Big data provides new ideas and technical support for precision poverty alleviation. It must actively utilize the advantages of big data technology to be fast, convenient and efficient, and accelerate the promotion of precision poverty alleviation.
The Chinese government attaches great importance to poverty alleviation and has helped more than 600 million poor people out of poverty through unremitting efforts. However, as of the end of 2015, more than 70 million people in China still did not get rid of poverty. In the past, a policy was introduced, and a group of people were able to get rid of poverty and get rich. Now all the rest are “hard bonesâ€, and it is more and more difficult to reduce poverty.
At the end of the "Thirteenth Five-Year Plan", we must ensure that 70 million people are all out of poverty on schedule. We must reduce at least 12 million people each year. We must reduce poverty by at least 1 million people every month. We must also set aside some time to consolidate the results. It should be said that the task is very difficult. Therefore, how to play the role of basic communication operators and use information technology such as big data, Internet of Things, cloud computing, mobile Internet, etc. to help the government to accurately alleviate poverty and accurately eliminate poverty is a topic worthy of consideration and research.
Apply big data technology to poverty alleviation, expand information collection channels, improve data analysis capabilities and processing efficiency, provide accurate, effective and reliable data support for poverty alleviation decision-making, and help precision poverty alleviation.
Eliminating poverty, improving people's livelihood, and gradually achieving common prosperity are the essential requirements of socialism. Over the past 30 years, China’s social productivity has been greatly improved, but the gap between the rich and the poor has further expanded. On the basis of liberating productive forces and developing productive forces, precise poverty alleviation and poverty alleviation are the only way to achieve common prosperity, and this is the essential requirement of socialism.
The big data revolution has made it more fair and responsible to acquire capital than ever before. So how can big data help the poor?
Even if you have the best ideas in the world, if you lack basic financial resources such as bank accounts or credit cards, it is almost impossible to start a small business.
Kiva Development Director Karen Little fully agrees with the above point. She said that there are many smart people in the world who have lost the opportunity to start a business because they cannot get the start-up funds. Banks have long neglected the demand for poor loans, mainly because the poor have no historical record of income, consumption habits and loan repayments. (Kiva is a microfinance network platform that helps the world's poor get microloans.)
The lack of more than one-third of the world's population financial data not only affects the individual's financing channels or the basic security of deposit accounts, it also deprives policymakers of the right to know about key issues in solving global poverty. Fortunately, the Internet and mobile phones are changing the status quo. About 85% of the world's population is using mobile phones. At such a high penetration rate, information about the location, behavior and needs of the poor can be collected through basic information technology. Poverty map. As the big data revolution spread to the world's poorest people, the big data revolution has made it easier than ever to get funding, while providing the best way to provide deeper insights and reforms for poverty.
one:Data includes 24/7 digital and network information generated by all businesses and users using mobile phones and connected devices. This information includes network activity, data collected from IoT sensors, and data generated by satellites, which all make up the data we generate every moment, with mobile data accounting for most of the user-generated data.
In the banking sector, we can get the best position for microfinance and bank offices in poor areas by analyzing mobile phone data. More importantly, digital access and digital payments create credit records for those who do not have a credit history, and paying records to non-bank lenders will ultimately be a credible proof of traditional bank loan requirements.
At the same time, more and more microfinance loans do not require proof of credit history, such as the Kiva website or Tala, Kenya's most popular financing application. Tala corrects personal credit levels through algorithms and 10,000 indicators, including the length of the message sent, the length of the call, and so on. David Pollak, Kiva's vice president of engineering, said, "We are trying to find out the factors that make borrowers' credits high and find borrowers who are more likely to repay through large amounts of data." In addition to evaluating the creditworthiness of new borrowers, big data analysis helps Kiva assess and track 300 independent microfinance institutions and social enterprises, over 2 million Kiva borrowers and more than 1.4 million Kiva lenders. 1 million loans.
Data analysis is also an important reason for Kiva's $7 million investment in the HP Enterprise Foundation's "Matter to a Million" project. Since 2014, lenders have lend more than $11.5 million to Kiva's borrowers, and the risk of borrowing is immeasurable without big data escorting. "If the power of data analysis is less than the power based on intuition, then Kiva can't exist." Pollak added, “The reason Kiva is able to connect payers and borrowers around the world is to use data analysis.â€
The next phase of big data is the use of data analysis to address the root causes of poverty. A research project at the State University of New York at Buffalo has been able to accurately map Senegal's poverty distribution maps via mobile phones. Traditional methods of obtaining poverty maps are inefficient, require intensive field surveys and can only be updated every three years. With the popularity of mobile phones, the cost of obtaining poverty maps is low and the results are accurate. Poverty maps can be updated in real time and shared with local decision makers to achieve accurate poverty alleviation.
From crime to natural disasters, the massive data flow mining can help to make accurate predictions and help develop. The Data-Pop Alliance is a global alliance focused on using big data as a tool for global growth. In cooperation with the Qatar Computing Institute, the Data-Pop Alliance analyzes millions of tweets to gather field information on poverty and general economic conditions in Egypt. Another project of the organization is to use Google Earth to estimate areas that are vulnerable to flooding, which are often poor. Finally, Letouze hopes that big data will support a bottom-up decision-making system where the poor can participate in poverty alleviation reforms and can exert political pressure on government representatives and departments. He said, "The key issue in solving poverty is not only how the authorities use the data, but also how the people who generate the data participate in poverty alleviation and improve their lives."
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