Regionalization of Social Interactions and Points-of-Interest Location Prediction With Geosocial Data

Psyllidis, Achilleas; Yang, Jie; Bozzon, Alessandro

doi:10.1109/access.2018.2850062

Cited by 23 publications

(14 citation statements)

References 56 publications

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“…We also adds to the growing body of research that uses geolocation data to study behaviors and social interactions (Psyllidis et al 2018;Bargain and Aminjonov 2020) and to the works that focus on the effects of political polarization on health behaviors (Iyengar et al 2019; Montoya-Williams and Fuentes-Afflick 2019). The papers closest to ours investigate the effect that partisan support has had on social distancing in the United States during the current COVID-19 crisis.…”

Section: Literaturementioning

confidence: 99%

Political orientation and adherence to social distancing during the COVID-19 pandemic in Italy

Barbieri

2021

Econ Polit

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Many governments have implemented social distancing and lockdown measures to curb the spread of the novel coronavirus (COVID-19). Using province-level geolocation data from Italy, we document that political disbelief can limit government policy effectiveness. Residents in provinces leaning towards extreme right-wing parties show lower rates of compliance with social distancing order. We also find that, during the Italian lockdown, provinces with high protest votes virtually disregarded all social distancing orders. On the contrary, in provinces with higher political support for the current political legislation, we found a higher degree of social distancing compliance. These results are robust to controlling for other factors, including time, geography, local COVID-19 cases and deaths, healthcare hospital beds, and other sociodemographic and economic characteristics. Our research shows that bipartisan support and national responsibility are essential to implement and manage social distancing efficiently. From a broader perspective, our findings suggest that partisan politics and discontent with the political class (i.e., protest voting) might significantly affect human health and the economy.

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Section: Literaturementioning

confidence: 99%

Political orientation and adherence to social distancing during the COVID-19 pandemic in Italy

Barbieri

2021

Econ Polit

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“…We also adds to the growing body of research that uses geolocation data to study behaviors and social interactions (Psyllidis et al 2018;Bargain and Aminjonov 2020) and to the works that focus on the effects of political polarization on health behaviors (Iyengar et al 2019;Montoya-Williams and Fuentes-Afflick 2019). The papers closest to ours investigate the effect that partisan support has had on social distancing in the United States during the current COVID-19 crisis.…”

Section: 2 Literaturementioning

confidence: 99%

Political Orientation and Adherence to Social Distancing during the COVID-19 Pandemic in Italy

Barbieri,

Bonini

2020

SSRN Journal

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Many governments have implemented social distancing and lockdown measures to curb the spread of the novel coronavirus . Using province-level geolocation data from Italy, we document that political disbelief can limit government policy effectiveness. Residents in provinces leaning towards extreme right-wing parties show lower rates of compliance with social distancing order. We also find that, during the Italian lockdown, provinces with high protest votes virtually disregarded all social distancing orders. On the contrary, in provinces with higher political support for the current political legislation, we found a higher degree of social distancing compliance. These results are robust to controlling for other factors, including time, geography, local COVID-19 cases and deaths, healthcare hospital beds, and other sociodemographic and economic characteristics. Our research shows that bipartisan support and national responsibility are essential to implement and manage social distancing efficiently. From a broader perspective, our findings suggest that partisan politics and discontent with the political class (i.e., protest voting) might significantly affect human health and the economy.

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“…Since Openshaw (1994), a geographical interest in dimensionality reduction using the Self-Organizing Map (Kohonen 1990) has sought to reduce high-dimensional structures in geographical processes and proven extensively useful. In an exploratory mode of analysis, the Self-Organizing Map and its variants (Bação et al 2004;Xu et al 2017;Clark et al 2017) have been consistently used in the analysis of complex, non-linear demographic relationships (Skupin and Fabrikant 2003;Agarwal and Skupin 2008;Pearson and Cooper 2012;Arribas-Bel, Nijkamp, et al 2011;Delmelle et al 2013;Spielman and Logan 2013;Psyllidis et al 2018). Another consistent interest is the use of Self-Organizing map for data-driven map reprojection (Skupin 2003;Henriques, Bação, et al 2009;Skupin and Esperbé 2011), which exploits the Self-Organizing Map's distinctive properties in order to build new or better map projections and transformations.…”

Section: Past Explorations and Prior Concerns For Geographic Dimension mentioning

confidence: 99%

“…But, Self-Organizing Maps are trained explicitly using "spatially-correlated learning" (Kohonen 1990(Kohonen , p. 1467, so the position of a point in the lower-dimensional embedding of the data incorporates the learning of surrounding points as well. These maps then allow for the exploration of complex multidimensional relationships in a two-dimensional grid amenable to exploratory visualization (Lobo et al 2012;Spielman and Logan 2013) or clustering (Skupin and Fabrikant 2003;Psyllidis et al 2018).…”

Section: Past Explorations and Prior Concerns For Geographic Dimension mentioning

confidence: 99%

Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning

Wolf¹,

Knaap²

2019

Preprint

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Dimension reduction is one of the oldest concerns in geographical analysis. Despite significant, longstanding attention in geographical problems, recent advances in non-linear techniques for dimension reduction, called manifold learning, have not been adopted in classic data-intensive geographical problems. More generally, machine learning methods for geographical problems often focus more on applying standard machine learning algorithms to geographic data, rather than applying true "spatially-correlated learning," in the words of Kohonen. As such, we suggest a general way to incentivize geographical learning in machine learning algorithms, and link it to many past methods that introduced geography into statistical techniques. We develop a specific instance of this by specifying two geographical variants of Isomap, a non-linear dimension reduction, or "manifold learning," technique. We also provide a method for assessing what is added by incorporating geography and estimate the manifold's intrinsic geographic scale. To illustrate the concepts and provide interpretable results, we conducting a dimension reduction on geographical and high-dimensional structure of social and economic data on Brooklyn, New York. Overall, this paper's main endeavor--defining and explaining a way to "geographize" many machine learning methods--yields interesting and novel results for manifold learning the estimation of intrinsic geographical scale in unsupervised learning.

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Regionalization of Social Interactions and Points-of-Interest Location Prediction With Geosocial Data

Cited by 23 publications

References 56 publications

Political orientation and adherence to social distancing during the COVID-19 pandemic in Italy

Political orientation and adherence to social distancing during the COVID-19 pandemic in Italy

Political Orientation and Adherence to Social Distancing during the COVID-19 Pandemic in Italy

Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning

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