Computing and Information Systems - Theses

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    Straggler Mitigation in Distributed Behavioural Simulations
    Bin Khunayn, Eman Abdulaziz A ( 2021)
    Behavioural simulation (B-SIM) has been widely used to understand real-world phenomena. Running such a large-scale simulation requires high computational power, which can be provided through parallel distributed computing. To preserve the correct behaviour in the system, the implementations follow the bulk synchronous parallel (BSP) computational model. The processing in BSP is divided into iterations running on multiple machines/workers, followed by a global barrier synchronisation. Unfortunately, the BSP model is plagued by the straggler problem, where a delay in any process at any iteration leads to a slowdown in the entire simulation. Stragglers may occur due to many reasons, including imbalanced workload distribution or communication and synchronisation delays. Moreover, this problem is further exacerbated by the system scaling up. Industry and academia have been working on addressing the straggler problem in distributed systems for a long time. Though mitigating the straggler effect in distributed B-SIM is very challenging, it is fundamental to improving the system performance, cost, and utilisation. This thesis presents straggler mitigation techniques in distributed B-SIM. It focuses on reducing the stragglers’ effect caused by computation, communication, and synchronisation delays in the BSP model that run on shared-nothing architectures. This thesis proposes novel techniques to mitigate the effect of computation and communication stragglers caused by computation and communication delays at the application level. The thesis makes the following key contributions to maximise B-SIM performance: - A GridGraph load balancing and partitioning method that reduces the computation stragglers by balancing the workload distribution in B-SIM among multiple workers. - The Priority Synchronous Parallel (PSP) model, a novel parallel computational model that exploits data dependencies to reduce the communication straggler’s effect. - The Priority Asynchronous Parallel (PAP) computational model that utilises the data dependencies further to reduce computation in addition to the communication straggler’s effect. - Neighbourhood Diffusion (NDiff), a dynamic load balancing (DLB) algorithm to dynamically mitigate computation stragglers caused by imbalanced workload distribution during the runtime. - The On-demand Dynamic Resource Allocation (On-demand DRA) method that dynamically allocates and releases computing resources (workers) to mitigate computation stragglers caused by highly imbalanced workload distribution. All proposed algorithms are implemented and evaluated using a microscopic traffic simulator, SMARTS [1] as an example of a B-SIM running a traffic simulation of three big cities—Melbourne, Beijing, and New York—with different traffic and straggler scenarios.
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    Traffic Optimization in The Era of Connected Autonomous Vehicles
    Motallebi, Sadegh ( 2021)
    Traffic optimization, especially at large scale, is an important and challenging problem due to the highly dynamic and unpredictable nature of traffic. The incoming era of connected autonomous vehicles (CAVs) offers a great opportunity for traffic optimization. The routes, which are followed by CAVs, can be assigned by a centralized system with better predictability. By collecting highly detailed real-time traffic data from sensors and CAVs, a traffic management system will have the full view of the entire road network, allowing it to plan traffic in a virtual world that replicates the real road network very closely. This thesis presents the problem of route assignment for CAVs, with the aim of optimizing traffic at network-level. We formulate the research problem as a streaming route assignment problem and address the intersecting routes which is a key reason causing delays and long queues at junctions. We focus on finding road network routes with few intersections for vehicles. We propose two route assignment algorithms to solve the problem, Local Detour Algorithm (LDA) and Multiple Intersection Reduction Algorithm (MIRA). LDA uses traffic information at road links or road junctions during route allocation, while MIRA enlarges the scope of traffic information to have a big picture of traffic conditions. LDA is an efficient algorithm due to searching detours in a small area around congested junctions. On the other hand, MIRA is more complicated as it searches for longer detours from not only congested junctions but also their surrounding junctions that will be congested in the near future. The results show that the route allocator can decrease traffic congestion significantly by utilizing MIRA. By assigning new routes on roads, traffic conditions can change significantly after a certain time, which means that the real-time traffic conditions will not be an accurate estimate of the near future. What if the route allocator can predict the effect of route assignments on roads? Traffic congestion prediction enables the route allocator to realize traffic conditions accurately. For a specific type of traffic road network in which intersections are signalized, we propose a predictive congestion model by modifying a real-time queue-based congestion model. The model predicts traffic congestion for each road link based on the corresponding number of route allocations. We also propose a route assignment algorithm, Traffic Congestion Aware Route Assignment Algorithm (TCARA), which uses the predictive congestion model. TCARA is designed for a specific type of road networks, while MIRA is a general route assignment algorithm with no prior assumption on road intersections (i.e., whether they are signalized or not), making it a good candidate for a wider range of road networks. Another reason that can change traffic conditions is varying traffic demand over time. Thus, if a route is optimized based on the current traffic conditions while the traffic demand is not stable, the route may be ineffective. What happens if prior temporal traffic data for particular traffic conditions of a road network is available? Temporal traffic data demonstrates historical traffic conditions collected at regular time intervals. The route allocator can be aware of changes in traffic demand that may occur in the near future. So, it can optimize traffic furthermore by having access to such data. We propose a new route assignment algorithm, Temporal Multiple Intersection Reduction Algorithm (T-MIRA), by extending MIRA to leverage prior temporal traffic data for reducing traffic congestions. In this thesis, MIRA is the main contribution. We extend it for situations when traffic conditions evolve due to the impact of route assignments or change of traffic demand. We investigate two different approaches, using a predictive congestion model in TCARA and using temporal traffic data in T-MIRA. An advanced traffic management system may utilize different approaches and solutions in an integrated way to achieve the best outcome in the real world.
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    Avoiding Bad Traffic: Analyzing the Past, Adapting the Present, and Estimating the Future
    Aldwyish, Abdullah Saleh ( 2021)
    Mobility is essential for modern societies. However, due to the increasing demand for mobility, traffic congestion poses a significant challenge to economic growth and advancement for many cities worldwide. At the same time, the widespread availability of location-aware devices has led to a sharp increase in the amount of traffic data generated, thereby, providing an opportunity for intelligent transportation systems to emerge as one of the main cost-effective methods for traffic congestion mitigation. This boost in traffic data has led to a new generation of live navigation services that depend on traffic estimation to provide up-to-date navigation advice. Intelligent transportation systems increase the utilization of existing infrastructure and support drivers to make better navigation decisions by providing actionable traffic information. However, a fundamental shortcoming of existing intelligent navigation systems is that they do not consider the evolution of traffic and route drivers based on snapshot traffic conditions. This is especially critical in the presence of traffic incidents, where the impact of the incident introduces significant variation in the traffic conditions around the incident as things unfold. This thesis proposes three contributions focusing on traffic estimation and forecasting to help drivers avoid bad traffic, especially around traffic incidents. The first contribution is an automated traffic management service to help drivers avoid traffic events based on analyses of historical trajectory data from other drivers. Users subscribe to the service and, when a traffic event occurs, the service provides advice based on all drivers' actions during a similar traffic event in the past. We present a solution that combines a graph search with a trajectory search to find the fastest path that was taken to avoid a similar event in the past. The intuition behind our solution is that we can avoid a traffic event by following the traces of the best driver from a similar situation in the past. The second contribution is a system that uses real-time traffic information and fast traffic simulations to adapt to traffic incident impact and generate navigation advice. In this work, we use faster than real-time simulations to model the evolution of traffic events and help drivers proactively avoid congestion caused by events. The system can subscribe to real-time traffic information and estimate the traffic conditions using fast simulations without the need for historical data. We evaluate our approach through extensive experiments to test the performance and accuracy, and quality of the navigation advice of our system with real data obtained from TomTom Traffic API. For the third contribution, we propose effective deep learning models for large-scale citywide traffic forecasting. In addressing this problem, our goal is to predict traffic conditions for thousands of sites across the city. Such large-scale predictions can be used by navigation systems to help drivers avoid congestion. We propose a traffic forecasting model based on deep convolutional networks to improve the accuracy of citywide traffic forecasting. Our proposed model uses a hierarchical architecture that captures traffic dynamics at multiple spatial resolutions. We apply a multi-task learning scheme based on this architecture, which trains the model to predict traffic at different resolutions. Our model helps provide a coherent understanding of traffic dynamics by capturing short and long spatial dependencies between different regions in a city. Experimental results on real datasets show that our model can achieve competitive results while being more computationally efficient.
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    Computational models of visual search and attention in natural images: from neural circuits to search strategies
    Rashidi, Shima ( 2021)
    Humans use visual search constantly in their daily activities, from searching for their keys to looking out for pedestrians while driving. Cortical visual attention and human eye movements are among the mechanisms that help with an effective visual search. In this thesis, we propose a theoretical model of eye movements in a visual search task as well as a computational model of neural mechanisms underlying visual attention. Towards computationally modeling human eye movements, we propose a model of target detectability in natural scenes which can be used by a Bayesian ideal searcher. The model uses a convolutional neural network as a feature extractor for extracting the features of target and background and uses signal detection theory to estimate detectability as the discriminability of the two feature distributions. We collect the detectability of a known target on 18 different backgrounds from human observers in a two-alternative forced-choice task. We use the collected detectabilities for verification and scaling of the ones estimated by our model. We further fed the collected detectabilities to a Bayesian ideal observer to predict human eye movements in a visual search task in natural scenes. We collected human eye movements on the same 18 natural backgrounds in a search for the known target and used it as the ground truth. Our model closely followed some statistical parameters of the collected eye movements, supporting the idea that humans search near-optimal in natural images. We further generalize the detectability model to any target and background and apply the Bayesian ideal observer model on a real-world dataset of pedestrians in various contexts. To the best of our knowledge, we are the first to provide an unsupervised gaze prediction algorithm on natural scenes which uses the Bayesian ideal observer and doesn't need human eye movements for training. The presented model can have potential applications in autonomous drivers. Towards computationally modeling the neural mechanisms of visual attention, we propose a large-scale computational model of attentional enhancement of visual processing, based on the idea of neural oscillations being fed back to attended features or locations in a scene. Our proposed model supports the idea of neural oscillation for mediating spatial attention and applies the same circuit for feature-based attention as well. The presented models can be used in various human gaze assisted Artificial Intelligent systems as well as enhancing our knowledge of the human visual system.
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    Scalable contrast pattern mining for network traffic analysis
    Alipourchavary, Elaheh ( 2021)
    Contrast pattern mining is a data mining technique that characterises significant changes between datasets. Contrast patterns identify nontrivial differences between the classes of a dataset, interesting changes between multiple datasets, or emerging trends in a data stream. In this thesis, we focus on the pattern of characterizing changes in Internet traffic using contrast pattern mining. For example, network managers require a compact yet informative report of significant changes in network traffic for security and performance management. In this context, contrast pattern mining is a promising approach to provide a concise and meaningful summary of significant changes in the network. However, the volume and high dimensionality of network traffic records introduce a range of challenges for contrast pattern mining. In particular, these challenges include the combinatorial search space for contrast patterns, the need to mine contrast patterns over data streams, and identifying new changes as opposed to rare recurring changes. In this thesis, we introduce a range of novel contrast mining algorithms to address these challenges. We first introduce the use of contrast pattern mining in network traffic analysis. We show that contrast patterns have strong discriminative power that make them suitable for data summarization, and finding meaningful and important changes between different traffic datasets. We also propose several evaluation metrics that reflect the interpretability of patterns for security managers. In addition, we demonstrate on real-life datasets that the vast majority of extracted patterns are pure, i.e., most change patterns correspond to either attack traffic or normal traffic, but not a mixture of both. We propose a method to efficiently extract contrast patterns between two static datasets.We extract a high-quality set of contrast patterns by using only the most specific patterns to generate a compact and informative report of significant changes for network managers. By elimination of minimal patterns in our approach, we considerably reduce the overlap between generated patterns, and by reducing the redundant patterns, we substantially improve the scalability of contrast pattern mining and achieve a significant speed-up. We also propose a novel approach to discriminate between new events and rare recurring events. Some changes in network traffic occur on a regular basis and show periodic behaviour, which are already known to network analysts. Thus, network managers may want to filter out these known recurring changes, and prioritize their focus on new changes, given their limited time and resources. Our approach to this problem is based on second order contrast pattern mining. Our work demonstrates the importance of higher order contrast pattern mining in practice, and provides an effective method for finding such higher order patterns in large datasets. Finally, based on the approaches that we introduced for contrast pattern mining in static datasets, we then propose two novel methods to extract contrast patterns over high dimensional data streams. We consider two incremental scenarios: (i) when one dataset is changing over time and the other dataset is static as a reference dataset, and (ii) when both datasets are changing over a data stream. In our approach, instead of regenerating all patterns from scratch, we reuse the previously generated contrast patterns wherever possible to mine the new set of patterns. Using this technique, we avoid expensive incremental computation and increase the efficiency and scalability of mining on dense and high dimensional data streams. As a result of this scalability, our method also can find the solutions for datasets where the other algorithms cannot. In addition to the substantial improvements in performance and scalability of our algorithm, we demonstrate that the quality of the generated patterns of our algorithm is quite comparable with the other algorithms. In summary, we propose several efficient contrast pattern mining approaches to extract significant changes between two static datasets or over a data stream. We also introduce a novel approach to identify new changes from the recurring changes. Our experimental results on different real-life datasets demonstrate the improvements in performance of our proposed algorithms compared to the existing approaches.
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    The Role of Explanations in Enhancing Algorithmic Fairness Perceptions
    Afrashteh, Sadaf ( 2021)
    Decision-makers employ machine learning algorithms for decision-making to gain insights from data and make better decisions. More specifically, advanced algorithms can help organizations classify their customers and predict their behavior at the highest accuracy levels. However, the opaque nature of those algorithms has raised concerns around some potential unintended consequences they might cause, unfair decisions at the center. Unfair decisions negatively impact on both organizations and customers. Customers may lose their trust in organizations as being treated unfairly and consequently, organizations’ reputations might be put at risk. Transparency provision has been introduced to organizations as a way of addressing the issue of algorithmic opacity. One approach for transparency provision is explaining algorithms’ performance and how they reach a decision to decision-makers. Therefore, explanations can consequently influence the fairness perceptions of the decision-makers about algorithms’ decisions. Understanding how explanations and the way of discoursing them impact on the fairness perceptions of the organizational decision-makers is important. However, little research has focused on the role of explanations in enhancing fairness perceptions. I seek to address this research gap answering the question of: “How does explanation influence decision-makers’ perceptions of fairness connected with an algorithm’s decisions?” I conduct three studies to answer this question. In study 1, I conduct a conceptual study to explore the dimensions of explanations that need to be studied in understanding the impact of explanations on fairness perceptions. In study 2, I develop a research model hypothesizing the role of perspective-taking in discoursing two different explanations with decision-makers in their fairness perceptions. I conducted a 2*2 experiment to test the hypotheses. In study 3, I develop a research model hypothesizing the influence of explanations restrictiveness in the decision’s fairness perceived by the decision-makers. I conducted a 2*2 experiment to test the hypotheses. The findings of this research result in three important insights about explanations and their role in enhancing algorithmic fairness perceptions; first, I propose four dimensions of explanations that need to be considered in understanding fairness perceptions including the content types of explanations, the explanations reasoning logic, the scope of explanations and explanations discourse. Second, taking different perspectives of organization or customer in communicating different types of explanations lead to different impact on the perception of fairness about algorithm’s performance and its made decision. Third, Framing explanations in a less restrictive way creates the space for the decision-makers to be cognitively more engaged with the algorithmic decision-making and practice their own judgment about that which consequently influences on their fairness perceptions.
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    Augmented Reality Annotations to Enhance Skill-Movement Acquisition in the Classroom
    Reinoso Naranjo, Martin Nicolas ( 2021)
    In a typical classroom, annotations are drawn in whiteboards to facilitate visual communication between teacher and students. It improves classroom interaction and results in enhanced learning. Annotations can add extra helpful information, guide student’s attention, and assess students’ understanding. However, annotations are not usually used in settings where students learn skilled-movements (e.g., dance, weightlifting, martial arts) because the content is the student’s movement itself and the dynamic nature of the movements . It is not a static representation of the movement on the whiteboard. Augmented Reality (AR) technology can change the skill-movement classroom, enabling annotations to leave the whiteboard and appear on/around the student performing a skilled-movement. These AR-annotations have the potential to improve teacher-student interaction in these settings, resulting in enhanced learning of skilled-movements. This thesis presents four studies investigating AR-Aannotations using the teacher-student communication model in a skilled-movement classroom that requires 3-steps: Demonstration, Performance , and Feedback. The context for these studies is physiotherapy and weightlifting. Study 1 gathered the requirements for an AR system that could enhance the practical physiotherapy classroom through a focus group and a field study. Study 2 builtds a system with the identified requirements and evaluated it in two practical physiotherapy classes. The study demonstrated that surface AR-annotations allowed teachers to guide the students’ focus of attention and assess students’ knowledge and understanding of the movement. However, the use of surface AR-annotations required the annotated object to be static, limiting AR-annotations used when movement is required. Study 3 investigated the Performance-step, where the aim is to find different types of AR-annotations to enhance the assessment of student’s performance. The study has shown that mid-air annotations (static, temporal, assisted, and morphing) can assist the teacher while assessing students’ performance. These AR-annotations can also be used on dynamic objects without requiring them to become static. Finally, Study 4 investigated the Feedback-step, where the problem is the different viewpoints of users. While annotating the student’s body, the physical worldview of the teacher and the student cannot be the same. The study a confirmationloop that enables a simple teacher-student interaction using mid-air annotations from different viewpoints. The thesis makes three contributions. The first contribution is a new skilled-movement acquisition model that adds a Knowledge-loop in which the teacher assesses the student’s movement knowledge. The teacher asks questions about the skilled-movement, and the student uses AR-annotations to demonstrate their understanding of the movement visually. The Knowledge-loop removes the need for performance to assess students’ knowledge of the skilled-movement, enhancing motivation and self-assessment. The second contribution is the addition of a morphing transition state to the current temporality dimension aspect of the taxonomy of AR-annotations since valuable information embedded in the creation process of annotations has not been considered in the field of AR-annotations. This information includes both the transition from the beginning to the end when creating an annotation as well as annotations that are morphing in nature. This additional information influences the intended message of annotations impacting the way they are perceived. The third contribution is the expansion of the understanding of egomotion and exocentric (viewpoint reference frame dimension) to switch the focus from a selfcontrolled to a shared controlled virtual viewpoint. This thesis explored the use of AR-annotations with multiple users and the challenges involved, where controlling the position of the virtual viewpoint becomes a shared task for all the users.
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    Efficient algorithms for graph clustering and searching
    Zhang, Xin ( 2021)
    This thesis aims to design efficient algorithms for challenges naturally arising from solving graph problems on large, diverse and noisy datasets. Motivated by these challenges in practice, we study two types of problems, graph clustering and graph searching, for their ubiquity and computational difficulty. We study carefully existing solutions and provide improvements with theoretical guarantees, leading to more efficient algorithms. For graph clustering, we first focus on solving a parametric clustering objective for all parameters, which yields a robust and comprehensive view on how similar nodes in the graph are grouped together. Our approach does not need the practitioner to manually set the parameter, avoiding the burden of needing domain-specific knowledge to the datasets. Specifically, our algorithm provides a family of clusterings that sufficiently approximate the clustering objective for all parameter values, by solving only O(log n) linear program, where n is the number of nodes. Moreover, the size of the family is at most twice as large as the minimum objective-approximating family. To handle massive datasets with the limited RAM of computers, we consider streaming algorithms that use little RAM in comparison to the data size, while making linear passes of data stored in slower mediums, such as disks. To this end, we study correlation clustering, another graph clustering problem, and implement a streaming algorithm efficient both in theory and in practice. For graph searching, we study the special case of interactively identifying the predecessor of a given target number in a sorted array of integers, i.e., a path graph. The search queries an index of the integer in the array, repeatedly to an oracle, which answers if the queried integer is greater than the target. The performance of a search algorithm is measured by query complexity, the worst-case number of queries needed to find the target. We provide a state-of-the-art, binary-search-based algorithm for the noisy and unbounded setting where the comparison result can be wrong with a probability and the size of the array is unbounded. Our algorithm improves previous results in this setting, reducing the constant factor in the leading term in the query complexity to one, closely matching the lower bound. Our approach can also be generalized to Cartesian products of paths, i.e., grid graphs, solving the problem of learning a user's personal preference on a set of items.
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    Verified Multi-Robot Planning Under Uncertainty
    Faruq, Fatma ( 2021)
    Multi-robot systems are being increasingly deployed to solve real-world problems, from warehouses to autonomous fleets for logistics, from hospitals to nuclear power plants and emergency search and rescue scenarios. These systems often need to operate in uncertain environments which can lead to robot failure, uncertain action durations or the inability to complete assigned tasks. In many scenarios, the safety or reliability of these systems is critical to their deployment. Therefore there is a need for robust multi-robot planning solutions that offer guarantees on the performance of the robot team. In this thesis we develop techniques for robust multi-robot task allocation and planning under uncertainty by building on techniques from formal verification. We present three algorithms that solve the problem of task allocation and planning for a multi-robot team operating under uncertainty. These algorithms are able to calculate the expected maximum number of tasks the multi-robot team can achieve, considering the possibility of robot failure. They are also able to reallocate tasks when robots fail. We formalise the problem of task allocation and robust planning for a multi-robot team using Linear Temporal Logic to specify the team's mission and Markov decision processes to model the robots. Our first solution method is a sampling based approach to simultaneous task allocation and planning. Our second solution method separates task allocation and planning for the same problem using auctioning for the former. Our final solution lies midway between the first two using simultaneous task allocation and planning in a sequential team model. We evaluate all solution approaches extensively using a set of tests inspired by existing benchmarks in related fields.%with a focus on scalability.
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    Effective Spatial Feature Extraction via Convolutional Neural Networks
    Zhao, Yunxiang ( 2021)
    Spatial data analysis has achieved great success in many real-world applications such as region similarity learning, crime prediction, and traffic prediction. In spatial data analysis, spatial features play an important role. In this thesis, we propose novel techniques based on the Convolutional Neural Network (CNN) and its variants to learn spatial features from two important aspects, namely Point of Interest (POI) features and POI relationships. We focus on the building height feature, the hexagonal geometrical layout relationship, and the pairwise relationship of POIs. We show the importance of learning these spatial features in spatial data analysis applications, in particular, region similarity learning. We make the following contributions in this thesis: (1) We propose a CNN-based building height estimation method that computes building height from street scene images. We first detect roofline candidates from such images. Then, we use a deep neural network called RoofNet to filter these candidates and select the best candidate via an entropy-based ranking algorithm. When the true roofline is identified, we compute building height via the pinhole camera model. Experimental results show that our overall building height estimation method is more accurate than the baseline by up to 11.9%. (2) We propose a novel native hexagonal CNN framework named HexCNN for hexagonal layout relationship learning. HexCNN takes hexagon-shaped input and performs forward and backward propagation on the input based on hexagon-shaped filters, hence avoiding computation and memory overheads caused by transforming the input into rectangular shapes to fit traditional CNN models. Experimental results show that, compared with the state-of-the-art models, which imitate hexagonal processing but use rectangle-shaped filters, HexCNN reduces training time by up to 42.2%. Meanwhile, HexCNN saves memory by up to 25% and 41.7% for loading the input and performing convolution, respectively. (3) We propose a Graph Convolutional Network (GCN) model with weighted structural features named WGCN for learning edge direction features between POIs. WGCN first captures nodes' structural fingerprints via a direction and degree-aware Random Walk with Restart algorithm. The walk is guided by both edge direction and nodes' in- and out-degrees. Then, the interactions between nodes' structural fingerprints are used as the weighted node structural features. To further capture nodes' high-order dependencies and graph geometry, WGCN embeds graphs into a latent space to obtain nodes' latent neighbors and geometrical relationships. Based on nodes' geometrical relationships in the latent space, WGCN differentiates latent, in-neighbors, and out-neighbors with an attention-based geometrical aggregation. Experiments on transductive node classification tasks show that WGCN outperforms the baseline models consistently and by up to 17.07% in terms of accuracy on five benchmark datasets. (4) To showcase the effectiveness of learning the above spatial features in spatial data analysis applications, we propose a triplet-based Graph Neural Network model named C-MPGCN for region representation learning, where POIs are represented as graphs. C-MPGCN uses POIs' height and size as node features in addition to POIs' category and geo-location features used in existing methods. C-MPGCN also captures the POIs' hexagonal layout relationship and the pairwise relationship (edge features). Experimental results show that C-MPGCN outperforms the state-of-the-art methods for region similarity learning consistently under different evaluation matrices.