Pub Date : 2019-01-01DOI: 10.1553/giscience2019_02_s129
J. Mund, Susanne Müller
Innovative and disruptive technological innovations trigger educational advances. Novel sensor-based distance and height measurement tools or wearable augmented realty (AR) devices and cameras have recently been introduced into several University curricula focusing on the environmental sector. Consumer gadgets and mobile GIS support students during self-organized fieldwork by displaying collected data in an immersive AR. This paper summarizes the authors’ experiences in implementing a module re-design integrating a new didactical approach to teaching empirical data collection for forest inventories with the use of AR tools and mobile data-collection methods. The new module combines blended and mobile learning and state-of-the-art IT in order to address future professional needs of the forestry sector. The piloting of the module from 2016 to 2018 demonstrated the potential for the forestry sector of mobile learning that uses geospatial information and AR technologies.
{"title":"Augmented Reality and Mobile GIS as Tools for Teaching Data-collection in the Context of Forest Inventories","authors":"J. Mund, Susanne Müller","doi":"10.1553/giscience2019_02_s129","DOIUrl":"https://doi.org/10.1553/giscience2019_02_s129","url":null,"abstract":"Innovative and disruptive technological innovations trigger educational advances. Novel sensor-based distance and height measurement tools or wearable augmented realty (AR) devices and cameras have recently been introduced into several University curricula focusing on the environmental sector. Consumer gadgets and mobile GIS support students during self-organized fieldwork by displaying collected data in an immersive AR. This paper summarizes the authors’ experiences in implementing a module re-design integrating a new didactical approach to teaching empirical data collection for forest inventories with the use of AR tools and mobile data-collection methods. The new module combines blended and mobile learning and state-of-the-art IT in order to address future professional needs of the forestry sector. The piloting of the module from 2016 to 2018 demonstrated the potential for the forestry sector of mobile learning that uses geospatial information and AR technologies.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80824836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1553/GISCIENCE_2019_01_S87
C. Steves
Using fishery-dependent observer data from National Marine Fisheries (NMFS) provides insight into the location and intensity of bottom-trawl fishing effort, and allows those areas most exposed to fishing pressure to be identified. In this study, the spatial and temporal extent of Alaskan bottom-trawl fishing effort in the Bering Sea, Aleutian Islands and Gulf of Alaska between 1993 and 2015 is explored within a space-time cube in ArcGIS Pro. The variables analysed were number of hauls per area and total catch per area. Statistical techniques were used to examine spatiotemporal clustering within the data. Results indicate that fishing was significantly clustered over space and time. A three-dimensional hotspot analysis shows which areas were most intensely fished and illustrates the trends over the relatively long study period. The data were then compared with sea ice concentration to determine the effect of changing climate on fishing activity. Sea ice had a limited effect on the spatial patterns of fishing effort, but certain areas in the Bering Sea exhibited increased fishing effort in years with less sea ice.
{"title":"Trends in the Alaskan Bottom-Trawl Fishery from 1993 to 2015: A GIS-based Spatiotemporal Analysis","authors":"C. Steves","doi":"10.1553/GISCIENCE_2019_01_S87","DOIUrl":"https://doi.org/10.1553/GISCIENCE_2019_01_S87","url":null,"abstract":"Using fishery-dependent observer data from National Marine Fisheries (NMFS) provides insight into the location and intensity of bottom-trawl fishing effort, and allows those areas most exposed to fishing pressure to be identified. In this study, the spatial and temporal extent of Alaskan bottom-trawl fishing effort in the Bering Sea, Aleutian Islands and Gulf of Alaska between 1993 and 2015 is explored within a space-time cube in ArcGIS Pro. The variables analysed were number of hauls per area and total catch per area. Statistical techniques were used to examine spatiotemporal clustering within the data. Results indicate that fishing was significantly clustered over space and time. A three-dimensional hotspot analysis shows which areas were most intensely fished and illustrates the trends over the relatively long study period. The data were then compared with sea ice concentration to determine the effect of changing climate on fishing activity. Sea ice had a limited effect on the spatial patterns of fishing effort, but certain areas in the Bering Sea exhibited increased fishing effort in years with less sea ice.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89772458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Describe! Analyse! Act! Geomedia and Sustainability: Results from a European School Project","authors":"Friedrich Barnikel, Floris Willems, Robert Plötz","doi":"10.1553/2019_02_s144","DOIUrl":"https://doi.org/10.1553/2019_02_s144","url":null,"abstract":"","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84005689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-29DOI: 10.1553/GISCIENCE2016_01_S176
C. Cheng
Green infrastructure serves as a critical no-regret strategy to address climate change mitigation and adaptation in climate action plans. Climate justice refers to the distribution of climate change-induced environmental hazards (e.g., increased frequency and intensity of floods) among socially vulnerable groups. Yet no index has addressed both climate justice and green infrastructure planning jointly in the USA. This paper proposes a spatial climate justice and green infrastructure assessment framework to understand social-ecological vulnerability under the impacts of climate change. The Climate Justice Index ranks places based on their exposure to climate change-induced flooding, and water contamination aggravated by floods, through hydrological modelling, GIS spatial analysis and statistical methodologies. The Green Infrastructure Index ranks access to biophysical adaptive capacity for climate change. A case study for the Huron River watershed in Michigan, USA, illustrates that climate justice hotspots are concentrated in large cities; yet these communities have the least access to green infrastructure. This study demonstrates the value of using GIS to assess the spatial distribution of climate justice in green infrastructure planning and thereby to prioritize infrastructure investment while addressing equity in climate change adaptation.
{"title":"Spatial Climate Justice and Green Infrastructure Assessment: A case study for the Huron River watershed, Michigan, USA","authors":"C. Cheng","doi":"10.1553/GISCIENCE2016_01_S176","DOIUrl":"https://doi.org/10.1553/GISCIENCE2016_01_S176","url":null,"abstract":"Green infrastructure serves as a critical no-regret strategy to address climate change mitigation and adaptation in climate action plans. Climate justice refers to the distribution of climate change-induced environmental hazards (e.g., increased frequency and intensity of floods) among socially vulnerable groups. Yet no index has addressed both climate justice and green infrastructure planning jointly in the USA. This paper proposes a spatial climate justice and green infrastructure assessment framework to understand social-ecological vulnerability under the impacts of climate change. The Climate Justice Index ranks places based on their exposure to climate change-induced flooding, and water contamination aggravated by floods, through hydrological modelling, GIS spatial analysis and statistical methodologies. The Green Infrastructure Index ranks access to biophysical adaptive capacity for climate change. A case study for the Huron River watershed in Michigan, USA, illustrates that climate justice hotspots are concentrated in large cities; yet these communities have the least access to green infrastructure. This study demonstrates the value of using GIS to assess the spatial distribution of climate justice in green infrastructure planning and thereby to prioritize infrastructure investment while addressing equity in climate change adaptation.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"176 1","pages":"176-190"},"PeriodicalIF":0.0,"publicationDate":"2016-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76851078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-01-01DOI: 10.1553/GISCIENCE2015S464
A. Keler, J. Krisp
People in growing urban areas are more and more influenced by emissions coming from numerous vehicles and factories. In this paper we inspect the concentration of particulate matter (PM2.5) visually over time. This information stems from a data set of air quality measurements from 36 static sensors in Beijing over one year (from 8.02.2013 till 8.02.2014). One possibility for creating an overview for 36 positions with varying PM2.5 measurements in time is the use of interpolation techniques. In our approach, we generate surfaces of PM2.5 concentration using inverse distance weighting (IDW). The resulting surfaces represent interpolated PM2.5 values, based on averaged PM2.5 information (e.g. average of one day). We create simple interactive visualizations using points as surface representations. Each surface point within the 3D visual analysis display exhibits its PM2.5 value by differing coloration and z-value (height component). The interactivity consists of using selection circles for stacked 3D displays of interpolated PM2.5 surfaces for different times (time series). The aim of this visual information analysis is the possible detection of periodical hotspots of high PM2.5 concentrations, which might be useful for people with respiratory diseases. For the detection of dynamic PM2.5 hotspot variations, we introduce thresholds for querying only the highest PM2.5 values of the surfaces. Afterwards, these points are aggregated into convex hulls (polygons), with the idea of comparing the size and shape of the PM2.5 hotspots in each created surface. The change of position and size of these polygons over time may be an indicator for air quality changes within an urban environment. Considering the above, this may be a starting point for the conception of a personalized routing solution for pedestrians or vehicle drivers with respiratory diseases, who want to avoid these hotspots of high PM2.5 concentrations.
{"title":"Spatio-temporal Visualization of Interpolated Particulate Matter (PM2.5) in Beijing","authors":"A. Keler, J. Krisp","doi":"10.1553/GISCIENCE2015S464","DOIUrl":"https://doi.org/10.1553/GISCIENCE2015S464","url":null,"abstract":"People in growing urban areas are more and more influenced by emissions coming from numerous vehicles and factories. In this paper we inspect the concentration of particulate matter (PM2.5) visually over time. This information stems from a data set of air quality measurements from 36 static sensors in Beijing over one year (from 8.02.2013 till 8.02.2014). One possibility for creating an overview for 36 positions with varying PM2.5 measurements in time is the use of interpolation techniques. In our approach, we generate surfaces of PM2.5 concentration using inverse distance weighting (IDW). The resulting surfaces represent interpolated PM2.5 values, based on averaged PM2.5 information (e.g. average of one day). We create simple interactive visualizations using points as surface representations. Each surface point within the 3D visual analysis display exhibits its PM2.5 value by differing coloration and z-value (height component). The interactivity consists of using selection circles for stacked 3D displays of interpolated PM2.5 surfaces for different times (time series). The aim of this visual information analysis is the possible detection of periodical hotspots of high PM2.5 concentrations, which might be useful for people with respiratory diseases. For the detection of dynamic PM2.5 hotspot variations, we introduce thresholds for querying only the highest PM2.5 values of the surfaces. Afterwards, these points are aggregated into convex hulls (polygons), with the idea of comparing the size and shape of the PM2.5 hotspots in each created surface. The change of position and size of these polygons over time may be an indicator for air quality changes within an urban environment. Considering the above, this may be a starting point for the conception of a personalized routing solution for pedestrians or vehicle drivers with respiratory diseases, who want to avoid these hotspots of high PM2.5 concentrations.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"17 1","pages":"464-474"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90997389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1553/giscience2021_02_s122
J. Růžička, Lukás Bruha
Spatial data are a key element of geographic information systems (GIS). With the growing computational power of modern GIS, the demand for accurate and up-to-date high definition (HD) spatial data grows accordingly and increases the requirements of data acquisition. To simplify and automate the process of obtaining HD road data, several methods have been created with different approaches and stages of automation. A new method combining high resolution aerial images and existing linear road data is presented in this article. The method models roads in a vector environment at the level of single driving lanes. Object-based image analysis (OBIA) is used to identify road surface markings (RSMs) in aerial images; the geometry of RSM polygons is analysed (skeletonization, neighbourhood and context analysis, pattern recognition) in order to obtain a coherent network of driving lanes. The technique is able to distinguish automatically between solid and broken lines. The method proposed was tested and proven to satisfactorily model driving lanes, including in complex situations like junctions, roundabouts or overor underpasses.
{"title":"Automatic Detection of Driving–Lane Geometry Based on Aerial Images and Existing Spatial Data","authors":"J. Růžička, Lukás Bruha","doi":"10.1553/giscience2021_02_s122","DOIUrl":"https://doi.org/10.1553/giscience2021_02_s122","url":null,"abstract":"Spatial data are a key element of geographic information systems (GIS). With the growing computational power of modern GIS, the demand for accurate and up-to-date high definition (HD) spatial data grows accordingly and increases the requirements of data acquisition. To simplify and automate the process of obtaining HD road data, several methods have been created with different approaches and stages of automation. A new method combining high resolution aerial images and existing linear road data is presented in this article. The method models roads in a vector environment at the level of single driving lanes. Object-based image analysis (OBIA) is used to identify road surface markings (RSMs) in aerial images; the geometry of RSM polygons is analysed (skeletonization, neighbourhood and context analysis, pattern recognition) in order to obtain a coherent network of driving lanes. The technique is able to distinguish automatically between solid and broken lines. The method proposed was tested and proven to satisfactorily model driving lanes, including in complex situations like junctions, roundabouts or overor underpasses.","PeriodicalId":29645,"journal":{"name":"GI_Forum","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87959902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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