globe. This natural hazard has particularly posed great challenge in the North, Central, and Eastern parts of the Najaf Abad Basin of Iran. This paper presents a comparative analysis of machine learning algorithms for earth fissure susceptibility assessment, and involves the creation of an earth fissure database by selecting and validating 200 fissure locations. To improve the accuracy of the fissure locations, various geographic data sources, such as Google Earth images, field surveys, and GPS measurements, were employed. The locations were divided into training and validation sets based on sixteen conditioning factors for finding the earth fissure susceptible zones using EBF (Evidential belief function), EBF – Multilayer Perceptron (MLP), EBF – Function Tree (FT), EBF – Alternating decision tree (ADtree), EBF – Adaptive Boosting (AdaBoost), EBF – Random SubSpace (RSS), EBF – Naive Bayes Tree (NBTree) models. The results show that earth fissures occur in greater concentrations where groundwater levels are lowest, and their prevalence is closely associated to both agricultural activities and the presence of clay, clayey sand, and silty clay soils. Susceptibility analysis results revealed that 15–21 % agricultural dependent population falls under the very high susceptibility risk zone in and around the major cities of the Najaf Abad region. The receiver operating characteristic (ROC) curve analysis showed that the EBF-RSS algorithm has higher accuracy (>99 %) compared to other algorithms (>85 %) in training and validation phases for earth fissure susceptibility prediction. Our findings emphasise the need to prioritise these susceptible areas for sustainable land and water management strategies and implement effective measures to mitigate the impact of earth fissures. Our study also advances our knowledge on geospatial analysis and earth fissures-related hazard assessment by applying five machine learning algorithms that shows spatial similarity in susceptibility categories with splendid accuracy performance.

New directions for malaria vector control using geography and geospatial analysis.

As we strive towards the ambitious goal of malaria elimination, we must embrace integrated strategies and interventions. Like many diseases, malaria is heterogeneously distributed. This inherent spatial component means that geography and geospatial data is likely to have an important role in malaria control strategies. For instance, focussing interventions in areas where malaria risk is highest is likely to provide more cost-effective malaria control programmes. Equally, many malaria vector control strategies, particularly interventions like larval source management, would benefit from accurate maps of malaria vector habitats - sources of water that are used for malarial mosquito oviposition and larval development. In many landscapes, particularly in rural areas, the formation and persistence of these habitats is controlled by geographical factors, notably those related to hydrology. This is especially true for malaria vector species like Anopheles funestsus that show a preference for more permanent, often naturally occurring water sources like small rivers and spring-fed ponds. Previous work has embraced geographical concepts, techniques, and geospatial data for studying malaria risk and vector habitats. But there is much to be learnt if we are to fully exploit what the broader geographical discipline can offer in terms of operational malaria control, particularly in the face of a changing climate. This chapter outlines potential new directions related to several geographical concepts, data sources and analytical approaches, including terrain analysis, satellite imagery, drone technology and field-based observations. These directions are discussed within the context of designing new protocols and procedures that could be readily deployed within malaria control programmes, particularly those within sub-Saharan Africa, with a particular focus on experiences in the Kilombero Valley and the Zanzibar Archipelago, United Republic of Tanzania.

A morphological analysis of Sukuma place names: A case of primary school names in Shinyanga, Tanzania

Place names are vital language forms as they are entrenched with data about their ethnic origins. They are crucial linguistic, historical, geographical, and socio-cultural data sources. A growing body of evidence suggests that place names have been studied mainly from a semantic standpoint. Studies focusing on the morphological analysis of place names are limited. This denies onomasticians access to knowledge regarding the structures and processes forming place names in many languages. This paper examines the morphological aspects of Sukuma place names, mainly names of primary schools in Shinyanga Rural District, Shinyanga Region, Tanzania, to uncover their underlying structural patterns and the word-formation processes. The data for this study were 125 names of primary schools found in the Shinyanga Rural District, collected through the documentary review method. The collected names were subjected to morphological analysis by decomposing them into constituent morphemes to establish their structures and word formation processes. The findings indicate that Sukuma place names minimally consist of a noun or verb stem, though most are composed of a prefix and a noun or verb stem. The study further establishes that Sukuma speakers use inflection, derivation, and compounding strategies to form place names. The results of this study support the view that names, like other words in a language, have elaborate linguistic structures that can be analysed morphologically. It is concluded that the structure of place names is as significant as the motivational factors for place naming in determining or contributing to the meaning of a place name.

Assessing spatial equity in urban park accessibility: an improve two-step catchment area method from the perspective of 15-mintue city concept

Ensuring equitable access to urban parks is crucial for promoting the sustainable development of cities. In the post-COVID era, the concept of the 15-minute city has gained significant attention, emphasizing human-scale urban design and fine-grained governance. This study introduces an improved two-step floating catchment area (2SFCA) method to accurately assess park accessibility and spatial equity in Guangzhou, considering the 15-minute city perspective. Firstly, a more effective framework is proposed to identify multiple park entrances that serve as supply points. Secondly, the population distribution is estimated using building areas and locationbased data through a random forest model. Furthermore, the supply capability index is estimated by considering the area of each park and its attractiveness index, derived from multiple geographic data sources. The results reveal unequal park accessibility in Guangzhou, with low-access areas mainly distributed on the fringe of study area in both walking and cycling scenarios due to the limited park supply or high-density population. With the influences of park entrances and the park attractiveness index, the spatial inequity of park accessibility may be more severe than previously estimated in other studies. This work enhances the understanding of park accessibility and facilitate effective planning for sustainable development and reducing environmental injustice.

Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data

Automatic damage assessment by analysing UAV-derived 3D point clouds provides fast information on the damage situation after an earthquake. However, the assessment of different damage grades is challenging given the variety in damage characteristics and limited transferability of methods to other geographic regions or data sources. We present a novel change-based approach to automatically assess multi-class building damage from real-world point clouds using a machine learning model trained on virtual laser scanning (VLS) data. Therein, we (1) identify object-specific point cloud-based change features, (2) extract changed building parts using k-means clustering, (3) train a random forest machine learning model with VLS data based on object-specific change features, and (4) use the classifier to assess building damage in real-world photogrammetric point clouds. We evaluate the classifier with respect to its capacity to classify three damage grades (heavy, extreme, destruction) in pre-event and post-event point clouds of an earthquake in L’Aquila (Italy). Using object-specific change features derived from bi-temporal point clouds, our approach is transferable with respect to multi-source input point clouds used for model training (VLS) and application (real-world photogrammetry). We further achieve geographic transferability by using simulated training data which characterises damage grades across different geographic regions. The model yields high multi-target classification accuracies (overall accuracy: 92.0%–95.1%). Classification performance improves only slightly when using real-world region-specific training data (< 3% higher overall accuracies). We consider our approach especially relevant for applications where timely information on the damage situation is required and sufficient real-world training data is not available.

THE DESIGN OF SCALABLE WEB GIS MICROSERVICE FRAMEWORK FOR UNDERGRADUATE EDUCATION

Abstract. As an interdisciplinary integration of GIS theory and network technology, Web GIS is of great significance for the comprehensive development of students’ GIS capability. Therefore, this paper targets undergraduate GIS education and presents a novel approach to designing a scalable Web GIS microservices framework, which consists of three essential components: scalable Web GIS microservices using RESTful APIs, scalable geographic data source provider using PostGIS, and scalable Web mapping and symbolization using JavaScript. In the GIS practice, students are expected to utilize Python, PostGIS, and JavaScript programming languages to develop RESTful microservice APIs, access geographic data, and perform mapping and symbolization using Web browser. Furthermore, they are encouraged to adjust the workload of GIS spatial analysis and algorithms based on the needs of the development environment, and implement different GIS applications. Ultimately, by guiding students to use this framework and participate in GIS practice, the aim of improving their GIS capability is achieved. The experimental results show that through the design and practice of the scalable Web GIS microservices framework, the relevant contents of Web GIS, GIS Software Development, GIS Principle, GIS Algorithm, and GeoDatabase courses are integrated. Students participate in GIS practice in various forms, such as open experiments, innovation projects, and professional competitions, which enhance their GIS capability at the undergraduate level and achieve favorable results.

Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)

There are many sources of geographic big data, and most of them come from heterogeneous environments. The data sources obtained in this case contain attribute information of different spatial scales, different time scales and different complexity levels. It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult. In this case, visualizing high-dimensional spatiotemporal trajectory data is extremely challenging. Therefore, i-tStar and its extension i-tStar (3D) proposed, a trajectory behavior feature for moving objects that are integrated into a view with less effort to display and extract spatiotemporal conditions, and evaluate our approach through case studies of an open-pit mine truck dataset. The experimental results show that this method is easier to mine the interaction behavior of multi-attribute trajectory data and the correlation and influence of various indicators of moving objects.

Mapping the food environment: The reliability of volunteered geographical information and institutional data sources in France

AbstractThe obesity epidemic and inequalities in access to food are prompting increasing numbers of food environment studies, which rely on secondary data sources for mapping. This article assesses the reliability of the two main food outlet data sources in France: the volunteered geographical information (VGI) collaborative map OpenStreetMap (OSM) and the national business register Sirene. Their information on food outlets was assessed through ground‐truthing in the city‐region of Montpellier. Sensitivity, positive predictive value, and concordance were computed for each database. We analyzed the socio‐spatial variability of these measures according to households' income level. The sensitivity of Sirene is good and that of OSM moderate, while the opposite holds for positive predictive value, and the concordance of both OSM and Sirene is fair. Sirene provides more reliable data on deprived neighborhoods and OSM on wealthy neighborhoods. Caution is recommended regarding the classifications on which they are based, the time required to update the institutional database, and socially influenced contributions to VGI.

Decoding the Geography of Natural TBEV Microfoci in Germany: A Geostatistical Approach Based on Land-Use Patterns and Climatological Conditions.

Background: Tickborne-encephalitis (TBE) is a potentially life-threating neurological disease that is mainly transmitted by ticks. The goal of the present study is to analyze the potential uniform environmental patterns of the identified TBEV microfoci in Germany. The results are used to calculate probabilities for the present distribution of TBEV microfoci in Germany based on a geostatistical model. Methods: We aim to consider the specification of environmental characteristics of locations of TBEV microfoci detected in Germany using open access epidemiological, geographical and climatological data sources. We use a two-step geostatistical approach, where in a first step, the characteristics of a broad set of environmental variables between the 56 TBEV microfoci and a control or comparator set of 3575 sampling points covering Germany are compared using Fisher’s Exact Test. In the second step, we select the most important variables, which are then used in a MaxEnt distribution model to calculate a high resolution (400 × 400 m) probability map for the presence of TBEV covering the entire area of Germany. Results: The findings from the MaxEnt prediction model indicate that multi annual actual evapotranspiration (27.0%) and multi annual hot days (22.5%) have the highest contribution to our model. These two variables are followed by four additional variables with a lower, but still important, explanatory influence: Land cover classes (19.6%), multi annual minimum air temperature (14.9%), multi annual sunshine duration (9.0%), and distance to coniferous and mixed forest border (7.0%). Conclusions: Our findings are based on defined TBEV microfoci with known histories of infection and the repeated confirmation of the virus in the last years, resulting in an in-depth high-resolution model/map of TBEV microfoci in Germany. Multi annual actual evapotranspiration (27%) and multi annual hot days (22.5%) have the most explanatory power in our model. The results may be used to tailor specific regional preventive measures and investigations.

Creating knowledge graphs for geographic data on the web

Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.

A System for Aligning Geographical Entities from Large Heterogeneous Sources

Aligning points of interest (POIs) from heterogeneous geographical data sources is an important task that helps extend map data with information from different datasets. This task poses several challenges, including differences in type hierarchies, labels (different formats, languages, and levels of detail), and deviations in the coordinates. Scalability is another major issue, as global-scale datasets may have tens or hundreds of millions of entities. In this paper, we propose the GeographicaL Entities AligNment (GLEAN) system for efficiently matching large geographical datasets based on spatial partitioning with an adaptable margin. In particular, we introduce a text similarity measure based on the local-context relevance of tokens used in combination with sentence embeddings. We then come up with a scalable type embedding model. Finally, we demonstrate that our proposed system can efficiently handle the alignment of large datasets while improving the quality of alignments using the proposed entity similarity measure.

Discovering the Legacy of Hispanic/Spanish and South American Landscapes through Geohistorical Sources: The Geographical and Topographical Relations of Philip II

Landscapes have history and memory, which are eloquent generators of testimonies and traces on the processes of the landscape that take place today, and that will take place in the future. In recent years, numerous methods of analysing land and landscape patterns have been developed and evaluated, based on the multiplicity of these type of geographic and historical data sources, which have developed the concept of the geohistorical source. The goal of these sources of information allows us to historically reconstruct landscapes. With this in mind, the basic objective of the present research is to approach a geohistorical source with a wide spatial spectrum in Europe and America: the geographical and topographical relations of Philip II. This source has been chosen for the quality, quantity, variety and systematization of the data it provides on the territory and landscape of the crown of Castile. In addition, it ended up being the model of how to obtain organized and homogeneous knowledge of a large spatial area, considering the geographical, anthropological and historical data of the different territories. This geohistorical source is reliable, because the local authorities, both secular and ecclesiastical, are questioned, as they are the ones who inhabit, use, and, at different levels, govern the territory and its people.

GPU UTILIZATION IN GEOPROCESSING BIG GEODATA: A REVIEW

Abstract. The expansion of data collection from remote sensing and other geographic data sources, as well as from other technology such as cloud, sensors, mobile, and social media, have made mapping and analysis more complex. Some geospatial applications continue to rely on conventional geospatial processing, where limitation on computation capabilities often lacking to attain significant data interpretation. In recent years, GPU processing has improved far more GIS applications than using CPU alone. As a result, numerous researchers have begun utilising GPUs for scientific, geometric, and database computations in addition to graphics hardware use. This paper summarizes parallel processing concept and architecture, the development of GPU geoprocessing for big geodata ranging from remote sensing and 3D modelling to smart cities studies. This paper also addresses the GPU future trends advancement opportunities with other technologies, machine learning, deep learning, and cloud-based computing.

Urban Functional Zone Recognition Integrating Multisource Geographic Data

As the basic spatial unit of urban planning and management, it is necessary to understand the real development trend of urban functional zones in time and carry out reasonable planning adjustment. Because of the complexity of urban functional zones, the automatic recognition of urban functional zones has become a significant scientific problem in urban research. Urban functional zones contain natural and socioeconomic characteristics, but the existing identification methods fail to comprehensively consider these features. This paper proposes a framework that integrates multisource geographic data to recognize urban functional zone. We used high-resolution remote sensing imagery, point-of-interest (POI) data and high-spatial-resolution nighttime light imagery to extract both natural and socioeconomic features for urban functional zone accurate interpretation. Various features provide more accurate and comprehensive description for complex urban functional zone, so as to improve the recognition accuracy of urban functional zone. At present, there are few studies on urban functional zone recognition based on the combination of high-resolution remote sensing image, POI and high-resolution nighttime light imagery. The application potential of the combination of these three geographical data sources in urban function zone recognition needs to be explored. The experimental results show that the accuracy of urban functional zone recognition was obviously improved by the three data sources combination, the overall accuracy reached 80.30% and a comprehensive evaluation index reached 68.26%. This illustrate that the combination of the three data sources is beneficial to the urban functional zone recognition.

Planning for Emerging Infectious Disease Pandemics

Problem, research strategy, and findings Planners have not paid enough attention to managing the risk of emerging infectious diseases (EIDs), of which COVID-19 is the most recent manifestation. Overlooking aggressive policies to manage this risk of zoonotic viruses reassorting between sick animals and humans misses the greatest opportunity for stopping future disease pandemics. In this study we review several disciplines, outline the scant planning literature on EIDs, and identify the increasing calls from virologists and medical professionals to address urbanization as a key EID driver. Using the case of avian influenza outbreaks in Vietnam in 2004 and 2005, we conceptualize a preventive planning approach to managing the risk of zoonotic transmission that results in EID pandemics. Takeaway for practice We make several recommendations for planners. Practicing planners should consider how their plans manage the risk of zoonotic disease transmission between animals and humans through land use planning and community planning. Planning education and certification organizations should develop positions regarding the role of planning for EIDs. Food systems planners should consider the importance of livestock practices in food production as a risk factor for EIDs. Diverse research teams should combine geographic scales, data sources, and disciplinary knowledge to examine how an extended series of upstream and downstream events can result in a global pandemic. Such empirical examination can lead to effective planning policies to greatly reduce this risk.

GDIS, a global dataset of geocoded disaster locations

This article presents a new open source extension to the Emergency Events Database (EM-DAT) that allows researchers, for the first time, to explore and make use of subnational, geocoded data on major disasters triggered by natural hazards. The Geocoded Disasters (GDIS) dataset provides spatial geometry in the form of GIS polygons and centroid latitude and longitude coordinates for each administrative entity listed as a disaster location in the EM-DAT database. In total, GDIS contains spatial information on 39,953 locations for 9,924 unique disasters occurring worldwide between 1960 and 2018. The dataset facilitates connecting the EM-DAT database to other geographic data sources on the subnational level to enable rigorous empirical analyses of disaster determinants and impacts.

A Generic FCN-Based Approach for the Road-Network Extraction From VHR Remote Sensing Images – Using OpenStreetMap as Benchmarks

As one road network is often the backbone in a geo-spatial dataset, capturing and/or updating the road networks using remote sensing imagery play an important role in traffic management, urban planning, vehicle navigation, and emergency management. Along with the progress of remote sensing launching technologies and the successful applications of deep learning in the field of computer vision, it has become more and more efficient and economical to employ deep learning methods for road-features extractions from very high-resolution (VHR) remote sensing imagery. Meanwhile, as one of the most significant and popular volunteer geographic information data sources, the OpenStreetMap (OSM) including the complete road networks in the wide world has been accumulated in the past decades. In this article, a generic and automatic approach for extracting road networks from VHR remote sensing images has been proposed based on fully convolutional neural network, in which the road centerlines from OSM have been employed to construct the labels for the model training and validation. In the conducted experiments on various VHR image datasets with two different spatial resolutions of 0.3 and 1 m, the proposed model has demonstrated quite satisfactory results – the overall completeness and correctness of the roads extraction from VHR remote sensing images exceed 94.0% and 98.0%, respectively.

A history of study and new records of terrestrial enchytraeids (Annelida, Clitellata, Enchytraeidae) from the Russian Far East.

A list of terrestrial enchytraeids of the Russian Far East is compiled based on literature and extensive field data collected by the authors in 2019. A database has been created consisting of geographic coordinates, habitat type, species, and data source. For some species collected by the authors, barcoding using COI, 16s, and 12s rRNA genes has been performed. In total, there are at least 62 species of enchytraeids belonging to 12 genera. Seven species (Achaeta macroampullacea, Cognettia sphagnetorum, Enchytraeus dichaetus, Fridericia cusanica, Globulidrilus riparius, Marionina southerni, Mesenchytraeus gigachaetus) are reported in the Russian Far East for the first time. Cognettia sphagnetorum and F. cusanica are most probably introduced. Taxonomic and biogeographical remarks on some of the species found and differences from the original descriptions are provided. Some of the specimens may be undescribed species, but this requires a more in-depth examination. The Russian Far East, especially its southeastern part, is of great interest as a possible location for new species of enchytraeids.

Residential proximity to power lines and risk of brain tumor in the general population

The effect of ELF-MF on human health is still controversial, particularly as regards long-term health effects like cancer. The literature does suggest, however, that they could be involved in the occurrence of brain tumors, although results concerning residential exposure are scarce. Our objective was to investigate the association between residential proximity to power lines and brain tumors among adults in France by using a geographical information system.CERENAT is a population-based case-control study carried out in France in 2004–2006. We used geographical data sources on power line location to create exposure scores based on distance between residence and power lines, and on the number of lines near residences. Conditional logistic regression for matched sets was used to estimate Odds Ratios (ORs) and 95% confidence intervals (95%CI).We found significant associations between cumulated duration living at <50 m to high voltage lines and: i) all brain tumors (OR 2.94; 95%CI 1.28–6.75); ii) glioma (OR 4.96; 95%CI 1.56–15.77). Further investigations are needed, particularly to improve the quality and availability of geographical and technical data on power lines.

The integration of geographic and territorial data sources into the base register of territorial and geographical entities

The integration of geographic and territorial data sources into the base register of territorial and geographical entities