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Information
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Big Spatial Data Management
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Big Spatial Data Management

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 17374

Special Issue Editor

Dr. Louai Alarabi

E-Mail Website
Guest Editor
Department of Computer Science, Umm Alqura University, Mecca 21955, Saudi Arabia
Interests: database; spatial computing; spatio-temporal computing; big data management; distributed computing

Special Issue Information

Dear Colleagues,

The special issue covers a wide spectrum of data types and information including (a) Raster data, e.g. Geoimages, (b) Vector data, e.g., Points, Lines, Polygons, and (c) Graph data, e.g. Road network graph. The special issue of “Big Spatial Data Management” deals with massive amounts of real-time spatial and spatio-temporal data obtained from billions of sensors, location-aware devices, remote sensing satellites, and various models of the physical world. The use of Big Spatial Data Management spans a variety of applications including crowdsourcing, social networks, earth sciences, transportation, communication networks, online maps, smart cities and urban planning, remote sensing, crisis and evacuation management, to name but a few.

List of topics:

  • Fundamentals and Theory of Big Spatial Data Management:
  • Big Spatial Data Management
  • Mining and Analysis of Big Spatial Data Management
  • Stream Processing of Big Spatial Data Management
  • Spatial Time Series Querying and Mining
  • Spatial Graph Processing
  • Descriptive, Predictive, and Prescriptive Models of Big Spatial Data Management
  • Information Privacy and Authentication of Big Spatial Data Management
  • Geosensing
  • Indexing of Big Spatial Data Management
  • Modern Hardware, High Performance, and Cloud Computing of Big Spatial Data Management
  • Visualization of Big Spatial Data Management
  • Deep Learning of Big Spatial Data Management
  • NoSQL and NewSQL Data Stores of Big Spatial Data Management
  • Data Frameworks of Big Spatial Data Management
  • Information Retrieval and Crowdsourcing of Big Spatial Data Management.
  • Geosocial Networks
  • Big Spatial Data Applications. Including, Smart Cities and Intelligent Transportation,Healthcare, Geosciences, etc.

Dr. Louai Alarabi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Database Applications
  • Spatial Information Applications
  • Spatial Database
  • Spatio-temporal System
  • Geographical Information Systems
  • Spatial Data Streaming and Processing
  • Spatial Crowdsourcing
  • Microblogs Data Management
  • Contact Tracing
  • Indoor/outdoor Trajectory

Published Papers (5 papers)

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Research

11 pages, 2498 KiB  
Article
Vehicle Routing Optimization for Non-Profit Organization Systems
by Ahmad Alhindi, Abrar Alsaidi and Amr Munshi
Information 2022, 13(8), 374; https://doi.org/10.3390/info13080374 - 4 Aug 2022
Cited by 1 | Viewed by 1439
Abstract
The distributor management system has long been a challenge for many organizations and companies. Overall, successful distribution involves several moving entities and methods, requiring a resilient distribution management strategy powered by data analysis. For nonprofit organizations, the distribution system requires efficient distribution and [...] Read more.
The distributor management system has long been a challenge for many organizations and companies. Overall, successful distribution involves several moving entities and methods, requiring a resilient distribution management strategy powered by data analysis. For nonprofit organizations, the distribution system requires efficient distribution and management. This includes minimizing time, distance, and cost. As a consequence, service quality and financial efficiency can be achieved. This paper proposes a methodology to tackle the vehicle routing problems (VRP) faced by nonprofit organizations. The methodology consists of four subsequent approaches—greedy, intraroute, interroute, and tabu search—to improve the functionality and performance of nonprofit organizations. The methodology was validated by applying it to a real nonprofit organization. Furthermore, the proposed system was compared to another state-of-the-art system; the achieved results were satisfactory and suggest that this methodology is capable of handling the VRP accordingly, improving the functionality and performance of nonprofit organizations. Full article
(This article belongs to the Special Issue Big Spatial Data Management)
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17 pages, 7461 KiB  
Article
A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
by Fahim Sufi and Musleh Alsulami
Information 2022, 13(3), 120; https://doi.org/10.3390/info13030120 - 28 Feb 2022
Cited by 7 | Viewed by 2968
Abstract
Social media platforms such as Twitter have been used by political leaders, heads of states, political parties, and their supporters to strategically influence public opinions. Leaders can post about a location, a state, a country, or even a region in their social media [...] Read more.
Social media platforms such as Twitter have been used by political leaders, heads of states, political parties, and their supporters to strategically influence public opinions. Leaders can post about a location, a state, a country, or even a region in their social media accounts, and the posts can immediately be viewed and reacted to by millions of their followers. The effect of social media posts by political leaders could be automatically measured by extracting, analyzing, and producing real-time geospatial intelligence for social scientists and researchers. This paper proposed a novel approach in automatically processing real-time social media messages of political leaders with artificial intelligence (AI)-based language detection, translation, sentiment analysis, and named entity recognition (NER). This method automatically generates geospatial and location intelligence on both ESRI ArcGIS Maps and Microsoft Bing Maps. The proposed system was deployed from 1 January 2020 to 6 February 2022 to analyze 1.5 million tweets. During this 25-month period, 95K locations were successfully identified and mapped using data of 271,885 Twitter handles. With an overall 90% precision, recall, and F1score, along with 97% accuracy, the proposed system reports the most accurate system to produce geospatial intelligence directly from live Twitter feeds of political leaders with AI. Full article
(This article belongs to the Special Issue Big Spatial Data Management)
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15 pages, 2683 KiB  
Article
Optimization of the Mashaer Shuttle-Bus Service in Hajj: Arafat-Muzdalifah Case Study
by Omar Hussain, Emad Felemban and Faizan Ur Rehman
Information 2021, 12(12), 496; https://doi.org/10.3390/info12120496 - 29 Nov 2021
Cited by 3 | Viewed by 4804
Abstract
Hajj, the fifth pillar of Islam, is held annually in the month of Dhul Al-Hijjah, the twelfth month, in the Islamic calendar. Pilgrims travel to Makkah and its neighbouring areas—Mina, Muzdalifah, and Arafat. Annually, about 2.5 million pilgrims perform spatiotemporally restricted rituals in [...] Read more.
Hajj, the fifth pillar of Islam, is held annually in the month of Dhul Al-Hijjah, the twelfth month, in the Islamic calendar. Pilgrims travel to Makkah and its neighbouring areas—Mina, Muzdalifah, and Arafat. Annually, about 2.5 million pilgrims perform spatiotemporally restricted rituals in these holy places that they must execute to fulfil the pilgrimage. These restrictions make the task of transportation in Hajj a big challenge. The shuttle bus service is an essential form of transport during Hajj due to its easy availability at all stages and ability to transport large numbers. The current shuttle service suffers from operational problems; this can be deduced from the service delays and customer dissatisfaction with the service. This study provides a system to help in planning the operation of the service for one of the Hajj Establishments to improve performance by determining the optimal number of buses and cycles required for each office in the Establishment. We will also present a case study in which the proposed model was applied to the non-Arab Africa Establishment shuttle service. At the same time, we will include the mechanism for extracting the information required in the tested model from the considerably large GPS data of 20,000+ buses in Hajj 2018. Full article
(This article belongs to the Special Issue Big Spatial Data Management)
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16 pages, 3182 KiB  
Article
Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images
by Sultan Daud Khan, Louai Alarabi and Saleh Basalamah
Information 2021, 12(6), 230; https://doi.org/10.3390/info12060230 - 28 May 2021
Cited by 24 | Viewed by 3763
Abstract
Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a [...] Read more.
Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a large number of high-resolution satellite images are readily available. However, these high-resolution satellite images are complex due to increased spatial resolution and data disruption caused by different factors involved in the acquisition process. Due to these challenges, an efficient land-cover semantic segmentation model is difficult to design and develop. In this paper, we develop a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net. This is carried out to obtain a pixel-wise classification of land cover. The contraction path of U-Net is replaced with DenseNet to extract features of multiple scales, while long-range connections of U-Net concatenate encoder and decoder paths are used to preserve low-level features. We evaluate the proposed hybrid network on a challenging, publicly available benchmark dataset. From the experimental results, we demonstrate that the proposed hybrid network exhibits a state-of-the-art performance and beats other existing models by a considerable margin. Full article
(This article belongs to the Special Issue Big Spatial Data Management)
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19 pages, 793 KiB  
Article
TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories
by Louai Alarabi, Saleh Basalamah, Abdeltawab Hendawi and Mohammed Abdalla
Information 2021, 12(5), 202; https://doi.org/10.3390/info12050202 - 6 May 2021
Cited by 10 | Viewed by 2829
Abstract
The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the transmission of infectious diseases. The [...] Read more.
The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the transmission of infectious diseases. The result of the contact tracing process is performing diagnostic tests, treating for suspected cases or self-isolation, and then treating for infected persons; this eventually results in limiting the spread of diseases. This paper proposes a technique named TraceAll that traces all contacts exposed to the infected patient and produces a list of these contacts to be considered potentially infected patients. Initially, it considers the infected patient as the querying user and starts to fetch the contacts exposed to him. Secondly, it obtains all the trajectories that belong to the objects moved nearby the querying user. Next, it investigates these trajectories by considering the social distance and exposure period to identify if these objects have become infected or not. The experimental evaluation of the proposed technique with real data sets illustrates the effectiveness of this solution. Comparative analysis experiments confirm that TraceAll outperforms baseline methods by 40% regarding the efficiency of answering contact tracing queries. Full article
(This article belongs to the Special Issue Big Spatial Data Management)
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