Dataset Information

Predicting socio-economic levels of urban regions via offline and online indicators.

ABSTRACT: Predicting the socio-economic level of an urban region is of great significance for governments and city managers when allocating resources and making decisions. However, the current approaches for estimating regional socio-economic levels heavily rely on census data, which demands significant effort in terms of time and money. With the ubiquitous usage of smart phones and the prevalence of mobile applications, massive amounts of data are generated by mobile networks that record people's behaviors. In this paper, we propose a low-cost approach of using humans' online and offline indicators to predict the socio-economic levels of urban regions. The results show that the socio-economic prediction model that is trained using online and offline features extracted from these data achieves a high accuracy over 85%. Notably, online features are showed to be tightly linked with socio-economic development. In environments where censuses are rarely held, our method provides an option for timely and accurate prediction of the economic status of urban regions.

SUBMITTER: Ren Y

PROVIDER: S-EPMC6619744 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Predicting socio-economic levels of urban regions via offline and online indicators.

Ren Yi Y Xia Tong T Li Yong Y Chen Xiang X

PloS one 20190710 7

Predicting the socio-economic level of an urban region is of great significance for governments and city managers when allocating resources and making decisions. However, the current approaches for estimating regional socio-economic levels heavily rely on census data, which demands significant effort in terms of time and money. With the ubiquitous usage of smart phones and the prevalence of mobile applications, massive amounts of data are generated by mobile networks that record people's behavio ...[more]

PMID: 31291296

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Project description:The need to consider the role of social factors in the efficacy of farm management and, consequently, in the onset and persistence of diseases typical to animal farms is increasingly being realized increasingly worldwide. Many risk analysis studies have been conducted to assess the role of various factors in the development of animal diseases; however, very few have accounted for the role of social factors. The aim of this work was to bridge this gap, with the main hypothesis that different socio-economic factors could be valid indicators for the occurrence of different animal diseases. A socio-economic analysis was performed using demographic characteristics of the farmers and data from 44 social indicators released by the Italian Statistician National Institute of Statistics (ISTAT) database. African swine fever (ASF) in wild boars (WB) and domestic pigs and other endemic animal diseases and zoonoses in Sardinia were considered, such as cistic echinococcosis (CE), contagious agalactia (CA), trichinellosis, West Nile disease (WND), and bluetongue (BT). Seven different negative binomial regression models were fitted using the number of cases between 2011-2017. Three indicators-cultural demand, employment rate, and legality-showed a statistically significant association with risk for all the diseases considered, but with varying effects. Some indicators, such as the age and sex of the farmer, material deprivation index, number of farms and animals, micro-criminality index, and rate of reported thefts were common to ASF, CA, trichinellosis, and CE cases. Others such as the forest surface and the energy produced from renewable sources were common to BT, WND, and ASF in WB. Tourism in seasons other than summer was a valid predictor of ASF and trichinellosis, while out-of-region hospital use had a statistically significant role in CE risk identification. These results may help understand the social context in which these diseases may occur and thus guide the design and implementation of additional risk management measures that go beyond well-known veterinary measures.

Dataset Information

Predicting socio-economic levels of urban regions via offline and online indicators.

Publications

Predicting socio-economic levels of urban regions via offline and online indicators.

Similar Datasets

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