Hi!
Hi 587ers! My name is Fernando and I am taking this course as part of the BI Certificate at the U of A.
I am originally from Spain, in the southwestern corner of Europe, and currently live in Tucson, Arizona. Below, I include a picture of my hometown, Alicante.
I specialize in Geographic Information Systems and have been working in academia for about 15 years now. Thanks to my work, I have got to live in several regions of the globe. After a few years getting to know the European peoples and some time in Japan, I moved to the USA for research collaborations with the US Department of Agriculture. More recently, I have been teaching at the University of Montana and the University of Arizona.
I am quiet interested in the most recent developments in data analytics, and how business intelligence and big data could be used in combination with GIS for spatial analysis. Looking forward to starting this course and chatting with you all!
Hi Fernando,
ReplyDeleteGlad to read you first post! The scenery in your photo is so beautiful.
As a master graduate in economics, I've learned that spatial analysis is also used in many regional economics research. Combining spatial analysis with big data is an excellent idea. What role will big data play in finding humanity patterns geographically? It's interesting to think about that.
DeleteI think Business Intelligence and GIScience share the problem of how to visualize multidimensional data and complex data formats. It looks like dynamic/interactive visualization techniques are helping to it. Regarding Big Data engineering, environments such as Google Earth engine and ArcGIS Pro claim to provide a broad access to big data storage, handling and analysis functionality, though these solutions are not open source so they do not make it easy to "democratize" tools and workflows. Another important aspect (and I think both open source and proprietary solutions offer solid alternatives) is the incorporation of Big Data, Machine Learning, and GIS in the same workflow. For instance, mapping the results of sentiment analysis or a social network, or mapping the item sets of the most interesting association rules. This could also be applied to web mining, and it has given us (in the unit I work for) additional insights about the geography of the visits to our website, so we can see what areas in the globe our website is most popular and where else we could try to expand.