With growing environmental concerns and the push for sustainable urban development, promoting green travel has become a critical initiative. Urban transit systems face the challenge of integrating green initiatives with efficient transport routes, while sophisticated graph modeling enhances travel efficiency. However, blending historical and contemporary elements introduces complex variations in traffic networks, complicating feature extraction and clustering for information retrieval due to multi-scale spatial heterogeneity. Traditional methods often overlook key nuances by oversimplifying data relationships. We proposed M3 and validated the integration of GIS-based Attention-Cluster-GCN with Dueling Double Deep Q Network across various cities, enhancing urban travel with detailed information on green attraction recommendations, considering the usage of Multi-Purpose and Multi-Stakeholder for Multi-Scale Spatial Heterogeneity scenarios. Utilizing Attention-Based Reinforcement Graph Clustering refines modeling and emphasizes vital connections, enhancing personalized recommendation precision and clustering performance. Our method surpasses both conventional and advanced GNN methods, even in graph convolution-based deep reinforcement learning, achieving superior cluster separation and accuracy. Our sampling and ablation studies confirm the pivotal role of the attention mechanism and multi-scale features, showing a significant performance decline without attention. Our findings underscore the potential of graph clustering in making public transport more engaging and aligned with green attractions policies by recommendations, even amidst significant spatial heterogeneity.

We present LINKin-PARK, an innovative system that seamlessly merges geographic visualization with an advanced Dual Attention Double Channel Convolutional Neural Network with Multilayer Perceptron (Dual Attention-DCCNN+MLP) to facilitate the efficient analysis of land valuation. LINKin-PARK provides robust visualization capabilities for intuitive comprehension. Our model outperforms traditional methods, e.g., linear regression, multilayer perceptron (MLP), Extreme Gradient Boosting (XGBoost), and the combination of CNN (Convolutional Neural Network) with MLP. An ablation study further evaluates the influence of specific components within the model, revealing that spatial and channel-wise attention mechanisms and the integration of DCCNN and skip connections are crucial for capturing spatial details and improving prediction accuracy. Users have the flexibility to explore and predict developable land valuation based on their specific requirements and provide their feedback to minimize errors in model prediction. For instance, this system can forecast future development potential and market demand for everywhere in an urban space, enabling users to make informed decisions before purchasing a property. Similarly, retailers can anticipate future revenues to aid in strategic decisions, such as selecting optimal locations for establishing new retail outlets. In summary, LINKin-PARK effectively combines geographic visualization and Dual Attention-DCCNN+MLP to assist users in analyzing and predicting land valuation and other scenarios.

We introduce a GIS-based AR to promote public transport and environmental awareness. It transforms bus rides into gamified journeys of virtual plant cultivation at bus stops. Featuring AR scanning for plant growth, navigation aids, and a digital herbarium, it enriches user interaction with their surroundings. The system integrates nature-inspired virtual installations, supported by a database and species map for green education, utilizing Cluster-GCN for plant, flower, and crop information and mapping into bus stops to recommend travel sites for passengers. An immersive AR interface enables users to access plant information at nearby stations through a custom graph clustering pipeline. User tests showed no significant change in bus ridership interest, slightly fluctuating from 52% to 43%, attributed to bus punctuality and frequency issues. However, the app significantly increased user engagement with the environment and species knowledge from 54% to 82%, underscoring a positive relationship between public transport and environmental awareness.

We introduce ACCEPT, a geospatial decision support system that merges robust, intuitive visualization with grid-based data processing and neural networks to enhance spatial data analysis and interpretation in context-sensitive scenarios. It offers versatile machine learning modules with multiple prediction models, tailored to specific requirements with user-defined configurable parameters and flexible predictive target selection. The system serves as an accessible introduction to geographic information systems (GIS) for the general public. The system maps Points of Interest (POIs) to grids, simplifying processes like weighting, intersection, and interpolation, enhancing data accessibility and manipulation. Our case studies show effective handling of spatial data, reflecting similar distribution patterns of POIs, spatial separation, local feature sensitivity, and proximity to infrastructure and kernel size affect evaluations. The extensible and user-friendly web interface includes geospatial data inquiries, overlay, import/export, statistic, and multiple map views, facilitating informed decisions in resource distribution and urban planning. It supports urban planners, analysts, and policymakers in achieving equitable resource distribution and enhancing residential justice, while also providing non-experts an introduction to advanced geospatial analyses, promoting wider engagement and understanding in spatial decision-making.