{"status":"public","department":[{"_id":"ChLa"}],"publist_id":"6906","type":"conference","publication_identifier":{"isbn":["978-153862714-3"]},"month":"12","scopus_import":1,"publication":"2017 IEEE International Conference on Big Data","year":"2017","oa_version":"None","page":"3760 - 3763","citation":{"ama":"Pielorz J, Prandtstetter M, Straub M, Lampert C. Optimal geospatial volunteer allocation needs realistic distances. In: <i>2017 IEEE International Conference on Big Data</i>. IEEE; 2017:3760-3763. doi:<a href=\"https://doi.org/10.1109/BigData.2017.8258375\">10.1109/BigData.2017.8258375</a>","ista":"Pielorz J, Prandtstetter M, Straub M, Lampert C. 2017. Optimal geospatial volunteer allocation needs realistic distances. 2017 IEEE International Conference on Big Data. Big Data, 3760–3763.","ieee":"J. Pielorz, M. Prandtstetter, M. Straub, and C. Lampert, “Optimal geospatial volunteer allocation needs realistic distances,” in <i>2017 IEEE International Conference on Big Data</i>, Boston, MA, United States, 2017, pp. 3760–3763.","mla":"Pielorz, Jasmin, et al. “Optimal Geospatial Volunteer Allocation Needs Realistic Distances.” <i>2017 IEEE International Conference on Big Data</i>, IEEE, 2017, pp. 3760–63, doi:<a href=\"https://doi.org/10.1109/BigData.2017.8258375\">10.1109/BigData.2017.8258375</a>.","chicago":"Pielorz, Jasmin, Matthias Prandtstetter, Markus Straub, and Christoph Lampert. “Optimal Geospatial Volunteer Allocation Needs Realistic Distances.” In <i>2017 IEEE International Conference on Big Data</i>, 3760–63. IEEE, 2017. <a href=\"https://doi.org/10.1109/BigData.2017.8258375\">https://doi.org/10.1109/BigData.2017.8258375</a>.","short":"J. Pielorz, M. Prandtstetter, M. Straub, C. Lampert, in:, 2017 IEEE International Conference on Big Data, IEEE, 2017, pp. 3760–3763.","apa":"Pielorz, J., Prandtstetter, M., Straub, M., &#38; Lampert, C. (2017). Optimal geospatial volunteer allocation needs realistic distances. In <i>2017 IEEE International Conference on Big Data</i> (pp. 3760–3763). Boston, MA, United States: IEEE. <a href=\"https://doi.org/10.1109/BigData.2017.8258375\">https://doi.org/10.1109/BigData.2017.8258375</a>"},"date_created":"2018-12-11T11:48:18Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2017-12-01T00:00:00Z","language":[{"iso":"eng"}],"publisher":"IEEE","title":"Optimal geospatial volunteer allocation needs realistic distances","doi":"10.1109/BigData.2017.8258375","publication_status":"published","day":"01","author":[{"full_name":"Pielorz, Jasmin","first_name":"Jasmin","id":"49BC895A-F248-11E8-B48F-1D18A9856A87","last_name":"Pielorz"},{"first_name":"Matthias","full_name":"Prandtstetter, Matthias","last_name":"Prandtstetter"},{"last_name":"Straub","first_name":"Markus","full_name":"Straub, Markus"},{"last_name":"Lampert","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","full_name":"Lampert, Christoph"}],"date_updated":"2021-01-12T08:13:55Z","abstract":[{"lang":"eng","text":"Modern communication technologies allow first responders to contact thousands of potential volunteers simultaneously for support during a crisis or disaster event. However, such volunteer efforts must be well coordinated and monitored, in order to offer an effective relief to the professionals. In this paper we extend earlier work on optimally assigning volunteers to selected landmark locations. In particular, we emphasize the aspect that obtaining good assignments requires not only advanced computational tools, but also a realistic measure of distance between volunteers and landmarks. Specifically, we propose the use of the Open Street Map (OSM) driving distance instead of he previously used flight distance. We find the OSM driving distance to be better aligned with the interests of volunteers and first responders. Furthermore, we show that relying on the flying distance leads to a substantial underestimation of the number of required volunteers, causing negative side effects in case of an actual crisis situation."}],"_id":"750","quality_controlled":"1","conference":{"end_date":"2017-12-14","start_date":"2017-12-11","name":"Big Data","location":"Boston, MA, United States"}}