Meysam Aliakbarian’s research while affiliated with University of Zurich and other places

While map apps on smartphones are abundant, their everyday usage is still an open empirical research question. With tappigraphy – the quantification of smartphone touchscreen interactions – we aimed to capture continuous data stream of behavioural human-map app usage patterns. The current study introduces a first tappigraphy analysis of the distribution of touchscreen interactions on map apps in 211 remotely observed smartphone users, accumulating a total of 42 days of tap data. We detail the requirements, setup, and data collection to understand how much, when, for how long, and how people use mobile map apps in their daily lives. Supporting prior research, we find that on average map apps are only sparsely used, compared to other apps. The longitudinal fluctuations in map use are not random and are partly governed by general daily and weekly human behaviour cycles. Smartphone session duration including map app use can be clearly distinguished from sessions without any map apps used, indicating a distinct temporal behavioural footprint surrounding map use. With the transfer of the tappigraphy approach to a mobile map app use context, we see a promising avenue to provide research communities interested in the underlying behavioural mechanisms of map use a continuous, in-situ momentary assessment method.

On a daily basis, a conventional internet user queries different internet services (available on different platforms) to gather information and make decisions. In most cases, knowingly or not, this user consumes data that has been generated by other internet users about his/her topic of interest (e.g. an ideal holiday destination with a family traveling by a van for 10 days). Commercial service providers, such as search engines, travel booking websites, video-on-demand providers, food takeaway mobile apps and the like, have found it useful to rely on the data provided by other users who have commonalities with the querying user. Examples of commonalities are demography, location, interests, internet address, etc. This process has been in practice for more than a decade and helps the service providers to tailor their results based on the collective experience of the contributors. There has been also interest in the different research communities (including GIScience) to analyze and understand the data generated by internet users. The research focus of this thesis is on finding answers for real-world problems in which a user interacts with geographic information. The interactions can be in the form of exploration, querying, zooming and panning, to name but a few. We have aimed our research at investigating the potential of using geographic user-generated content to provide new ways of preparing and visualizing these data. Based on different scenarios that fulfill user needs, we have investigated the potential of finding new visual methods relevant to each scenario. The methods proposed are mainly based on pre-processing and analyzing data that has been offered by data providers (both commercial and non-profit organizations). But in all cases, the contribution of the data was done by ordinary internet users in an active way (compared to passive data collections done by sensors). The main contributions of this thesis are the proposals for new ways of abstracting geographic information based on user-generated content contributions. Addressing different use-case scenarios and based on different input parameters, data granularities and evidently geographic scales, we have provided proposals for contemporary users (with a focus on the users of location-based services, or LBS). The findings are based on different methods such as semantic analysis, density analysis and data enrichment. In the case of realization of the findings of this dissertation, LBS users will benefit from the findings by being able to explore large amounts of geographic information in more abstract and aggregated ways and get their results based on the contributions of other users. The research outcomes can be classified in the intersection between cartography, LBS and GIScience. Based on our first use case we have proposed the inclusion of an extended semantic measure directly in the classic map generalization process. In our second use case we have focused on simplifying geographic data depiction by reducing the amount of information using a density-triggered method. And finally, the third use case was focused on summarizing and visually representing relatively large amounts of information by depicting geographic objects matched to the salient topics emerged from the data.

Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and thus a complete automated system for geological map generalization is not yet available. In particular, while in other areas of map generalization constraint-based techniques have become the prevailing approach in the past two decades, generalization methods for geological maps have rarely adopted this approach. This paper seeks to fill this gap by presenting a methodology for the automation of geological map generalization that builds on size constraints (i.e., constraints that deal with the minimum area and distance relations in individual or pairs of map features). The methodology starts by modeling relevant size constraints and then uses a workflow consisting of generalization operators that respond to violations of size constraints (elimination/selection, enlargement, aggregation, and displacement) as well as algorithms to implement these operators. We show that the automation of geological map generalization is possible using constraint-based modeling, leading to improved process control compared to current approaches. However, we also show the limitations of an approach that is solely based on size constraints and identify extensions for a more complete workflow.

The growth of user-generated content in quantity and quality has changed the way people use digital services, including geo-services. The process of map generalization is not an exception to this phenomenon. Earlier research has considered user-generated content as data sources for the generalization process. However, little work has been accomplished to date considering the knowledge that may be extracted from those sources, in particular from special place-related semantics captured in user-contributed feature tags. This study considers doing so from the perspective of folksonomies, presenting some first steps in that direction. In particular, this short paper shows, on the example of OpenStreetMap, how different similarity measures can be used to exploit folksonomy-based semantics in map generalization. And it shows how these semantics can be used to introduce behaviour changes in generalization operators, in particular in the selection and aggregation operators, respectively.