Choropleth map animation is widely used to show the development of spatial processes over time. Although animation congruently depicts change, the rapid succession of complex map scenes easily exceeds the human cognitive capacity, causing map users to miss important information. Hence, a reduction of the visual complexity of map animations is desirable. This article builds on research related to complexity reduction of static choropleth maps. It proposes value generalization of choropleth time-series data in space and time, by using a method that adapts to the degree of global spatiotemporal autocorrelation within the dataset. A combination with upstream algorithms for local outlier detection results in less complex map animations focusing on large-scale patterns while still preserving significant local deviations in space and time. An according software application allows for in-depth exploration of the spatial and temporal autocorrelation structures in time-series data and provides control over the whole process of generalization.

The definition of classes for choropleth maps is commonly based on nonspatial attribute values, ignoring the spatially autocorrelated nature of almost all geographical data. This ‘blindness’ toward spatial configuration during the classification process leads to relatively complex and fragmented spatial patterns that confuse visual perception and impair the subsequent cognitive processes involved in map interpretation. This article presents a new approach to cartographic classification of univariate, quantitative polygonal data. The proposed method adapts to the degree of spatial autocorrelation in data by utilizing the Moran's I scatter plot in combination with the Fisher–Jenks algorithm. When data are spatially autocorrelated, the resulting maps are visually less complex than those derived using equivalent nonspatial classification approaches. However, the resulting classes might overlap in the value domain. The cartographic concept that we present therefore combines the advantages of traditional classification with those of our proposed method: it allows the visual assignment of individual polygons to mutually exclusive value ranges, while still preserving visual clarity of patterns.

Time series animation of choropleth maps easily exceeds our perceptual limits. In this empirical research, we investigate the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends and local deviations thereof. Comparing generalization in space, in time, and in a combination of both dimensions, value smoothing based on a first order spatial neighborhood facilitated the detection of local outliers best, followed by the spatiotemporal and temporal generalization variants. We did not find any evidence that value generalization helps in detecting global trends.

Walking is recognised as a healthy and sustainable mode of transport. Providing adequate infrastructure is pivotal for the promotion of walking and, subsequently, for achieving the benefits derived from its numerous positive effects. However, efficiently measuring the walkability at the street level remains challenging. In this paper, we present an indicator-based assessment model that can be used with open spatial data to evaluate segment-based walkability. The model incorporates eleven indicators describing the street segments and their close surroundings that are relevant for pedestrians, such as the presence and type of pedestrian infrastructure, road category, noise levels, and exposure to green and blue space. A weighted average calculation results in walkability index values for each street segment within a road network graph. The model’s generic approach and the ability to be used with open data ensure its reproducibility, adaptability, and scalability. The feasibility of the walkability model was shown using a case study for Salzburg, Austria. The model’s validity was evaluated through a large-scale study involving 660 full responses to an online survey. Participants provided ratings on the walkability of randomly selected street segments in Salzburg, which were compared with the calculated index, revealing a strong correlation (Spearman’s rank correlation = 0.82).