{"id":7353,"date":"2026-09-08T15:49:44","date_gmt":"2026-09-08T20:49:44","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=7353"},"modified":"2026-09-08T15:49:44","modified_gmt":"2026-09-08T20:49:44","slug":"robinson-week-2","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2026\/09\/08\/robinson-week-2\/","title":{"rendered":"Robinson Week 2"},"content":{"rendered":"

Chapter 1:<\/span><\/p>\n

The first chapter discusses a general introduction to GIS analysis. A multi-step process when it comes to viewing the relationships and patterns between multiple features. It starts with framing a question to determine what information is needed. Who it will be used for and how also comes into play. Which is really important. Once the information is gathered, we need to understand the data. Using certain data types and features will help us better determine which method to use. What approach should be used? That depends entirely on the question and how the results will be used (Remember this: \u201cWho it will be used for and how… \u201d). After that, we need to process that data and look at the results. Then display them in various ways (charts, tables, maps, etc.).<\/span><\/p>\n

There are many types of geographic features.\u00a0 They play a role in our analysis process. Discrete features represent precise locations, whether the feature is there or not (ex: legal boundaries or barriers). Continuous phenomena (like rainfall or temperature) can be measured anywhere, with no gaps. Data represented by density or counts of multiple features\u00a0 shown in a given area.<\/span> For example, the number of parks or schools in a place. These features can be shown in 2 ways. By a vector model, as a row contained in a table with x, y coordinates. Their features are represented as lines or events. The other way is the raster model, where features are shown as a group of cells that create layers. (Vectors remind me of C++, which is used to alter arrays in a data set.)<\/span><\/p>\n

The features in question have values attributed to them. Some include categories that help organize and group similar data together. Ranks assert the importance of multiple factors or provide a direct measurement when it is hard to capture. These are noncontinuous values. Counts give us the total numbers. On the other hand, amounts are quantifiable associations within features. Ratios can show us the connection between 2 numbers. They are divided by another one for each feature present. The last 3 are known as continuous values. We put these values into data tables to help us characterize and summarize data. 3 operators are used for tables. They are selecting (assigning values), calculating (pretty self-explanatory), and summarizing (summing specific traits to get data).<\/span><\/p>\n

 <\/p>\n

Chapter 2:<\/span><\/p>\n

In the last chapter, we got a general introduction and learned some GIS concepts. This part of the book focuses on mapping: deciding, preparing, making, and analyzing a map. Describing how certain features on maps can be used to show patterns of correlation. Note that it should always represent the intended audience. (You don\u2019t want to make a map representing customer data and show it to a bunch of zoologists.)\u00a0<\/span><\/p>\n

Each feature needs a coordinate and a code to assign a type (one or more categories). Groups of features with similar characteristics are called categories. To map (features) them, we simply draw them, represented by a marker. The same applies to categories, but with colors or patterns. The downside is that users are limited to seven. The reason why is that most people can\u2019t differentiate 7 patterns or colors on a map. This has something to do with our cognitive ability to recognize patterns. (This topic interested me; I spent some time looking into it.) If a person tries to use more than that, it\u2019ll make the map harder for the user to read. It is recommended to try to group these together.\u00a0<\/span><\/p>\n

We can combine them in various ways. Assigning a code, using a linked table, or assigning categories by using symbols.\u00a0 All this data is stored in a table, including the description of the shape you gave it. (Which I found was cool.) When mapping features, they have to be the right size for viewers to visualize the categories. Scaling also plays an important role, as long as the map\u2019s characteristics are defined and easy to view. Symbols and lines, including line width, have their own criteria for user visibility. Text labels can also help distinguish categories. Using distinguishable landmarks like rivers, roads, buildings, and more can make a map unique.<\/span><\/p>\n

Chapter 3:<\/span><\/p>\n

The last chapter focused on mapping where things are. This chapter discusses why mapping the most and least of the datasets can show correlations between locations. Including the features that help put everything together. A potential map should include the following features: discrete features, represented by lines or symbols; continuous phenomena, shown with 3-D perspective (view), colors, or contours. Or data summarization; displaying category amounts by shading in a given area.\u00a0<\/span><\/p>\n

To plot this data, it must be given a quantity. Some include continuous or noncontinuous data. (These types were discussed in chapter 1 but also mentioned in this chapter.) Once the quantities are given. It\u2019s time to either group them together with continuous data. Or plot them individually with the usage of ranks. You can group similar values with their features, or manually create groups under certain conditions. You can do this using standard classification schemes based on how the data is laid out.\u00a0<\/span><\/p>\n

There are 4 common types. Natural breaks indicate jumps in values when data is not evenly distributed. Quantile, in which each class is used and has the same number of features. Equal interval, showing the gap between the highest and lowest values in every class. The last type is standard deviation, where it adds or subtracts the standard deviation from the mean. This creates the class breaks. With all this data at hand, it’s time to create a map. Using graduated symbols, colors, charts, contours, or even 3d perspectives to display our data. Each has its use cases, upsides, and downsides. (This includes the use of standard classification schemes.) As long as the data matches the visual map. We can easily compare and contrast different areas within it. For example, we can see where certain parts of a map contain lacking, concentrated, or scattered values.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Chapter 1: The first chapter discusses a general introduction to GIS analysis. A multi-step process when it comes to viewing the relationships and patterns between multiple features. It starts with framing a question to determine what information is needed. Who it will be used for and how also comes into play. Which is really important. Once the information is gathered, we need to understand the data. Using certain data types and features will help us better determine which method to use. What approach should be used? That depends entirely on the question and how the results will be used (Remember this: \u201cWho it will be used for and how… \u201d). After that, we need to process that data and look at the results. Then display them in various ways (charts, tables, maps, etc.). There are many types of geographic features.\u00a0 They play a role in our analysis process. Discrete features represent precise locations, whether the feature is there or not (ex: legal boundaries or barriers). Continuous phenomena (like rainfall or temperature) can be measured anywhere, with no gaps. Data represented by density or counts of multiple features\u00a0 shown in a given area. For example, the number of parks or schools in a place. These features can be shown in 2 ways. By a vector model, as a row contained in a table with x, y coordinates. Their features are represented as lines or events. The other way is the raster model, where features are shown as a group of cells that create layers. (Vectors remind me of C++, which is used to alter arrays in a data set.) The features in question have values attributed to them. Some include categories that help organize and group similar data together. Ranks assert the importance of multiple factors or provide a direct measurement when it is hard to capture. These are noncontinuous values. Counts give us the total numbers. On the other hand, amounts are quantifiable associations within features. Ratios can show us the connection between 2 numbers. They are divided by another one for each feature present. The last 3 are known as continuous values. We put these values into data tables to help us characterize and summarize data. 3 operators are used for tables. They are selecting (assigning values), calculating (pretty self-explanatory), and summarizing (summing specific traits to get data).   Chapter 2: In the last chapter, we got a general introduction and learned some GIS concepts. This part of the book focuses on mapping: deciding, preparing, making, and analyzing a map. Describing how certain features on maps can be used to show patterns of correlation. Note that it should always represent the intended audience. (You don\u2019t want to make a map representing customer data and show it to a bunch of zoologists.)\u00a0 Each feature needs a coordinate and a code to assign a type (one or more categories). Groups of features with similar characteristics are called categories. To map (features) them, we simply draw them, represented by a marker. The same applies to categories, but with colors or patterns. The downside is that users are limited to seven. The reason why is that most people can\u2019t differentiate 7 patterns or colors on a map. This has something to do with our cognitive ability to recognize patterns. (This topic interested me; I spent some time looking into it.) If a person tries to use more than that, it\u2019ll make the map harder for the user to read. It is recommended to try to group these together.\u00a0 We can combine them in various ways. Assigning a code, using a linked table, or assigning categories by using symbols.\u00a0 All this data is stored in a table, including the description of the shape you gave it. (Which I found was cool.) When mapping features, they have to be the right size for viewers to visualize the categories. Scaling also plays an important role, as long as the map\u2019s characteristics are defined and easy to view. Symbols and lines, including line width, have their own criteria for user visibility. Text labels can also help distinguish categories. Using distinguishable landmarks like rivers, roads, buildings, and more can make a map unique. Chapter 3: The last chapter focused on mapping where things are. This chapter discusses why mapping the most and least of the datasets can show correlations between locations. Including the features that help put everything together. A potential map should include the following features: discrete features, represented by lines or symbols; continuous phenomena, shown with 3-D perspective (view), colors, or contours. Or data summarization; displaying category amounts by shading in a given area.\u00a0 To plot this data, it must be given a quantity. Some include continuous or noncontinuous data. (These types were discussed in chapter 1 but also mentioned in this chapter.) Once the quantities are given. It\u2019s time to either group them together with continuous data. Or plot them individually with the usage of ranks. You can group similar values with their features, or manually create groups under certain conditions. You can do this using standard classification schemes based on how the data is laid out.\u00a0 There are 4 common types. Natural breaks indicate jumps in values when data is not evenly distributed. Quantile, in which each class is used and has the same number of features. Equal interval, showing the gap between the highest and lowest values in every class. The last type is standard deviation, where it adds or subtracts the standard deviation from the mean. This creates the class breaks. With all this data at hand, it’s time to create a map. Using graduated symbols, colors, charts, contours, or even 3d perspectives to display our data. Each has its use cases, upsides, and downsides. (This includes the use of standard classification schemes.) As long as the data matches the visual map. We can easily compare and contrast different areas within it. For example, we can see where certain parts of a map contain lacking, concentrated, or scattered values.<\/p>\n","protected":false},"author":2434,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7353","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\/7353","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\/2434"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7353"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7353\/revisions"}],"predecessor-version":[{"id":7354,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7353\/revisions\/7354"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7353"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7353"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7353"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}