Research on Spatialization of Crowdsourcing Contingency Geographic Information Based on K-means Algorithm

doi:10.13474/j.cnki.11-2246.2018.0222

Abstract

Abstract: This paper analyzes the shortcomings of the common spatialization way in the crowdsourcing emergency geographical information extraction.It puts forward the consideration of geographical info interspace based on the sourcing contingency events.K-means algorithm is used to solve the problem that the map service cannot extract the multi-sourcing affairs' spatial material.The spatial circumstance combines the advantages of attribute data and geography.This method carries out the optimal hazard trouble identification and storage.It makes the surveying and mapping better serve the society and the nation more closely related to people.With the geospatial cognitive model,this essay can discuss the spatial relations of toponomy in more specific aspects.This provides a guarantee of data quality for the next step in the visualization of the outburst geographic accident.Trial runs results show that the space of breakthrough geoscience's mishap not only can draw the Chinese placename data,but also identify non-Chinese country.The measure also offers a reference to other relevant areas of research.The study can supply more accurate spatial geonomy's malfunction needs for flying into disaster relief departments.They get to the source of the breaking out record displayed on the plat accurately.

Key words: crowdsourcing, emergency, K-means algorithm, spatial cognition model, spatialization

CLC Number:

P208

YANG Bo, WANG Jizhou, MAO Xi, MA Weijun. Research on Spatialization of Crowdsourcing Contingency Geographic Information Based on K-means Algorithm[J]. 测绘通报, 2018, 0(7): 112-115.

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