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Final CALL FOR PAPERS: ISCRAM has extended the Deadline to December 10

15th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2018)
May 20-23, 2018, Rochester, NY, USA
https://iscram2018.rit.edu/submissions

Track: Geospatial Technologies and Geographic Information Science for Crisis Management (GIS)
https://iscram2018.rit.edu/sites/rit.edu.iscram2018/files/docs/ISCRAM_2018_track_GIS.pdf

Deadline for paper submissions: December 10, 2017

Track Description

With disasters and disaster management being an “inherently spatial” problem, geospatial information and technologies have been widely employed for supporting disaster and crisis management. This includes SDSS and GIS architectures, VGI, spatial databases, spatial-temporal methods, as well as geovisual analytics technologies, which have a great potential to build risk map, estimate damaged areas, define evacuation routes, and plan resource distribution. Collaborative platforms like OSM have been also employed to support disaster management (e.g., near real-time mapping). Nevertheless, all these geospatial big data pose new challenges for not only geospatial data visualization, but also data modeling and analysis; existing technologies, methodologies, and approaches now have to deal with data shared in various formats, different velocities, and uncertainties. Furthermore, new issues have been also emerging in urban computing and smart cities for making communities more resilient against disasters. In line with this year’s conference theme, the GIS Track particularly welcomes submissions addressing aspects of geovisualization in disaster risk and crisis research. This includes SDSS, near-real-time mapping, situational awareness, VGI, spatio-temporal modeling, urban computing, and other related aspects. We seek conceptual, theoretical, technological, methodological, empirical contributions, as well as research papers employing different methodologies, e.g., design-oriented research, case studies, and action research. Solid student contributions are welcome.

Track topics are therefore focused on but not limited to the following list.

Geospatial data analytics for crisis management
Location-based services for crisis management
Location-based technologies for crisis management
Geospatial ontology for crisis management
Geospatial big data in the context of disaster and crisis management
Geospatial linked data for crisis management
Urban computing and geospatial aspects of smart cities for crisis management
Spatial Decision Support Systems for crisis management
Remote sensing for crisis management
Geospatial intelligence for crisis management
Spatial data management for crisis management
Spatial data infrastructure for crisis management
Geovisual analytics for crisis management
Spatial-temporal modeling in disaster and crisis context
Crisis mapping and geovisualization
Crowdsourcing and VGI in the context disaster and crisis management
Spatial analysis of OpenStreetMap (OSM) data for crisis management
Spatial analysis of social media messages in the context of crisis management
Interoperability aspects regarding disaster-related geodata

Important Dates

Submission of Completed Research papers (CoRe): December 10, 2017
Acceptances or otherwise of Completed Research papers (CoRe): January 7, 2018
Submission of Work in Progress (WiPe) papers, demos and posters: January 21, 2018.
Acceptances or otherwise of Work in Progress (WiPe) papers, demos and posters: February 18, 2018.
Camera Ready Paper: March 4, 2018.

iscram 2018
Paper submission guidelines

https://iscram2018.rit.edu/submissions

Track Chairs

Dr João Porto de Albuquerque (primary contact)
University of Warwick, United Kingdom

Prof. Dr. Alexander Zipf
University of Heidelberg, Germany

Dr Flávio E. A. Horita
University of São Paulo, Brazil

Software Engineer OSM Routing Services, Backend & Algorithms
Heidelberg Institute for Geoinformation Technology (HeiGIT)

You genuinely enjoy developing open source Geoinformation Services used by thousands on a daily basis? You are a highly motivated Java Backend Developer? And you love using and enhancing OpenStreetMap for high-performance services for global coverage? Then we actually might have a suitable and interesting job for you.

In order to promote technology transfer and applied research in the area of ​​Geoinformatics, the Heidelberg Institute for Geoinformation Technology (HeiGIT http://www.heigit.org) is currently being established with the support of the Klaus-Tschira Foundation. In the future this endeavour is to be continued in the future as an independent institute. To this end we are looking for a motivated Software Developer in the field of Routing Services (Java Backend). Depending on your experience the tasks are related to at least one of the following areas:

  • Route Planning, Smart Mobility and Navigation Intelligence
  • Development and design of innovative routing services, algorithms and extensions for the well-known open source project http://OpenRouteService.org (Java)
  • Extension of the services infrastructure of various location-based services (LBS) using OpenStreetMap
  • Development of highly performant algorithms, methods and GI web services, especially for the analysis and data enrichment of heterogeneous global geodata sets e.g. from the Social Web, OpenStreetMap, etc., especially in the domain of routing, traffic and navigation etc.

We offer an attractive position in an interdisciplinary team in a highly dynamic and growing market. HeiGIT is and will be related closely to the Department of Geoinformatics, which is member of the Interdisciplinary Center for Scientific Computing (IWR) and the Heidelberg Center for the Environment (HCE). We offer a stimulating interdisciplinary research environment with many personal development opportunities.

We expect a highly motivated backend developer with solid experience in Java or C++ and an above-average university degree in one of the subjects such as Geoinformatics, Computer Science, Geography, Mathematics or similar.

The position is to be filled as soon as possible and for administrative reasons initially limited to end of June 2019 - with the option of sustainable extension. Please send application documents (CV, certificates, references, etc.) as soon as possible to zipf@uni-heidelberg.de (best before 08.01.2018)

http://openrouteservice.org

http://go.openrouteservice.org

http://disaster.openrouteservice.org

This week the 11th Workshop on Geographic Information Retrieval (GIR’17) will be held at Mathematikon, Heidelberg University, Germany. This workshop will address all aspects of Geographic Information Retrieval - such as the provision of methods to identify geographic scope, retrieve and relevance rank documents or other resources from both unstructured and partially structured collections on the basis of queries specifying both theme and geographic scope.

Christian Jensen gives a keynote talk at the workshop. The full workshop programme is available here GIR’17 is held in cooperation with ACM SIGSPATIAL and organized by Chris Jones and Ross Purves.

Enjoy the Christmas Market ;-)

Today we present the latest version and details on the technical API of OpenRouteService at the 7th GeoIT WhereCamp Conference Berlin. Tim from the Heidelberg Institute for Geoinformation Technology (HeiGIT) will talk about

Consuming Open Data for Highly Customizable Route Planning

He will show some examples of the very rich feature set and API of OpenRouteService like many specialized routing profiles (from heavy vehicles with many options to several specialized bike profiles (e-Bike, MTB, secure etc with further options.), or even healthy, quiet or wheelchair routing - even with crossing open spaces). Further API endpoints and services include fast and highly configurable isochrones for accessibility analysis, geocoder, the Locations API and the brand new Matrix API,, as well as navigation features such as Landmark based navigation. ORS is open source, based on OpenStreetMap data and is also the routing engine of the German national “Bundesamt für Kartographie und Geodesie” (BKG).

If you are eager to play around with these features feel free to browse to OpenRouteService.org and if you can’t wait to use the API directly, please sign up for a key at go.openrouteservice.org

Openrouteservice
Openrouteservice API documentation

Statistical parameters often vary across geographic landscapes. For instance, when the the variance of georeferenced random variables is not stable, this might hint on variegated processes or contextual conditions in different sub-regions. Analysing such heterogeneous areas is challenging because an explicit consideration of this so-called spatial heterogeneity is required. Various approaches exist that take account of spatial heterogeneity. For instance, geographically weighted regression estimates local regression coefficients and adapted spatial hot-spot estimators like the O-statistic introduced by Keith Ord & Arthur Getis takes account of global structures when evaluating local conditions.

A related objective is the analysis of the heterogeneity itself, instead of taking account of it in the assessment of other statistical characteristics. This leads to the question of the role of the geographic arrangement ofspatial random variables for parameters such as the variance. The way how variables are situated in relation to each other in geographic space may either increase or decrease the variance, or it might have no effect at all. These insights would make an important contribution to a better characterization of the processes in different sub-regions.

In a recent paper we put forward a novel statistical test called ‘Local Spatial Dispersion’ (LSD) that allows to draw inference about relationships between the geographic arrangement of random variables and their variance. The presented test relates spatially weighted local residuals with their randomized averages estimated from local contexts. Hypotheses about relationships are thereby only tested locally, without taking account of other parts of the analysed region. Because local subsets might become small quickly and because this can cause small-sample issues, a Bayesian estimation technique for generating additional synthetic local mean values is proposed. This ensures that Monte-Carlo randomizations can be carried out even in local subregions that are sparsely sampled.

Application of the method to a LiDAR-derived change dataset shows promising results. For example, it is possible to characterize a boundary between a mown and an unmown part of a meadow in detail, and to disclose haystacks and other features in other sub-regions. Our experiments also show little correlation of the proposed technique with the general level of variability. This means that the technique is driven by the influence of spatial pattern on the variance, as it is intended. Further, our results suggest that there is only a negligible relationship with Moran’s I, an estimator of spatial autocorrelation, which shows that our proposed method is able to reveal additional structures in datasets.

Westerholt, R., Resch, B., Mocnik, F.-B., and Hoffmeister, D. (2017): A statistical test on the local effects of spatially structured variance. International Journal of Geographical Information Science, volume and issue pending, pp. pending. DOI: 10.1080/13658816.2017.1402914.

The joint interpretation of LOSH (a technique for assessing local variance patterns in relation to the overall region) and our proposed method LSD shows a detailed characterization of spatial patterns within the variance of the mapped random variables. The western part of the map shows the unmown part, whereas the eastern part visualizes a mown meadow.

The joint interpretation of LOSH (a technique for assessing local variance patterns in relation to the overall region) and our proposed method LSD shows a detailed characterization of spatial patterns within the variance of the mapped random variables. The western part of the map shows an unmown area, whereas the eastern part visualizes the mown part of a meadow.

On November 23d, the 9th “Fachaustausch Geoinformation“, a yearly networking  event  for professionals in the geodata and geoinformation domain organised by GeoNet.MRN, took place in Heidelberg’s Print Media Academy. GIScience research group at Heidelberg University and HeiGIT (Heidelberg Center for Geographic Information Technology) presented current research and projects at the event’s exhibition. Theresia Bauer, Baden-Württemberg’s Minister for Science, Research, and the Arts visited our stand to learn about the Global Climate Protection Map, an interactive, map-based information source for retrieving relevant information on the topics of energy and sustainability from OpenStreetMap data. Prof. Dr. Alexander Zipf gave a keynote about analysis and usage potential of free and open geodata from a Heidelberg perspective. A talk by our team member Dr. Tessio Novack presented new approaches for green and healthy routing. Many interesting discussions developed around these and other topics during this well-visited one-day event. We are very much looking forward to participating again next year.

Finding the shortest path through open spaces is a well-known challenge for pedestrian routing engines. A common solution is routing on the open space boundary, which causes in most cases an unnecessarily long route. A possible alternative is to create a subgraph within the open space. In a recently published paper authors from GIScience Research Group Heidelberg (Hahmann et al. 2017) assess this approach and investigate its implications for routing engines. A number of algorithms (Grid, Spider-Grid, Visibility, Delaunay, Voronoi, Skeleton) have been evaluated by four different criteria:

(i) Number of additional created graph edges,

(ii) additional graph creation time,

(iii) route computation time,

(iv) routing quality.

It is shown that each algorithm has advantages and disadvantages depending on the use case. They identify the algorithms Visibility with a reduced number of edges in the subgraph and Spider-Grid with a large grid size to be a good compromise in many scenarios.

An enhanced version of the visibility algorithm has been implemented in the Open Online Route planning system OpenRouteService.org (Schmitz et al. 2008; Neis & Zipf 2008) using OSM data. The implementation is in particular also useful for e.g. wheelchair routing (Zipf et al. 2016).

You can test the result for pedestrian routing including open areas explicitly labeled as squares in OSM at the recently introduced research platform labs.openrouteservice.org for currently all of Germany.

Hahmann, S., J. Miksch, B. Resch, J. Lauer & A. Zipf (2017): Routing through open spaces – a performance comparison of algorithms.
Geo-spatial Information Science (GSIS). Taylor & Francis. Online First. Issue and pp pending. Open Access. http://www.tandfonline.com/doi/full/10.1080/10095020.2017.1399675

we cordially invite everybody interested to our next open GIScience colloquium talk

State of the Art of Event Detection from Geo-tagged Twitter Data

Diao Lin
Chair of Cartography, Technical University of Munich

Time and date: Mon, November 27, 2:15 pm
Venue: INF 348, Room 015, Department of Geography, Heidelberg University

The speaker tries to give a structured and comprehensive overview of event detection from geo-tagged twitter data and present some open questions. Precisely, it starts with an introduction of the basic conceptions to answer questions like: what is event and event detection in social media, then an overall workflow of event detection will be given. Based on existed techniques applied in recent papers, three different approaches of event detection focus on: clustering driven approaches, anomaly detection driven approaches, and topic modelling driven approaches will be analysed in terms of their algorithms and advantages and disadvantages. The talk ends with some open questions regarding the research challenges (e.g. scale problem), and trends (e.g. multi-source event detection) in the field of event detection from the perspective of GIScience.

The Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University is a leading institution in research and education in scientific computing in a multitude of interdisciplinary topics - covering science, engineering and humanities. Computational methods are a very important foundation in GIScience / Geoinformatics with particular focus on the investigation of geospatial data and geospatial computational methods. Alexander Zipf has been PI for GIScience / Geoinformatics at IWR for many years now.

Recently, Bernhard Höfle has been appointed new head of a main research group at IWR and thus the second group from GIScience Heidelberg. The particular focus of Bernhard’s group will be the development of new computational methods for “3D Geospatial Data Processing” and interdisciplinary applications in the broad field of digital and computational environmental sciences. Furthermore, he also becomes investigator at the Heidelberg Graduate School MathComp (HGS MathComp) where several collaborations with other IWR groups are already running.

Not undercover but on the cover: We were selected as cover story of the ISPRS International Journal of Geo-Information, Volume 6, Issue 11.

In our research on “Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM“, we apply rapid landscape line detection to extract historic vegetable garden walls based on topographic information from a LiDAR-derived high-resolution digital elevation model (DEM). The spatial information on slope, curvature, and openness enables the identification of the location of anthropogenic landscape features. Three rapid processes used in this study include the derivation of topographic parameters, line extraction and aggregation. Results show that wall line detection with multiple topographic parameter images is a simple means of obtaining essential historic wall feature information. The aggregation of three individual detection results from slope, curvature, and openness increases the accuracy of identification of historic anthropogenic features. Further research will consider the unmanned aerial vehicle for collecting point clouds and spectral information of image photos. View the paper

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