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S17: Kartographische Visualisierung
Freitag, 09.03.2018:
13:15 - 14:45

Chair der Sitzung: Jana Moser
Ort: Hörsaal 2750

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SNmultimodal – a Pilot Study for Semantically Enriched and User-Oriented Multimodal Navigation

E. Bogucka, C. Murphy

Technische Universität München, Deutschland

Within the mFUND research initiative, the Federal Ministry of Transport and Infrastructure (BMVI) has been funding research and development proposals for digital data-driven applications for Mobility 4.0. Among them is SNmultimodal, a project executed at the Chair of Cartography of the Technische Universität München (TUM). This pilot study demonstrates the feasibility of enriching individually tailored mobility routes with the information on different user’s needs, modes of transport and event data from Volunteered Geographic Information (VGI) and social networks.

Case studies of the urban areas in Berlin and Munich are implemented for this preliminary project. The proposed methodology covers the acquisition of geo- and semantic data from public domain repositories (i.e. mCLOUD), VGI (i.e. OpenStreetMap ) and social networks (i.e. Twitter), as well as processes of data transformation, harmonisation and map matching. As a base for multimodal networks, public transportation services (e.g. buses, trains, trams) and modern urban modes of mobility (car- and bike sharing) were chosen. Traffic network datasets were converted into network graphs of the individual modes of transport and merged to a multimodal dataset using the switch point concept. These switch points act as connecters at places where a change of the travel modality is possible. The upcoming work will concentrate on extending routing schemes by integrating context-aware information such as landmarks, city lights and negative traffic events detected from social media.

The visual techniques and tools for conveying multimodal navigation options will be tested within a web-based prototype application. In order to represent the routing possibilities, easy to grasp cartographic representations are created for individual travels. User-oriented routing suggestions will be displayed according to different map zoom levels, in order to assist users in their choice and provide step-by-step movement instructions. The proposed approach has the potential to extend currently available route planning systems and to improve personalized routing visualizations in the real-time navigation applications. In the final step of this project it is planned to develop a mobile app that runs the extended multimodal navigation for the two pilot cities and offers user-oriented routing enhanced by visualisation of event-based traffic events.

Modeling and visualizing the spatial uncertainty of moving transport hubs in urban spaces - a case study in NYC with taxi and boro taxi trip data

A. Keler

Technische Universität München (TUM), Deutschland

1 Introduction

Urban human mobility relies on the presence of transport hubs, which connect different modes of transport, public and private. This paper proposes a technique for relating taxi trip origins and destination hotspots for gaining knowledge on the spatial uncertainties of transport hubs, more precisely their movements within specific times. The outcomes of applying the techniques are matter of further investigation of spatial uncertainty perception, representation, and visualization. In the stages of the approach, the outcomes of transport hub movements are related with more general functional transport regions resulting from NYC public transport services.

2 Motivation

Urban environments are characterized by complex and dynamically changing human mobility. Vehicle movement trajectories of urban vehicle fleets, as taxi fleets, can help extracting this knowledge. Taxi trip data is usable in many different applications of different research domains.

Due to often-immense sizes of the data extracts, many applications make only use of taxi trip origin and destination points. In an aggregated view, these points may be clustered into origin and destination hotspots, which are connectable, especially, when there are different taxi fleets with specific operational restrictions. This promotes the automated deduction of local knowledge on smaller scales of investigation.

Understanding time-dependencies and dynamics of moving transport hubs is beneficial for planning, decision-making, and various ongoing research in connection with smart cities and intelligent transportation systems (ITS).

3 Methods

The methods of the proposed technique include density-based point clustering, convex hull estimations, polygon matching, and intersection. The components are part of an extendible workflow. The aim of the mentioned polygonization of point clusters is, besides providing visibility at certain map scales, representing positional accuracies together with uncertainties in density-based connectivity of trip origins and destinations.

Matching between different types of areal polygons relies on the Dimensionally Extended nine-Intersection Model (DE-9IM) as introduced by Clementini et al. (1994).

4 Results

The outcomes of applying the techniques are matter of further inspections and visual analysis. The visualization of the polygon matching results aims to show the spatial uncertainty of boro taxi trip destination hotspots for at least four successive time windows.

Besides the resulting boro taxi trip destination hotspots, there are also extractable yellow taxi trip origin hotspots and functional transport regions resulting from public transport stations of subway and bus lines. This has a semantic connection to the restricted yellow zone and specific commuting patterns between Manhattan and the outer boroughs of NYC. The further steps include matching these spatiotemporal hotspots with characteristic transport hubs, and testing a specific visualization scheme for representing spatial and spatiotemporal uncertainties of the mentioned transport hubs.

5 References

Clementini, E.; Sharma, J. & Egenhofer, M. J., 1994: Modelling topological spatial relations: Strategies for query processing. Computers & Graphics, 18 (6), 815-822.

Visualising Spatial Uncertainty of Social Media Documents

C. E. Murphy

Lehrstuhl für Kartographie, Technische Universität München

Uncertainty Visualisation is one the current great challenges in cartographic research. The awareness of Cartographers to communicate various kinds of uncertainty has become considerably higher with the emergence of big data. There is no all-embracing methodology to display the nature of uncertainty, as a useful visualisation depends strongly on the type of uncertainty, the spatial data feature type as well as the source of uncertainty. This work tackles two location uncertainty problems within case studies that commonly appear when mapping geo-tagged microblogs of social media; (1) the communication of multiple uncertainty levels of georeferenced microblogs; and (2) the indication of spatial uncertainty caused by generalising distributions. While the latter case study empowers the user to intuitively depict the centres of gravity and the major orientation of multiple geo-tagged text document sets, both visualisation proposals allow a coincident visualisation that indicate positional uncertainty and semantic information such as keywords on one map face, which is particularly suitable for laymen.

In the past years, many related works address to analyse real world events and to reveal spatial patterns from social media such as Twitter. However, users publish tweets in different locational accuracy levels. Geo-tagged tweets are associated either directly with coordinates by a GPS enabled device, or, in a more inaccurate level, with a place (e.g. city) that is defined by a regional bounding box polygon. When geo-tagged tweets of contrasting accuracy levels are mixed in processing and display the need for indicating the uncertainty level becomes necessary. In this work, symbolisation strategies are to be presented for single depiction and aggregated sets of tweets that enable the user to understand the contrasting locational accuracy levels.

Furthermore, the uncertainty following generalisation processes is described. Large quantities of geo-tagged text documents have to be generalised within the visualisation process in order to keep a visualisation understandable and free from clutter. Commonly used spatial temporal clustering algorithms are extended by a semantic dimension that express the text similarity between tweets. The results are presented in form of the error ellipse, a well-known statistical graphic tool, which visually encodes statistics as a compact distributional summary. Each cluster is represented by one error ellipse. The form of the drawn ellipse shows the cluster´s centre of gravity, the major orientation of the distribution as well as indicates the extent of the geo-tagged text documents occurrence. The ellipse´s surface is graphically encoded with high incidence text keywords. These strategies of displaying the positional uncertainty of social media documents are presented by analysing Twitter microblogs during the Munich Oktoberfest.

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