With the wide use of web technologies, service-oriented architecture (SOA), and cloud computing, more and more geographical information systems are served as GIServices. Under such circumstance, quality of geographic information services (QoGIS) has emerged as an important research topic of geoinformatics. However, it is not easy to understand the field since QoGIS has no formal standards, which is not only in regard to server-side performance and capabilities, but is also related with the quality of experience (QoE) during user interaction with GIServices. In this paper, we compare quality of service (QoS) and QoGIS research to understand the uniqueness of QoGIS. A conceptual framework is proposed to organize and interpret QoGIS research from the perspective of quality modeling, acquisition, and application, and we discuss the status, limitations, and future directions of this area. Overall, our analysis shows that new quality metrics will evolve from existing metrics to match the needs in concrete QoGIS applications, and user preferences need to be considered in quality modeling for GIServices. We discuss three approaches for the provision of QoGIS information and find that user feedback mining is an important supplementary source of quality information. Gaps between QoS and QoGIS research suggest that the GIService performance enhancement must not only consider the unique features of spatial data models and algorithms, but also system architecture, deployment, and user spatiotemporal access behaviors. Advanced service selection algorithms must be introduced to tackle the quality optimization problems of geoprocessing workflow planning. Moreover, a QoGIS-aware GIServices framework must be established to facilitate and ensure GISerivce discovery and interaction. We believe this bibliographic review provides a helpful guide for GIS researchers.

Modern technology has given thousands of amateur cartographers not only the opportunity but also the means to make a valuable contribution to mapping at all scales. However, web maps made by amateurs are prone to legibility shortcomings, such as coalescence, complexity, and congestion. These problems can be solved by map generalization; however, for amateurs participating in web mapping, the crucial decision is when to activate generalization. Such a decision is difficult, as the original and target map scales are sometimes uncertain owing to heterogeneous data quality and different output devices. In this study, starting from the identification of different pixels in a rasterized line, we developed a new scale-specific measure to evaluate the degree of legibility (DoL) of individual lines. Our experimental results showed that this measure could reflect the legibility of a line at a given map scale effectively, and could facilitate accurate decisions on whether generalization is necessary. In addition, we fitted a multi-scale legibility function (MSLF) for each line based on DoLs at multiple scales. This function can be used to estimate the legible scale for each line accurately. Our measures are easy to understand, tolerant of data quality, and have great potential for multi-scale spatial data processing.

Taking location-based service (LBS) as the research scenario and aiming at the limitation of visualizing LBS points of interest (POI) in conventional web maps, this article proposes a visualization method of LBS-POI based on tag cloud, which is called “LBS tag cloud”. In this method, the user location is taken as the layout center, and the name of the POI is converted into a text tag and then placed around the center. The tags’ size, color, and placement location are calculated based on other attributes of the POI. The calculation of placement location is at the core of the LBS tag cloud. Firstly, the tag’s initial placement position and layout priority are calculated based on polar coordinates, and the tags are placed in the initial placement position in the order of layout priority. Then, based on the force-directed model, a repulsive force is applied to the tag from the layout center to make it move to a position without overlapping with other tags. During the move, the quadtree partition of the text glyph is used to optimize the detection of overlaps between tags. Taking scenic spots as an example, the experimental results show that the LBS tag cloud can present the attributes and distribution of POIs completely and intuitively and can effectively represent the relationship between the POIs and user location, which is a new visualization form suitable for spatial cognition.