Analysing objective and subjective data in social sciences: implications for smart cities

Research output: Contribution to journalArticlepeer-review

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Analysing objective and subjective data in social sciences : implications for smart cities. / Erhan, Laura ; Ndubuaku, Maryleen ; Ferrara, Enrico; Richardson, Miles ; Sheffield, David; Ferguson, Fiona J.; Brindley, Paul; Liotta, Antonio.

In: IEEE Access, Vol. 7, 04.02.2019, p. 19890-19906.

Research output: Contribution to journalArticlepeer-review

Harvard

Erhan, L, Ndubuaku, M, Ferrara, E, Richardson, M, Sheffield, D, Ferguson, FJ, Brindley, P & Liotta, A 2019, 'Analysing objective and subjective data in social sciences: implications for smart cities', IEEE Access, vol. 7, pp. 19890-19906. https://doi.org/10.1109/ACCESS.2019.2897217

APA

Erhan, L., Ndubuaku, M., Ferrara, E., Richardson, M., Sheffield, D., Ferguson, F. J., Brindley, P., & Liotta, A. (2019). Analysing objective and subjective data in social sciences: implications for smart cities. IEEE Access, 7, 19890-19906. https://doi.org/10.1109/ACCESS.2019.2897217

Vancouver

Author

Erhan, Laura ; Ndubuaku, Maryleen ; Ferrara, Enrico ; Richardson, Miles ; Sheffield, David ; Ferguson, Fiona J. ; Brindley, Paul ; Liotta, Antonio. / Analysing objective and subjective data in social sciences : implications for smart cities. In: IEEE Access. 2019 ; Vol. 7. pp. 19890-19906.

Bibtex

@article{29481d3c33914e7b8c550a2041a67eb3,
title = "Analysing objective and subjective data in social sciences: implications for smart cities",
abstract = "The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data from our cities. In this paper, we investigate a novel way of analyzing data from social sciences studies by employing machine learning and data science techniques. This enables us to maximize the insight gained from this type of studies by fusing both objective (sensor information) and subjective data (direct input from the users). The pilot study is concerned with better understanding the interactions between citizens and urban green spaces. A field experiment was carried out in Sheffield, U.K., involving 1870 participants for two different time periods (7 and 30 days). With the help of a smartphone app, both objective and subjective data were collected. Location tracking was recorded as people entered any of the publicly accessible green spaces. This was complemented by textual and photographic information that users could insert spontaneously or when prompted (when entering a green space). By employing data science and machine learning techniques, we identify the main features observed by the citizens through both text and images. Furthermore, we analyze the time spent by people in parks as well as the top interaction areas. This paper allows us to gain an overview of certain patterns and the behavior of the citizens within their surroundings and it proves the capabilities of integrating technology into large-scale social studies.",
keywords = "data analysis, data science, smart cities, social science, urban analytics, urban planning",
author = "Laura Erhan and Maryleen Ndubuaku and Enrico Ferrara and Miles Richardson and David Sheffield and Ferguson, {Fiona J.} and Paul Brindley and Antonio Liotta",
year = "2019",
month = feb,
day = "4",
doi = "10.1109/ACCESS.2019.2897217",
language = "English",
volume = "7",
pages = "19890--19906",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "IEEE Xplore",

}

RIS

TY - JOUR

T1 - Analysing objective and subjective data in social sciences

T2 - implications for smart cities

AU - Erhan, Laura

AU - Ndubuaku, Maryleen

AU - Ferrara, Enrico

AU - Richardson, Miles

AU - Sheffield, David

AU - Ferguson, Fiona J.

AU - Brindley, Paul

AU - Liotta, Antonio

PY - 2019/2/4

Y1 - 2019/2/4

N2 - The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data from our cities. In this paper, we investigate a novel way of analyzing data from social sciences studies by employing machine learning and data science techniques. This enables us to maximize the insight gained from this type of studies by fusing both objective (sensor information) and subjective data (direct input from the users). The pilot study is concerned with better understanding the interactions between citizens and urban green spaces. A field experiment was carried out in Sheffield, U.K., involving 1870 participants for two different time periods (7 and 30 days). With the help of a smartphone app, both objective and subjective data were collected. Location tracking was recorded as people entered any of the publicly accessible green spaces. This was complemented by textual and photographic information that users could insert spontaneously or when prompted (when entering a green space). By employing data science and machine learning techniques, we identify the main features observed by the citizens through both text and images. Furthermore, we analyze the time spent by people in parks as well as the top interaction areas. This paper allows us to gain an overview of certain patterns and the behavior of the citizens within their surroundings and it proves the capabilities of integrating technology into large-scale social studies.

AB - The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data from our cities. In this paper, we investigate a novel way of analyzing data from social sciences studies by employing machine learning and data science techniques. This enables us to maximize the insight gained from this type of studies by fusing both objective (sensor information) and subjective data (direct input from the users). The pilot study is concerned with better understanding the interactions between citizens and urban green spaces. A field experiment was carried out in Sheffield, U.K., involving 1870 participants for two different time periods (7 and 30 days). With the help of a smartphone app, both objective and subjective data were collected. Location tracking was recorded as people entered any of the publicly accessible green spaces. This was complemented by textual and photographic information that users could insert spontaneously or when prompted (when entering a green space). By employing data science and machine learning techniques, we identify the main features observed by the citizens through both text and images. Furthermore, we analyze the time spent by people in parks as well as the top interaction areas. This paper allows us to gain an overview of certain patterns and the behavior of the citizens within their surroundings and it proves the capabilities of integrating technology into large-scale social studies.

KW - data analysis

KW - data science

KW - smart cities

KW - social science

KW - urban analytics

KW - urban planning

U2 - 10.1109/ACCESS.2019.2897217

DO - 10.1109/ACCESS.2019.2897217

M3 - Article

VL - 7

SP - 19890

EP - 19906

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -