Validity of historical volunteered geographic information: Evaluating citizen data for mapping historical geographic phenomena

Studies on volunteered geographic information (VGI) have focused on examining its validity to reveal geographic phenomena in relatively recent periods. Empirical evaluation of the validity of VGI to reveal geographic phenomena in historical periods (e.g., decades ago) is lacking, although such evaluation is desirable for assessing the possibility of broadening the temporal scope of VGI applications. This article presents an evaluation of the validity of VGI to reveal historical geographic phenomena through a citizen data-based habitat suitability mapping case study. Citizen data (i.e., sightings) of the black-and-white snub-nosed monkey (Rhinopithecus bieti) were elicited from local residents through three-dimensional (3D) geovisualization interviews in Yunnan, China. The validity of the elicited sightings to reveal the historical R. bieti distribution was evaluated through habitat suitability mapping using the citizen data in historical periods. The results of controlled experiments demonstrated that suitability maps predicted using the historical citizen data had a consistent spatial pattern (correlation above 0.60) that reflects the R. bieti distribution (Boyce index around 0.90) in areas free of significant environmental change across historical periods. This in turn suggests that citizen data have validity for mapping historical geographic phenomena. It provides supporting empirical evidence for potentially broadening the temporal scope of VGI applications.

[1]  G. Colli,et al.  Revisiting the historical distribution of Seasonally Dry Tropical Forests: new insights based on palaeodistribution modelling and palynological evidencegeb , 2011 .

[2]  Henry P. Huntington,et al.  USING TRADITIONAL ECOLOGICAL KNOWLEDGE IN SCIENCE: METHODS AND APPLICATIONS , 2000 .

[3]  A. Zhu,et al.  Can citizen science assist digital soil mapping , 2015 .

[4]  Weihua Dong,et al.  Exploring differences of visual attention in pedestrian navigation when using 2D maps and 3D geo-browsers , 2017 .

[5]  Anthony Stefanidis,et al.  Assessing Completeness and Spatial Error of Features in Volunteered Geographic Information , 2013, ISPRS Int. J. Geo Inf..

[6]  F. Danielsen,et al.  Biodiversity monitoring in developing countries: what are we trying to achieve? , 2003, Oryx.

[7]  Mark S. Boyce,et al.  Modelling distribution and abundance with presence‐only data , 2006 .

[8]  Xiaolin,et al.  Report on the distribution, population, and ecology of the yunnan snub-nosed monkey (Rhinopithecus bieti) , 1994, Primates.

[9]  Michael F. Goodchild,et al.  Assuring the quality of volunteered geographic information , 2012 .

[10]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[11]  Thierry Toutin,et al.  Comparison of stereo-extracted DTM from different high-resolution sensors: SPOT-5, EROS-a, IKONOS-II, and QuickBird , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Jakob van Zyl,et al.  The Shuttle Radar Topography Mission (SRTM): a breakthrough in remote sensing of topography , 2001 .

[13]  Wei Ding,et al.  Habitat Degradation of Rhinopithecus bieti in Yunnan, China , 2003, International Journal of Primatology.

[14]  Lucy Bastin,et al.  Usability of VGI for validation of land cover maps , 2015, Int. J. Geogr. Inf. Sci..

[15]  Guo-Peng Ren,et al.  Black-and-white snub-nosed monkey (Rhinopithecus bieti) feeding behavior in a degraded forest fragment: clues to a stressed population , 2017, Primates.

[16]  Steffen Fritz,et al.  Assessing the Accuracy of Volunteered Geographic Information arising from Multiple Contributors to an Internet Based Collaborative Project , 2013, Trans. GIS.

[17]  A. Hirzel,et al.  Habitat suitability modelling and niche theory , 2008 .

[18]  Steven Mackinson,et al.  Points of View: Combining Local and Scientific Knowledge , 1998, Reviews in Fish Biology and Fisheries.

[19]  Hansi Senaratne,et al.  A review of volunteered geographic information quality assessment methods , 2017, Int. J. Geogr. Inf. Sci..

[20]  Wen Xiao,et al.  Seasonality of reproduction of wild black-and-white snub-nosed monkeys (Rhinopithecus bieti) at Mt. Lasha, Yunnan, China , 2012, Primates.

[21]  Pedro J. Leitão,et al.  Effects of species and habitat positional errors on the performance and interpretation of species distribution models , 2009 .

[22]  Maria Antonia Brovelli,et al.  Public participation in GIS via mobile applications , 2016 .

[23]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[24]  M. Donoghue,et al.  Historical biogeography, ecology and species richness. , 2004, Trends in ecology & evolution.

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

[26]  Bryan C. Carstens,et al.  Historical Species Distribution Models Predict Species Limits in Western Plethodon Salamanders. , 2015, Systematic biology.

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

[28]  Jorge Soberón,et al.  Niches and distributional areas: Concepts, methods, and assumptions , 2009, Proceedings of the National Academy of Sciences.

[29]  Michael F. Goodchild,et al.  Please Scroll down for Article International Journal of Digital Earth Crowdsourcing Geographic Information for Disaster Response: a Research Frontier Crowdsourcing Geographic Information for Disaster Response: a Research Frontier , 2022 .

[30]  Bin Jiang,et al.  Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information , 2016, ISPRS Int. J. Geo Inf..

[31]  M. Boyce,et al.  Evaluating resource selection functions , 2002 .

[32]  S. Gorman,et al.  Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake , 2010 .

[33]  J. Anadón,et al.  Linking local ecological knowledge and habitat modelling to predict absolute species abundance on large scales , 2010, Biodiversity and Conservation.

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

[35]  Vítezslav Moudrý,et al.  Influence of positional accuracy, sample size and scale on modelling species distributions: a review , 2012, Int. J. Geogr. Inf. Sci..

[36]  Hong S. He,et al.  Comparing Predicted Historical Distributions of Tree Species Using Two Tree-based Ensemble Classification Methods , 2012 .

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

[38]  Steven Mackinson,et al.  Integrating Local and Scientific Knowledge: An Example in Fisheries Science , 2001, Environmental management.

[39]  I. Pérez,et al.  Evaluation of Local Ecological Knowledge as a Method for Collecting Extensive Data on Animal Abundance , 2009, Conservation biology : the journal of the Society for Conservation Biology.

[40]  Xiao Wen,et al.  Distribution and conservation status of Rhinopithecus strykeri in China , 2014, Primates.

[41]  A. Peterson,et al.  New developments in museum-based informatics and applications in biodiversity analysis. , 2004, Trends in ecology & evolution.

[42]  Yongcheng Long,et al.  Status and conservation strategy of the Yunnan snub-nosed monkey , 1996 .

[43]  C. Carbonell Carrera,et al.  Augmented reality as a digital teaching environment to develop spatial thinking , 2017 .

[44]  Krzysztof Janowicz,et al.  A data-synthesis-driven method for detecting and extracting vague cognitive regions , 2017, Int. J. Geogr. Inf. Sci..

[45]  A. Peterson,et al.  Ecological niches and present and historical geographic distributions of species: a 15-year review of frameworks, results, pitfalls, and promises , 2015, Folia Zoologica.

[46]  D. Nogues‐Bravo,et al.  Predicting the past distribution of species climatic niches. , 2009 .

[47]  J. Ragle,et al.  IUCN Red List of Threatened Species , 2010 .

[48]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

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

[50]  Craig Moritz,et al.  Historical climate modelling predicts patterns of current biodiversity in the Brazilian Atlantic forest , 2008 .