January 24th-26th, 2022

• I chose the noble state of Pennsylvania

• What is the overall purpose of your map?

  • To see how the population of pennsylvania shifts from county to county over the 20th century

• Who is the intended audience?

  • For college students in an intro level geography class

• What do you expect the viewers of the maps you create to know?

  • I expect them to know basic information about Pennsylvania, such as major cities and the location of the state on a national map. I also expect them to be able to read the map without many guides.

• What is the final medium for your map (it will be on the WWW) and how might this affect the way your map is created and put together?

  • It will be put on a website, which will make it necessary for me to assess the dimensions of the map as well as what symbols I use. For example, pictures and colors will show up well, but if it is too wordy, some people may have a hard time seeing it.

• The sources I used to find data:

  • https://pasdc.hbg.psu.edu/Data/Research-Briefs/Historic-Population-in-Pennsylvania

    • Html - But, I was able to export the data into an excel sheet on my computer in the GIS Lab. This gives me data from 1900-2010.

  • https://www2.census.gov/library/publications/decennial/1990/population-of-states-and-counties-us-1790-1990/population-of-states-and-counties-of-the-united-states-1790-1990.pdf

    • PDF - page 135-137 has population status of between 1900-2010.

    • The location of this pdf is on Lab computer, as I cannot open that large of a PDF on my macbook.

  • For years 2010-2020, I have downloaded a PDF from Penn State that has the information. The location is in my google drive, as well as in my folder on my designated computer in the GIS lab

• At first, finding the data wasn’t that hard, as one of the first sources available was an HTML source from the University of Pennsylvania that was an itemized list of population by county from 1900-2020. It gave me all of the information needed, yet finding another source was a little harder. Most of the ones I found were HTML sources or were PDFs that were so long that they didn't load on my laptop. I went through a variety of ways in looking for data.

  • 1) I just searched “population of Pennsylvania by county 1900-2010” on google, which is how I found my first source.

  • 2) When I couldn’t find specific sources, I took away certain words within the original search phrase, such as the restrictive years and “by county.” Eventually, I came across another website which I thought would deliver me a nice PDF of the Pennsylvania decennial census, but it never loaded. I believe that this PDF is too large to load on my macbook.

  • 3) The PDF in #2 was able to be downloaded on the computer in the GIS lab. I only downloaded the pages that were needed instead of the 300 page document.

• I would like to say though, that I thought it was going to be a lot easier to find the data than it was. Of course, my first document was amazing, and way better than what other people found, I was under the impression that finding a second source would be just as easy. Yet, it was not.

• Websites that describe population

  • https://pasdc.hbg.psu.edu/Data/Research-Briefs/Historic-Population-in-Pennsylvania#:~:text=Pennsylvania's%20growth%20was%20highest%20in,to%201930%20%5BFigure%202%5D.

    • This describes population trends in Pennsylvania

  • https://www.timelines.ws/states/PENNSYLVANIA.HTML

    • Literally a timeline of Pennsylvania history, including times of epidemics, labor strikes and influences, and economic reasoning

  • https://philadelphiaencyclopedia.org/archive/twentieth-century-to-1945/

    • Describes economic trends that influenced Pennsylvania population, such as the textile industry boom in the 1920s.

  • https://www.usnews.com/news/best-states/pennsylvania/articles/2021-03-06/county-seeks-ways-to-stop-decades-long-population-decline

    • This describes how the rust towns are failing

  • https://apnews.com/article/pennsylvania-census-2020-suburbs-3aa246994b12f6ef8e045aadd4e51b0c

    • This one talks about population trends within the state, such as city and suburb growth

    • It also talks about immigration

  • https://eh.net/book_reviews/the-face-of-decline-the-pennsylvania-anthracite-region-in-the-twentieth-century/

    • This describes the decline of the coal industry in pennsylvania

  • https://archives.upenn.edu/exhibits/penn-history/flu

    • Spanish Flu outbreak, which hit Pennsylvania harder than other states.

• The locations that I have saved on Google Earth:

  • 30th street train station in Philadelphia - interstate commerce

  • the city of Philadelphia.

  • Pennsylvania train station in Pittsburgh - interstate commerce

  • Fayette County, which is an area that used to be booming with the mining industry. It is not now.

  • Another abandoned mining town labeled “ghost town” in the North of the state.

  • Erie, Pa, which is (though, not as much as it used to be) integral in the transport of machinery.

  • Presbyterian Hospital, which was hit hard by the Spanish Flu when it came to Philadelphia in 1918.

  • The Statehouse complex in Harrisburg

  • Allegheny Portage National Railroad site - rail was integral in the development of pennsylvania

  • Gettysburg National Military Park

• February 1st-7th, 2022

• For this lab, we created different web pages that we will use to organize and showcase our progress throughout the semester. This involved us to learn the basics of HTML in order to make the different websites presentable.

• There are four websites that were created:

  1. A main personal page that gives random information about myself. Since we were told to be creative with this page, I made it a little bit chaotic. There are plenty of cat photos, including one of my two cats, and a link of 500 chaotic cat photos. There are also links to things to my other interests, such as the Columbus Blue Jackets, oil painting, and the bands that I like. I also made the background color a light cornflower blue and the font a Prussian blue, using their corresponding hex codes. If you haven't guessed from the accent colors of this blog and the colors chosen on the websites, my favorite color is blue.

This site took the most time to complete, as I added the most things to it. I changed the color many times, added many photos, and changed the font and font size. It also took quite a long time to get the bulleted list to work. Since I had so many sections and subsections between the listed items and links, it was difficult to figure how to get the format to work, which means that I have many downloads of the website saved to my macbook, as there isn't really a way for to simply refresh the page like I could on the desktop in the lab. It also took me a long time to create two columns on my webpage instead of one, simple column. For some reason, when I moved the links over to the left side of the page, it caused the left column to fail, which just made the page look too simple. Eventually, I got two of the links to work, but the other three links are still struggling, so I kept the the links to the other pages at the bottom of the right, larger column, which do still work.

• 2. A projects page that will present population change in Pennsylvania, which is the state I chose in the first lab. It has a couple pieces of information about the state, such as its state motto and its state bird. The original website from Dr. Krygier had a state word, but I couldn't find one for Pennsylvania, so I modified it to state bird, which is the Ruffed Grouse. There is a link to a wikipedia page about this bird as well. I also added an image

  3. A maps page that will have my maps loaded when I complete them. This page is relatively boring at this moment, but I currently do not have anything to really add.

  4. A maps mashup page that has a map of Pennsylvania with ten important locations. These locations in include important cities, such as Erie, Pittsburgh, and Philadelphia, as well as important places within these cities, and other places in Pennsylvania that are important to its history and population change. I included old mining towns/counties to ensure that economic booms could be traced, and locations where epidemics hit hard, such as specific hospitals, where population would change dramatically. This map was created by saving a series of KML files off of google earth pro, then uploading it to the OWU server, under the webpages assigned to my username.

1. February 9th-14th, 2022

2. For this lab, we transformed data so that we can use it in ArcGIS.

   • First, we downloaded several different files from the United States Census Bureau that listed the population of every county in the United States from 1900-1990. Since my project is only for Pennsylvania, I had to delete thousands of rows of data from other states that did not apply. This was a little frustrating because Pennsylvania is right in the middle of the alphabet, so while scrolling and selecting, it was easy to miss it. After this happened, we had to transform the FIPS code that were given by the data (a FIPS code is an identifying code given to county by the federal government) into text, which was also tedious. It was purely tedious because Pennsylvania has 67 counties, and for each record within the FIPS code field, I had to force the number to be read as text.

Second, we found data for 2000-2010, which was on a different form on the census bureau. All we did for this was download the document and then copy and paste the data into the document where the other populations were listed.

Finally, we found the population estimates for 2020 and put those into the final column of data in the spreadsheet, and did the required transformations on those to make sure that all of the formatting matched. After transforming the data, we saved it as a DBF.

3. Definitions:

• .txt - This is a file that stores computer data

• .csv - Comma Separated Values file. This is like a .txt file, but the data is separated by commas

• .xls - Spreadsheet file, meaning that it stores data in a table-like format.

• .dbf - A file that stores data within a series of fields and records.

1. February 16th-21st, 2022

2. Lab 4: Introduction to ArcGIS

   • For this lab, we simply introduced ourselves to arcGIS in both the desktop and the online versions. In class, we worked on creating basic maps and how to use all of the different features in the program, such as queries, map layers, symbology, and different map projections.

This lab was ultimately a little challenging, as I did not know how to use the program before and the sheer amount of selections that you can choose from overwhelmed me. However, as I continue to work on ArcGIS, I am sure that these reasons will work themselves out.

There weren't any specific problems with this lab except for human error. I had trouble finding some of the folders (since connecting the project with the folder has a lot of steps), but eventually, I got it worked out.

2. • For the online portion, it was a little easier to work out, as I was not making my own project but following a tutorial program provided by ArcGIS.

ArcGIS Getting Started comments:

2. • It’s cool how you can configure different layers for each map you create, and then turn them off and on as you please. This can allow you to visually assess different relationships between variables that you might not have understood otherwise.

   • I love how detailed you can make scenes within the map. I haven’t seen many maps that incorporate details like daylight into the scene creation.

   • Apparently, in local scenes, measurements are displayed as euclidean values, which I had to research because I had no idea what that was. BUT, I sort of understand now that it’s a system of measurement used by arcgis that relies on proximity of other sources in a straight line. I understand the concept now, but I think I’ll truly understand it if/when I see it practice during the following labs.

3. ArcGIS basic training:

   • I like how organized the different levels of sharing are. It's easy to plan who you want to share your project with and why.

   • I also love how there's so many options about which base map you use to build your layers on top of. Not only is this a bonus in the effectiveness of a project, but an aesthetic one as well.

   • Since there are so many options, I think that I'll need to play around on it until I can effectively use arcgis without step-by-step instructions.

4. 
   

• February 16th-28th, 2022

• Lab #5

• In this lab, we added layers onto the map to reflect the research and data transformations that we did in the past four labs.

  • In specific, I first made a map layer of my own state (Pennsylvania!) and then added the four states (Maryland, West Virginia, Ohio, and New York) that surrounded it. At this point, I included a JPG of my map. (See attached image).

  • After, I began to work on the data portion of my Pennsylvania map layer. At first, I joined the previously listed data with the data that I gathered and transformed from the US Census Bureau. However, I had a small issue with the join, as all of my data came back as "null," which meant that the data was correctly joined with the FIPS codes. I believe that this is because I had uploaded the data into my google drive, then downloaded it to my laptop, and then back again, causing some strange formatting error that it beyond my knowledge of computers. However, I simply deleted the join, and then I forced the FIPS codes into text a second time, which fixed the problem.

  • After the data was joined in the corresponding attribute table, I cleared out all of the unnecessary fields, such as population parameters by race, gender, economic background, etc. This leaves only the FIPS fields and the decade populations by county. I then implemented the field calculator so that I could examine the population change from between each decade.

  • Other information

    • Definitions of the following

      • .DBF / Dbase file - a type of database file that makes it easier to shift data between processing systems

      • Select by Attributes

        • it's a query function in ArcGIS that selects data by a chosen feature or characteristic

      • Query

        • it's a search function of sorts. By typing a specifically written sentence, it will find/highlight certain things for you.

      • Fields (in a Table)

        • It stores data from a particular subject, like county populations in a decade. They are the columns.

      • Records (in a Table)

        • While fields are the columns in the table, records are the rows. They represent an individual feature as it changes across the fields. So, you can look at one county, and see how the population changes.

      • Attributes

        • data for a specific subject that is not spacial

      • Relational Database: a database that holds different kinds of information so that we can explore the relationships between them

      • Join function

        • This combines two sets of data into the same table

      • Monitor Fire - what I thought was going to happen when I saw 400 nulls appear on my attribute table :/

What happens if you try and too much on a computer and it overheats.

• • • • Calculate / Field Calculator -

        • you can use this function to preform many calculations in your data without having to do an individual calculation for each record, such as calculating population change.

• March 18th - March 30th

• For this lab, we could finally see the results of this semester's hard work. First, we took our remaining export layer from previous labs and duplicated it until we had twelve. Each one represented one decade of change. By the end, we also twelve additional graduated symbol maps that represent total county populations for each decade.

• For my choropleth/graduated color maps, I used a range of blues. I did this for multiple reasons. Primarily, it seems that blue is a relatively universal color that does not have too many negative associations. Additionally, blue is one of Pennsylvania's state colors, so the color represents the state as well. However, as stated by Dr. Krygier in class, elderly people have a harder time seeing different shades of blue. This would be a problem as the shades of blue correspond with the magnitude of the population change. Yet, my map's audience (college students, who are typically young adults), will likely not have an issue with the color.

For the classification scheme, I used a unique scheme:

25.01 -> 113.36

12.01 -> 25.00

5.01 -> 12.00

0.01 -> 0.00

-4.99 -> - 5.00

-14.99 -> - 5 .00

-30.71 -> -15.00

• I chose this scheme because Pennsylvania's population is very stable. As one of the oldest states in the country, all of the counties in the state had been established for decades, so there weren't many huge shifts in the populations. For the most part, there is a pretty even transition between counties who don't change that much between two decades (0-> +/- 5%), to moderate change (5-20% ranges), and then there are few counties that are +/- 25 %. I combined these last ranges with the one that are above 100 because there are only a few counties/decades that fit there too. Together, the rarity of these last ranges pair nicely together

• For the graduated symbol maps, I again chose blue. The background is a light periwinkle blue, and then the circles (which are the shape I chose for the graduated symbols), are navy blue. I chose blue for the same reasons as my choropleth maps.

The classification scheme is again a unique scheme:

1000000.01 -> 2071605.00

6000000.01 -> 10000000.00

400000.01 -> 600000.00

200000.01 -> 400000.00

80000.01 -> 200000.00

40000.01 ->80000.00

10000.01 -> 40000.00

4361.00 -> 10000.00

Again, since Pennsylvania is a relatively big state (in land and in population), there is a very even spread between high, moderate, and low populations, so I wanted to reflect that with the scheme. Low populations, which represent the more rural counties, mostly seemed to hover around 40,000, with the max around 80,000. Then, I wanted a section that represented counties that have more of a moderately sized population, roughly between 80,000 and 400,000 thousand. Though this section only has two of the eight sections, it probably reflects the most counties. I did it like this because most of the counties were very similar, and a higher or lower category would make them an outlier. Finally, 400,000-2,000,000 were the higher populations, with 400000-600000 being the low side of the populations. I wanted to keep these sections together because the tended to show more urban populations, as the majority of these counties have bigger cities in them, or surround the counties that have these cities.

• Finally, even though this lab could supposedly have the most errors technology wise, I didn't have that many problems creating the maps. That was nice, as typically there's something that seems to go wrong. Yet, this time nothing was too terrible. There was a slight error when the years of 2000 and 2010 wouldn't show up on the list to be mapped, but that turned out to be a formating error. That was easily fixed by moving the data into a new field.