Dynamic Land-Use Map Based on Twitter Data

Yuyun,; Nuzir, Fritz Akhmad; Dewancker, Bart

doi:10.3390/su9122158

Cited by 14 publications

(7 citation statements)

References 27 publications

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“…Tu et al [22] and Cao et al [23] couple mobile phone data and social media check-in data to infer urban land function zones. Yuyun et al [24] use Twitter data to acquire dynamic land use map. Tu et al [25] and Liu et al [26] demonstrate that public transport mobility data also implies urban land use variation.…”

Section: Land Use and Land Cover Classificationmentioning

confidence: 99%

Integrating Aerial and Street View Images for Urban Land Use Classification

et al. 2018

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Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.

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Section: Land Use and Land Cover Classificationmentioning

confidence: 99%

Integrating Aerial and Street View Images for Urban Land Use Classification

et al. 2018

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“…In addition, social media data is also adopted to characterize urban region functions. Yuyan et al [28] utilized social media data to generate dynamic functional land use map. Pei et al [13] utilized mobile phone data to classify urban land use.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Modal Fusion Network Guided by Feature Co-Occurrence for Urban Region Function Recognition

ZHANG

WANG

et al. 2022

IEICE Trans. Inf. & Syst.

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Recently, multi-modal fusion methods based on remote sensing data and social sensing data have been widely used in the field of urban region function recognition. However, due to the high complexity of noise problem, most of the existing methods are not robust enough when applied in real-world scenes, which seriously affect their application value in urban planning and management. In addition, how to extract valuable periodic feature from social sensing data still needs to be further study. To this end, we propose a multi-modal fusion network guided by feature cooccurrence for urban region function recognition, which leverages the cooccurrence relationship between multi-modal features to identify abnormal noise feature, so as to guide the fusion network to suppress noise feature and focus on clean feature. Furthermore, we employ a graph convolutional network that incorporates node weighting layer and interactive update layer to effectively extract valuable periodic feature from social sensing data. Lastly, experimental results on public available datasets indicate that our proposed method yeilds promising improvements of both accuracy and robustness over several state-of-the-art methods.

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“…Pan and Yang (2017) analyzed search engine results, website traffic and weekly weather forecast to predict short-time occupancy of hotels. Yuyun et al (2017) analyzed social networking check-ins, timestamps and users' status text or post activities to characterize dynamic urban land use. Kantarci et al (2017) 2016) analyzed tourists' opinions about destinations and tourism services that affect reservation decisions of potential tourists by using sentiment analysis of Big Data.…”

Section: Literature Review Of Big Data In Hospitality Industrymentioning

confidence: 99%

On modeling of responses generated by travel 2.0 implementation: fuzzy rule-based systems

Başaran

Doğan

Kantarcı

2020

IJCHM

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Purpose Web 2.0 applications enable travelers to evaluate several services and assessment attributes. Constructed websites in several languages trigger a new way of data collections resulting in data streams leading to the accumulation of vast amounts of data, called big data. The need for analysis is in high demand. This study aims to construct a model to investigate which single attribute or interrelated ones having an impact on the performances of hotels. Design/methodology/approach The total number of 1,137 observations collected from the website HolidayCheck.de are used from the hotels in the Bavaria region in 2016. Bavaria is a region where both domestic and foreign travelers mostly prefer to visit. Fuzzy rule-based systems, which is a combination of fuzzy set theory (FST) and fuzzy logic, are used. Although the FST is used to convert linguistically expressed perceptions by travelers into mathematically usable data, fuzzy logic is used to construct a model between service attributes and price-performance (PP) to attain the set of single and interrelated attributes on the assessment of PP. Findings No single attribute plays a key role in PP assessment. However, two or more interrelated combinations have different impacts on PP. For example, when “Food—Drink” and “Room” moves together from average to good level, PP reaches the highest level of assessment. Research limitations/implications Accessibility to too much data is difficult. Practical implications A model can be continuously run so that any changes can be observed during the incoming of data. Social implications As the consumer reviews and ratings are the crucial source of information for other travelers, hoteliers must monitor and respond them on time in order to deal with the complaints. Originality/value Travelers’ perceptions or evaluations are treated with a FST that measures the impression of human beings. New modeling enables researchers to observe not only any single attribute but also interrelated ones on the PP.

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Dynamic Land-Use Map Based on Twitter Data

Cited by 14 publications

References 27 publications

Integrating Aerial and Street View Images for Urban Land Use Classification

Integrating Aerial and Street View Images for Urban Land Use Classification

A Multi-Modal Fusion Network Guided by Feature Co-Occurrence for Urban Region Function Recognition

On modeling of responses generated by travel 2.0 implementation: fuzzy rule-based systems

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