Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering

A Study on Crowdsourcing Geospatial Data Mining Based on Spatial Statistics

Authors
Jiyuan Geng, Weidong Song, Shangyu Sun
Corresponding Author
Jiyuan Geng
Available Online October 2016.
DOI
https://doi.org/10.2991/epee-16.2016.56How to use a DOI?
Keywords
spatial data mining; spatial statistics; exploratory spatial data analysis; the source of geospatial data; plasticity area unit
Abstract

By analyzing and mining the source geospatial data, provide a reference for the government macro decision-making and public opinion monitoring and provide the basis for enterprise precision marketing and the individuality service. The experimental results show that, within the scope of the study area tend to check data and its associated attribute value spatial clustering model, clustering of statistically significant hot spots are mainly distributed in the city school, station, district position, city hot spots can be obtained through the sign-in data detection coverage is consistent with the actual urban planning scheme, and has obvious directivity, have very strong application value.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
10.2991/epee-16.2016.56
ISSN
2352-5401
DOI
https://doi.org/10.2991/epee-16.2016.56How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jiyuan Geng
AU  - Weidong Song
AU  - Shangyu Sun
PY  - 2016/10
DA  - 2016/10
TI  - A Study on Crowdsourcing Geospatial Data Mining Based on Spatial Statistics
BT  - Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering
PB  - Atlantis Press
SP  - 248
EP  - 251
SN  - 2352-5401
UR  - https://doi.org/10.2991/epee-16.2016.56
DO  - https://doi.org/10.2991/epee-16.2016.56
ID  - Geng2016/10
ER  -