Chapter 1<\/b><\/p>\n
GIS analysis is the process of looking at geographic patterns in your data and at relationships between features. Understanding GIS analysis is important in making accurate decisions about what to expect and how to prepare for future conditions. Throughout this chapter, I was able to learn the steps of performing a GIS analysis and the necessary geographic features and attributes. To start an analysis, you need to form a specific question based on the information you need and how it will be used. Based on the chosen question, you then must choose an analysis method that best fits your data and features. There are three types of features used in GIS: discrete, continuous phenomena, or summarized by the area. Each geographic feature has one or more attributes that help identify it. These types of attribute values include categories, ranks, counts, amounts, and ratios. Features can be represented by two models: vector or raster. After choosing the method, you then must process the data and analyze the results. Analysis is done through summary statistics. The three most common types of summary statistics used in GIS include selecting, calculating, and summarizing. Finally, after the analysis, it is important to decide whether your information is valid and useful or if it is necessary to rerun the analysis.\u00a0<\/span><\/p>\n Key Concepts\/Definitions:<\/b><\/p>\n Discrete features: Features with specific locations that are either present or absent at any given point.\u00a0<\/span><\/p>\n Continuous phenomena: Can be found or measured everywhere.\u00a0<\/span><\/p>\n Summarized by the area: Represents the counts or density of individual features within area boundaries.\u00a0<\/span><\/p>\n Vector Model: Each feature is a row in a table and feature shapes are defined by x,y locations. Areas in the vector model are defined by borders and are represented as closed polygons. Discrete, summarized by area, and continuous categories.<\/span><\/p>\n Raster Model: Features are represented as a matrix of cells in continuous space. Each layer represents one attribute and most analysis occurs by combining the layers to create new layers with different values. Continuous numeric values.<\/span><\/p>\n Selecting Statistics: Select features to work with a subset or assign a new attribute value to just those features.<\/span><\/p>\n Calculating Statistics: Calculate the attribute values to assign new values to features such as rank or ratios.<\/span><\/p>\n Summarizing Statistics: Summarize the values for specific attributes to get the statistics such as mean or frequency.<\/span><\/p>\n Chapter 2<\/b><\/p>\n Throughout this course, I learned that GIS\u2019s ability to map where things are is an important visual in solving real world problems. In order to do this however, one must have a deep understanding of GIS analysis and the ability to create a suitable map. In this chapter, I was introduced to a variety of steps and suggestions to convey an appropriate analysis of data through mapping.<\/span><\/p>\n When deciding what to map, the information must be appropriate for the audience and the issue being addressed. To prepare your data, each feature in your map needs geographic coordinates. You can also map by type, categorize similar features, or map by subset categories. I was then intrigued to ask when it is most beneficial to divide major categories into subtypes? The chapter later explains that the general rule of mapping is no more than seven categories. However, if the features are dispersed or the map is smaller, your number of categories can vary. Later, the chapter explains when making your map there are several different ways you can display data including single type, subset feature, or by categories. Mainly, what I have gathered about the mapping process is that there is a delicate balance between being too informative and including as much data into the map as possible. The chapter, however, gives several map making tips on grouping categories, choosing appropriate symbols, and mapping reference features to make the process easier. The chapter concluded by introducing several patterns to look for in analysis such as clustered, uniformly spaced, and random distribution.<\/span><\/p>\n Key Concepts\/Definitions:<\/b><\/p>\n Single Type Map: To map features of a single type. Same symbol used for all features. Basic map to reveal patterns. May suggest differences in the features to further explore.<\/span><\/p>\n Subset Feature Map: A map of all features in a data layer or subset based on category value. Can reveal patterns that aren\u2019t apparent when mapping all features. Commonly done for individual locations.<\/span><\/p>\n Category Map: Maps features by category. Features represented by different symbols for each category value. Provides understanding of how a place functions.<\/span><\/p>\n Chapter 3<\/b><\/p>\n In this chapter, I was introduced to the features and process of accurately mapping the most and least features. Mapping where the most and least occur is extremely important in visualizing the relationships between places. This type of mapping is based on the quantity associated with each feature. When the data is discrete or continuous, you should map using counts or amounts. When summarizing by area, however, using counts or amounts can skew the patterns so it is useful to use ratios or ranks instead. Since there can be many different values in mapping, mapping by class allows the reader to compare the data more efficiently. The four most common classification schemes are natural breaks, quantile, equal interval, and standard deviation. When choosing a classification scheme, you need to know how the data values are distributed across the range. Creating a bar chart is a helpful way to see that data. If there is an outlier, you need to pay close attention to it as it can heavily skew your data on the map. One of the most useful things I learned throughout the chapter, however, was how to appropriately use graduated symbols, graduated colors, charts, contour lines, and 3D perspective views to map effectively. Having this knowledge on how to map the most and least is crucial in creating an informative and respectable map through GIS analysis.<\/span><\/p>\n Key Concepts\/Definitions<\/b><\/p>\n Counts: Actual number of features on the map.<\/span><\/p>\n Amounts: Any measurable quantity associated with a feature.<\/span><\/p>\n Ratios: The relationship between two quantities and are created by dividing one quantity by another for each feature. Can display the average, proportion, or density of certain features.<\/span><\/p>\n Ranks: Feature in order from high to low. Show relative values rather than measured values.<\/span><\/p>\n Class: Features with similar values represented by the same symbol.\u00a0<\/span><\/p>\n Natural Breaks: Set where there is a jump in values so block groups having similar values are placed into the same class. Unevenly distributed data.<\/span><\/p>\n Quantile: Each class contains an equal number of features. Evenly distributed and emphasis on the relative difference between features.<\/span><\/p>\n Equal Interval: The difference between high and low values is the same for every class. Evenly distributed and emphasis on the difference between features.<\/span><\/p>\n Standard Deviation: Features are placed in classes based on how much their values vary from the mean. Evenly distributed and emphasis on the difference between features.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" Chapter 1 GIS analysis is the process of looking at geographic patterns in your data and at relationships between features. Understanding GIS analysis is important in making accurate decisions about what to expect and how to prepare for future conditions. Throughout this chapter, I was able to learn the steps of performing a GIS analysis and the necessary geographic features and attributes. To start an analysis, you need to form a specific question based on the information you need and how it will be used. Based on the chosen question, you then must choose an analysis method that best fits your data and features. There are three types of features used in GIS: discrete, continuous phenomena, or summarized by the area. Each geographic feature has one or more attributes that help identify it. These types of attribute values include categories, ranks, counts, amounts, and ratios. Features can be represented by two models: vector or raster. After choosing the method, you then must process the data and analyze the results. Analysis is done through summary statistics. The three most common types of summary statistics used in GIS include selecting, calculating, and summarizing. Finally, after the analysis, it is important to decide whether your information is valid and useful or if it is necessary to rerun the analysis.\u00a0 Key Concepts\/Definitions: Discrete features: Features with specific locations that are either present or absent at any given point.\u00a0 Continuous phenomena: Can be found or measured everywhere.\u00a0 Summarized by the area: Represents the counts or density of individual features within area boundaries.\u00a0 Vector Model: Each feature is a row in a table and feature shapes are defined by x,y locations. Areas in the vector model are defined by borders and are represented as closed polygons. Discrete, summarized by area, and continuous categories. Raster Model: Features are represented as a matrix of cells in continuous space. Each layer represents one attribute and most analysis occurs by combining the layers to create new layers with different values. Continuous numeric values. Selecting Statistics: Select features to work with a subset or assign a new attribute value to just those features. Calculating Statistics: Calculate the attribute values to assign new values to features such as rank or ratios. Summarizing Statistics: Summarize the values for specific attributes to get the statistics such as mean or frequency. Chapter 2 Throughout this course, I learned that GIS\u2019s ability to map where things are is an important visual in solving real world problems. In order to do this however, one must have a deep understanding of GIS analysis and the ability to create a suitable map. In this chapter, I was introduced to a variety of steps and suggestions to convey an appropriate analysis of data through mapping. When deciding what to map, the information must be appropriate for the audience and the issue being addressed. To prepare your data, each feature in your map needs geographic coordinates. You can also map by type, categorize similar features, or map by subset categories. I was then intrigued to ask when it is most beneficial to divide major categories into subtypes? The chapter later explains that the general rule of mapping is no more than seven categories. However, if the features are dispersed or the map is smaller, your number of categories can vary. Later, the chapter explains when making your map there are several different ways you can display data including single type, subset feature, or by categories. Mainly, what I have gathered about the mapping process is that there is a delicate balance between being too informative and including as much data into the map as possible. The chapter, however, gives several map making tips on grouping categories, choosing appropriate symbols, and mapping reference features to make the process easier. The chapter concluded by introducing several patterns to look for in analysis such as clustered, uniformly spaced, and random distribution. Key Concepts\/Definitions: Single Type Map: To map features of a single type. Same symbol used for all features. Basic map to reveal patterns. May suggest differences in the features to further explore. Subset Feature Map: A map of all features in a data layer or subset based on category value. Can reveal patterns that aren\u2019t apparent when mapping all features. Commonly done for individual locations. Category Map: Maps features by category. Features represented by different symbols for each category value. Provides understanding of how a place functions. Chapter 3 In this chapter, I was introduced to the features and process of accurately mapping the most and least features. Mapping where the most and least occur is extremely important in visualizing the relationships between places. This type of mapping is based on the quantity associated with each feature. When the data is discrete or continuous, you should map using counts or amounts. When summarizing by area, however, using counts or amounts can skew the patterns so it is useful to use ratios or ranks instead. Since there can be many different values in mapping, mapping by class allows the reader to compare the data more efficiently. The four most common classification schemes are natural breaks, quantile, equal interval, and standard deviation. When choosing a classification scheme, you need to know how the data values are distributed across the range. Creating a bar chart is a helpful way to see that data. If there is an outlier, you need to pay close attention to it as it can heavily skew your data on the map. One of the most useful things I learned throughout the chapter, however, was how to appropriately use graduated symbols, graduated colors, charts, contour lines, and 3D perspective views to map effectively. Having this knowledge on how to map the most and least is crucial in creating an informative and respectable map through GIS analysis. Key Concepts\/Definitions Counts: Actual number of features on the map. Amounts: Any measurable quantity associated with a feature. Ratios: The relationship between two quantities and are created by dividing one quantity by another for each feature. Can display the average, proportion, or density of certain features. Ranks: Feature in order from high to low. Show relative values rather than measured values. Class: Features with similar values represented by the same symbol.\u00a0 Natural Breaks: Set where there is a jump in values so block groups having similar values are placed into the same class. Unevenly distributed data. Quantile: Each class contains an equal number of features. Evenly distributed and emphasis on the relative difference between features. Equal Interval: The difference between high and low values is the same for every class. Evenly distributed and emphasis on the difference between features. Standard Deviation: Features are placed in classes based on how much their values vary from the mean. Evenly distributed and emphasis on the difference between features.<\/p>\n","protected":false},"author":2413,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7263","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\/7263","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\/2413"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7263"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7263\/revisions"}],"predecessor-version":[{"id":7264,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7263\/revisions\/7264"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7263"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7263"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7263"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}