Tag: Data review

https://tradingeconomics.com/united-states/average-weekly-hours-of-all-employees-total-private-in-st-louis-mo-il-msa-fed-data.html

1.Data set profile:

Who is credited as the creator and/or contributors of this data set? Who are they?

The Data set is on the trading economics page and comes from to The United States Federal Reserve. This website goes over current employment stats- such as Hours, earnings, and employment. There are no Individual creditors because it comes from an organization the website only repackages the information and post it on there website

What are the sources of their data?

The Sources come form the BLS or the Bureau of Labor Statistics which takes around 622,000 individual work sites. Most company’s are mandated to report work hours and earning data. so In other words its is easy to access this information because it is mandated by the government for organizations to report. Data is collected through web reporting.

Why did they create or compile it?

They do this to monitor employment hours and earning to track market labor conditions. STL does this specifically

How has it been used?

The site doesn’t seem to do much with the information just tracking and recording the conditions. These types of sites are used by economist and business to track labor markets and to make sure they are following the standard. I think also to Identify trends and what they can do to improve work conditions. After words I think they transfer the data to state records to track the economic cycle.

What format is the data set in?

its in a bar chart that follows each month. This info goes back 10 years at least on the website.

2. Data set Evaluation:

Take a look at the data itself. How have they structured it? What fields have they chosen? What effect might that have on how it can be used?

The data is structured by Date and average weekly hours. That’s pretty much it it is very straight forward and is minimal, but you can go back and see previous data

Read the creators’ description of the data set. Have they described the choices they made in cleaning the data, and if so, how? What effect might those choices have on the data?

The site doesn’t say much because it just shows the data and the weeks, but it does say that it follow the handbook which includes things such as

“All microdata are edited for correctness and consistency with prior months.
Strict edit tests (mandatory rules, e.g., hours ≤ 168 per week, no negative values) exclude failing data from estimation until corrected.
Non-strict and screening tests flag outliers based on percentage/level changes over 1/2/12 months (using industry-specific thresholds derived from historical variance).”

So I believe they just follow standard state and global regulations.

Consider the creators’ identities and goals in creating the data set. How might those things have shaped the data, either intentionally or inadvertently?

This website does not shape the understanding of the data for their own personal goals, It is simply used to represent data and give the public access to the data.

What would you use this data for?

If I owned a Business i would use this data to compare and contrast it to my own company and make sure we are on the right track. I would also find a way to increase the amount of hours worked in the November and December months because they have the lowers amount of hours worked each year. I also would use it to track long term trends to analyze business cycles in the area.

This project was sponsored by the Illinois Department of Natural Resources and Illinois Natural History Survey. The Illinois Department of Natural Resources (IDNR) is a state agency responsible for managing, conserving and protecting Illinois’ natural, recreational and cultural resources. The Illinois Natural History Survey (INHS) is a premier scientific research organization; it is part of the Prairie Research Institute at the University of Illinois Urbana-Champaign. Since 1858, they have been the guardian and recorder of the biological resources of Illinois-state’s biological memory. According to their manifesto, their mission is to “investigate the diversity, life histories and ecology of the plants and animals of the state; to publish result so that those resources can be managed wisely” (https://inhs.illinois.edu/about/about-inhs/).

The source of the data are plat maps and field notebooks that contained details about the survey, as well as notes that have the quality of the landscape, mines, salt lick, watercourses, springs, mill seats and other “remarkable and permanent things”.

The data was used by Surveyors and Cartographers to create a more complete map of the township. It was also used by the Illinois Natural History Survey to create the Early 1800’s land cover map. The format of the data set is in digital vector coverage (shapefiles/geodatabase) representing forests, prairies and wetlands, managed by the Illinois Natural History Survey.

The data wasn’t made available in this case, although the land code value was provided with definitions of some keywords in a tabular format.

A few data clean ups were made. Areas that were mislabeled were corrected, areas that were mis-digitized and had incorrect label had the missing line added and the labels corrected. The effect this cleanup has on the data is clarity, accuracy and trust. For example, the present-day Adams County, a prairie area was mis-digitized and incorrectly labeled as forest. This can cause confusion and misrepresentation, but correcting the data made it more presentable and understandable to anyone with access to that information, making it possible for people to trust the data from the creators.

The goal of creating the data is one of preservation, fostering public understanding and appreciation of resources, safety of natural resources for present and future generation. This might have shaped the data in making sure it is more detailed and accurate.

I could use the data to learn and analyze the soil nutrient composition, health and contamination level of a particular area.

The dataset “Illinois Landcover in the Early 1800s” was likely created by environmental researchers, historians, and geographers working through universities or state organizations. These contributors study how the land looked before major urban development. The data probably comes from historical maps, written records, and ecological reconstructions rather than direct measurements, since no one was systematically recording land cover at that time.

The main reason this dataset was created is to help people understand what Illinois looked like before industrialization and large-scale farming changed the landscape. It’s often used in environmental studies, conservation planning, and education to compare past and present ecosystems. The dataset is usually presented in map form or as categorized geographic data, showing different land types like forests, prairies, wetlands, and rivers.

When looking at how the data is structured, it’s organized by land type and geographic area. This makes it easy to visualize patterns, like where prairies were dominant versus forested areas. However, the categories chosen (like “prairie” or “wetland”) can oversimplify the landscape. Nature doesn’t always fit into clean categories, so some details may be lost.

Because this dataset is based on historical interpretation, the creators had to make decisions about how to “clean” or fill in missing information. They might have combined multiple sources or estimated land types in areas without clear records. While this makes the dataset usable, it also introduces uncertainty. Not everything can be 100% accurate when reconstructing the past.

The creators’ goals mainly involve understanding how environmental change shapes the dataset to highlight natural landscapes before human impact. This could unintentionally downplay the role Indigenous people had in shaping the land, which is an important limitation to recognize.

I would use this dataset to compare how much Illinois has changed over time, especially in terms of agriculture and urbanization. One challenge is that it’s not exact; it’s more of an informed reconstruction. Overall, it’s a really valuable dataset, but it’s important to remember that it reflects both historical evidence and modern interpretation.

Ioannis Koupepides

After scanning the data set on the St. Louis, MO Monthly and Seasonal Mean Temperature set I was provided, I had to do a little digging. This data set seems to have came from the NOAA, who are responsible for collecting and analyzing climate data and environment data. After making sure that this administration is highly credible I also learned that they do these surveys of the environment to this day consistently. Though they are responsible for the data set presented the data probably comes from a long standing weather station in the St. Louis area with the proper tools and instruments to record the data.

The purpose of compiling this data set is to track the temperature trends over time and provide insight into St. Louis’s long term weather patterns. The data could be used to monitor the environment by scientists and researchers. Or to relate it to my area of interest it could be used by investors for urban planning.

The data is presented in a table format with columns and rows. When referring to structure I think this table achieves its purpose in presenting the data clearly and plainly. It shows the yearly data broken down into monthly averages and seasonal averages. The organization makes it useful to identify weather trends over the decades. However, because it only tracks averages, the data does not represent outliers like the weather phenomena we are experiencing this week (3/19). This could cause an issue and makes the data less reliable.

The methods to “clean” this data aren’t mentioned in the PDF provided, but following my NOAA statement made prior; the NOAA typically applies many procedures to ensure the consistency and quality of the data. Some effects to the data may still be present, things like weather tracking technology and station location. Another that came to mind was the slimming size of St. Louis since they began to conduct these weather surveys, I wonder if it could have gotten colder because there is less population? Surely there is less foot traffic, construction, etc…

The creator’s goals likely focused on long term averages and effects rather than short term effects. This could unintentionally downplay odd short term weather events and phenomena, especially in recent memory.

I think that the data set would be most useful to predict future weather patterns. Visualizations like charts or graphs representing the most large changes, this could also prove useful to provide insights into the growing climate change concern.

This dataset on average weekly hours worked by private employees in the St. Louis metropolitan area was made by Trading Economics, but the original data comes from government sources like the Federal Reserve’s FRED database and the U.S. Bureau of Labor Statistics. These organizations collect employment data through surveys and reports from businesses. The purpose of compiling this data is to help researchers, businesses, and policymakers understand labor trends and overall economic conditions. It has been used for economic research, forecasting, and news reporting. The dataset is available online in graph form and can also be downloaded as a spreadsheet.

The data itself is structured in a simple way, mainly focusing on the date and the average number of hours worked each week. This makes it useful for looking at trends over time, such as whether people are working more or fewer hours during certain months, years, and periods of time. However, because the dataset only focuses on averages, it does not show differences between industries, job types, or worker demographics. This could affect how the data is interpreted since part-time and full-time workers are included in the same average.

The creators mention that the data has been standardized and sometimes seasonally adjusted to make comparisons easier. While this helps make the data cleaner and more consistent, it may also hide unusual spikes or drops that could be important for understanding real economic changes. The goals of the organizations involved also shape the dataset. Government agencies focus on measurable indicators like hours worked because they are important for tracking employment and productivity. Trading Economics then decides how to present the data, which can influence what users focus on.

I would use this dataset to study economic trends in the St. Louis region, especially how working hours change during recessions or periods of growth. I would also look at why fewer hours are worked in certain months of the year vs others. Overall, the dataset is useful, but it should be used carefully because it simplifies complex labor patterns into one average number. It also doesn’t account for certain factors, like part-time vs full-time employees.