Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation
Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/1/55 |
| _version_ | 1797493528026152960 |
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| author | Guiming Zhang |
| author_facet | Guiming Zhang |
| author_sort | Guiming Zhang |
| collection | DOAJ |
| description | Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics. |
| first_indexed | 2024-03-10T01:21:17Z |
| format | Article |
| id | doaj.art-c04c1e11ccac4f9a9683a18bf0fd8529 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T01:21:17Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-c04c1e11ccac4f9a9683a18bf0fd85292023-11-23T14:00:25ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-01-011115510.3390/ijgi11010055Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density EstimationGuiming Zhang0Department of Geography & the Environment, University of Denver, Denver, CO 80208, USAVolunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics.https://www.mdpi.com/2220-9964/11/1/55volunteered geographic information (VGI)geospatial big datapoint pattern analysiskernel density estimationhot-spot detection and visualizationspatial bias |
| spellingShingle | Guiming Zhang Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation ISPRS International Journal of Geo-Information volunteered geographic information (VGI) geospatial big data point pattern analysis kernel density estimation hot-spot detection and visualization spatial bias |
| title | Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation |
| title_full | Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation |
| title_fullStr | Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation |
| title_full_unstemmed | Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation |
| title_short | Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation |
| title_sort | detecting and visualizing observation hot spots in massive volunteer contributed geographic data across spatial scales using gpu accelerated kernel density estimation |
| topic | volunteered geographic information (VGI) geospatial big data point pattern analysis kernel density estimation hot-spot detection and visualization spatial bias |
| url | https://www.mdpi.com/2220-9964/11/1/55 |
| work_keys_str_mv | AT guimingzhang detectingandvisualizingobservationhotspotsinmassivevolunteercontributedgeographicdataacrossspatialscalesusinggpuacceleratedkerneldensityestimation |