THE LOCATIONAL DETERMINANTS OF WESTERN NONMETRO HIGH TECH MANUFACTURERS: AN ECONOMETRIC ANALYSIS

The Tobit estimation procedure was used to determine the factors which influence the location and size of high technology manufacturers in nonmetro areas in the West. The results indicate that high tech branch plants tend to locate in populous counties adjacent to Metropolitan Statistical Areas (MSAs). Percent of local employment in manufacturing and agriculture was inversely related to branch plant employment, and the stock of human capital was not significantly related to employment. High tech unit plants also exhibited a propensity to locate in the more populous counties. Unlike branch plants, the unit concerns were more likely to develop or locate in communities with a highly educated work force and at greater distances from metro areas. The unit plants better fit the perception of high tech plants selecting high amenity locations with abundant skilled labor.Industrial Organization, Labor and Human Capital,

Regional differentiation of human potential indicators

The purpose of the study presented in this article is an interregional analysis of human potential. The quality of the population proper is investigated at the regional level far less than the quality of the population life. The article provides an extended characteristic of human potential in seven directions: economic activity, demographic processes, physical health, the cultural potential of population, social health, educational potential, the attitude of population to the environment. On the basis of official statistics for 2008–2012, there were selected 63 indicators characterizing human potential in all these directions. In the final result, the correlation analysis has led to the substantiation of the system of indicators for the level of human potential development, consisting of 10 indicators. The system included 3 economic indicators and 7 social indicators characterizing human potential. Upon these indicators the Russian regions were divided in two types of regions by means of hierarchic agglomerative (combining) methods of cluster analysis: the regions with economic indicators and without them. The performed calculations provided the typology of regions by the human potential indicators being stable over time and covering 74.4 % of the Russian population. A substantial interpretation of breaking down regions by groups, identification of both strong and weak aspects of each cluster were made, finding out specific features of the regions falling under the clusters. The obtained results can be used when working out measures for reducing the interregional inequality in the levels of human potential development. To find out what measures can be effective, it is possible to examine the strategic directions of regions’ development in the cluster that is the most successful with respect to the human potential characteristics under investigation

Database integrated analytics using R : initial experiences with SQL-Server + R

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Most data scientists use nowadays functional or semi-functional languages like SQL, Scala or R to treat data, obtained directly from databases. Such process requires to fetch data, process it, then store again, and such process tends to be done outside the DB, in often complex data-flows. Recently, database service providers have decided to integrate “R-as-a-Service” in their DB solutions. The analytics engine is called directly from the SQL query tree, and results are returned as part of the same query. Here we show a first taste of such technology by testing the portability of our ALOJA-ML analytics framework, coded in R, to Microsoft SQL-Server 2016, one of the SQL+R solutions released recently. In this work we discuss some data-flow schemes for porting a local DB + analytics engine architecture towards Big Data, focusing specially on the new DB Integrated Analytics approach, and commenting the first experiences in usability and performance obtained from such new services and capabilities.Peer ReviewedPostprint (author's final draft

OpenML: networked science in machine learning

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.Comment: 12 pages, 10 figure