Yaru Li

As the diversity and complexity of geographic statistical data continue to increase, it becomes increasingly important to present multi-level information in order to meet a broader range of needs. In response to the limitations of existing visualization methods in representing the geographic distribution of statistical data, this paper proposes a geographical hierarchical data visualization method based on rectangular cartograms. First, a new rectangular cartograms construction algorithm is adopted in this paper, which can effectively preserve relatively accurate orientation and adjacency relationships between geographic regions, while also effectively preserving the statistical data features. Then, a treemap layout algorithm is applied within the rectangular cartogram to further partition the geographic regions, thereby visualizing the hierarchical structure of the data. Through experimental validation using real datasets and usability testing, the results demonstrate that the method presented in this paper excels in geographic distribution representation, hierarchical relationship visualization, and information readability. Compared to traditional thematic map methods, this approach demonstrates significant advantages in terms of information transmission efficiency and shows promising performance in expressive effectiveness, providing strong support for the analysis and decision making of geographical hierarchical data.

The equity of medical services is crucial for the quality of life of a population and the sustainable development of cities. Current research on optimizing the maximal equity of medical facilities has the following limitations: (1) In the accessibility calculation models for multiple transportation modes, the impact of factors such as public transport transfers and travel distance on the choice of transportation mode is often overlooked. (2) Existing spatial equity indicators are mostly derived from the overall study area, failing to fully consider the differences in population distribution and development gaps within different development zones inside the region. This study proposes a novel Incorporating Multiple Transportation Two-Step Floating Catchment Area (IMT-2SFCA) and a Hierarchical Theil-based Maximal Accessibility Equality model (HT-MAE) to optimize the equity of access to tuberculosis medical institutions in Wuhan. The findings reveal that, compared to single-mode transportation accessibility models, the multimodal accessibility model integrates the characteristics of four transportation modes, providing a more realistic reflection of residents’ access to medical services. The optimization results show a significant improvement in the equity of access to medical services across Wuhan and among different economic development zones, although the equity indicators in non-central urban areas have declined. The results of this study provide a theoretical basis and practical insights for alleviating the inequality of access to medical services across different urban layers.

The rectangular cartogram is a geospatial visualization method that blends the characteristics of maps and charts. By simplifying geographic regions into rectangles and using the area of each rectangle to represent statistical data, it enables efficient geovisualization. This paper summarizes and analyzes the advantages and limitations of two main approaches used in current rectangular cartogram construction algorithms. To address the issues of high computational cost and inadequate preservation of adjacency and relative positional relationships in existing algorithms, we propose and implement a new rectangular cartogram construction algorithm. This algorithm simplifies the layout computation process while ensuring that the adjacency and relative positional relationships between regions during the layout generation process have only minor errors. In adjusting rectangle areas to match attribute values, the algorithm adopts a “region-by-region placement” strategy, ensuring that errors in area accuracy remain within a small range, while also keeping errors in adjacency and relative positional relationships minimal. Finally, by comparing the results of our algorithm with those of existing algorithms using real-world data with varying distribution characteristics, we demonstrate its effectiveness. The results show that the proposed algorithm not only improves computational efficiency but also effectively displays the adjacency and relative positional relationships between regions.