Spatial and Temporal Patterns in Volunteer Data Contribution Activities: A Case Study of eBird

Volunteered geographic information (VGI) has great potential to reveal spatial and temporal dynamics of geographic phenomena. However, a variety of potential biases in VGI are recognized, many of which root from volunteer data contribution activities. Examining patterns in volunteer data contribution activities helps understand the biases. Using eBird as a case study, this study investigates spatial and temporal patterns in data contribution activities of eBird contributors. eBird sampling efforts are biased in space and time. Most sampling efforts are concentrated in areas of denser populations and/or better accessibility, with the most intensively sampled areas being in proximity to big cities in developed regions of the world. Reported bird species are also spatially biased towards areas where more sampling efforts occur. Temporally, eBird sampling efforts and reported bird species are increasing over the years, with significant monthly fluctuations and notably more data reported on weekends. Such trends are driven by the expansion of eBird and characteristics of bird species and observers. The fitness of use of VGI should be assessed in the context of applications by examining spatial, temporal and other biases. Action may need to be taken to account for the biases so that robust inferences can be made from VGI observations.

[1]  A-Xing Zhu,et al.  A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena , 2019, Int. J. Geogr. Inf. Sci..

[2]  Bo Markussen,et al.  What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements , 2016 .

[3]  D. Fink,et al.  Spatiotemporal exploratory models for broad-scale survey data. , 2010, Ecological applications : a publication of the Ecological Society of America.

[4]  M. Goodchild,et al.  Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr , 2013 .

[5]  Alexander Zipf,et al.  Temporal Analysis on Contribution Inequality in OpenStreetMap: A Comparative Study for Four Countries , 2016, ISPRS Int. J. Geo Inf..

[6]  W. Kendall,et al.  First-Time Observer Effects in the North American Breeding Bird Survey , 1996 .

[7]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[8]  Greg Brown,et al.  A Review of Sampling Effects and Response Bias in Internet Participatory Mapping (PPGIS/PGIS/VGI) , 2017, Trans. GIS.

[9]  Alexander Zipf,et al.  Coupling maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists , 2018, Int. J. Geogr. Inf. Sci..

[10]  Xueming Li,et al.  Density and diversity of OpenStreetMap road networks in China , 2015 .

[11]  Tao Pei,et al.  A citizen data-based approach to predictive mapping of spatial variation of natural phenomena , 2015, Int. J. Geogr. Inf. Sci..

[12]  Pascal Neis,et al.  Analyzing the Contributor Activity of a Volunteered Geographic Information Project - The Case of OpenStreetMap , 2012, ISPRS Int. J. Geo Inf..

[13]  D. Roy,et al.  Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour , 2016, Scientific Reports.

[14]  Guillaume Touya,et al.  Quality Assessment of the French OpenStreetMap Dataset , 2010, Trans. GIS.

[15]  Mao Ning Tuanmu,et al.  A global 1‐km consensus land‐cover product for biodiversity and ecosystem modelling , 2014 .

[16]  Stephen R. Baillie,et al.  Estimating species distributions from spatially biased citizen science data , 2020 .

[17]  S. Nielsen,et al.  Accounting for spatially biased sampling effort in presence‐only species distribution modelling , 2015 .

[18]  Sarah McCaffrey,et al.  Social media approaches to modeling wildfire smoke dispersion: spatiotemporal and social scientific investigations , 2017 .

[19]  C. Bittner Diversity in volunteered geographic information: comparing OpenStreetMap and Wikimapia in Jerusalem , 2017 .

[20]  F. A. La Sorte,et al.  Survey completeness of a global citizen‐science database of bird occurrence , 2019, Ecography.

[21]  Steve Kelling,et al.  Estimates of observer expertise improve species distributions from citizen science data , 2018 .

[22]  Aaron M Ellison,et al.  Observer bias and the detection of low-density populations. , 2009, Ecological applications : a publication of the Ecological Society of America.

[23]  Thomas G. Dietterich,et al.  The eBird enterprise: An integrated approach to development and application of citizen science , 2014 .

[24]  Georgina M. Mace,et al.  Distorted Views of Biodiversity: Spatial and Temporal Bias in Species Occurrence Data , 2010, PLoS biology.

[25]  M. Goodchild Citizens as sensors: the world of volunteered geography , 2007 .

[26]  Guiming Zhang,et al.  Enhancing VGI application semantics by accounting for spatial bias , 2019, Big Earth Data.

[27]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[28]  Brian L. Sullivan,et al.  eBird: Engaging Birders in Science and Conservation , 2011, PLoS biology.

[29]  Rodolphe Devillers,et al.  The life cycle of contributors in collaborative online communities -the case of OpenStreetMap , 2018, Int. J. Geogr. Inf. Sci..

[30]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[31]  Kathleen M Griffiths,et al.  Describing the distribution of engagement in an Internet support group by post frequency: A comparison of the 90-9-1 Principle and Zipf's Law , 2014 .

[32]  María B. García,et al.  A Novel Method to Handle the Effect of Uneven Sampling Effort in Biodiversity Databases , 2013, PloS one.

[33]  Daniel Fink,et al.  Correcting for bias in distribution modelling for rare species using citizen science data , 2018 .

[34]  Melinda Laituri,et al.  The art and science of multi-scale citizen science support , 2011, Ecol. Informatics.

[35]  H. Sauermann,et al.  Crowd science user contribution patterns and their implications , 2015, Proceedings of the National Academy of Sciences.

[36]  M. Huijbregts,et al.  Global patterns of current and future road infrastructure , 2018 .

[37]  Brian L. Sullivan,et al.  eBird: A citizen-based bird observation network in the biological sciences , 2009 .

[38]  W. Link,et al.  Observer differences in the North American Breeding Bird Survey , 1994 .

[39]  A-Xing Zhu,et al.  The representativeness and spatial bias of volunteered geographic information: a review , 2018, Ann. GIS.

[40]  Giles M. Foody,et al.  Crowdsourced geospatial data quality: challenges and future directions , 2019, Int. J. Geogr. Inf. Sci..

[41]  Marshall J. Iliff,et al.  Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves? , 2015, PloS one.

[42]  Jun Yu,et al.  A Human/Computer Learning Network to Improve Biodiversity Conservation and Research , 2012, AI Mag..

[43]  Vyron Antoniou,et al.  MEASURES AND INDICATORS OF VGI QUALITY: AN OVERVIEW , 2015 .