{"id":7321,"date":"2026-09-05T14:30:00","date_gmt":"2026-09-05T19:30:00","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=7321"},"modified":"2026-09-05T14:30:00","modified_gmt":"2026-09-05T19:30:00","slug":"holbrooks-week-2","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2026\/09\/05\/holbrooks-week-2\/","title":{"rendered":"Holbrooks Week 2"},"content":{"rendered":"

Chapter 1: Introducing GIS Analysis<\/em><\/p>\n

The introduction of this chapter explains well the many different ways that GIS can be used. I appreciate the layout for formulating a research question, as it reminds me of our previous class together, where we created TPGs.\u00a0<\/span><\/p>\n

This chapter also discusses <\/span>discrete features,<\/b> where a feature is either present or absent; <\/span>continuous phenomena,<\/b> which blanket the entire area of focus; and <\/span>summarized data,<\/b> which counts the density within a specific area of a feature. I never knew these terms before, and did not realize there was such a concrete definition\/method for each of these measurements.\u00a0<\/span>I liked learning that both the <\/span>vector<\/b> and <\/span>raster models<\/b> can be used to plot any type of feature. Discrete features are typically mapped with vectors, which makes the most sense to me as well. (I would have assumed that discrete features can <\/span>only<\/span><\/i> be plotted by vector models.)\u00a0<\/span><\/p>\n

Page 14 says, \u201cAll map projections distort the shapes of the features being displayed, as well as measurements of area, distance, and direction. In general, if you\u2019re mapping a relatively small area, such as a town or county, this distortion is negligible. It may be more of a concern if you\u2019re mapping a large area such as a state, country, or the entire world, because the curvature of the Earth comes into play.\u201d I find this interesting\u2026 <\/span>what is the exact amount of distortion that happens at each scale size? Where should that line be drawn when an area becomes \u2018too big\u2019 to attempt a map projection?\u00a0<\/span><\/span><\/p>\n

I liked the refresher about <\/span>proportions <\/b>and<\/span> densities<\/b>, as well. Proportions show you what part of a total each value is, and densities show the distribution of that feature across a certain area. I\u2019ve seen ratios and ranks on maps before, but have never really understood what they meant well until learning about proportions and densities. I\u2019m excited to learn more about density specifically in chapter 4. <\/span>I also learned that <\/span>calculating<\/b> is far simpler than I imagined, and allows you to assign values directly to each feature for what you\u2019d like to learn\/discover. Also, looking at the figures included for summarization helped me a <\/span>lot<\/span><\/i> to understand the concept and what it is we\u2019re actually doing.\u00a0<\/span><\/p>\n

Chapter 2: Mapping Where Things Are<\/span><\/em><\/p>\n

Throughout the beginning of this section, I enjoyed learning about the ways that mapping and being able to recognize patterns are important for understanding how things got to be the way they are. I understand now that being able to compare these patterns to other variables or areas helps us to further understand the first pattern we\u2019re concerned about. I enjoyed learning that with GIS we can <\/span>toggle<\/b> these <\/span>features <\/b>or categories to focus on specific features\/patterns.\u00a0<\/span><\/p>\n

The section \u201cWhat GIS does\u201d for mapping really helped my understanding of what the program does to actually capture a feature that may not be as simple as a single dot. Linear features, for example, are a series of coordinate pairs that are then connected by drawn lines. Or, for parcels\/pieces of land, the lines are then connected or filled in with a color or pattern. Though short, I like how this section gave me the perfect amount of background information to better understand the process that\u2019s going on as I input the data.\u00a0<\/span><\/p>\n

Similarly, \u201cWhat the GIS does\u201d for mapping by category helped me to picture how I\u2019ll be completing the work in the program before even doing it. I now get that assigning a specific <\/span>value<\/b> (or, I think of it as a \u2018<\/span>code<\/b>\u2019) will be stored separately from the characteristics of symbols I specified to draw for each value. I can envision displaying the features and the GIS working to look up the symbol for each feature\/rule and display\/draw that feature on the map separately. \u201cGrouping Categories\u201d also made it much easier to envision how the features will be categorized in a broad or umbrella-type sense, and the figure on page 41 helped me to identify how they\u2019d be displayed on the map. I like that we have so many abilities through GIS to look at extremely finite or niche details, yet also compare those features to broad patterns across an area or to somewhere completely different. This chapter was very helpful in solidifying my understanding of what the program is actually doing as we input data.\u00a0<\/span><\/p>\n

Chapter 3: Mapping the Most and Least<\/span><\/em><\/p>\n

As I stated for chapter 1, I enjoyed learning more about ratios and proportions. I tend to struggle with math and statistics, specifically. I appreciate how the reading gives really understandable examples for each of these topics, and, again, really helps me to envision and prepare for the work we\u2019ll be doing in the desktop program. For example, on page 60, Mitchell says, \u201cProportions show you what part of a whole each quantity represents. To calculate a <\/span>proportion<\/b>, you divide quantities that use the same measure. For example, dividing the number of 18- to 29-year-olds in each tract by the total population of each tract gives you the proportion of people aged 18 to 29 in each tract.\u201d This step-by-step guide and example format, along with the figures showing how that will look on a map and index\/key, was a great review in ratios for me.\u00a0<\/span><\/p>\n

In my writing for chapter 2, I also discussed how seeing the different ways things can be categorized and compared, from big to small, solidified my understanding of <\/span>Grouping Categories<\/b> and how they\u2019ll appear on the map in our work. In chapter 3, the section \u201cCreating Classes\u201d built on this information and discussed how we\u2019ll actually be assigning the values their own symbol and\/or grouping the values into <\/span>classes<\/b>. \u2018Creating classes manually,\u2019 \u2018Using standard classification schemes\u2019, \u2018comparing standard classification schemes,\u2019 and \u2018Dealing with outliers\u2019 were the most helpful in giving me a basis of the different classification processes and how finite they get. Some are detailed and will be better understood after I\u2019ve gone into the program and practiced, but I really appreciated this baseline understanding that I got through these sections. Many of the others were very understandable, like \u2018Deciding on how many classes\u2019 or \u2018Making the classes easier to read,\u2019 but still were nice to read through and feel confident going into next week\u2019s computer lab work. <\/span>I\u2019m looking forward to actually getting into the program and seeing these processes through with unique data! <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Chapter 1: Introducing GIS Analysis The introduction of this chapter explains well the many different ways that GIS can be used. I appreciate the layout for formulating a research question, as it reminds me of our previous class together, where we created TPGs.\u00a0 This chapter also discusses discrete features, where a feature is either present or absent; continuous phenomena, which blanket the entire area of focus; and summarized data, which counts the density within a specific area of a feature. I never knew these terms before, and did not realize there was such a concrete definition\/method for each of these measurements.\u00a0I liked learning that both the vector and raster models can be used to plot any type of feature. Discrete features are typically mapped with vectors, which makes the most sense to me as well. (I would have assumed that discrete features can only be plotted by vector models.)\u00a0 Page 14 says, \u201cAll map projections distort the shapes of the features being displayed, as well as measurements of area, distance, and direction. In general, if you\u2019re mapping a relatively small area, such as a town or county, this distortion is negligible. It may be more of a concern if you\u2019re mapping a large area such as a state, country, or the entire world, because the curvature of the Earth comes into play.\u201d I find this interesting\u2026 what is the exact amount of distortion that happens at each scale size? Where should that line be drawn when an area becomes \u2018too big\u2019 to attempt a map projection?\u00a0 I liked the refresher about proportions and densities, as well. Proportions show you what part of a total each value is, and densities show the distribution of that feature across a certain area. I\u2019ve seen ratios and ranks on maps before, but have never really understood what they meant well until learning about proportions and densities. I\u2019m excited to learn more about density specifically in chapter 4. I also learned that calculating is far simpler than I imagined, and allows you to assign values directly to each feature for what you\u2019d like to learn\/discover. Also, looking at the figures included for summarization helped me a lot to understand the concept and what it is we\u2019re actually doing.\u00a0 Chapter 2: Mapping Where Things Are Throughout the beginning of this section, I enjoyed learning about the ways that mapping and being able to recognize patterns are important for understanding how things got to be the way they are. I understand now that being able to compare these patterns to other variables or areas helps us to further understand the first pattern we\u2019re concerned about. I enjoyed learning that with GIS we can toggle these features or categories to focus on specific features\/patterns.\u00a0 The section \u201cWhat GIS does\u201d for mapping really helped my understanding of what the program does to actually capture a feature that may not be as simple as a single dot. Linear features, for example, are a series of coordinate pairs that are then connected by drawn lines. Or, for parcels\/pieces of land, the lines are then connected or filled in with a color or pattern. Though short, I like how this section gave me the perfect amount of background information to better understand the process that\u2019s going on as I input the data.\u00a0 Similarly, \u201cWhat the GIS does\u201d for mapping by category helped me to picture how I\u2019ll be completing the work in the program before even doing it. I now get that assigning a specific value (or, I think of it as a \u2018code\u2019) will be stored separately from the characteristics of symbols I specified to draw for each value. I can envision displaying the features and the GIS working to look up the symbol for each feature\/rule and display\/draw that feature on the map separately. \u201cGrouping Categories\u201d also made it much easier to envision how the features will be categorized in a broad or umbrella-type sense, and the figure on page 41 helped me to identify how they\u2019d be displayed on the map. I like that we have so many abilities through GIS to look at extremely finite or niche details, yet also compare those features to broad patterns across an area or to somewhere completely different. This chapter was very helpful in solidifying my understanding of what the program is actually doing as we input data.\u00a0 Chapter 3: Mapping the Most and Least As I stated for chapter 1, I enjoyed learning more about ratios and proportions. I tend to struggle with math and statistics, specifically. I appreciate how the reading gives really understandable examples for each of these topics, and, again, really helps me to envision and prepare for the work we\u2019ll be doing in the desktop program. For example, on page 60, Mitchell says, \u201cProportions show you what part of a whole each quantity represents. To calculate a proportion, you divide quantities that use the same measure. For example, dividing the number of 18- to 29-year-olds in each tract by the total population of each tract gives you the proportion of people aged 18 to 29 in each tract.\u201d This step-by-step guide and example format, along with the figures showing how that will look on a map and index\/key, was a great review in ratios for me.\u00a0 In my writing for chapter 2, I also discussed how seeing the different ways things can be categorized and compared, from big to small, solidified my understanding of Grouping Categories and how they\u2019ll appear on the map in our work. In chapter 3, the section \u201cCreating Classes\u201d built on this information and discussed how we\u2019ll actually be assigning the values their own symbol and\/or grouping the values into classes. \u2018Creating classes manually,\u2019 \u2018Using standard classification schemes\u2019, \u2018comparing standard classification schemes,\u2019 and \u2018Dealing with outliers\u2019 were the most helpful in giving me a basis of the different classification processes and how finite they get. Some are detailed and will be better understood after I\u2019ve gone into the program and practiced, but I really appreciated this baseline understanding that I got through these sections. Many of the others were very understandable, like \u2018Deciding on how many classes\u2019 or \u2018Making the classes easier to read,\u2019 but still were nice to read through and feel confident going into next week\u2019s computer lab work. I\u2019m looking forward to actually getting into the program and seeing these processes through with unique data!<\/p>\n","protected":false},"author":2361,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7321","post","type-post","status-publish","format-standard","hentry","category-course-student-work"],"_links":{"self":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7321","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/users\/2361"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7321"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7321\/revisions"}],"predecessor-version":[{"id":7322,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7321\/revisions\/7322"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7321"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7321"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7321"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}