TY - JOUR
T1 - Scale, context, and heterogeneity
T2 - the complexity of the social space
AU - Balsa-Barreiro, José
AU - Menendez, Mónica
AU - Morales, Alfredo J.
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - The social space refers to physical or virtual places where people interact with one another. It decisively influences the emergence of human behaviors. However, little is known about the nature and complexity of the social space, nor its relationship to context and spatial scale. Recently, the science of complex systems has bridged between fields of knowledge to provide quantitative responses to fundamental sociological questions. In this paper, we analyze the shifting behavior of social space in terms of human interactions and wealth distribution across multiple scales using fine-grained data collected from both official (US Census Bureau) and unofficial data sources (social media). We use these data to unveil how patterns strongly depend upon the observation scale. Therefore, it is crucial for any analysis to be framed within the appropriate context to avoid biased results and/or misleading conclusions. Biased data analysis may lead to the adoption of fragile and poor decisions. Including context and a proper understanding of the spatial scale are essential nowadays, especially with the pervasive role of data-driven tools in decision-making processes.
AB - The social space refers to physical or virtual places where people interact with one another. It decisively influences the emergence of human behaviors. However, little is known about the nature and complexity of the social space, nor its relationship to context and spatial scale. Recently, the science of complex systems has bridged between fields of knowledge to provide quantitative responses to fundamental sociological questions. In this paper, we analyze the shifting behavior of social space in terms of human interactions and wealth distribution across multiple scales using fine-grained data collected from both official (US Census Bureau) and unofficial data sources (social media). We use these data to unveil how patterns strongly depend upon the observation scale. Therefore, it is crucial for any analysis to be framed within the appropriate context to avoid biased results and/or misleading conclusions. Biased data analysis may lead to the adoption of fragile and poor decisions. Including context and a proper understanding of the spatial scale are essential nowadays, especially with the pervasive role of data-driven tools in decision-making processes.
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U2 - 10.1038/s41598-022-12871-5
DO - 10.1038/s41598-022-12871-5
M3 - Article
C2 - 35641578
AN - SCOPUS:85130995487
SN - 2045-2322
VL - 12
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 9037
ER -