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Relevant bibliographies by topics / Crowdsourced Mapping

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Journal articles

Academic literature on the topic 'Crowdsourced Mapping'

Author: Grafiati

Published: 24 February 2024

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Journal articles on the topic "Crowdsourced Mapping"

1

Soney, Johns. "Crowdsourced Pothole Mapping and Route Navigation." International Journal of Wireless Communications and Network Technologies 8, no. 3 (2019): 21–24. http://dx.doi.org/10.30534/ijwcnt/2019/05832019.

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2

Dodge, Martin, and Rob Kitchin. "Crowdsourced Cartography: Mapping Experience and Knowledge." Environment and Planning A: Economy and Space 45, no. 1 (2013): 19–36. http://dx.doi.org/10.1068/a44484.

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3

Jestico, Ben, Trisalyn Nelson, and Meghan Winters. "Mapping ridership using crowdsourced cycling data." Journal of Transport Geography 52 (April 2016): 90–97. http://dx.doi.org/10.1016/j.jtrangeo.2016.03.006.

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4

Rice, Rebecca M., Ahmad O. Aburizaiza, Matthew T. Rice, and Han Qin. "Position Validation in Crowdsourced Accessibility Mapping." Cartographica: The International Journal for Geographic Information and Geovisualization 51, no. 2 (2016): 55–66. http://dx.doi.org/10.3138/cart.51.2.3143.

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5

Gkeli, Maria, and Chryssy Potsiou. "3D crowdsourced parametric cadastral mapping: Pathways integrating BIM/IFC, crowdsourced data and LADM." Land Use Policy 131 (August 2023): 106713. http://dx.doi.org/10.1016/j.landusepol.2023.106713.

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6

Groß, Simon, Benjamin Herfort, Sabrina Marx, and Alexander Zipf. "Exploring MapSwipe as a Crowdsourcing Tool for (Rapid) Damage Assessment: The Case of the 2021 Haiti Earthquake." AGILE: GIScience Series 4 (June 6, 2023): 1–11. http://dx.doi.org/10.5194/agile-giss-4-5-2023.

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Abstract. Fast and reliable geographic information is vital in disaster management. In the late 2000s, crowdsourcing emerged as a powerful method to provide this information. Base mapping through crowdsourcing is already well-established in relief workflows. However, crowdsourced post-disaster damage assessment is researched but not yet institutionalized. Based on MapSwipe, an established mobile application for crowdsourced base mapping, a damage assessment approach was developed and tested for a case study after the 2021 Haiti earthquake. First, MapSwipe’s damage mapping results are assessed for quality by using a reference dataset in regard to different aggregation methods. Then, the MapSwipe data was compared to an already established rapid damage assessment method by the Copernicus Emergency Management Service (CEMS). Crowdsourced building damage mapping achieved a maximum F1-score of 0.63 in comparison to the reference data set. MapSwipe and CEMS data showed only slight agreement with Cohen’s Kappa values reaching a maximum of 0.16. The results highlight the potential of crowdsourcing damage assessment as well as the importance for a scientific evaluation of the quality of CEMS data. Next steps for further integrating the presented workflow into MapSwipe are discussed.

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7

McCullagh, M., and M. Jackson. "CROWDSOURCED MAPPING – LETTING AMATEURS INTO THE TEMPLE?" ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W1 (May 22, 2013): 399–432. http://dx.doi.org/10.5194/isprsarchives-xl-1-w1-399-2013.

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8

Pipelidis, Georgios, Omid Moslehi Rad, Dorota Iwaszczuk, Christian Prehofer, and Urs Hugentobler. "Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data." Sensors 18, no. 2 (2018): 480. http://dx.doi.org/10.3390/s18020480.

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9

Branion-Calles, Michael, Trisalyn Nelson, and Meghan Winters. "Comparing Crowdsourced Near-Miss and Collision Cycling Data and Official Bike Safety Reporting." Transportation Research Record: Journal of the Transportation Research Board 2662, no. 1 (2017): 1–11. http://dx.doi.org/10.3141/2662-01.

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Official sources of cyclist safety data suffer from underreporting and bias. Crowdsourced safety data have the potential to supplement official sources and to provide new data on near-miss incidents. BikeMaps.org is a global online mapping tool that allows cyclists to record the location and details of near misses and collisions they experience. However, little is known about how the characteristics of near-miss and collision events compare. Further, the question remains whether the characteristics of crowdsourced collision data are similar to those of collision data captured by official insurance reports. The objectives of this study were twofold: ( a) to assess similarities and differences in near misses and collisions reported to BikeMaps.org and ( b) to assess similarities and differences in collisions reported to BikeMaps.org and to an official insurance data set. Logistic regression was used first to model the odds of crowdsourced near-miss reports as opposed to collision reports and then to model the odds of crowdsourced as opposed to official insurance collision reports, as a function of incident circumstances. The results indicated higher odds of crowdsourced reports of near misses than of crowdsourced collision reports for commute trips, interactions with motor vehicles, and in locations without bicycle-specific facilities. In addition, relative to insurance reports, crowdsourced collision reports were associated with peak traffic hours, nonintersection locations, and locations where bicycle facilities were present. These analyses indicated that crowdsourced collision data have potential to fill in gaps in reports to official collision sources and that crowdsourced near-miss reporting may be influenced by perceptions of risk.

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10

Lingua, Federico, Nicholas C. Coops, Valentine Lafond, Christopher Gaston, and Verena C. Griess. "Characterizing, mapping and valuing the demand for forest recreation using crowdsourced social media data." PLOS ONE 17, no. 8 (2022): e0272406. http://dx.doi.org/10.1371/journal.pone.0272406.

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Mapping and valuing of forest recreation is time-consuming and complex, hampering its inclusion in forest management plans and hence the achievement of a fully sustainable forest management. In this study, we explore the potential of crowdsourced social media data in tackling the mapping and valuing of forest recreation demand. To do so, we assess the relationships between crowdsourced social media data, acquired from over 350,000 Flickr geotagged pictures, and demand for forest recreation in British Columbia (BC) forests. We first identify temporal and spatial trends of forest recreation demand, as well as the countries of origin of BC forests visitors. Second, we estimate the average number of annual recreational visits with a linear regression model calibrated with empirically collected secondary data. Lastly, we estimate recreational values by deriving the average consumer surpluses for the visitors of BC forested provincial parks. We find that annually, on average, over 44 million recreational experiences are completed in BC forests, with peaks during the summer months and during the weekends. Moreover, a crowdsourced travel cost approach allowed us to value the recreational ecosystem service in five forested provincial parks ranging from ~2.9 to ~35.0 million CAN$/year. Our findings demonstrate that social media data can be used to characterize, quantify and map the demand for forest recreation (especially in peri-urban forests), representing a useful tool for the inclusion of recreational values in forest management. Finally, we address the limitations of crowdsourced social media data in the study of forest recreation and the future perspectives of this rapidly growing research field.

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