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Article
Publication date: 10 May 2021

Zachary Hornberger, Bruce Cox and Raymond R. Hill

Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces…

Abstract

Purpose

Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors.

Design/methodology/approach

This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering.

Findings

As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands.

Originality/value

This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.

Details

Journal of Defense Analytics and Logistics, vol. 5 no. 1
Type: Research Article
ISSN: 2399-6439

Keywords

Article
Publication date: 28 November 2023

Jiaying Chen, Cheng Li, Liyao Huang and Weimin Zheng

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep…

Abstract

Purpose

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

Design/methodology/approach

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

Findings

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

Practical implications

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

Originality/value

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

目的

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

设计/方法/途径

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成:时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

研究结果

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

实践意义

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

原创性/价值

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

Objetivo

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Diseño/metodología/enfoque

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Conclusiones

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

Implicaciones prácticas

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Originalidad/valor

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

Article
Publication date: 3 April 2019

Saffet Erdoğan and Abdulkadir Memduhoğlu

The purpose of this paper is to examine the real estate sales in Turkey on a district basis to reveal the current state of real estate sales and any meaningful changes in the last…

Abstract

Purpose

The purpose of this paper is to examine the real estate sales in Turkey on a district basis to reveal the current state of real estate sales and any meaningful changes in the last period. The real estate market is important and is an indicator of the country’s general economic health, as real estate is seen as an investment.

Design/methodology/approach

As a powerful method of spatial analysis and evaluation, geographic information systems have been used to examine real estate data in both spatial and temporal ways. In this study, 14 years of sales data covering the years 2004 to 2017 obtained from government agencies on a district basis were evaluated using spatiotemporal methods. Several maps were produced using Getis-Ord Gi* and local Moran’s I indices, which showed the spatiotemporal change of sales and sales rates.

Findings

When looking at the maps, provinces such as Istanbul, Ankara, Izmir, Antalya and their surrounding districts have buoyant real estate markets compared to the other side of the country. Real estate sales are more stagnant in the eastern and northern parts of the country. In addition, the authors found that the growth rate of annual average real estate sales was approximately seven times higher than the annual average population growth.

Originality/value

This spatiotemporal study, which presents 14 years of performance data of the real estate market and, by extension, the economic situation, also highlights the regions that stand out for investment planning throughout the country. The results of spatiotemporal analysis also present a new way of real estate market visualization using maps with well-designed categorizations.

Details

Journal of European Real Estate Research, vol. 12 no. 2
Type: Research Article
ISSN: 1753-9269

Keywords

Article
Publication date: 16 August 2022

Liyao Huang, Cheng Li and Weimin Zheng

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors…

Abstract

Purpose

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

Design/methodology/approach

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

Findings

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

Practical implications

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

Originality/value

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

Details

International Journal of Contemporary Hospitality Management, vol. 35 no. 1
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 31 January 2024

Tan Zhang, Zhanying Huang, Ming Lu, Jiawei Gu and Yanxue Wang

Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on…

Abstract

Purpose

Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features.

Design/methodology/approach

The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Findings

Through these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery.

Originality/value

The authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Details

Industrial Lubrication and Tribology, vol. 76 no. 2
Type: Research Article
ISSN: 0036-8792

Keywords

Article
Publication date: 29 August 2023

Qingqing Li, Ziming Zeng, Shouqiang Sun, Chen Cheng and Yingqi Zeng

The paper aims to construct a spatiotemporal situational awareness framework to sense the evolutionary situation of public opinion in social media, thus assisting relevant…

Abstract

Purpose

The paper aims to construct a spatiotemporal situational awareness framework to sense the evolutionary situation of public opinion in social media, thus assisting relevant departments in formulating public opinion control measures for specific time and space contexts.

Design/methodology/approach

The spatiotemporal situational awareness framework comprises situational element extraction, situational understanding and situational projection. In situational element extraction, the data on the COVID-19 vaccine, including spatiotemporal tags and text contents, is extracted. In situational understanding, the bidirectional encoder representation from transformers – latent dirichlet allocation (BERT-LDA) and bidirectional encoder representation from transformers – bidirectional long short-term memory (BERT-BiLSTM) are used to discover the topics and emotional labels hidden in opinion texts. In situational projection, the situational evolution characteristics and patterns of online public opinion are uncovered from the perspective of time and space through multiple visualisation techniques.

Findings

From the temporal perspective, the evolution of online public opinion is closely related to the developmental dynamics of offline events. In comparison, public views and attitudes are more complex and diversified during the outbreak and diffusion periods. From the spatial perspective, the netizens in hotspot areas with higher discussion volume are more rational and prefer to track the whole process of event development, while the ones in coldspot areas with less discussion volume pay more attention to the expression of personal emotions. From the perspective of intertwined spatiotemporal, there are differences in the focus of attention and emotional state of netizens in different regions and time stages, caused by the specific situations they are in.

Originality/value

The situational awareness framework can shed light on the dynamic evolution of online public opinion from a multidimensional perspective, including temporal, spatial and spatiotemporal perspectives. It enables decision-makers to grasp the psychology and behavioural patterns of the public in different regions and time stages and provide targeted public opinion guidance measures and offline event governance strategies.

Details

The Electronic Library , vol. 41 no. 5
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 8 September 2023

Xiancheng Ou, Yuting Chen, Siwei Zhou and Jiandong Shi

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the…

Abstract

Purpose

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

Design/methodology/approach

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Findings

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

Research limitations/implications

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

Originality/value

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

Details

International Journal of Web Information Systems, vol. 19 no. 5/6
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 23 July 2020

James R. DeLisle, Terry V. Grissom and Brent Never

The purpose of this study is to explore spatiotemporal factors that affect the empirical analysis of whether crime rates in buffer areas surrounding abandoned properties…

Abstract

Purpose

The purpose of this study is to explore spatiotemporal factors that affect the empirical analysis of whether crime rates in buffer areas surrounding abandoned properties transferred to a Land Bank that differed among three regimes: before transfer, during Land Bank stewardship and after disposition and whether those differences were associated with differences in relative crime activity in the neighborhoods in which they were located.

Design/methodology/approach

This study analyzed crime incidents occurring between 2010 and 2018 in 0.1-mile buffer areas surrounding 31 abandoned properties sold by the Land Bank and their neighborhoods in which those properties were located. Using Copulas, researchers compared concordance/discordance in the buffer areas across the three regime states for each property and approximately matched time periods for associated neighborhoods.

Findings

In a substantial number of cases, the relative crime activity levels for buffer areas surrounding individual sold properties as measured by the Copulas shifted from concordant to discordant states and vice versa. Similarly, relative crime activity levels for neighborhoods shifted from concordant to discordant states across three matched regimes. In some cases, the property and neighborhood states matched, while in other cases they diverged. These cross-level interactions indicate that criminal behavioral patterns and target selection change over time and relative criminal activity. The introduction of Copulas can improve the reliability of such models over time and when and where they should be customized to add more granular insights needed by law enforcement agencies.

Research limitations/implications

The introduction of Copulas can improve the spatiotemporal reliability of the analysis of criminal activity over space and time.

Practical implications

Spatiotemporal considerations should be incorporated in setting interventions to manage criminal activity.

Social implications

This study provides support for policies supporting renovation of abandoned properties.

Originality/value

To the best of authors’ knowledge, this research is the first application of Copulas to crime impact studies. As noted, Copulas can help reduce the risk of applying intervention or enforcement programs that are no longer reliable or lack the precision provided by insights into convergent/divergent patterns of criminal activity.

Details

Journal of European Real Estate Research, vol. 15 no. 1
Type: Research Article
ISSN: 1753-9269

Keywords

Article
Publication date: 18 June 2019

Maryam Shafiei Sabet, Ali Asgary and Adriano O. Solis

Responding to emergency incidents by emergency response organizations such as fire, ambulance and police during large disaster and emergency events is very important. The purpose…

Abstract

Purpose

Responding to emergency incidents by emergency response organizations such as fire, ambulance and police during large disaster and emergency events is very important. The purpose of this paper is to provide some insights into response patterns during the 2013 ice storm in the city of Vaughan, Ontario, Canada, using temporal and spatial analyses.

Design/methodology/approach

The City of Vaughan Fire and Rescue Service data set containing all responses to fire and other emergency incidents from January 1, 2009 to December 31, 2016 was used. The 2013 Southern Ontario ice storm occurred from December 20, 2013 to January 1, 2014, and, for this study, December 20–31 is considered the “study period.” Temporal, spatial and spatiotemporal analyses of responses during the study period are carried out and are compared with the same period in other years (2009–2012 and 2014–2016).

Findings

The findings show that temporal patterns of response attributes changed significantly during the 2013 ice storm. Similarly, the spatial pattern of responses during the 2013 ice storm showed some major differences with other years. The spatiotemporal analyses also demonstrate significant variations in responses in the city during different hours of the day in the ice storm days.

Originality/value

This study is the first study to examine the spatiotemporal patterns of responses made by a fire department during the 2013 ice storm in Canada. It provides some insights into the differences between response volumes, temporal and spatial distributions during large emergency events (e.g. ice storm) and normal situations. The results will help in mitigating the number of responses in the future through public education and technological changes. Moreover, the results will provide fire departments with information that could help them prepare for such events by possible reallocation of resources.

Details

International Journal of Emergency Services, vol. 8 no. 3
Type: Research Article
ISSN: 2047-0894

Keywords

Abstract

Details

Freight Transport Modelling
Type: Book
ISBN: 978-1-78190-286-8

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