Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico

Roberto Zagal; Felix Mata; Christophe Claramunt

doi:10.1145/3021304.3021307

Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico

Roberto Zagal, Felix Mata, Christophe Claramunt

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Nowadays, experts and citizens at large are keen to express their opinions using social networks on many issues, this generating a new form of participatory democracy. The research presented in this paper proposes a preliminary research work that combines semantics processing and machine learning to derive geographic and semantic knowledge implicitly derived from the perceptions and opinions as expressed by social networks, digital media and institutional data. The results are mapped to the geographical structure of the city in order to study differences and commonalities at the neighborhood level. The whole approach is applied and illustrated in the context of the city of Mexico and pollution perception as a case study. The figures that emerge show evidence of a significant impact of the structure of the city over the way citizens perceive pollution.

Original language	English
Title of host publication	Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450345866
DOIs	https://doi.org/10.1145/3021304.3021307
State	Published - 31 Oct 2016
Event	9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016 - Burlingame, United States Duration: 31 Oct 2016 → 3 Nov 2016

Publication series

Name	Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016

Conference

Conference	9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016
Country/Territory	United States
City	Burlingame
Period	31/10/16 → 3/11/16

Keywords

GKD
Machine learning
Social analysis

Access to Document

10.1145/3021304.3021307

Cite this

Zagal, R., Mata, F., & Claramunt, C. (2016). Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico. In Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016 Article a3 (Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016). Association for Computing Machinery, Inc. https://doi.org/10.1145/3021304.3021307

Zagal, Roberto ; Mata, Felix ; Claramunt, Christophe. / Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico. Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016. Association for Computing Machinery, Inc, 2016. (Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016).

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title = "Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico",

abstract = "Nowadays, experts and citizens at large are keen to express their opinions using social networks on many issues, this generating a new form of participatory democracy. The research presented in this paper proposes a preliminary research work that combines semantics processing and machine learning to derive geographic and semantic knowledge implicitly derived from the perceptions and opinions as expressed by social networks, digital media and institutional data. The results are mapped to the geographical structure of the city in order to study differences and commonalities at the neighborhood level. The whole approach is applied and illustrated in the context of the city of Mexico and pollution perception as a case study. The figures that emerge show evidence of a significant impact of the structure of the city over the way citizens perceive pollution.",

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Zagal, R , Mata, F & Claramunt, C 2016, Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico. in Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016., a3, Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016, Association for Computing Machinery, Inc, 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016, Burlingame, United States, 31/10/16. https://doi.org/10.1145/3021304.3021307

Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico. / Zagal, Roberto ; Mata, Felix; Claramunt, Christophe.
Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016. Association for Computing Machinery, Inc, 2016. a3 (Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico

AU - Zagal, Roberto

AU - Mata, Felix

AU - Claramunt, Christophe

PY - 2016/10/31

Y1 - 2016/10/31

N2 - Nowadays, experts and citizens at large are keen to express their opinions using social networks on many issues, this generating a new form of participatory democracy. The research presented in this paper proposes a preliminary research work that combines semantics processing and machine learning to derive geographic and semantic knowledge implicitly derived from the perceptions and opinions as expressed by social networks, digital media and institutional data. The results are mapped to the geographical structure of the city in order to study differences and commonalities at the neighborhood level. The whole approach is applied and illustrated in the context of the city of Mexico and pollution perception as a case study. The figures that emerge show evidence of a significant impact of the structure of the city over the way citizens perceive pollution.

AB - Nowadays, experts and citizens at large are keen to express their opinions using social networks on many issues, this generating a new form of participatory democracy. The research presented in this paper proposes a preliminary research work that combines semantics processing and machine learning to derive geographic and semantic knowledge implicitly derived from the perceptions and opinions as expressed by social networks, digital media and institutional data. The results are mapped to the geographical structure of the city in order to study differences and commonalities at the neighborhood level. The whole approach is applied and illustrated in the context of the city of Mexico and pollution perception as a case study. The figures that emerge show evidence of a significant impact of the structure of the city over the way citizens perceive pollution.

KW - GKD

KW - Machine learning

KW - Social analysis

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M3 - Contribución a la conferencia

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Zagal R , Mata F, Claramunt C. Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico. In Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016. Association for Computing Machinery, Inc. 2016. a3. (Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016). doi: 10.1145/3021304.3021307

Geographical knowledge discovery applied to the social perception of pollution in the city of Mexico

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