Spatial and Temporal Patterns in Volunteer Data Contribution Activities
A Case Study of eBird
Bibliographic Data
| ID | 22033802 |
|---|---|
| Authors | Guiming Zhang (0000-0001-7064-2138, University of Denver, corresponding author) |
| Year | 2020 |
| Volume | 9 |
| Issue | 10 |
| Pages | 597 |
| Publication date | 2020-10-11 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9100597 |
| OpenAlex | W3092450323 |
| Language | EN |
| Citations received | 8 |
| References cited | 45 |
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
Biology · Cartography · Citizen science · Crowdsourcing · Data science · Geography · Remote sensing · Spatial contextual awareness · Temporal scales · Volunteered Geographic Information · World Wide Web · Computer Science · Data-Driven Disease Surveillance · Geographic Information Systems Studies · Species Distribution and Climate Change · Ecology
Community-Centred Environmental Discourse
Infrastructure and the ethnographic-cartographic production of urban bird species richness
Multi-GPU-Parallel and Tile-Based Kernel Density Estimation for Large-Scale Spatial Point Pattern Analysis
Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation
Exploring visitors’ nightlife using geo-tagged social media
A framework for contextualizing social‐ecological biases in contributory science data
Temporal trends in opportunistic citizen science reports across multiple taxa
Hills thought to be mountains
OpenStreetMap
A Review of Sampling Effects and Response Bias in Internet Participatory Mapping (PPGIS/PGIS/VGI)
eBird
Maximum entropy modeling of species geographic distributions
The eBird enterprise
Modeling of species distributions with Maxent
Quality Assessment of the French OpenStreetMap Dataset
Social media approaches to modeling wildfire smoke dispersion
Global patterns of current and future road infrastructure
Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr
Diversity in volunteered geographic information
Citizens as sensors
| Unique citing works | 8 |
|---|---|
| Citations per year | 2 |
| Citation span | 2022 - 2025 (4) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 7 |