High-level geospatial information discovery and fusion for geocoded multimedia

Ennis, Andrew; Chen, Liming; Nugent, Chris; Ioannidis, George; Stan, Alexandru

doi:10.1108/ijpcc-09-2013-0026

Cited by 12 publications

(6 citation statements)

References 5 publications

(12 reference statements)

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“…In our framework, by assuming that the Earth is spherical, we deal with the shortest path between two users’ locations measured along the surface of the Earth, instead of the rather naive straight-line Euclidean distance. Following an approach similar to that employed in Huang et al [40] and Ennis et al [41], the distance between two locations on the Earth’s surface can be computed according to the spherical law of cosines. 11 Then, when we denote the distance between the two users measured from ( x u 0 , y u 0 , t 0 ) and ( x v 1 , y v 1 , t 1 ) by d uv ( 0 ) , we obtain 12…”

Section: Methodsmentioning

confidence: 99%

A new understanding of friendships in space: Complex networks meet Twitter

Shin

Singh

Cho

et al. 2015

Journal of Information Science

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Studies on friendships in online social networks involving geographic distance have so far relied on the city location provided in users' profiles. Consequently, most of the research on friendships have provided accuracy at the city level, at best, to designate a user's location. This study analyzes a Twitter dataset because it provides the exact geographic distance between corresponding users. We start by introducing a strong definition of "friend" on Twitter (i.e., a definition of bidirectional friendship), requiring bidirectional communication. Next, we utilize geo-tagged mentions delivered by users to determine their locations, where "@username" is contained anywhere in the body of tweets. To provide analysis results, we first introduce a friend counting algorithm. From the fact that Twitter users are likely to post consecutive tweets in the static mode, we also introduce a two-stage distance estimation algorithm. As the first of our main contributions, we verify that the number of friends of a particular Twitter user follows a well-known power-law distribution (i.e., a Zipf's distribution or a Pareto distribution). Our study also provides the following newly-discovered friendship degree related to the issue of space: The number of friends according to distance follows a double power-law (i.e., a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides concrete evidence that Twitter can be a useful platform for assigning a more accurate scalar value to the degree of friendship between two users.

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Section: Methodsmentioning

confidence: 99%

A new understanding of friendships in space: Complex networks meet Twitter

Shin

Singh

Cho

et al. 2015

Journal of Information Science

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“…In our previous work, we have developed a system, known as MediaPlace, to extract geospatial information from multiple geospatial data sources, including Geonames, DBpedia, Google Places, and OpenStreetMaps [ 14 , 15 ]. The types of information extracted from these datasets are latitude, longitude, place name, place feature type and feature description, city, country, and elevation.…”

Section: Geospatial Data Modelingmentioning

confidence: 99%

“…The system, developed in our previous work uses the GPS coordinates in a photograph to query the datasets and extract the different items of information and fuse the result sets together. This fusion process combines duplicates that may appear in the multiple datasets or combinations of two places that are similar enough to be assumed the same place, based on given criteria [ 14 , 15 ]. The criterion of the fusion process involves initially checking if any of the extracted Point Of Interests (POI) have a reference to each other, such as a “sameAs” attribute.…”

Section: Geospatial Data Modelingmentioning

confidence: 99%

“…To handle the data model, the MediaPlace architecture was modified, as shown in Figure 1 , so that in the POI Handler layer contains the RDF Enrichment Model Generator component, which creates the sets of triples representing the extracted objects and their attributes, such as latitude, longitude, place name, and place feature (building, statue, etc .) [ 14 , 15 ]. A POI is a significant location that may be of interest and, in relation to MediaPlace, is usually a building, statue, or business location.…”

Section: Geospatial Data Modelingmentioning

confidence: 99%

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A Geospatial Semantic Enrichment and Query Service for Geotagged Photographs

Ennis

Nugent

Morrow

et al. 2015

Sensors

Self Cite

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With the increasing abundance of technologies and smart devices, equipped with a multitude of sensors for sensing the environment around them, information creation and consumption has now become effortless. This, in particular, is the case for photographs with vast amounts being created and shared every day. For example, at the time of this writing, Instagram users upload 70 million photographs a day. Nevertheless, it still remains a challenge to discover the “right” information for the appropriate purpose. This paper describes an approach to create semantic geospatial metadata for photographs, which can facilitate photograph search and discovery. To achieve this we have developed and implemented a semantic geospatial data model by which a photograph can be enrich with geospatial metadata extracted from several geospatial data sources based on the raw low-level geo-metadata from a smartphone photograph. We present the details of our method and implementation for searching and querying the semantic geospatial metadata repository to enable a user or third party system to find the information they are looking for.

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“…When the shortest path between two geo-tags' locations is measured along the surface of the Earth, the distance between two locations can be computed according to the spherical law of cosines[14].…”

mentioning

confidence: 99%

An Improved Approach for Estimating Social POI Boundaries With Textual Attributes on Social Media

Tran

Vu²,

Shin

2020

Preprint

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It has been insufficiently explored how to perform density-based clustering by exploiting textual attributes on social media. In this paper, we aim at discovering a social point-of-interest (POI) boundary, formed as a convex polygon. More specifically, we present a new approach and algorithm, built upon our earlier work on social POI boundary estimation (SoBEst). This SoBEst approach takes into account both relevant and irrelevant records within a geographic area, where relevant records contain a POI name or its variations in their text field. Our study is motivated by the following empirical observation: a fixed representative coordinate of each POI that SoBEst basically assumes may be far away from the centroid of the estimated social POI boundary for certain POIs. Thus, using SoBEst in such cases may possibly result in unsatisfactory performance on the boundary estimation quality (BEQ), which is expressed as a function of the  -measure. To solve this problem, we formulate a joint optimization problem of simultaneously finding the radius of a circle and the POI's representative coordinate by allowing to update . Subsequently, we design an iterative SoBEst (I-SoBEst) algorithm, which enables us to achieve a higher degree of BEQ for some POIs. The computational complexity of the proposed I-SoBEst algorithm is shown to scale linearly with the number of records. We demonstrate the superiority of our algorithm over competing clustering methods including the original SoBEst.

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High-level geospatial information discovery and fusion for geocoded multimedia

Cited by 12 publications

References 5 publications

A new understanding of friendships in space: Complex networks meet Twitter

A new understanding of friendships in space: Complex networks meet Twitter

A Geospatial Semantic Enrichment and Query Service for Geotagged Photographs

An Improved Approach for Estimating Social POI Boundaries With Textual Attributes on Social Media

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