scholarly journals Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing

2020 ◽  
Vol 12 (22) ◽  
pp. 9662 ◽  
Author(s):  
Disheng Yi ◽  
Yusi Liu ◽  
Jiahui Qin ◽  
Jing Zhang
Keyword(s):  
Traffic Management ◽  
Spatial Clustering ◽  
Spatial Interaction ◽  
The Other ◽  
Temporal Data ◽  
Trajectory Data ◽  
Data Field ◽  
Spatio Temporal ◽  
The Impact

Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

scholarly journals Stochastic Fuzzy Algorithms for Impairment of Assets Management

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Marcel Ioan Bolos ◽  
Victoria Bogdan ◽  
Ioana Alexandra Bradea ◽  
Claudia Diana Sabau Popa ◽  
Dorina Nicoleta Popa
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Fuzzy Numbers ◽  
Cash Flows ◽  
The Other ◽  
Dual Purpose ◽  
Fuzzy Algorithms ◽  
Tangible Assets ◽  
The Impact

The present paper aims to analyze the impairment of tangible assets with the help of artificial intelligence. Stochastic fuzzy numbers have been introduced with a dual purpose: on one hand to estimate the cash flows generated by tangible assets exploitation and, on the other hand, to ensure the value ranges stratifications that define these cash flows. Estimation of cash flows using stochastic fuzzy numbers was based on cash flows generated by tangible assets in previous periods of operation. Also, based on the Lagrange multipliers, were introduced: the objective function of minimizing the standard deviations from the recorded value of the cash flows generated by the tangible assets, as well as the constraints caused by the impairment of tangible assets identification according to which the cash flows values must be equal to the annual value of the invested capital. Within the determination of the impairment value and stratification of the value ranges determined by the cash flows using stochastic fuzzy numbers, the impairment of assets risk was identified. Information provided by impairment of assets but also the impairment risks, is the basis of the decision-making measures taken to mitigate the impact of accumulated impairment losses on company’s financial performance.

scholarly journals Spatio-Temporal Change Characteristics of Spatial-Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China

2019 ◽  
Vol 8 (6) ◽  
pp. 273 ◽  
Author(s):  
Jing Yang ◽  
Disheng Yi ◽  
Bowen Qiao ◽  
Jing Zhang
Keyword(s):  
Urban Areas ◽  
Traffic Management ◽  
Spatial Interaction ◽  
Small World ◽  
Core Layer ◽  
Vital Role ◽  
Interaction Networks ◽  
Ring Road ◽  
The Core ◽  
Spatio Temporal

Spatial-interaction networks are an important factor in geography that could help in the exploration of both human spatial-temporal behavior and the structure of urban areas. This paper analyzes changes in the spatio-temporal characteristics of the Spatial-Interaction Networks of Beijing (SINB) in three consecutive steps. To begin with, we constructed 24 sequential snapshots of spatial population interactions on the basis of points of interest (POIs) collected from Dianping.com and various taxi GPS data in Beijing. Then, we used Jensen–Shannon distance and hierarchical clustering to integrate the 24 sequential network snapshots into four clusters. Finally, we improved the weighted k-core decomposition method by combining the complex network method and weighted distance in a geographic space. The results showed: (1) There are three layers in the SINB: a core layer, a bridge layer, and a periphery layer. The number of places greatly varies, and the SINB show an obvious hierarchical structure at different periods. The core layer contains fewer places that are between the Second and Fifth Ring Road in Beijing. Moreover, spatial distribution of places in the bridge layer is always in the same location as that of the core layer, and the quantity in the bridge layer is always superior to that in the core layer. The distributions of places in the periphery layer, however, are much greater and wider than the other two layers. (2) The SINB connected compactly over time, bearing much resemblance to a small-world network. (3) Two patterns of connection, each with different connecting ratios between layers, appear on weekdays and weekends, respectively. Our research plays a vital role in understanding urban spatial heterogeneity, and helps to support decisions in urban planning and traffic management.

scholarly journals Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns

2018 ◽  
Author(s):  
Ali Zonoozi ◽  
Jung-jae Kim ◽  
Xiao-Li Li ◽  
Gao Cong
Keyword(s):  
Traffic Management ◽  
State Of The Art ◽  
Recurrent Network ◽  
Temporal Data ◽  
Periodic Patterns ◽  
Temporal Correlations ◽  
Density Prediction ◽  
Crowd Density ◽  
Spatial Domains ◽  
Spatio Temporal

Time-series forecasting in geo-spatial domains has important applications, including urban planning, traffic management and behavioral analysis. We observed recurring periodic patterns in some spatio-temporal data, which were not considered explicitly by previous non-linear works. To address this lack, we propose novel `Periodic-CRN' (PCRN) method, which adapts convolutional recurrent network (CRN) to accurately capture spatial and temporal correlations, learns and incorporates explicit periodic representations, and can be optimized with multi-step ahead prediction. We show that PCRN consistently outperforms the state-of-the-art methods for crowd density prediction across two taxi datasets from Beijing and Singapore.

scholarly journals Monitoring of the ice caps area changes on Galindez, Winter and Skua Islands (Argentine Islands, West Antarctica)

2020 ◽  
pp. 42-49
Author(s):  
І. Savchyn ◽  
◽  
Ye. Shylo ◽  
Keyword(s):  
Data Sets ◽  
Temporal Data ◽  
Single System ◽  
West Antarctica ◽  
Linear Rate ◽  
Ice Caps ◽  
Basic Parameters ◽  
Spatio Temporal ◽  
The Antarctic

Due to global warming, the glaciers and ice systems of Antarctica and the Antarctic Peninsula have been significantly changing in shape and size in recent decades. Therefore, to control, forecast and prevent such processes, it is necessary to constantly monitor and analyse changes in the basic parameters of glaciers and ice systems. This paper proposes a study of changes in the area of ice caps located on the Galindez, Winter and Skua islands (Argentine Islands, West Antarctica). The study is based on the integration of different spatio-temporal datasets into a single system for retrospective geographical monitoring of changes in the area of Galindez, Winter and Skua islands ice caps. The system for integrating space-time datasets is the UTM coordinate system (zone 20, South). Using transformed archival cartographic materials, as well as recently obtained orthophotos, the boundaries of the glaciers in different periods of research were digitized. Based on the identified boundaries, the significance of changes in the area of island glaciers, as well as the rate of their change during 1935–2019, were determined. Based on the integration of different spatio-temporal datasets into a single system, retrospective-geographical monitoring of changes in icecaps area during 1935–2019 was performed. The ice caps were found to be experiencing systematic decrease in area with average linear rate of decrease from –0.30%/year to –0.37%/year. A detailed analysis of all integrated spatio-temporal data sets including determination of the cause of changes in the area of the Galindez, Winter and Skua islands' ice caps is a promising topic for further research.

scholarly journals A Fuzzy Best Worst approach to the determination of the importance level of digital supply chain on sustainability

2021 ◽  
Vol 9 (4) ◽  
pp. 1366-1379
Author(s):  
Kevser Arman ◽  
Arzu Organ
Keyword(s):  
Supply Chain ◽  
Direct Relationship ◽  
Digital Technologies ◽  
The Other ◽  
Social Dimension ◽  
Other Hand ◽  
The Impact ◽  
Day By Day ◽  
Best Worst Method

In today's world where the importance of digitalization is increasing day by day, companies to increase their competitiveness have focused on digital supply chain instead of traditional supply chain. In a world where resources are constantly decreasing, the concept of sustainability has become very crucial in every part of life. Digital technologies, on the other hand, have a direct relationship with sustainability. Sustainability has three main dimensions: economic, environmental, and social. Therefore, the aim of this study is to evaluate digital supply chain on 3 basic dimensions of sustainability. For this purpose, Fuzzy Best Worst Method (F-BWM) was used to define the importance level of criteria. Findings reveal that the concept of sustainability in textile firms in Turkey is generally perceived within an economic and environmental area, rather than within a social dimension. This study is very important in putting forward digital technologies which utilizing in supply chain and the impact of the digital supply chain on sustainability.

scholarly journals MODELING OF THE OPTIMAL DISTRIBUTION OF MOTORWAY OVERPASSES ON THE EXAMPLE OF THE A4 MOTORWAY SECTION

2018 ◽  
Author(s):  
Stanisław BACIOR ◽  
Barbara PRUS ◽  
Małgorzata DUDZIŃSKA
Keyword(s):  
Agricultural Production ◽  
Agricultural Land ◽  
Negative Impact ◽  
The Other ◽  
Toxic Impact ◽  
Innovative Method ◽  
Alternative Location ◽  
Under Construction ◽  
The Impact

The developed, innovative method of estimating the impact of motorway on agricultural land allows determination of all the losses associated with the directions of this impact. The basis for the determination of losses is the analysis of variability in land use and the quality classes and location of access roads to the land along the axis of the planned motorway. The approved measure of the multidirectional impact of the motorway on agricultural land is a change in the value of land, which is designated taking into account the differentiation of their suitability for agricultural production. The developed method of determining the impact of motorway on agricultural land was presented on the example of A4 motorway section between Bratkowice and Mrowla. The existing section of motorway was assessed and then for the same section the calculations were made again, but with an alternative location of the motorway overpasses. In the case of the existing section, the construction of one kilometer of the section of motorway under consideration will result in a reduction in the value of agricultural land of 1725 cereal units. Acquisition of land for the construction of the motorway and its negative impact cover about 69% of the total reduction in value of agricultural land. The remaining 31% of the land value reduction is related to the increase in transport and the deterioration of the parcels layout. On the other hand, in the case of the section with alternative arrangement of viaducts, the value of agricultural land is reduced of 1538 cereal units. Acquisition of land under construction and its toxic impact will be equal 75%, while the combined effect of transport growth and deterioration of the layout makes 25%.

scholarly journals The Visualization Representation of Space-Time-Path in The Space-Time-Cube

2021 ◽  
Vol 906 (1) ◽  
pp. 012030
Author(s):  
Bingbing Song ◽  
Yanlin Wang ◽  
Fang Li
Keyword(s):  
Three Dimensional ◽  
Temporal Changes ◽  
Space Time ◽  
Temporal Data ◽  
Processing Technologies ◽  
Spatial Object ◽  
Time Path ◽  
Visual Clutter ◽  
Spatio Temporal ◽  
The Impact

Abstract Map is a traditional visualization tool to represent distribution and interaction of spatial objects or spatial phenomenon. However, with the continuous development of acquisition and processing technologies for spatio-temporal data, traditional map can hardly meet the visualization requirement for this type of data. In other words, the dynamic information about spatial object or phenomenon cannot be expressed fully by traditional map. The Space-Time-Cube (STC), as a three-dimensional visualization environment, whose base represents the two-dimensional geographical space and whose height represents the temporal dimension, can simultaneously represent the spatial distribution as well as the temporal changes of spatio-temporal data. For some spatial object or phenomenon, its moving trajectory can be visualized in STC as a Space-Time-Path (STP), by which the speed and state of motion can be clearly reflected. Noticeably, the problem of visual clutter about STP is inevitably due to the complexity of three-dimensional visualization. In order to reduce the impact of visual clutter, this paper discusses different aspects about visualization representation of STP in the STC. The multiple scales representation and the multiple views display can promote interactive experience of users, and the application of different visual variables can help to represent different kinds of attribute information of STP. With the visualization of STP, spatio-temporal changes and attributive characters of spatial object or phenomenon can be represented and analysed.

Moving Beyond Traditional Decision Support Systems

2017 ◽  
Vol 8 (2) ◽  
pp. 74-87
Author(s):  
Noura Azaiez ◽  
Jalel Akaichi ◽  
Jeffrey Hsu
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Moving Objects ◽  
Temporal Data ◽  
Trajectory Data ◽  
Reduced Costs ◽  
Spatio Temporal ◽  
Traditional Approaches ◽  
Dairy Milk ◽  
Made In

Integrating the concept of mobility into the professional and organizational realm offers the possibility of reducing geographical disparities related to organization services. The advances made in technology, geographic information systems and pervasive systems equipped with global positioning (GPS) technologies have been able to bring about an evolution from classic data approaches towards the modeling of trajectory data resulting from moving activities of moving objects. As such, trajectory data needs first to be loaded into a Data Warehouse for analysis purposes. However, the traditional approaches used are poorly suited to handle spatio-temporal data features and also the decision making tasks related to mobility issues. Because of this mismatch, the authors propose to move beyond traditional approaches and propose a repository that is able to analyse trajectories of moving objects. Improving decision making and extracting pertinent knowledge with reduced costs and time expended are the main goals of this revised analysis approach. Thus, the authors propose an approach in which they employ the Bottom-up approach to modeling a Decision Support System which is designed to support Trajectory Data. As an example to illustrate this approach, the authors use a creamery and dairy milk mobile cistern application to demonstrate the effectiveness of their approach.

scholarly journals Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 182
Author(s):  
Elias Dritsas ◽  
Andreas Kanavos ◽  
Maria Trigka ◽  
Gerasimos Vonitsanos ◽  
Spyros Sioutas ◽  
...  
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Privacy Preservation ◽  
Nearest Neighbor ◽  
Data Representation ◽  
Privacy Preserving ◽  
Temporal Data ◽  
K Nearest Neighbor ◽  
Trajectory Data ◽  
Spatio Temporal

Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection. The k-anonymity is considered a key solution that has been widely employed to prevent data re-identifcation and concerns us in the context of this work. Data modeling has also gained significant attention from the big data perspective. It is believed that the advancing distributed environments will provide users with several solutions for efficient spatio-temporal data management. GeoSpark will be utilized in the current work as it is a key solution that has been widely employed for spatial data. Specifically, it works on the top of Apache Spark, the main framework leveraged from the research community and organizations for big data transformation, processing and visualization. To this end, we focused on trajectory data representation so as to be applicable to the GeoSpark environment, and a GeoSpark-based approach is designed for the efficient management of real spatio-temporal data. Th next step is to gain deeper understanding of the data through the application of k nearest neighbor (k-NN) queries either using indexing methods or otherwise. The k-anonymity set computation, which is the main component for privacy preservation evaluation and the main issue of our previous works, is evaluated in the GeoSpark environment. More to the point, the focus here is on the time cost of k-anonymity set computation along with vulnerability measurement. The extracted results are presented into tables and figures for visual inspection.

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