Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API

Abdulkarim, Bilal; Kamberov, Rustam; Hay, Geoffrey J.

doi:10.3390/rs6109691

Cited by 11 publications

(7 citation statements)

References 19 publications

(35 reference statements)

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“…The assessment could be used to assess the potential for a citizen science dataset to be combined with a conventionally collected dataset and determine the fitness for the hybridized dataset. As noted earlier in this article, hybrid datasets can fill in gaps and create a more comprehensive and complete dataset (Batty et al, 2010;Connors et al, 2012;Parker et al, 2012;Abdulkarim et al, 2014;Bruce et al, 2014;Upton et al, 2015). The assessment should be tested with standalone datasets to determine how the assessment can evaluate fitness for use when there are no conventionally collected datasets for comparison.…”

Section: Discussionmentioning

confidence: 99%

“…Hybrid datasets involve integrating the citizen science data with conventionally collected data (Elwood et al, 2012;Parker et al, 2012;Upton et al, 2015). Combined datasets (e.g., data mash-ups, hybrid datasets, or cross-validation) allow researchers to test out the accuracy or combine the datasets to fill in gaps (Batty et al, 2010;Connors et al, 2012;Parker et al, 2012;Abdulkarim et al, 2014;Bruce et al, 2014;Upton et al, 2015). Wentz and Shimizu (2018) suggest that an adaption of their assessment would be appropriate for citizen science/VGI data.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Fitness for Use of Citizen Science Data for Wildlife Monitoring

2021

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Contributory citizen science programs focused on ecological monitoring can produce fine-grained and expansive data sets across spatial and temporal scales. With this data collection potential, citizen scientists can significantly impact the ability to monitor ecological patterns. However, scientists still harbor skepticism about using citizen science data in their work, generally due to doubts about data quality. Numerous peer-reviewed articles have addressed data quality in citizen science. Yet, many of these methods are not useable by third-party scientists (scientists who are not directly involved in the citizen science program). In addition, these methods generally capture internal data quality rather than a dataset’s potential to be used for a specific purpose. Assessing data fitness for use represents a promising approach to evaluating data accuracy and quality for different applications and contexts. In this article, we employ a Spatial, Temporal, Aptness, and Application (STAAq) assessment approach to assess data fitness for use of citizen science datasets. We tested the STAAq assessment approach through a case study examining the distribution of caribou in Denali National Park and Preserve. Three different datasets were used in the test, Map of Life data (a global scale citizen science mobile application for recording species observations), Ride Observe and Record data (a program sponsored by the park staff where incentivized volunteers observe species in the park), and conventionally collected radio collar data. The STAAq assessment showed that the Map of Life and Ride Observe and Record program data are fit for monitoring caribou distribution in the park. This data fitness for use approach is a promising way to assess the external quality of a dataset and its fitness to address particular research or monitoring questions. This type of assessment may help citizen science skeptics see the value and potential of citizen science collected data and encourage the use of citizen science data by more scientists.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the Fitness for Use of Citizen Science Data for Wildlife Monitoring

2021

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“…An interesting solution that helps reduce the impact of urban areas on climate change is also monitoring energy consumption in buildings through the use of automatic thermal modeling of building structures. This approach proposed in previous studies [49,50] creates new challenges for the community of incorporating existing GIS vector data and engages the public to provide voluntary information on urban facilities from which a new knowledge base is created to support urban energy efficiency.…”

Section: Discussionmentioning

confidence: 99%

An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

et al. 2020

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Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Góra quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas.

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“…In the majority of CS projects, scientists provide opportunities and structures within which volunteers can engage in the scientific process, strengthen connections to their local community, become more scientifically literate, increase self-efficacy for doing science, and increase their social well-being (Bonney et al 2015;Jordan, Gray, and Howe 2011). Scientists benefit as participants contribute to their research or monitoring programs through data collection, analysis, and at times program design and research-question development, contributing in crucial ways to positive scientific outcomes (Abdulkarim, Kamberov, and Hay 2014;Bonney et al 2009;Couvet et al 2008;Gouveia et al 2004;Tulloch et al 2013;Dickinson, Zuckerberg, and Bonter 2010;Silvertown 2009). Benefits for program participants range tremendously and depend on the type of CS activity and the agenda, need, and background of participants.…”

Section: Benefits Of Citizen Sciencementioning

confidence: 99%

Going Beyond Hooked Participants: The Nibble-and-Drop Framework for Classifying Citizen Science Participation

Fischer

Cho

Storksdieck

2021

Citizen Science: Theory and Practice

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Many citizen science (CS) programs aim to grow and sustain a pool of enthusiastic participants who consistently contribute their efforts to a specific scientific endeavor. Consequently, much research has explored CS participants' motivations and their relationship to participant recruitment and retention. However, much of this research has focused on actively participating citizen scientists. If researchers want to elucidate the relationship between participant factors (such as demographics and motivations) and participant retention, it is necessary to develop a more comprehensive picture of the different degrees of participation in CS. This paper presents a framework for classifying participation throughout the participant's engagement in a CS project/program. We suggest a CS participation model that captures the dynamic nature of participation across an arc of volunteering. Called the Nibble-and-Drop Framework, the model describes multiple exit points and stages of contribution typical of participation in a CS program. Applying the framework to the NASA GLOBE Observer (GO) CS program, we found that it captured the dynamics of participation in a global-scale, mobile, app-based, contributorystyle CS project. The framework guided our analysis of how different participant factors correlate with degrees of participation. We found that participants were motivated to initially participate because they wanted to contribute to NASA research and science. Participants who dropped out of the program at various points often initially engaged through specific collection events and did not feel the need to continue contributing beyond the event; other drop-outs doubted whether their contributions were meaningful, showing again the need to ensure that participants understand the value of their engagement in a CS project.

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Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API

Cited by 11 publications

References 19 publications

Evaluating the Fitness for Use of Citizen Science Data for Wildlife Monitoring

Evaluating the Fitness for Use of Citizen Science Data for Wildlife Monitoring

An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

Going Beyond Hooked Participants: The Nibble-and-Drop Framework for Classifying Citizen Science Participation

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