NCeSS Project : Data mining for social scientists

We will discuss the work being undertaken on the NCeSS data mining project, a one year project at the University of Manchester which began at the start of 2007, to develop data mining tools of value to the social science community. Our primary goal is to produce a suite of data mining codes, supported by a web interface, to allow social scientists to mine their datasets in a straightforward way and hence, gain new insights into their data. In order to fully define the requirements, we are looking at a range of typical datasets to find out what forms they take and the applications and algorithms that will be required. In this paper, we will describe a number of these datasets and will discuss how easily data mining techniques can be used to extract information from the data that would either not be possible or would be too time consuming by more standard methods

Injuries, Illnesses, and Fatal Injuries in Mining in 2010

[Excerpt] Workers in the mining industry continue to face a higher risk of fatal injury than average American workers. Although the rate of nonfatal injuries and illnesses in mining is less than the average reported for private industry, these injuries are often of a severe nature, as evidenced by the higher median days away from work. Fires and explosions were the leading causes of workplace fatal injuries. Contact with objects and equipment was the leading cause of nonfatal injuries and illnesses

The Unemployed and Job Openings: A Data Primer

New information that adds to the mix of labor market indicators may be useful to Congress. The ratio of unemployed persons per job opening provides information on how many unemployed persons on average there are for every job opening. It adds to the current mix of labor market indicators such as the unemployment rate, which is a measure of the excess supply of workers. In addition, it adds to employment statistics, which measures the demand for workers that have already been met by employers. By dividing the number of unemployed persons with the number of job openings, the ratio gauges the excess supply of workers relative to the demand, where job openings serve as a measure of the unmet need for workers. The resultant statistic compares the number of persons who are actively searching for jobs to the number of available opportunities. Four key findings arise from this analysis: 1. The ratio of unemployed persons per job opening is highly correlated with the unemployment rate between 2001 and 2012. 2. The ratio of unemployed persons per job opening rises during the recessionary periods covered in this data set. In the 2007-2009 recession, the ratio rises to very high levels, especially in the goods-producing industries (construction, manufacturing, mining and logging). 3. Although the ratio is highly correlated with changes in the unemployment rate, the ratio saw modest improvements coming out of the recent recession sooner than the reductions in the unemployment rate. 4. Even though the ratio has reduced, it remains at higher levels than prior to the 2007-2009 recession

Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"

Affected by urbanization, centralization and the decrease of overall population, Japan has been making efforts to revitalize the rural areas across the country. One particular effort is to increase tourism to these rural areas via regional branding, using local farm products as tourist attractions across Japan. Particularly, a program subsidized by the government called Michinoeki, which stands for 'roadside station', was created 20 years ago and it strives to provide a safe and comfortable space for cultural interaction between road travelers and the local community, as well as offering refreshment, and relevant information to travelers. However, despite its importance in the revitalization of the Japanese economy, studies with newer technologies and methodologies are lacking. Using sales data from establishments in the Kyushu area of Japan, we used Support Vector to classify content from Twitter into relevant topics and studied their causal relationship to the sales for each establishment using LiNGAM, a linear non-gaussian acyclic model built for causal structure analysis, to perform an improved market analysis considering more than just correlation. Under the hypotheses stated by the LiNGAM model, we discovered a positive causal relationship between the number of tweets mentioning those establishments, specially mentioning deserts, a need for better access and traf^ic options, and a potentially untapped customer base in motorcycle biker groups