PeerJ Computer Science Preprints: Spatial and Geographic Information Systemshttps://peerj.com/preprints/index.atom?journal=cs&subject=11700Spatial and Geographic Information Systems articles published in PeerJ Computer Science PreprintsRethinking the usage and experience of clustering markers in web mappinghttps://peerj.com/preprints/278582019-08-272019-08-27Loïc Fürhoff
Although the notion of ‘too many markers’ have been mentioned in several research, in practice, displaying hundreds of Points of Interests (POI) on a web map in two dimensions with an acceptable usability remains a real challenge. Web practitioners often make an excessive use of clustering aggregation to overcome performance bottlenecks without successfully resolving issues of perceived performance. This paper tries to bring a broad awareness by identifying sample issues which describe a general reality of clustering, and provide a pragmatic survey of potential technologies optimisations. At the end, we discuss the usage of technologies and the lack of documented client-server workflows, along with the need to enlarge our vision of the various clutter reduction methods.
Although the notion of ‘too many markers’ have been mentioned in several research, in practice, displaying hundreds of Points of Interests (POI) on a web map in two dimensions with an acceptable usability remains a real challenge. Web practitioners often make an excessive use of clustering aggregation to overcome performance bottlenecks without successfully resolving issues of perceived performance. This paper tries to bring a broad awareness by identifying sample issues which describe a general reality of clustering, and provide a pragmatic survey of potential technologies optimisations. At the end, we discuss the usage of technologies and the lack of documented client-server workflows, along with the need to enlarge our vision of the various clutter reduction methods.Grounded Design and GIScience - A framework for informing the design of geographical information systems and spatial data infrastructureshttps://peerj.com/preprints/278222019-06-252019-06-25Alexander KmochEvelyn UuemaaHermann Klug
Geographical Information Science (GIScience), also Geographical Information Science and Systems, is a multi-faceted research discipline and comprises a wide variety of topics. Investigation into data management and interoperability of geographical data and environmental data sets for scientific analysis, visualisation and modelling is an important driver of the Information Science aspect of GIScience, that underpins comprehensive Geographical Information Systems (GIS) and Spatial Data Infrastructure (SDI) research and development. In this article we present the 'Grounded Design' method, a fusion of Design Science Research (DSR) and Grounded Theory (GT), and how they can act as guiding principles to link GIScience, Computer Science and Earth Sciences into a converging GI systems development framework. We explain how this bottom-up research framework can yield holistic and integrated perspectives when designing GIS and SDI systems and software. This would allow GIScience academics, GIS and SDI practitioners alike to reliably draw from interdisciplinary knowledge to consistently design and innovate GI systems.
Geographical Information Science (GIScience), also Geographical Information Science and Systems, is a multi-faceted research discipline and comprises a wide variety of topics. Investigation into data management and interoperability of geographical data and environmental data sets for scientific analysis, visualisation and modelling is an important driver of the Information Science aspect of GIScience, that underpins comprehensive Geographical Information Systems (GIS) and Spatial Data Infrastructure (SDI) research and development. In this article we present the 'Grounded Design' method, a fusion of Design Science Research (DSR) and Grounded Theory (GT), and how they can act as guiding principles to link GIScience, Computer Science and Earth Sciences into a converging GI systems development framework. We explain how this bottom-up research framework can yield holistic and integrated perspectives when designing GIS and SDI systems and software. This would allow GIScience academics, GIS and SDI practitioners alike to reliably draw from interdisciplinary knowledge to consistently design and innovate GI systems.GIS analysis of geological surfaces orientations: the qgSurf plugin for QGIShttps://peerj.com/preprints/276942019-04-302019-04-30Mauro Alberti
GIS techniques enable the quantitative analysis of geological structures. In particular, topographic traces of geological lineaments can be compared with the theoretical ones for geological planes, to determine the best fitting theoretical planes. qgSurf, a Python plugin for QGIS, implements this kind of processing, in addition to the determination of the best-fit plane to a set of topographic points, the calculation of the distances between topographic traces and geological planes and also basic stereonet plottings. By applying these tools to a case study of a Cenozoic thrust lineament in the Southern Apennines (Calabria, Southern Italy), we deduce the approximate orientations of the lineament in different fault-delimited sectors and calculate the misfits between the theoretical orientations and the actual topographic traces.
GIS techniques enable the quantitative analysis of geological structures. In particular, topographic traces of geological lineaments can be compared with the theoretical ones for geological planes, to determine the best fitting theoretical planes. qgSurf, a Python plugin for QGIS, implements this kind of processing, in addition to the determination of the best-fit plane to a set of topographic points, the calculation of the distances between topographic traces and geological planes and also basic stereonet plottings. By applying these tools to a case study of a Cenozoic thrust lineament in the Southern Apennines (Calabria, Southern Italy), we deduce the approximate orientations of the lineament in different fault-delimited sectors and calculate the misfits between the theoretical orientations and the actual topographic traces.GeoNode: an open source framework to build spatial data infrastructureshttps://peerj.com/preprints/275342019-02-132019-02-13Paolo CortiFrancesco BartoliAlessio FabianiCristiano GiovandoAthanasios Tom KralidisAngelos Tzotsos
GeoNode is an open source framework designed to build geospatial content management systems (GeoCMS) and spatial data infrastructure (SDI) nodes. Its development was initiated by the Global Facility for Disaster Reduction and Recovery (GFDRR) in 2009 and adopted by a large number of organizations in the following years. Using an open source stack based on mature and robust frameworks and software like Django, OpenLayers, PostGIS, GeoServer and pycsw, an organization can build on top of GeoNode its SDI or geospatial open data portal. GeoNode provides a large number of user friendly capabilities, broad interoperability using Open Geospatial Consortium (OGC) standards, and a powerful authentication/authorization mechanism. Supported by a vast, diverse and global open source community, GeoNode is an official project of the Open Source Geospatial Foundation (OSGeo).
GeoNode is an open source framework designed to build geospatial content management systems (GeoCMS) and spatial data infrastructure (SDI) nodes. Its development was initiated by the Global Facility for Disaster Reduction and Recovery (GFDRR) in 2009 and adopted by a large number of organizations in the following years. Using an open source stack based on mature and robust frameworks and software like Django, OpenLayers, PostGIS, GeoServer and pycsw, an organization can build on top of GeoNode its SDI or geospatial open data portal. GeoNode provides a large number of user friendly capabilities, broad interoperability using Open Geospatial Consortium (OGC) standards, and a powerful authentication/authorization mechanism. Supported by a vast, diverse and global open source community, GeoNode is an official project of the Open Source Geospatial Foundation (OSGeo).Open educational resources for validation of global high-resolution land cover mapshttps://peerj.com/preprints/272142019-01-022019-01-02Candan E KilsedarGorica BraticMonia E MolinariMarco MinghiniMaria A Brovelli
Land cover (LC) maps are crucial to analyze and understand several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, we observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on validation through open source and easy-to-use software. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.
Land cover (LC) maps are crucial to analyze and understand several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, we observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on validation through open source and easy-to-use software. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.Machine learning with remote sensing data to locate uncontacted indigenous villages in Amazoniahttps://peerj.com/preprints/273072018-10-302018-10-30Robert S WalkerMarcus J Hamilton
Background. The world’s last uncontacted indigenous societies in Amazonia have only intermittent and often hostile interactions with the outside world. Knowledge of their locations is essential for urgent protection efforts, but their extreme isolation, small populations, and semi-nomadic lifestyles make this a challenging task.
Methods. Remote sensing technology with Landsat satellite sensors is a non-invasive methodology to track isolated indigenous populations through time. However, the small-scale nature of the deforestation signature left by uncontacted populations clearing villages and gardens has similarities to those made by contacted indigenous villages. Both contacted and uncontacted indigenous populations often live in proximity to one another making it difficult to distinguish the two in satellite imagery. Here we use machine learning techniques applied to remote sensing data with a training dataset of 500 contacted and 25 uncontacted villages.
Results. Uncontacted villages generally have smaller cleared areas, reside at higher elevations, and are farther from populated places and satellite-detected lights at night. A random forest algorithm with an optimally-tuned detection cutoff has a leave-one-out cross-validated sensitivity and specificity of over 98%. A grid search around known uncontacted villages led us to identify 3 previously-unknown villages using predictions from the random forest model. Our efforts can improve policies toward isolated populations by providing better near real-time knowledge of their locations and movements in relation to encroaching loggers, settlers, and other external threats to their survival.
Background. The world’s last uncontacted indigenous societies in Amazonia have only intermittent and often hostile interactions with the outside world. Knowledge of their locations is essential for urgent protection efforts, but their extreme isolation, small populations, and semi-nomadic lifestyles make this a challenging task.Methods. Remote sensing technology with Landsat satellite sensors is a non-invasive methodology to track isolated indigenous populations through time. However, the small-scale nature of the deforestation signature left by uncontacted populations clearing villages and gardens has similarities to those made by contacted indigenous villages. Both contacted and uncontacted indigenous populations often live in proximity to one another making it difficult to distinguish the two in satellite imagery. Here we use machine learning techniques applied to remote sensing data with a training dataset of 500 contacted and 25 uncontacted villages.Results. Uncontacted villages generally have smaller cleared areas, reside at higher elevations, and are farther from populated places and satellite-detected lights at night. A random forest algorithm with an optimally-tuned detection cutoff has a leave-one-out cross-validated sensitivity and specificity of over 98%. A grid search around known uncontacted villages led us to identify 3 previously-unknown villages using predictions from the random forest model. Our efforts can improve policies toward isolated populations by providing better near real-time knowledge of their locations and movements in relation to encroaching loggers, settlers, and other external threats to their survival.An example of SAR-derived image segmentation for landslides detectionhttps://peerj.com/preprints/272122018-10-202018-10-20Giuseppe EspositoAlessandro Cesare MondiniIvan MarchesiniPaola ReichenbachPaola SalvatiMauro Rossi
A rapid assessment of the areal extent of landslide disasters is one of the main challenges facing by the scientific community. Satellite radar data represent a powerful tool for the rapid detection of landslides over large spatial scales, even in case of persistent cloud cover. To define landslide locations, radar data need to be firstly pre-processed and then elaborated for the extraction of the required information. Segmentation represents one of the most useful procedures for identifying land cover changes induced by landslides. In this study, we present an application of the i.segment module of GRASS GIS software for segmenting radar-derived data. As study area, we selected the Tagari River valley in Papua New Guinea, where massive landslides were triggered by a M7.5 earthquake on February 25, 2018. A comparison with ground truth data revealed a suitable performance of i.segment. Particular segmentation patterns, in fact, resulted in the areas affected by landslides with respect to the external ones, or to the same areas before the earthquake. These patterns highlighted a relevant contrast of radar backscattering values recorded before and after the landslides. With our procedure, we were able to define the extension of the mass movements that occurred in the study area, just three days after the M7.5 earthquake.
A rapid assessment of the areal extent of landslide disasters is one of the main challenges facing by the scientific community. Satellite radar data represent a powerful tool for the rapid detection of landslides over large spatial scales, even in case of persistent cloud cover. To define landslide locations, radar data need to be firstly pre-processed and then elaborated for the extraction of the required information. Segmentation represents one of the most useful procedures for identifying land cover changes induced by landslides. In this study, we present an application of the i.segment module of GRASS GIS software for segmenting radar-derived data. As study area, we selected the Tagari River valley in Papua New Guinea, where massive landslides were triggered by a M7.5 earthquake on February 25, 2018. A comparison with ground truth data revealed a suitable performance of i.segment. Particular segmentation patterns, in fact, resulted in the areas affected by landslides with respect to the external ones, or to the same areas before the earthquake. These patterns highlighted a relevant contrast of radar backscattering values recorded before and after the landslides. With our procedure, we were able to define the extension of the mass movements that occurred in the study area, just three days after the M7.5 earthquake.ALBIS: integrated system for risk-based surveillance of invasive mosquito Aedes albopictushttps://peerj.com/preprints/272512018-10-032018-10-03Milan P. AntonovicMassimiliano CannataAndrea DananiLukas EngelerEleonora FlacioFrancesca MangiliDamiana RavasiDaniele StrigaroMauro Tonolla
According to predictions bases on a climate-driven large-scale model the areas surrounding Lake Léman and, to some extent, the Swiss Plateau are suitable for the spread of Ae. albopictus North of the Alps, while other areas in Switzerland (e.g., the city of Zürich) seem currently too cold in winter for the survival of eggs. However, this model does not take into account particular micro-climate conditions in urban areas where the specie thrives. Climate conditions in urban micro-habitats (in particular catch basins) increase the probability of the survival of diapausing eggs in the winter season favoring the colonization of new cities that were thought to be too cold for the survival of the eggs. Therefore, there is an urgent need for appropriate monitoring tools and risk-based surveillance of Ae. albopictus populations. In 2018 a multidisciplinary group of researchers from the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) has joined launching the project ALBIS (Albopictus Integrated System). The designed system focuses on the monitoring of urban catch basins, primarily on micro-climate environmental sensing, data transmission, data acquisition and data dissemination. The gathered data are the input for an empirical machine learning model for the prediction of spatial and temporal distribution of the Ae. albopictus. The first real time monitoring tests are in progress in the pilot area in the city of Lugano in the Canton Ticino. Fully functional prototypes have been engineered by the Institute of Earth Science in collaboration with a local electronics manufacturer (TECinvent) combined with the Open Source istSOS OGC Sensor Observation Service software for data acquisition and dissemination, and in the first tests cases have demonstrated good quality in terms of energy efficiency, data quality and data transmission reliability. The first results demonstrated that temperature in catch basins can be different from outside temperature that is detected by traditional terrain measures: in February 2018 during a period of cold air temperature in Canton Ticino of down to -8°C, the prototype sensor monitoring the catch basins' wall surface shows temperatures up to 6°C higher. Considering that one of the Ae. albopictus establishment thresholds is to have a mean January temperature of >0°C to allow egg overwintering, taking into account this micro-climate environments could lead to more realistic predictions.
According to predictions bases on a climate-driven large-scale model the areas surrounding Lake Léman and, to some extent, the Swiss Plateau are suitable for the spread of Ae. albopictus North of the Alps, while other areas in Switzerland (e.g., the city of Zürich) seem currently too cold in winter for the survival of eggs. However, this model does not take into account particular micro-climate conditions in urban areas where the specie thrives. Climate conditions in urban micro-habitats (in particular catch basins) increase the probability of the survival of diapausing eggs in the winter season favoring the colonization of new cities that were thought to be too cold for the survival of the eggs. Therefore, there is an urgent need for appropriate monitoring tools and risk-based surveillance of Ae. albopictus populations. In 2018 a multidisciplinary group of researchers from the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) has joined launching the project ALBIS (Albopictus Integrated System). The designed system focuses on the monitoring of urban catch basins, primarily on micro-climate environmental sensing, data transmission, data acquisition and data dissemination. The gathered data are the input for an empirical machine learning model for the prediction of spatial and temporal distribution of the Ae. albopictus. The first real time monitoring tests are in progress in the pilot area in the city of Lugano in the Canton Ticino. Fully functional prototypes have been engineered by the Institute of Earth Science in collaboration with a local electronics manufacturer (TECinvent) combined with the Open Source istSOS OGC Sensor Observation Service software for data acquisition and dissemination, and in the first tests cases have demonstrated good quality in terms of energy efficiency, data quality and data transmission reliability. The first results demonstrated that temperature in catch basins can be different from outside temperature that is detected by traditional terrain measures: in February 2018 during a period of cold air temperature in Canton Ticino of down to -8°C, the prototype sensor monitoring the catch basins' wall surface shows temperatures up to 6°C higher. Considering that one of the Ae. albopictus establishment thresholds is to have a mean January temperature of >0°C to allow egg overwintering, taking into account this micro-climate environments could lead to more realistic predictions.GeoSQL Journey - A gamified learning experience to introduce (or demystify) geospatial SQL querieshttps://peerj.com/preprints/272472018-10-022018-10-02Romain SandozSarah CompostoSandrine DivorneOlivier ErtzJens Ingensand
In a digital world in the making, digital natives develop new learning profiles, interests, and way of working. Simultaneously teachers are facing students with lack of engagement and motivation with quite traditional learning process that has probably to be reframed considering the effects of digital transformation in the education sector. This issue is acute when it comes to complex subject of study, such as SQL geospatial to manipulate the geospatial characteristic of data. Indeed, some common difficulties have been identified by teachers from HEIG-VD university both in Media Engineering and Geomatics fields of study. The user-centered approach aims at creating digital products highly responding to the user’s needs through techniques improving the user experience. Various aspects have to be considered, including emotions. In education, gamification, along with user experience, interface design and usability best practices is one promising approach able to increase the learner's engagement, interest and motivation. It aims to implement game mechanics within non-game context, in order to motivate the learner to accomplish a task and increase the ability to learn new skills. Using a gamification layer within a given context, being digital or not, act as a motivational trigger. It helps giving meaningful, enjoyable and empowering experience. SQL Island is a project from Kaiserslautern University of Technology which illustrates very well a gamified learning experience of the SQL special-purpose programming language. The GeoSQL Journey project goes further, tackling SQL geospatial to learn in a fun way how to manipulate the geospatial characteristic of data. It is a gamified pedagogical application to introduce the students to the practice of SQL geospatial during the first hours or days of the course. Serving as an initiation, it is designed to focus on intrinsic motivation (personal development, quest, challenge and fulfillment) with learning objectives determined and integrated with an engaging and coherent game world and narrative. This paper describes the early work of conceptual design of the GeoSQL Journey project. Game mechanics and game interface has been conceived and brought together according to the literature in the domain and best practices on this matter. The following step for this project is to elaborate a testing method without yet having to develop an application prototype (e.g. organizing a fairly raw tabletop game associated with a classic SQL console) so as to challenge the design with students and teachers to get their feedbacks. Also, it is envisioned to evaluate how existing open source gamification tools and frameworks would be suitable to develop the first prototype planned for the 2019-2020 academic year.
In a digital world in the making, digital natives develop new learning profiles, interests, and way of working. Simultaneously teachers are facing students with lack of engagement and motivation with quite traditional learning process that has probably to be reframed considering the effects of digital transformation in the education sector. This issue is acute when it comes to complex subject of study, such as SQL geospatial to manipulate the geospatial characteristic of data. Indeed, some common difficulties have been identified by teachers from HEIG-VD university both in Media Engineering and Geomatics fields of study. The user-centered approach aims at creating digital products highly responding to the user’s needs through techniques improving the user experience. Various aspects have to be considered, including emotions. In education, gamification, along with user experience, interface design and usability best practices is one promising approach able to increase the learner's engagement, interest and motivation. It aims to implement game mechanics within non-game context, in order to motivate the learner to accomplish a task and increase the ability to learn new skills. Using a gamification layer within a given context, being digital or not, act as a motivational trigger. It helps giving meaningful, enjoyable and empowering experience. SQL Island is a project from Kaiserslautern University of Technology which illustrates very well a gamified learning experience of the SQL special-purpose programming language. The GeoSQL Journey project goes further, tackling SQL geospatial to learn in a fun way how to manipulate the geospatial characteristic of data. It is a gamified pedagogical application to introduce the students to the practice of SQL geospatial during the first hours or days of the course. Serving as an initiation, it is designed to focus on intrinsic motivation (personal development, quest, challenge and fulfillment) with learning objectives determined and integrated with an engaging and coherent game world and narrative. This paper describes the early work of conceptual design of the GeoSQL Journey project. Game mechanics and game interface has been conceived and brought together according to the literature in the domain and best practices on this matter. The following step for this project is to elaborate a testing method without yet having to develop an application prototype (e.g. organizing a fairly raw tabletop game associated with a classic SQL console) so as to challenge the design with students and teachers to get their feedbacks. Also, it is envisioned to evaluate how existing open source gamification tools and frameworks would be suitable to develop the first prototype planned for the 2019-2020 academic year.Crossing SSH and STEM approaches in a MapDesign course using open data and softwarehttps://peerj.com/preprints/272372018-09-262018-09-26Massimiliano CannataGiovanni ProfetaMichela VoegeliManuel LüscherLaura Morandi
This paper presents the design, realization and evaluation of a Map Design course conducted using an open source GIS (QGIS) to students of the bachelor in Visual Communication. The specific challenge was teaching approaches from Social Science and Humanities (SSH) and Science, Technology, Engineering and Mathematics (STEM) disciplines to integrate rigorous cartographic methodologies for map production with aesthetic visual aspects. This was successfully addressed with an hybridization approach that discuss themes from the two disciplines point of view and a goal-oriented course organization that produced as an output real map products. The general evaluation of this new course by students and teachers was positive. Despite the main criticism was related to the complexity of the used tools with respect to the course duration, the quality of the outputs demonstrated a very good capacity of students in learning and fusing of STEM and SSH concepts.
This paper presents the design, realization and evaluation of a Map Design course conducted using an open source GIS (QGIS) to students of the bachelor in Visual Communication. The specific challenge was teaching approaches from Social Science and Humanities (SSH) and Science, Technology, Engineering and Mathematics (STEM) disciplines to integrate rigorous cartographic methodologies for map production with aesthetic visual aspects. This was successfully addressed with an hybridization approach that discuss themes from the two disciplines point of view and a goal-oriented course organization that produced as an output real map products. The general evaluation of this new course by students and teachers was positive. Despite the main criticism was related to the complexity of the used tools with respect to the course duration, the quality of the outputs demonstrated a very good capacity of students in learning and fusing of STEM and SSH concepts.