Extracting spatial information from social media in support of agricultural management decisions

Golubovic, Nevena; Krintz, Chandra; Wolski, Rich; Lafia, Sara; Hervey, Thomas; Kuhn, Walther

doi:10.1145/3003464.3003468

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…This key can be used to provide a time series for mortar falling. * geocoding forms a fundamental part of spatial analysis in a variety of research disciplines especially in extracting spatial coordinates from social media [Golubovic, 2017]. The algorithm used to solve geocoding the data is similar to the gazetteer algorithm [Churches, 2002].…”

Section: Methodsmentioning

confidence: 99%

Untitled

2021

ICIGIS

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Сборник включает материалы, представленные на Международную конференцию «ИнтерКарто. ИнтерГИС 27». В статьях рассматриваются теоретические и методические аспекты геоинформационного и картографического обеспечения экологических, экономических и социальных вопросов устойчивого развития, геоинформатики, картографии, создания атласов, дистанционного зондирования Земли. Также рассматриваются аспекты картографического и геоинформационного обеспечения решения вопросов общественного здоровья, сельского хозяйства и землепользования, туризма, природного и культурного наследия, исторической географии и географического образования. Сборник выпускается 1 раз в год, начиная с 1994 г.

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Section: Methodsmentioning

confidence: 99%

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2021

ICIGIS

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“…Most of the LSBN services allow users to create new venues using various methods, 6 especially when they unable to nd their current place during their check-in process. Apart from that, LBSNs users can add "tags" to venues or leave "tips" to venues, which are crucial for assisting users in searching and exploring new places as well as for developing recommendation services [1,14,36].…”

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confidence: 99%

Place-Type Detection in Location-Based Social Networks

Hasanuzzaman

Way

2017

Proceedings of the 28th ACM Conference on Hypertext and Social Media

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While most prior studies in Location-Based Social Networks (LS-BNs) have mainly centered around areas such as Point-of-Interest (POI) recommendation and place tag annotation, there exists no works looking at the problem of associating place-type to venues in LBSNs. Determining the type of places in location-based social networks may contribute to the success of various downstream tasks such as Point-of-Interest recommendation, location search, automatic place name database creation, and data cleaning. In this paper, we propose a multi-objective ensemble learning framework that (i) allows the accurate tagging of places into one of the three categories: public, private, or virtual, and (ii) identifying a set of solutions thus o ering a wide range of possible applications. Based on the check-in records, we compute two types of place features from (i) speci c pa erns of individual places and (ii) latent relatedness among similar places. e features extracted from speci c pa erns (SP) are derived from all check-ins at a speci c place. e features from latent relatedness (LR) are computed by building a graph of related places where similar types of places are connected by virtual edges. We conduct an experimental study based on a dataset of over 2.7M check-in records collected by crawling Foursquare-tagged tweets from Twi er. Experimental results demonstrate the e ectiveness of our approach to this new problem and show the strength of taking various methods into account in feature extraction. Moreover, we demonstrate how place type tagging can be bene cial for place name recommendation services.

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Multimedia and geographic data integration for cultural heritage information retrieval

Purificato

Rinaldi

2018

Multimed Tools Appl

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Extracting spatial information from social media in support of agricultural management decisions

Cited by 7 publications

References 5 publications

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Place-Type Detection in Location-Based Social Networks

Multimedia and geographic data integration for cultural heritage information retrieval

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