Martin Loidl

A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic

In this paper we present a visual analytics approach for deriving spatio-temporal patterns of collective human mobility from a vast mobile network traffic data set. More than 88 million movements between pairs of radio cells—so-called handovers—served as a proxy for more than two months of mobility within four urban test areas in Northern Italy. In contrast to previous work, our approach relies entirely on visualization and mapping techniques, implemented in several software applications. We purposefully avoid statistical or probabilistic modeling and, nonetheless, reveal characteristic and exceptional mobility patterns. The results show, for example, surprising similarities and symmetries amongst the total mobility and people flows between the test areas. Moreover, the exceptional patterns detected can be associated to real-world events such as soccer matches. We conclude that the visual analytics approach presented can shed new light on large-scale collective urban mobility behavior and thus helps to better understand the “pulse” of dynamic urban systems.

Autocorrelation-Based Regioclassification – a self-calibrating classification approach for choropleth maps explicitly considering spatial autocorrelation

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 Morans 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.

Spatial Information for Safer Bicycling

The need for sustainable modes of transport is obvious, especially in urban areas. Because of the large number of trips within cities and distances lesser than 5 km, the bicycle is regarded as optimal mode of transport, both for utilitarian and leisure trips. Nevertheless, safety concerns are among the most relevant factors that hamper an increasing bicycle usage. Geographical Information Systems (GIS) with their ability to model and analyze road infrastructure and users in an explicitly spatial context can significantly contribute to meet these safety concerns. They can be employed in all stages of better understanding bicycle safety as a spatio-temporal phenomenon and provide the basis for informed decisions in the context of planning, information provision and cycling promotion.

Considering Spatial Factors in Promoting Active, Healthy Commuting

Although mobility is spatial by its very nature, spatial factors are rarely explicitly considered in the promotion of active commuting. However, active commuting bears great potential for increasing physical activity among employees and can thus contribute to health promotion. We argue that spatial models and analyses are building blocks for more efficient strategies in corporate mobility and health promotion measures. Specifically, we propose (a) a routing algorithm that optimizes commuting journeys in terms of health effectiveness; (b) assessment models, which express the suitability of workplace environments for active commuting; (c) spatial analyses that estimate the potentials of different modes for any given location, and (d) spatial analyses that support awarenessraising for active mobility. The elements are conceptualized on a generic level and then applied to a case study in Salzburg, Austria. In this case study, we demonstrate the integrative power of a geo-spatial approach which facilitates holistic perspectives on healthy commuting and has the potential to serve as an evidence base in targeted interventions.

Reviewing Software for Agent-based Bicycle Flow Models

Agent-based models are able to simulate traffic flows and reveal emergent phenomena which tend to remain hidden in traditional traffic modelling frameworks. Until now Agent-Based Modelling has primarily been employed to simulate bicycle traffic flows at a local level, but hardly ever at a regional level. The present review examines the suitability of available agent-based platforms for modelling bicycle traffic flows at regional level, outlining the requirements for doing this. From a wide range of available tools, we considered GAMA, NetLogo and MATSim for an in-depth evaluation.