We tend to think in nice round numbers, such as fives, tens, hundreds. Despite being a nice round number, 2020 will always be the year with the asterisk.
Researchers will seek to account for the social, economic, and political events of the year by assuming 2020 can be “normalized”. This is too simple a concept. If the economy can be represented as a factory that can be stopped and started, then concerns over 2020’s prospects are unfounded. However, this ignores the many activities that require multiple years to complete, such as capital programs, public services, or other planning and permitting activities. The challenge will be to see how activities with longer horizons perform during 2020. It may be many years to get to the new “normal”.
Data is an abstraction or a physical activity. When describing data we are measuring one element that may actually have multiple variables that influence its outcome.
On Monday, the LSU Tigers will play the Clemson Tigers for the College Football National Championship. Before, during and after the game, reporters, fans, and announcers will compare many metrics. They will discuss turnovers, first downs, penalties, etc. but the most common statistic (beyond the score) will be offensive yards. Offensive yardage represents many things: the quality of the offensive line (or its lack of execution), each coach’s play-calling, and the quality of the quarterback/receivers play. For example, Joe Burrow’s highlight against Georgia represents a 71 yard pass and that is all. The duration of the play, etc., are compressed into one small data point.
1st & 10 at LSU 20
(3:57 – 3rd) Joe Burrow pass complete to Justin Jefferson for 71 yds to the Geo 9 for a 1ST down
So, when you are watching the game, remember the announcers often describe an action by a single variable, one which is influenced by many things. And for some items, “data” fails to describe the variables that create these memorable moments.
There is the old nursery rhyme about how a kingdom is lost because a horseshoe falls off. The poem refers to paying attention to little things that can make a difference, as the casual relationship of minor things failing can evolve into major problems (the Space Shuttle Colombia is but one of many examples). While one could argue its importance on military logistics or other more mundane tasks (such as learning the basics when mastering any skill), the same logic could be applied to not only the development of data but to data applications.
In the age of “Big Data”, we see where more information can provide insights that were unavailable just five years ago. The use of Artificial Intelligence and Machine Learning will transform how we collect, manage and process data, providing insights that will assist researchers and decision makers. However, the casual relationships between collecting/using data with any unintended consequences remain.
For example, one could argue that I represent three people: a physical me who eats, sleeps and walks around, while there is a legal me, who signs legal documents and has financial interests. There is an emerging digital me, where I live and work in a virtual world. My information is collected, processed, and analyzed, as I become “a product” sold to others. In many ways, the data collected from millions of digital actions are creating better horseshoe nails for business, governments and others, but will this lead us to lose the kingdom of our individualism?
Description and Presentations: While freight activity utilizes a mix of different networks, from the global to the local, each movement depends on the same transportation system. In many cases, a study area’s geography may be small when compared the users on the system (a local connector study) or so broad that geography may not matter (national traffic patterns). Not all freight transportation data can be used at every geographic level; some data can only be used for macro-level geographic analyses, while other data are only appropriate to use at small-scale or micro-level geographies. For transportation agencies and companies that are interested in conducting freight transportation analyses for larger geographies, such as for an entire state or along an entire multijurisdictional corridor, or for smaller geographies, such as for a metropolitan area or county, conducting those analyses can be challenging because data may not be useful for the required level of analysis without additional, analytical rigor. There are also different of uses for freight data, ranging from simply education to project prioritization, which are not necessarily the traditional mode, commodity, and origin/destination freight data approach.
There are various transportation data sources in the public and private sector. Some of these sources are freight transportation-specific, like the Freight Analysis Framework, while others contain more general measures (demographic, economic, etc.) or geographic data (roadway networks, traffic counties, etc.) that can be adopted into a freight study. Many challenges exist when transforming data to the proper geographic scope, where the planner’s needs are aligned with the required planning needs.
This webinar will discuss how different transportation entities are examining freight transportation using geography as the research goal, and are trying to make freight data “fit into” the study area. The presenters will focus on the challenges they have faced in conducting freight analyses at both large and small-scale geographies, and provide insights concerning where data gaps exist and/or future research needs regarding program management, operations, performance metrics, or general planning needs.
Using Freight Transportation Data to Understand the Differences between Metropolitan Areas within a State
A series of presenters will provide an overview of a state DOT’s efforts to understand freight flows within their state through research programs to address freight data gaps.
Joel Worrell, Florida Department of Transportation
Thomas Hill, Florida Department of Transportation
Holly Cohen, Florida Department of Transportation
Utilizing Freight Transportation Data to Help Prioritize Projects along Key Freight Corridors (SmartScale) and Address Truck Parking Needs
This presentation will discuss how Virginia DOT has identified large and small-scale project needs along key freight corridors within the state.
Erik Johnson, Virginia Department of Transportation
Using Freight Transportation Data to Examine Last Mile Freight Transportation Needs
This presentation will examine how freight traffic volume information can be integrated into regional and local land use planning.
Michael Brown, Metro Analytics
Chandler Duncan, Metro Analytics
If you have not yet participated in Talking Freight, I encourage you to do so. These monthly seminars, sponsored by the Federal Highway Administration, are held via web conference, which means that you view the PowerPoint presentations over the Internet while listening to the presenters over your computer or the telephone. There is no cost involved and you do not have to leave your desk to participate. More information about Talking Freight is available at http://www.ops.fhwa.dot.gov/freight/fpd/talking_freight/index.htm Links to past presentations and recordings are available on http://www.fhwa.dot.gov/freightplanning/talking.htm.
When I was younger I read the story of the three vinegar tasters in “The Tao of Pooh“. What does vinegar taste like maybe a question for a cooking class, but as a researcher, its relevance is more important as “why do people need this information”. Everyone who is asking a question does so for many reasons, but they can be grouped into some very large clusters:
information to impress (everyone wants HUGE numbers),
wisdom to inform a decision maker (help make a decision), or
to satisfy a program requirement (support a decision already made).
As a researcher, people come and ask you for a question to be answered. The challenge is you may have to help them ask the correct question, which sometimes they may not understand why formulating their request remains a critical step for a successful study. In the short story, “Ask A Foolish Question”, by Robert Sheckley, there is a machine capable of answering any question, named the “Answerer”. The problem is that people asking do not know as much as the computer (or in some cases, the researcher’s knowledge on the topic). The Questioner must know something about the answer for the Answerer to provide the correct information. In some ways, every researcher must learn how to pass on information, but they must also inform the people asking the question to help them both format their question and understand the answer. (Richard Feynmn would argue if you can’t explain it to a five-year old, do you really understand the topic?) Sometimes people feel like Ralph where there is too much ambiguity to even phrase the question.
In helping people frame their research, often we simply have to listen, asking them what they want and how will they use the information. Oftentimes this becomes a collaborative process between the answerer and the asker.
With the advent of the internet, many assume the information they seek is readily available, often ignoring the work and effort it takes to transform data into something useful, This failure to understand could lead to the discounting of the work associated with exploring the question, assisting in organizing the research, and providing a satisfying answer. So, please a little patience goes a long way for everyone to agree on how vinegar should taste.
The Ancient Mariner could have as easily said “Data, Data, everywhere”, as discussions regarding big data and other analytical approaches seem to be the order of the day Wall Street Journal. We often make data out to be this mysterious element, but data requires context to be useful to anyone if they intend to make a decision, as I hope to show below. For example, data becomes information when it is categorized, which then becomes intelligence when a decision can be made, and ultimately wisdom when an action occurs based on intelligence.
One of my favorite quotes about Coffee comes from Charles Maurice de Talleyrand-Périgor, who said that the perfect cup of coffee should be: “Black as the devil, hot as hell, pure as an angel, sweet as love.” I would agree that a good cup of coffee is a bargain at any price, so let’s think about how the decision to buy a cup of coffee can explain transforming data into wisdom.
The objective: I would like to purchase a cup of coffee at Giddyup Coffee in Folsom, LA. So, I asked the two baristas if I could do take a few pictures for this blog post. They thought it funny that someone would actually do this, but they agreed. So, the research question is: do I have enough loose change to purchase a cup of coffee.
There will be a cost of purchasing a cup of coffee, so I look into my coin purse. These loose coins are simply data points, each representing a certain value. (Coins contain other information, such as their size, year and place it was minted, as well as the coins condition based on circulation. These data points are not relevant for this purpose are ignored.) However, beyond knowing that I have coins, I do not know if these coins are enough to purchase a cup of coffee.
Information: The loose coins now need to be organized before I can actually make a decision, so the coins were put into different categories. This act of putting the coins into categories, based on the relative value of the coins, resulted in the data about the coins becoming information.
Intelligence: Now that I know the relative value of the coins, I next have to make a comparison. Do I have enough to actually purchase the coffee with the coins that I have? So, I add a new data element, namely the posted value of a cup of coffee. So, the addition of the information posted on the menu allowed me to determine if I could make a purchase with my loose change.
Wisdom: I bought the cup of coffee, once I had enough intelligence to make a decision based on the cost of the cup of coffee and my loose change. (Wisdom is the only attribute with a future component, namely, data, information, intelligence are all static elements at the moment a decision is made, but Wisdom will influence my future actions.)
I learned this paradigm as the more formal DIKW: Data, Information, Knowledge, Wisdom. While one could argue that the DIKW is based upon filtering data to make a decision, I changed Knowledge into Intelligence. I see Knowledge represents a broad body of information, based on many factors, including not only the data itself but the cultural, contextual relationship of the researcher to the topic being researched. For example, I could have taken the coins to a Coinstar or a bank, or made a different decision concerning these coins. For my father who abhors coffee (his loss), the research question (can I purchase a cup of coffee) would mean nothing to him, (much like saying “what does that have to do with the price of tea in China?”!) For me, knowledge serves not as a filter of the data/information as normally discussed in the DIKW paradigm, but rather a filter through the act of transforming data to wisdom can even occur.
Finally, we can not remove the researcher from the research, but a good researcher should understand what data elements are useful to become transformed into intelligence based on understanding what answer is required.