{"id":7393,"date":"2026-09-11T22:05:09","date_gmt":"2026-09-12T03:05:09","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=7393"},"modified":"2026-09-11T22:05:09","modified_gmt":"2026-09-12T03:05:09","slug":"robinson-week-3","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2026\/09\/11\/robinson-week-3\/","title":{"rendered":"Robinson Week 3"},"content":{"rendered":"

Chapter 4:<\/span><\/p>\n

In the previous chapter, we learned how correlations can be shown when using data sets to map the most and least of this information. This chapter focuses on mapping density, its importance, and the various ways we can use and build it. This is crucial because it shows viewers where high concentrations of features are located. GIS allows you to map features or feature values; each will result in different outputs (based on what the user inputs). To make either useful, we need to create a density map. One way to do that is to define an area. This can be done graphically with a dot map or by calculating each value per area. Using calculations requires a couple of things. First, by adding a new field for a density table based on the coverage area of the polygon. Then, assign density values and finally divide the values by the polygon area. This method may need a conversion equation if the density units don’t equal area units. This creates a map by total amount or counts, while noting what each dot signifies (e.g., 10 or 10,000 people). Another way is by density surface, created through a raster layer.\u00a0 It divides the total amount of features by the search radius field. When it comes to calculating density sizes, a few things are required: cell size (to define patterns, its size is given by the length of the cell), searching for a radius (used to find objects on a map), calculation methods (which have 2 methods that can be used: simple and ring series), and units (measurement). How do we display a density surface? We can display it using gradient colors and contours. (This topic was discussed in Chapter 3.) The results will depend heavily on how the density surface is created.<\/span><\/p>\n

Chapter 5:<\/span><\/p>\n

Previously, we learned about map density, importance, and their use cases. Within this chapter, discussing the importance of mapping its inner contents. Helping the reader compare fewer and more areas (reviewed in the previous chapter). To define our evaluation, there are a couple of things the user wants to know. How many areas do you have, and whether they are inside one area (e.g., a shopping mall) or have multiple areas (e.g., zip codes)? Each area must be identifiable by a unique name. Drawing areas and features like surfaces and lines works well when the user is trying to find what\u2019s inside and outside a given area. It’s easy to use, but it relies heavily on visual information. Selecting features in a region is useful if you want a summary or list of what’s inside that region. Its downside is that it works for only one area, not multiple. Overlaying has the benefits of both drawing and selection (only within), but it requires more processing power. The only time a user should choose overlaying is if they have multiple areas (examples: zip codes, disjuncts: pieces of land that are separated, or nested: smaller areas inside larger ones) and want a summary of each area; a single area (examples: a shopping mall, buffer, an administrative or natural boundary, manually drawn area, or a result of a model) and want a list of discrete features; or a single area and want a summary of continuous values. Overlaying with discrete features is used when the user wants to know which features are inside or outside a given space. A use case is finding the number of bears in a specific park. With continuous data, we blend gradients to find patterns. A pretty common example is varying temperatures or rainfall across a certain region.<\/span><\/p>\n

Chapter 6:<\/span><\/p>\n

In the prior chapter, we learned about mapping inner contents. This chapter covers mapping nearby areas. People define distance as a measurement (think inches, feet, and miles), while others measure it by cost. The best method for your analysis entirely depends on a couple of things. Maybe you want to use list, count, or summary (discussed in previous chapters). Or you might want to know how many distance ranges you\u2019ll need. There are 2: inclusive rings, used to find the total increase as the distance rises. The other includes distance bands, used for when you want to compare distance to different characteristics. How do you find what\u2019s nearby, though? There\u2019s straight-line distance, used to create boundaries or select features around an area. Cost over a network, which finds what\u2019s within travel distance. Then there is cost over a surface, which not only measures distance but also calculates the area within a given range. Buffers create a boundary line to show what’s far away. To create one, the user defines the source feature (or features) and the buffer distance. The aftermath lets people see details in a given area. Special ones are used for finding multiple features. To find objects in several distance ranges, create multiple proximity zones. To pick features close to many objects, the user selects and tags the feature with a code. For features within several distance ranges, you select each object once for each distance. Feature-to-feature is great for tracking distance from a source object, which has its own use cases. There are several options when making a map for this case. Out of the many maps used in this chapter. The one that caught my attention the most was the spider diagram (not really a map, but close enough). It looks like a firework going off in multiple locations.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Chapter 4: In the previous chapter, we learned how correlations can be shown when using data sets to map the most and least of this information. This chapter focuses on mapping density, its importance, and the various ways we can use and build it. This is crucial because it shows viewers where high concentrations of features are located. GIS allows you to map features or feature values; each will result in different outputs (based on what the user inputs). To make either useful, we need to create a density map. One way to do that is to define an area. This can be done graphically with a dot map or by calculating each value per area. Using calculations requires a couple of things. First, by adding a new field for a density table based on the coverage area of the polygon. Then, assign density values and finally divide the values by the polygon area. This method may need a conversion equation if the density units don’t equal area units. This creates a map by total amount or counts, while noting what each dot signifies (e.g., 10 or 10,000 people). Another way is by density surface, created through a raster layer.\u00a0 It divides the total amount of features by the search radius field. When it comes to calculating density sizes, a few things are required: cell size (to define patterns, its size is given by the length of the cell), searching for a radius (used to find objects on a map), calculation methods (which have 2 methods that can be used: simple and ring series), and units (measurement). How do we display a density surface? We can display it using gradient colors and contours. (This topic was discussed in Chapter 3.) The results will depend heavily on how the density surface is created. Chapter 5: Previously, we learned about map density, importance, and their use cases. Within this chapter, discussing the importance of mapping its inner contents. Helping the reader compare fewer and more areas (reviewed in the previous chapter). To define our evaluation, there are a couple of things the user wants to know. How many areas do you have, and whether they are inside one area (e.g., a shopping mall) or have multiple areas (e.g., zip codes)? Each area must be identifiable by a unique name. Drawing areas and features like surfaces and lines works well when the user is trying to find what\u2019s inside and outside a given area. It’s easy to use, but it relies heavily on visual information. Selecting features in a region is useful if you want a summary or list of what’s inside that region. Its downside is that it works for only one area, not multiple. Overlaying has the benefits of both drawing and selection (only within), but it requires more processing power. The only time a user should choose overlaying is if they have multiple areas (examples: zip codes, disjuncts: pieces of land that are separated, or nested: smaller areas inside larger ones) and want a summary of each area; a single area (examples: a shopping mall, buffer, an administrative or natural boundary, manually drawn area, or a result of a model) and want a list of discrete features; or a single area and want a summary of continuous values. Overlaying with discrete features is used when the user wants to know which features are inside or outside a given space. A use case is finding the number of bears in a specific park. With continuous data, we blend gradients to find patterns. A pretty common example is varying temperatures or rainfall across a certain region. Chapter 6: In the prior chapter, we learned about mapping inner contents. This chapter covers mapping nearby areas. People define distance as a measurement (think inches, feet, and miles), while others measure it by cost. The best method for your analysis entirely depends on a couple of things. Maybe you want to use list, count, or summary (discussed in previous chapters). Or you might want to know how many distance ranges you\u2019ll need. There are 2: inclusive rings, used to find the total increase as the distance rises. The other includes distance bands, used for when you want to compare distance to different characteristics. How do you find what\u2019s nearby, though? There\u2019s straight-line distance, used to create boundaries or select features around an area. Cost over a network, which finds what\u2019s within travel distance. Then there is cost over a surface, which not only measures distance but also calculates the area within a given range. Buffers create a boundary line to show what’s far away. To create one, the user defines the source feature (or features) and the buffer distance. The aftermath lets people see details in a given area. Special ones are used for finding multiple features. To find objects in several distance ranges, create multiple proximity zones. To pick features close to many objects, the user selects and tags the feature with a code. For features within several distance ranges, you select each object once for each distance. Feature-to-feature is great for tracking distance from a source object, which has its own use cases. There are several options when making a map for this case. Out of the many maps used in this chapter. The one that caught my attention the most was the spider diagram (not really a map, but close enough). It looks like a firework going off in multiple locations.<\/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-7393","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\/7393","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=7393"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7393\/revisions"}],"predecessor-version":[{"id":7394,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7393\/revisions\/7394"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7393"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7393"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7393"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}