About the US Minerals Databrowser

This page gives advice on using the US Minerals Databrowser and interpreting results. You may show or hide any section by clicking on the section header.

Background

The United States Geological Survey (USGS) is the administrative branch in charge of managing information regarding mineral resources within the United States. The following description comes from the USGS Minerals About Page:

Today the United States is the world's largest user of mineral commodities. Every year, about 25,000 lbs. of new non-fuel mineral materials is extracted from the Earth for every person in the United States just to satisfy the needs of the growing U.S. economy.

Mineral materials processed domestically accounted for more than $575 billion in the U.S. economy in 2007. U.S. manufacturers and consumers require increasing amounts of imported mineral materials. Making informed decisions about supply and development of mineral commodities that are critical to our economy and security requires current and reliable information about both mineral resources and the consequences of their development.

The goal of the US Minerals Databrowser is to make it easier to extract meaningful information from this valuable dataset.

More about databrowsers

A databrowser is a web based interface that allows non-technical users to interact with scientific data. This databrowser was built and is hosted by mazamascience.com. Other examples of databrowsers can be seen at their examples page.

Making sense of complex datasets depends upon two very different kinds of machines. Silicon based number crunchers (computers) perform complex mathematical calculations at lightning speed with essentially zero errors while carbon based pattern recognizers (our brains) detect visual patterns much faster than any computer and use these patterns to develop further questions about the data.

People enjoy looking at informative scientific graphics if the barrier to creating them is low. When this happens, our species' extraordinary capabilities as pattern recognizers enable us to convert what we see in excellent scientific graphics into a deeper understanding. The problem in many fields of science is that the barriers to creating excellent graphics are discouragingly high.

A bottleneck exists where information is transferred between number crunchers and pattern recognizers. It can take a large amount of time to organize, format and analyze data before generating the graphics that tell the story of the data. Often, the role of data management and analysis is handed over to computer experts rather than the scientist end users with a real interest in the data. With no easy way to create the graphics that they need, the ability of scientits, managers and interested members of the public to develop their intuition about a dataset is greatly impaired.

Scientific databrowsers attempt to solve this problem by hiding the details of data management and analysis while providing simple, intuitive interfaces to the kinds of analysis that are appropriate for a particular dataset. These analyses are typically vetted statistical routines that are written in code in such a way as to be driven by input from a web browser user interface. In this manner, end users including both experts and non-experts can harness the power of (server side) number crunchers as well as their own (client side) pattern recognizers without having to learn the arcana of data management and scientific analysis software.

Building a databrowser.

The process of building a databrowser involves several steps:

  1. cleaning up any problems with the source data so that they are consistent and well organized
  2. writing code that allows vetted statistical analyses to be run interactively
  3. writing code to create high quality scientific graphics based on the results of the analysis
  4. embedding the analysis and visualization code in a web-server based databrowser engine
  5. creating a user interface that allows users to quickly and easily send requests to the analysis and visualization engine running on the server

When properly designed, the code behind a good databrowser can encapsulate a huge amount of institutional memory about the scientific process. Ideally, databrowser graphics should be of high enough quality that they are immediately ready to be included in scientific publications.

Interpreting Results

The US Minerals Databrowser provides several different visualization styles, each tailored to answer a specific set of question regarding the USGS Minerals dataset.

US Production / Exports

This visualization uses the production, apparent consumption, imports and exports fields from the dataset. Net exports are calculated as exports - imports. This plot highlights how sustainable US use of a particular mineral is. Missing data in the dataset are displayed in a row above the time axis so that it is clear when values in the chart are genuinely zero as opposed to simply missing.

World Production / Price

This visualization plots both US and world annual production of each mineral. The nominal price per ton as well as the inflation adjusted price (in 1998 dollars) are overlain. This plot highlights the interplay between price and production as anticipated by basic economics -- high prices should bring about increased production for globally fungible commodoties. Because of the century long nature of the dataset, other features are evident in many plots:

  1. long term price decreases due to mechanization
  2. massive growth in world production since the end of World War II
  3. commodoties speculation around 1980 (gold, silver, molybdenum)

Price Evolution

Plotting inflation adjusted prices against global production create interesting plots that give insight into how well the price signal works for a particular mineral. Standard economics teaches that increased demand causes the price to increase which, intern causes production to increase.

In the Price Evolution plot this would be seen as movement to the right (i.e.toward increasing production). In a well supplied market the trend will be toward the right or lower right (nitrogen); in a tight market toward the upper right (nickel).

Speculative booms and busts are seen as rapid increases straight up and then straight down (gold, molybdenum).

Movement to the left indicates a decline in production with movement to the lower left implying a decline in demand (mercury), while movment to the upper right implies unmet demand (gold since 2002).

Usage History

Usage patterns for any mineral will change as new uses are found and older applications are discontinued. The Usage History plots historical timelines of consumption in all the major usage categories so that hanges in usage patterns can be easily followed.

The categories are listed in order of cumulative consumption over the entire time series. In the case of aluminum we can see that 'Containers and packaging' is the largest category overall even though it was overtaken by 'Transportation' in 1994.

Usage Pie

Pie charts provide a quick, intuitive view of relative consumption in each category for a single year to answer the question: "What is this mineral used for?"

By default, the year chosen is the most recent year that has data. Categories of consumption with missing or withheld data are displayed but are represented in the pie by a wedge of size 0%. By reviewing the Usage History plots, users can determine whether this 0% assumption is reasonable or not. Category colors match those of the Usage History plots.