Table of Links Abstract and 1 Introduction 1.1. Spatial Digital Twins (SDTs) 1.2. Applications 1.3. Different Components of SDTs 1.4. Scope of This Work and Contributions 2. Related Work and 2.1. Digital Twins and Variants 2.2. Spatial Digital Twin Case Studies 3. Building Blocks of Spatial Digital Twins and 3.1. Data Acquisition and Processing 3.2. Data Modeling, Storage and Management 3.3. Big Data Analytics System 3.4. Maps and GIS Based Middleware 3.5. Key Functional Components 4. Other Relevant Modern Technologies and 4.1. AI & ML 4.2. Blockchain 4.3. Cloud Computing 5. Challenges and Future Work, and 5.1. Multi-modal and Multi-resolution Data Acquisition 5.2. NLP for Spatial Queries and 5.3. Benchmarking the Databases and Big Data Platform for SDT 5.4. Automated Spatial Insights and 5.5. Multi-modal Analysis 5.6. Building Simulation Environment 5.7. Visualizing Complex and Diverse Interactions 5.8. Mitigating the Security and Privacy Concerns 6. Conclusion and References 5.8. Mitigating the Security and Privacy Concerns Researchers have emphasised on the importance of integrating security and privacy solutions in digital twins to ensure the quality of services like providing data-driven decisions [104, 105]. Reliability, trust, transparency, integrity, authenticity, anonymity and selective disclosure are some of the security and privacy aspects that need attention to make DTs a success. The security and privacy challenges that have been identified for DTs in general also apply for SDTs. Besides, the use of location data imposes additional security and privacy concerns for the SDTs as location data may act as an identifier of an individual and may reveal sensitive information [106, 107]. For example, although mobility data of an individual allows an SDT to monitor traffic in indoor and outdoor spaces or contact tracing [108], continuous sharing of locations of an individual will allow others to know the individual’s movement trajectory and infer the places visited by the individual. If a place represents an individual’s office then the individual would be identified, and if a place is a liver clinic then it may reveal the individual’s sensitive health information. Researchers have developed techniques like obfuscation, k-anonymity and space transformation that trad-off between utility and privacy of location data. There are a few works [109, 110, 111] that proposed countermeasures to prevent security and privacy attacks for DTs. In these works, blockchain technology [109], federated learning models [111], and cryptographic protocols [110] have been shown as techniques to address various security and privacy concerns. Considering that location data may raise new threats, future research should focus on identifying new security and privacy attacks, designing solutions to protect them, and investigating the applicability of existing security and privacy solutions for SDTs. 6. Conclusion In this paper, a thorough and organized collection of spatial technologies is presented, forming the fundamental components of Spatial Digital Twins (SDTs). The building blocks of SDTs are categorized into four layers, and a comprehensive overview of key spatial technologies is provided for each layer, covering aspects such as data acquisition, processing, storage, and visualization. We have also presented how modern technologies like AI &ML, blockchain, and cloud computing can facilitate more efficient and effective SDTs. It is important to note that there is currently no existing study that specifically focuses on identifying these crucial technologies essential for the development of SDTs. 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Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1. Spatial Digital Twins (SDTs) 1.1. Spatial Digital Twins (SDTs) 1.2. Applications 1.2. Applications 1.3. Different Components of SDTs 1.3. Different Components of SDTs 1.4. Scope of This Work and Contributions 1.4. Scope of This Work and Contributions 2. Related Work and 2.1. Digital Twins and Variants 2. Related Work and 2.1. Digital Twins and Variants 2.2. Spatial Digital Twin Case Studies 2.2. Spatial Digital Twin Case Studies 3. Building Blocks of Spatial Digital Twins and 3.1. Data Acquisition and Processing 3. Building Blocks of Spatial Digital Twins and 3.1. Data Acquisition and Processing 3.2. Data Modeling, Storage and Management 3.2. Data Modeling, Storage and Management 3.3. Big Data Analytics System 3.3. Big Data Analytics System 3.4. Maps and GIS Based Middleware 3.4. Maps and GIS Based Middleware 3.5. Key Functional Components 3.5. Key Functional Components 4. Other Relevant Modern Technologies and 4.1. AI & ML 4. Other Relevant Modern Technologies and 4.1. AI & ML 4.2. Blockchain 4.2. Blockchain 4.3. Cloud Computing 4.3. Cloud Computing 5. Challenges and Future Work, and 5.1. Multi-modal and Multi-resolution Data Acquisition 5. Challenges and Future Work, and 5.1. Multi-modal and Multi-resolution Data Acquisition 5.2. NLP for Spatial Queries and 5.3. Benchmarking the Databases and Big Data Platform for SDT 5.2. NLP for Spatial Queries and 5.3. Benchmarking the Databases and Big Data Platform for SDT 5.4. Automated Spatial Insights and 5.5. Multi-modal Analysis 5.4. Automated Spatial Insights and 5.5. Multi-modal Analysis 5.6. Building Simulation Environment 5.6. Building Simulation Environment 5.7. Visualizing Complex and Diverse Interactions 5.7. Visualizing Complex and Diverse Interactions 5.8. Mitigating the Security and Privacy Concerns 5.8. Mitigating the Security and Privacy Concerns 6. Conclusion and References 6. Conclusion and References 5.8. Mitigating the Security and Privacy Concerns Researchers have emphasised on the importance of integrating security and privacy solutions in digital twins to ensure the quality of services like providing data-driven decisions [104, 105]. Reliability, trust, transparency, integrity, authenticity, anonymity and selective disclosure are some of the security and privacy aspects that need attention to make DTs a success. The security and privacy challenges that have been identified for DTs in general also apply for SDTs. Besides, the use of location data imposes additional security and privacy concerns for the SDTs as location data may act as an identifier of an individual and may reveal sensitive information [106, 107]. For example, although mobility data of an individual allows an SDT to monitor traffic in indoor and outdoor spaces or contact tracing [108], continuous sharing of locations of an individual will allow others to know the individual’s movement trajectory and infer the places visited by the individual. If a place represents an individual’s office then the individual would be identified, and if a place is a liver clinic then it may reveal the individual’s sensitive health information. Researchers have developed techniques like obfuscation, k-anonymity and space transformation that trad-off between utility and privacy of location data. There are a few works [109, 110, 111] that proposed countermeasures to prevent security and privacy attacks for DTs. In these works, blockchain technology [109], federated learning models [111], and cryptographic protocols [110] have been shown as techniques to address various security and privacy concerns. Considering that location data may raise new threats, future research should focus on identifying new security and privacy attacks, designing solutions to protect them, and investigating the applicability of existing security and privacy solutions for SDTs. 6. Conclusion In this paper, a thorough and organized collection of spatial technologies is presented, forming the fundamental components of Spatial Digital Twins (SDTs). The building blocks of SDTs are categorized into four layers, and a comprehensive overview of key spatial technologies is provided for each layer, covering aspects such as data acquisition, processing, storage, and visualization. We have also presented how modern technologies like AI &ML, blockchain, and cloud computing can facilitate more efficient and effective SDTs. It is important to note that there is currently no existing study that specifically focuses on identifying these crucial technologies essential for the development of SDTs. 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Authors: Authors: (1) Mohammed Eunus Ali, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (2) Muhammad Aamir Cheema, Faculty of Information Technology, Monash University, 20 Exhibition Walk, Clayton, 3164, VIC, Australia; (3) Tanzima Hashem, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (4) Anwaar Ulhaq, School of Computing, Charles Sturt University, Port Macquarie, 2444, NSW, Australia; (5) Muhammad Ali Babar, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv
