Falko Schmid

Semantic Trajectory Compression

In the light of rapidly growing repositories capturing the movement trajectories of people in spacetime, the need for trajectory compression becomes obvious. This paper argues for semantic trajectory compression (STC) as a means of substantially compressing the movement trajectories in an urban environment with acceptable information loss. STC exploits that human urban movement and its large---scale use (LBS, navigation) is embedded in some geographic context, typically defined by transportation networks. STC achieves its compression rate by replacing raw, highly redundant position information from, for example, GPS sensors with a semantic representation of the trajectory consisting of a sequence of events . The paper explains the underlying principles of STC and presents an example use case.

Knowledge-based wayfinding maps for small display cartography

Small displays are currently gaining importance as interfaces for geographic information. More specifically, mobile position-aware devices, such as mobile phones equipped with globally positioning system, are increasingly used for mobile wayfinding assistance. But their constrained displays are too small to reproduce conventional maps without an increasing effort for the user. For example, they have to zoom in and out, and to scroll through the map to understand the details and configurational relationships of the involved entities of a route. This fragmentation of the information is not just inconvenient, but actually affects the cognitive processing of the given information and lowers the effectiveness of the assistance. One way to attack this problem is to tailor maps to the individual knowledge of a user. If an assistance system knows about the places and paths a user knows, it can generate maps according to this information: those parts of a route, which the user has good knowledge of, can be displayed with less detail and parts with no or little knowledge can be emphasised. However, the transformation of maps with respect to previous knowledge is a yet unexplored field and requires new and basic considerations about map generation. In this work, we analyse prototypical spatial configurations, geographic veridicality and assistance scenarios. We demonstrate first prototypes of personalised maps for small display cartography.

Semantic trajectory compression: Representing urban movement in a nutshell ∗

There is an increasing number of rapidly growing repositories capturing the movement of people in space-time. Movement trajectory compression becomes an ob- vious necessity for coping with such growing data volumes. This paper introduces the concept of semantic trajectory compression (STC). STC allows for substantially compressing trajectory data with acceptable information loss. It exploits that human urban mobility typically occurs in transportation networks that define a geographic context for the move- ment. In STC, a semantic representation of the trajectory that consists of reference points localized in a transportation network replaces raw, highly redundant position information (e.g., from GPS receivers). An experimental evaluation with real and synthetic trajectories demonstrates the power of STC in reducing trajectories to essential information and illus- trates how trajectories can be restored from compressed data. The paper discusses possible application areas of STC trajectories.

Ambiguity and plausibility: managing classification quality in volunteered geographic information

With the ubiquity of technology and tools, current Volunteered Geographic Information (VGI) projects allow the public to contribute, maintain, and use geo-spatial data. One of the most prominent and successful VGI project is OpenStreetMap (OSM), where more than one million volunteers collected and contributed data that is obtainable for everybody. However, this kind of contribution mechanism is usually associated with data quality issues, e.g., geographic entities such as gardens or parks can be assigned with inappropriate classification by volunteers. Based on the observation that geographic features usually inherit certain properties and characteristics, we propose a novel classification-based approach allowing the identification of entities with inappropriate classification. We use the rich data set of OSM to analyze the properties of geographic entities with respect to their implicit characteristics in order to develop classifiers based on them. Our developed classifiers show high detection accuracies. However, due to the absence of proper training data we additionally performed a user study to verify our findings by means of intra-user-agreement. The results of our study support the detections of our classifiers and show that our classification-based approaches can be a valuable tool for managing and improving VGI data.

Situated local and global orientation in mobile you-are-here maps

This paper presents a novel solution to the focus-and-context problem of mobile maps provided for local and global orientation. Our solution is inspired by the design principles of static You-Are-Here maps and realizes principles of human spatial cognition to enable efficient communication of location information. We further propose selective interaction with the presented information to improve the speed and accuracy of interpretation of the geographic information. Tests show strong evidence for the cognitive and interaction efficiency of the resulting maps, as users were faster and more accurate than with conventional mobile maps.

Data Quality Assurance for Volunteered Geographic Information

The availability of technology and tools enables the public to participate in the collection, contribution, editing, and usage of geographic information, a domain previously reserved for mapping agencies or companies. The data of Volunteered Geographic Information (VGI) systems, such as OpenStreetMap (OSM), is based on the availability of technology and participation of individuals. However, this combination also implies quality issues related to the data: some of the contributed entities can be assigned to wrong or implausible classes, due to individual interpretation of the submitted data, or due to misunderstanding about available classes. In this paper we propose two methods to check the integrity of VGI data with respect to hierarchical consistency and classification plausibility. These methods are based on constraint checking and machine learning methods. They can be used to check the validity of data during contribution or at a later stage for collaborative manual or automatic data correction.

Route Aware Maps: Multigranular Wayfinding Assistance

Abstract In unfamiliar environments, people need assistance to find their way. One predominant form of such assistance is maps. In constructing these maps, there is a conflict between concentrating on the essential information for wayfinding, namely the route, and providing overview information of the environment. The former eases information extraction by reducing visual clutter, the latter allows for reorientation in the environment even if the route has been left. In this paper we present route aware maps, an approach that combines the best of both (map) worlds. We argue how route information can be embedded in its surrounding environment, i.e., the global spatial context, without introducing unnecessary visual clutter. We present a construction process that results in route aware maps and detail each step of this process. Route aware maps shall ease information extraction by focusing on the route as the crucial piece of information and at the same time impart the feeling of efficient and safe navigation by keeping the wayfinder in global context. Providing a global context in route following invokes spatial awareness with respect to the overall environment and, thus, decreases the (felt) risks of making wayfinding errors.

Mobile4D: crowdsourced disaster alerting and reporting

Small and large-scale disasters are a major factor for poverty. When information is sent out at an early stage and directly to people affected, impact on environment, people, livestock, crop, and belongings can be minimized. We present Mobile4D, an integrated mobile crowdsourcing-based disaster alerting and reporting system tested in Lao PDR. With Mobile4D it is possible to gather information from affected people, to establish direct communication channels between affected people and administrative units, and to rapidly distribute information to regions and people struck by disasters.

What Do Focus Maps Focus On

Maps are an important, everyday medium to communicate spatial information. We are faced with a great variety of different maps used for different purposes. While many of these maps are task-specific and concentrate on specific pieces of information, often they do not support map reading to extract the information relevant for the task at hand. In this paper, we explore the concept of focus maps. This concept has been previously presented with a restricted scope, however it covers a range of different kinds of maps that all focus a map users attention on the relevant information, be it specific features or areas. We discuss their general properties and the importance of context for designing such maps, and introduce a toolbox for constructing schematic mapsthat provides a generic way of generating the different kinds of maps discussed. Furthermore, we provide empirical evidence supporting our approach and outline how navigation in 3D virtual environments may benefit from a transfer of the proposed concept of focus maps from 2D to 3D.

Micro-mapping with smartphones for monitoring agricultural development

Monitoring and evaluating progress and impact of development projects is a critical aspect. In this paper, we show how we can use a smartphone based system to intuitively retrieve the exact geometry of smaller objects, making it suitable to assess agricultural entities like fields or ponds with their exact extent and location. The systems simple and intuitive workflow can be used by laymen and thus allows for crowdsourcing geo-data on a local level.