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 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.

Parking behaviors near popular destinations often exhibit a preference for proximity, resulting in poor habits, limited parking availability, and a range of consequential issues such as traffic chaos, economic challenges due to congestion, and imbalanced parking utilization. Taiwan has also faced escalating challenges in this regard. To effectively address these issues, the Government of Taiwan has initiated the Smart City program, encompassing various initiatives to enhance urban functionality. One notable solution implemented under this program is the Smart Parking Meter System (SPMS), designed to enhance the overall parking experience. The SPMS incorporates intelligent billing and secure parking data transmission, ensuring a safer and improved parking environment. In this paper, we propose ParkFlow, a comprehensive software-based solution that seamlessly integrates with smart parking hardware, presenting a holistic approach to tackling these challenges. ParkFlow intelligently disperses parking shortages in highly frequented areas and addresses the problem from multiple perspectives, including user, engineering, and government scenarios. By exploring and addressing these scenarios, we aim to provide valuable insights and inspiration to regions worldwide grappling with similar parking-related difficulties. Based on historical data analysis, the implementation of ParkFlow in resolving the parking imbalance problem is anticipated to lead to a significant increase of up to 10% to 20% in available parking hours in popular areas of Tainan, Taiwan. ParkFlow is in the process of being integrated into the Tainan City Government's Parking application, indicating its potential to address real-world parking challenges.