Assessing uncertainty in VGI for emergency response

Camponovo, M.E.; Freundschuh, Scott M.

doi:10.1080/15230406.2014.950332

Cited by 35 publications

(17 citation statements)

References 14 publications

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“…While an exact match rate of 76.8% is very good, and is better than categorical accuracy found in research such as Camponovo and Freundschuh (2014), there is still some explanation necessary for some matching errors and disagreement in obstacle classification. Though the moderators are trained and given a rubric that declares the criteria for each obstacle type, there is still some ambiguity.…”

Section: Resultsmentioning

confidence: 71%

“…We also use Camponovo and Freundschuh's (2014) 69 research on prior knowledge of categorical accuracy of geocrowdsourced data. The data used in Camponovo and Freundschuh (2014) is from the Ushahidi Project, a humanitarian relief project that took place during the 2010 Haiti earthquake, where emergency responders were asked to categorize incoming messages into a series of primary and secondary categories. The accuracy rates for placement into primary categories were 50% and subcategories were only 27%.…”

Section: Social Moderation For Crowdsourced Geospatial Datamentioning

confidence: 99%

“…While the reports are mainly consistent, in that their positioning is within 3 meters for all obstacles (and less than 1 meter for non-area features), and in agreement for almost all obstacle types, there is still some inherent ambivalence in selecting a location as well as for determining obstacle type. However, as the research from Camponovo and Freundschuh (2014) shows, ambivalence may be an intrinsic part of the geocrowdsourcing process. In order to improve the consistency and accuracy of moderator's decisions, it is critical that we ensure they are trained and are allowed to make joint decisions when validating incoming data.…”

Section: F Fi Ig Gu Ur Re E 4 44 4 T To Ot Ta Al L C Co Ou Un Nt Tmentioning

confidence: 99%

See 2 more Smart Citations

Social Moderation and Dynamic Elements in Crowdsourced Geospatial Data: A Report on Quality Assessment, Dynamic Extensions and Mobile Device Engagement in the George Mason University Geocrowdsourcing Testbed

Rice¹,

Curtin²,

Pfoser³

et al. 2015

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A A r re ep po or rt t o on n q qu ua al li it ty y a as ss se es ss sm me en nt t, , d dy yn na am mi ic c e ex xt te en ns si io on ns s, , a an nd d m mo ob bi il le e d de ev vi ic ce e e en ng ga ag ge em me en nt t i in n t th he e G Ge eo or rg ge e M Ma as so on n U Un ni iv ve er rs si it ty y G Ge eo oc cr ro ow wd ds so ou ur rc ci in ng g T Te es st tb be ed AbstractThis report documents the development of the George Mason University Geocrowdsourcing Testbed (GMU-GcT), whose purpose is to provide a platform for studying the dynamics, limitations, and best practices of geocrowdsourcing. We present a comprehensive study of the social moderation process in the GMU-GcT and the quality parameters of information in the GMU-GcT. We present an analysis of device-based positioning in mobile geocrowdsourcing, and use the study and our analysis as a context for dynamic application extensions of the GMU-GcT in the areas of field-based obstacle moderation, obstacle interaction, and accessible routing.

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Section: Resultsmentioning

confidence: 71%

Section: Social Moderation For Crowdsourced Geospatial Datamentioning

confidence: 99%

Section: F Fi Ig Gu Ur Re E 4 44 4 T To Ot Ta Al L C Co Ou Un Nt Tmentioning

confidence: 99%

See 1 more Smart Citation

Social Moderation and Dynamic Elements in Crowdsourced Geospatial Data: A Report on Quality Assessment, Dynamic Extensions and Mobile Device Engagement in the George Mason University Geocrowdsourcing Testbed

Rice¹,

Curtin²,

Pfoser³

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Obstacle characterization in the GMU-GcT requires a shared understanding of obstacle categories between the system administrators and system end-users. This categorical characterization process, as noted by Camponovo et al (2014) in the context of the Haiti Earthquake response, is problematic. They report that over fifty percent of the messages to the Ushahidi web platform were mischaracterized with regard to emergency need.…”

Section: Moderation and Quality Assessment In The Gmu Geocrowdsourcinmentioning

confidence: 99%

Obstacle Characterization in a Geocrowdsourced Accessibility System

Qin

Aburizaiza

Rice

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission II, WG II/4KEY WORDS: Transitory Obstacles, Accessibility, Geocrowdsourcing, Data Quality, Semantics ABSTRACT:Transitory obstacles -random, short-lived and unpredictable objects -are difficult to capture in any traditional mapping system, yet they have significant negative impacts on the accessibility of mobility-and visually-impaired individuals. These transitory obstacles include sidewalk obstructions, construction detours, and poor surface conditions. To identify these obstacles and assist the navigation of mobility-and visually-impaired individuals, crowdsourced mapping applications have been developed to harvest and analyze the volunteered obstacles reports from local students, faculty, staff, and residents. In this paper, we introduce a training program designed and implemented for recruiting and motivating contributors to participate in our geocrowdsourced accessibility system, and explore the quality of geocrowdsourced data with a comparative analysis methodology.

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“…Given the increasing involvement of the public in the creation and use of geographic information through social media and citizen science projects, such considerations are also relevant for GIScience in general, as users may come up with diverse ontologies of the geographic domain. However, so far, much attention in the literature has been on using the data acquired through crowd-sourcing in a myriad of ways [67][68][69]. In contrast, more conceptual questions about crowd-sourced geographic data have so far received relatively little scientific attention [66,70,71].…”

Section: Implications For Using Sketch Maps In Decision-makingmentioning

confidence: 99%

What’s (Not) on the Map: Landscape Features from Participatory Sketch Mapping Differ from Local Categories Used in Language

Wartmann

Purves

2017

Land

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Participatory mapping of local land use as the basis for planning and decision-making has become widespread around the globe. However, still relatively little is known about the conceptual underpinnings of geographic information produced through participatory mapping in given cultural and linguistic settings. In this paper, we therefore address the seemingly simple question of what is (not) represented on maps through an exploratory case study comparing land use categories participants represented on sketch maps with categories elicited through more language-focused ethnographic fieldwork. To explore landscape categorization, we conducted sketch mapping with 29 participants and in-depth ethnographic fieldwork with 19 participants from the Takana indigenous people in the Bolivian Amazon. Sketch mapping resulted in 74 different feature types, while we elicited 156 landscape categories used in language, of which only 23 overlapped with feature types from the sketch mapping. Vegetation categories were highly diversified in language but seldom represented on maps, while more obviously anthropogenic features were represented on sketch maps. Furthermore, participants seldom drew culturally important landscape categories such as fallow plots or important plant harvesting sites on maps, with important potential consequences for natural resource management.

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Assessing uncertainty in VGI for emergency response

Cited by 35 publications

References 14 publications

Social Moderation and Dynamic Elements in Crowdsourced Geospatial Data: A Report on Quality Assessment, Dynamic Extensions and Mobile Device Engagement in the George Mason University Geocrowdsourcing Testbed

Social Moderation and Dynamic Elements in Crowdsourced Geospatial Data: A Report on Quality Assessment, Dynamic Extensions and Mobile Device Engagement in the George Mason University Geocrowdsourcing Testbed

Obstacle Characterization in a Geocrowdsourced Accessibility System

What’s (Not) on the Map: Landscape Features from Participatory Sketch Mapping Differ from Local Categories Used in Language

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