Footprint generation using fuzzy-neighborhood clustering

Parker, Jonathon K.; Downs, Joni A.

doi:10.1007/s10707-012-0152-0

Cited by 12 publications

(2 citation statements)

References 28 publications

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“…The virtual geography experiments demonstrate clearly that the fuzzy alpha-shape method is extremely robust to biased sampling (Figure 7), which is an important issue for natural history data used to map species distributions [43] and for crowdsourced and big geographic data more generally [3,23]. Where biased sampling is not an issue and the density of point samples is a reliable indication of the greater possibility of a spatial object, then there are existing density based methods for fuzzy mapping of spatial objects, such as density peak clustering [25] and density-based spatial clustering with noise [45], that could also be considered. The virtual geography experiments also demonstrate that the fuzzy alpha-shape method is robust to low levels of error, with virtually no effect on performance when errors consisted of around 3% of the sample, and minimal effects on performance when errors consisted of around 6% of the sample (Figure 6).…”

Section: Discussionmentioning

confidence: 99%

Mapping uncertain spatial object extents from point samples using fuzzy alpha-shapes

Etherington

2023

JOSIS

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Mapping the extent of spatial objects from point samples is a fundamental process in geographical analysis. Computational geometry methods are commonly used, and one method that has been proposed is the alpha-shape as it is insensitive to both bias and errors that are common in crowdsourced geographic data and big geographic data more generally. However, many spatial objects are uncertain in nature, with vague boundaries that are not well represented by the current use of discrete alpha-shapes. Fuzzy alpha-shapes are presented as a highly generic and adaptable methodology that can produce maps of spatial objects that recognise the vague and uncertain nature of many geographies. A series of virtual geography experiments demonstrate that fuzzy alpha-shapes avoid the need for binary thresholds, create a model that better represents the uncertain boundaries of some spatial objects, while also retaining the robustness to errors and bias that motivated the original use of alpha-shapes for mapping spatial objects.

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Section: Discussionmentioning

confidence: 99%

Mapping uncertain spatial object extents from point samples using fuzzy alpha-shapes

Etherington

2023

JOSIS

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“…In addition, this approach might not define the boundaries for places with less geo-tagged photos. In another study, Parker and Downs [3] proposed a novel approach to generate geometric footprints, which delineate the region occupied by a spatial point pattern, by clustering data points and then creating a minimum convex envelope to enclose each cluster. This study utilizes two density-based clustering techniques for footprint generation.…”

Section: A Spatial Extent Estimation Of Geographic Entities and Poi I...mentioning

confidence: 99%

Estimating Bounding Box for Point of Interest Using Social Media Geo-Tagged Photos

Bui

2023

IEEE Access

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The accuracy and completeness of information in geographical databases are very important for many location-based applications and services. However, the incompleteness in geographical databases is currently an issue. One consequence of this is that the geographic bounding boxes of many points of interests (POIs) have not been known. This paper studies the problem of estimating geographic bounding boxes for POIs using geo-tagged photos contributed by public users on social media. We present a novel approach using relevant geo-tagged photos of POIs to estimate geographic bounding boxes for the POIs. In the proposed method, we extend to apply survival analysis with random distance variable for our estimation. We demonstrate the superiority and effectiveness of our proposed approach over competing methods.INDEX TERMS Geographic bounding box, POI, survival analysis, geo-tagged photos, Flickr.

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Intuitionistic Fuzzy Density Based Spatial Clustering of Applications with Noise: IFDBSCAN

Ünver

Erginel

2019

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making

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Footprint generation using fuzzy-neighborhood clustering

Cited by 12 publications

References 28 publications

Mapping uncertain spatial object extents from point samples using fuzzy alpha-shapes

Mapping uncertain spatial object extents from point samples using fuzzy alpha-shapes

Estimating Bounding Box for Point of Interest Using Social Media Geo-Tagged Photos

Intuitionistic Fuzzy Density Based Spatial Clustering of Applications with Noise: IFDBSCAN

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