{"id":7277,"date":"2026-09-03T08:54:32","date_gmt":"2026-09-03T13:54:32","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=7277"},"modified":"2026-09-03T08:54:32","modified_gmt":"2026-09-03T13:54:32","slug":"sisler-week-2","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2026\/09\/03\/sisler-week-2\/","title":{"rendered":"Sisler Week 2"},"content":{"rendered":"

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

Some key concepts from this chapter include what GIS is, geographic features, and geographic attributes.\u00a0<\/span><\/p>\n

Definitions:<\/span><\/p>\n

GIS analysis- Geographic Information System analysis is a process of looking at data to find relationships, patterns, and the features of the data.\u00a0<\/span><\/p>\n

Geographic Features- The type of feature affects the steps of the analysis process; the three types are discrete, continuous phenomena, and summarized by area.\u00a0<\/span><\/p>\n

Geographic attributes- A way to identify what the geographic feature is by describing it, or representing some magnitude from the feature.\u00a0<\/span><\/p>\n

Comments\/Notes on the chapter-<\/span><\/p>\n

One of the first steps to GIS analysis is framing the question. This is to find what information you need, how it will be used, and who it is for. The second step of GIS analysis is to understand your data and to choose a method of getting information. There are 2-3 types of ways to get information: fast with approximate information, and other types that take longer but have more precise results. Once you have picked a model, the next step is to process the data and look at the results.\u00a0 <\/span>There are different types of geographic features. Discrete features are locations and lines; they are either shown or not. Continuous phenomena start out as a series of sample points; on the map there will be no gaps. Continuous data can also be enclosed by boundaries; the boundaries indicate where things are more similar than not. Summarized data represents the counts or densities of individual features within a boundary.\u00a0 <\/span>There are two ways of representing data, vector and raster. The vector model is shown as points, sets of coordinates, or enclosed areas. When working with data this way, the analysis involves working with the attributes in the layers data table. The raster model is represented as a matrix of cells in a continuous space. The analysis of raster data involves combining layers of data to create new layers with new cell values. Discrete and summarized data are normally represented by vector models. Continuous categories are represented by either, while continuous numerical data is normally represented by raster models.\u00a0 <\/span>There are five geographic attributes: categories, ranks, counts, amounts, and ratios. Categories are a group of similar things, while ranks are putting things into an order from high to low. Counts and amounts show you a total number, a count is the actual number of features on the map and amounts are any measurable quantity with a feature. Ratios show the relationship between two quantities.\u00a0<\/span><\/p>\n

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

Comments\/Notes on this chapter-<\/span><\/p>\n

Maps are a big part of GIS analysis. When looking at the distribution of features on a map you can see patterns that help people understand the area and where action is needed. When deciding what to map you have to look for geographic patterns in your data set. The information from the map depends on what you want to know, whether that’s where cops need to be stationed due to crimes or where a new store should be built depending on where customers are.\u00a0\u00a0<\/span><\/p>\n

When creating the map you want the features to have geographic coordinates and a category attribute for each value. The categories on the map can have subtypes. When making the map you can tell the GIS which features to display and what symbols for each feature. The GIS can store the location of each feature as a pair of geographic coordinates or a set of pairs to define its shape, line, or point. When mapping a category, you can map the subsets as different colors for individual locations to show relationships between the areas. If patterns are complex, you can create separate maps for each category, to see the patterns easier. When mapping roads, you can have different types of roads as different line thickness to help differentiate between highways and backroads for example. When displaying categories, you don\u2019t want more than seven categories; however depending on the scale of the map you could need more categories than seven to show a pattern. When using a large scale, more than seven categories can make the pattern hard to see. When grouping categories, you want to group similar things alike, and to help distinguish the pattern. Depending on how you group things can change how readers perceive the information. The base of the map should include landmarks, and should be a simple monochrome color to not distract from the data you want to show.\u00a0<\/span><\/p>\n

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

In this chapter it discusses how to best compare places, and understand relationships by using maps. When mapping it depends on what type of features you want to map. When looking for relationships or patterns in the data, it is helpful to look at the data in high and low detail and look at different ways to display it. This can help you find patterns easier, and distinguish when it is harder for the intended audience to see the pattern or relationship. When mapping you want to know what type of quantities you are mapping to display the data the best. When mapping with counts or amounts, you can map ratios to help represent the distribution of features. Ranks are useful when direct measures of data are hard to make, or if the quantity represents a combination of different factors. When mapping the determined quantities, you have to decide to assign each its own value or group them together into a class. When using classes you assign them the same symbol on the map, and define the range by determining which features fall into each class. When grouping data there are a few different ways, natural breaks, quantile, equal intervals, and standard deviation. Each grouping way shows different patterns in the data, so depending on what relationship you want to display. Each grouping also has its own advantages and disadvantages. There are also different classification schemes to know when mapping. There are ways to make the classes easier to when, for example making the legend easier to understand.\u00a0<\/span><\/p>\n

Key words\/ definitions<\/span><\/p>\n

Quantities- Quantities can be counts or amounts, ratios, or ranks.\u00a0<\/span><\/p>\n

Counts- The actual number of each feature on the map.<\/span><\/p>\n

Amounts- The total of a value that is associated with each feature.\u00a0<\/span><\/p>\n

Ratios- Ratios show the relationship between two quantities and are created by dividing one quantity by another quantity.\u00a0<\/span><\/p>\n

Ranks- Ranks put features in order from high to low and show relative values.\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Chapter 1- Some key concepts from this chapter include what GIS is, geographic features, and geographic attributes.\u00a0 Definitions: GIS analysis- Geographic Information System analysis is a process of looking at data to find relationships, patterns, and the features of the data.\u00a0 Geographic Features- The type of feature affects the steps of the analysis process; the three types are discrete, continuous phenomena, and summarized by area.\u00a0 Geographic attributes- A way to identify what the geographic feature is by describing it, or representing some magnitude from the feature.\u00a0 Comments\/Notes on the chapter- One of the first steps to GIS analysis is framing the question. This is to find what information you need, how it will be used, and who it is for. The second step of GIS analysis is to understand your data and to choose a method of getting information. There are 2-3 types of ways to get information: fast with approximate information, and other types that take longer but have more precise results. Once you have picked a model, the next step is to process the data and look at the results.\u00a0 There are different types of geographic features. Discrete features are locations and lines; they are either shown or not. Continuous phenomena start out as a series of sample points; on the map there will be no gaps. Continuous data can also be enclosed by boundaries; the boundaries indicate where things are more similar than not. Summarized data represents the counts or densities of individual features within a boundary.\u00a0 There are two ways of representing data, vector and raster. The vector model is shown as points, sets of coordinates, or enclosed areas. When working with data this way, the analysis involves working with the attributes in the layers data table. The raster model is represented as a matrix of cells in a continuous space. The analysis of raster data involves combining layers of data to create new layers with new cell values. Discrete and summarized data are normally represented by vector models. Continuous categories are represented by either, while continuous numerical data is normally represented by raster models.\u00a0 There are five geographic attributes: categories, ranks, counts, amounts, and ratios. Categories are a group of similar things, while ranks are putting things into an order from high to low. Counts and amounts show you a total number, a count is the actual number of features on the map and amounts are any measurable quantity with a feature. Ratios show the relationship between two quantities.\u00a0 Chapter 2- Comments\/Notes on this chapter- Maps are a big part of GIS analysis. When looking at the distribution of features on a map you can see patterns that help people understand the area and where action is needed. When deciding what to map you have to look for geographic patterns in your data set. The information from the map depends on what you want to know, whether that’s where cops need to be stationed due to crimes or where a new store should be built depending on where customers are.\u00a0\u00a0 When creating the map you want the features to have geographic coordinates and a category attribute for each value. The categories on the map can have subtypes. When making the map you can tell the GIS which features to display and what symbols for each feature. The GIS can store the location of each feature as a pair of geographic coordinates or a set of pairs to define its shape, line, or point. When mapping a category, you can map the subsets as different colors for individual locations to show relationships between the areas. If patterns are complex, you can create separate maps for each category, to see the patterns easier. When mapping roads, you can have different types of roads as different line thickness to help differentiate between highways and backroads for example. When displaying categories, you don\u2019t want more than seven categories; however depending on the scale of the map you could need more categories than seven to show a pattern. When using a large scale, more than seven categories can make the pattern hard to see. When grouping categories, you want to group similar things alike, and to help distinguish the pattern. Depending on how you group things can change how readers perceive the information. The base of the map should include landmarks, and should be a simple monochrome color to not distract from the data you want to show.\u00a0 Chapter 3- In this chapter it discusses how to best compare places, and understand relationships by using maps. When mapping it depends on what type of features you want to map. When looking for relationships or patterns in the data, it is helpful to look at the data in high and low detail and look at different ways to display it. This can help you find patterns easier, and distinguish when it is harder for the intended audience to see the pattern or relationship. When mapping you want to know what type of quantities you are mapping to display the data the best. When mapping with counts or amounts, you can map ratios to help represent the distribution of features. Ranks are useful when direct measures of data are hard to make, or if the quantity represents a combination of different factors. When mapping the determined quantities, you have to decide to assign each its own value or group them together into a class. When using classes you assign them the same symbol on the map, and define the range by determining which features fall into each class. When grouping data there are a few different ways, natural breaks, quantile, equal intervals, and standard deviation. Each grouping way shows different patterns in the data, so depending on what relationship you want to display. Each grouping also has its own advantages and disadvantages. There are also different classification schemes to know when mapping. There are ways to make the classes easier to when, for example making the legend easier to understand.\u00a0 Key words\/ definitions Quantities- Quantities can be counts or amounts, ratios, or ranks.\u00a0 Counts- The actual number of each feature on the map. Amounts- The total of a value that is associated with each feature.\u00a0 Ratios- Ratios show the relationship between two quantities and are created by dividing one quantity by another quantity.\u00a0 Ranks- Ranks put features in order from high to low and show relative values.\u00a0<\/p>\n","protected":false},"author":2415,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7277","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\/7277","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\/2415"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7277"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7277\/revisions"}],"predecessor-version":[{"id":7279,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7277\/revisions\/7279"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7277"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7277"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}