Detection of POI boundaries through geographical topics

Bui, Thanh-Hieu; Han, Yong-Jin; Park, Seong-Bae

doi:10.1109/35021bigcomp.2015.7072827

Cited by 6 publications

(7 citation statements)

References 15 publications

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Section: Related Workmentioning

confidence: 97%

“…First, the approaches to discovering an AOI on a small scale were studied in [5,20,46], which lies in the same line as our work. In [5], geo-tagged Flickr textual data, Wikipedia concepts, and links among them with a topical similarity method were employed to discover a POI boundary. In [20], the boundary of an AOI was estimated using kernel density estimation and support vector machine (SVM) via geo-tagged Flickr photos.…”

Section: Related Workmentioning

confidence: 97%

“…AOI discovery. The term AOI is also referred to as place-of-interest [33], region-of-interest [43], or POI boundary [5,46]. In a broad sense, the research on AOI discovery can be categorized into the following two problems: estimation of a small-scale AOI (i.e., a single POI boundary) and estimation of large-scale AOIs (i.e., multiple POI boundaries).…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Tran

Vu²,

Shin

2020

Preprint

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“…POI clusters can be used to form POI areas. The approaches for discovering a single POI boundary were studied in [10], [11], and [13]. Bui et al [10] introduced a novel method, namely Boundarydependent Explicit Semantic Analysis (BESA), to identify the boundary of POI.…”

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

Self Cite

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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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“…The authors formulated the Integer Linear Programming approach to identify the visited POI which is useful in handling the POI recommendation [4]. Researchers proposed detection of the POI boundaries using Boundary-dependent Explicit Semantic analysis [5]. POI data density is used to identify the urban functional area which can be used for future planning in that region [6].…”

Section: A Poi Data Verificationmentioning

confidence: 99%

Fine-tuned Predictive Model for Verifying POI Data

Monika¹,

Bundele²,

Bothale³

et al. 2021

IJACSA

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Mapping websites and geo portals are playing a vital role in daily life due to the availability of geo-tagged data. From booking a cab to search a place, getting traffic information, review of the place, searching for a doctor or best school available in the locality, we are heavily dependent on the map services and geo portals available for finding such information. There is voluminous data available on these sources and it is getting increasing every moment. These data are majorly collected through crowdsourcing methods where people are contributing. As a basic principle of Garbage in garbage out, the quality of this data impacts the quality of the services based on this data. Therefore, it is highly desired to have a model which can predict the quality/accuracy of the geotagged Point of interest data. We propose a novel Fine-Tuned Predictive Model to check the accuracy of this data using the best suitable supervised machine learning approach. This work focuses on the complete life cycle of the model building, starting from the data collection to the fine-tuning of the hyperparameters. We covered the challenges particularly to the geotagged POI data and remedies to resolve the issues to make it suitable for predictive modeling for classifying the data based on their accuracy. This is a unique work that considers multiple sources including ground truth data to verify the geotagged data using a machine learning approach. After exhaustive experiments, we obtained the best values for hyperparameters for the selected predictive model built on the real data set prepared specifically to target the proposed solution. This work provides a way to develop a robust pipeline for predicting the accuracy of crowdsourced geotagged data.

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Detection of POI boundaries through geographical topics

Cited by 6 publications

References 15 publications

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

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

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

Fine-tuned Predictive Model for Verifying POI Data

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