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Chapter 1<\/b>– I learned about concepts and how to correctly read and identify locations and lines for example\u2026<\/span><\/p>\n Businesses, symbolized by the number of employees, are an example of individual locations. Streams are linear features. Parcels, color-coded by land value, are an example of discrete areas.<\/span><\/p>\n <\/p>\n Some new things I learned that I think are really important to remember are\u2026 <\/span>Continuous phenomena <\/b>such as precipitation or temperature can be found or measured anywhere. EX.) Temperature, precipitation, and elevation are examples. For instance, elevation changes continuously as you move across the landscape rather than existing at only one point.\u00a0<\/span><\/p>\n Also <\/span>Continuous data<\/b> which are areas enclosed by boundaries. <\/span>Summarize data, <\/b>which counts individual features within boundaries.<\/span><\/p>\n The book then discussed the process of overlaying boundaries and businesses to be able to get a sense of how many business locations are within certain zip codes.\u00a0<\/span><\/p>\n <\/p>\n Is cell size referring to pixel size? Also having a hard time understanding the concept of a raster and vector, although I can clearly see the difference within the quality of the map.\u00a0<\/span><\/p>\n What is a tract??<\/span><\/p>\n <\/p>\n Other terms to keep in mind:\u00a0<\/span><\/p>\n Categories<\/b>– Groups of similar things, helps organize data.\u00a0<\/span><\/p>\n <\/p>\n Ranks<\/b>– Feasturers in order from highest to lowest<\/span><\/p>\n <\/p>\n Counts and Amounts<\/b>– Count is the total number of features in a map and amount is the measurable quantity of that feature. EX.) how many employees at a business.\u00a0<\/span><\/p>\n Ratios<\/b>– Relationship between 2 quantities and are created by dividing one feather by another for each feature.\u00a0\u00a0<\/span><\/p>\n <\/p>\n Continuous and noncontinuous values<\/b>– Categories and ranks are not continuous values. Counts, amounts and ratios are continuous values.\u00a0<\/span><\/p>\n <\/p>\n Polygon\/Area:<\/b> A representation of a feature covering an area, such as a county, lake, parcel, or park.<\/span><\/p>\n <\/p>\n I have just come to the understanding that GIS is very much like a quantitative\/stats class that involves coding.\u00a0<\/span><\/p>\n <\/p>\n Chapter 2<\/b>– Goes over why location matters, deciding what to map, preparing the data, creating the map, and analyzing the patterns that appear.<\/span><\/p>\n Mapping where things are can help people make decisions. Someone could map crimes to see where certain crimes occur so they can make a decision on where they might want to move, businesses to see consumer patterns, or environmental features to understand where habitats or populations are located. We need to figure out what we need to actually map.<\/span> \u00a0We might not have to map every feature. Categories describe different types of features.\u00a0<\/span><\/p>\n EX.), crimes could be separated into assaults, burglaries, thefts, and auto thefts. Displaying too many categories on one map can make the map difficult to understand. It is recommended keeping a single map to around seven less categories to avoid confusion.\u00a0<\/span><\/p>\n We have to learn how to symbolize the features. Different symbols and colors can represent different categories. The symbols should make the map reading and\u00a0 patterns easy to understand. Features might be clustered in certain areas, spread evenly throughout an area, we can\u00a0 compare categories to determine whether different types of features seem to occur near each other.<\/span><\/p>\n Distribution-<\/b> The way geographic features are arranged across an area.<\/span><\/p>\n Geographic pattern-<\/b> A recognizable spatial arrangement of features.<\/span><\/p>\n Category-<\/b> A group of features that share the same characteristic or type.<\/span><\/p>\n Categorical data-<\/b> Information that places features into named groups instead of measuring an amount.<\/span><\/p>\n Symbol-<\/b>A visual representation of a geographic feature on a map.<\/span><\/p>\n Symbology-<\/b> The system of colors, shapes, lines, and other symbols used to represent map features.<\/span><\/p>\n Map scale-<\/b> The relationship between distance on a map and distance in the real world.<\/span><\/p>\n Coordinate pair-<\/b> Two coordinate values used together to identify a geographic location.<\/span><\/p>\n Data preparation-<\/b>Organizing and checking geographic information before performing GIS analysis.<\/span><\/p>\n Attribute table-<\/b> A table containing descriptive information associated with mapped features.<\/span><\/p>\n Cluster-<\/b> A group of features located relatively close together.<\/span><\/p>\n Spatial distribution-<\/b>The arrangement of features across geographic space.<\/span><\/p>\n I need to figure out the definition and concept of a parcel.<\/span><\/p>\n <\/p>\n Chapter 3<\/b>– Instead of only showing the location or category of a feature, GIS can represent the quantity of a feature. This makes it possible to compare places. EX.) Instead of only mapping businesses, you could show the number of employees at each business. Quantities can include counts\/ amounts, ratios, and ranks. A count might be the total number of people living in a county. A ratio compares one quantity with another. Population density, for example, compares population with land area. A rank puts features\/categories in an ordered position based on their values.<\/span><\/p>\n Again showing every individual value may make the map too complicated. Instead, GIS can group values into ranges called classes. EX.)Counties could be divided into groups representing the range of low, medium, and high populations.<\/span><\/p>\n Natural break<\/b>s looks for natural groupings and gaps in the dataset and places class boundaries around those groups. It is useful when values are unevenly distributed. Standard deviation groups features according to how far their values are above or below the dataset’s mean.<\/span> Outliers<\/b> are another important issue. An outlier is a value that is extremely high or low compared with the other values.<\/span><\/p>\n There are also several ways of visually showing quantities EX.) graduated symbols, graduated colors, charts, contours, and 3D perspective views. Graduated symbols change size according to magnitude, while graduated colors use differences in shading to represent value ranges. Contours for continuous phenomena.\u00a0<\/span><\/p>\n Quantity-<\/b> A numerical amount associated with a geographic feature.<\/span><\/p>\n Class<\/b>– A range of numerical values grouped together for mapping.<\/span><\/p>\n Classification-<\/b> The process of dividing numerical data into classes.<\/span><\/p>\n Class break-<\/b> The numerical boundary separating one class from another.<\/span><\/p>\n Natural breaks<\/b> – A classification method that creates classes based on natural groupings and gaps in the values.<\/span><\/p>\n Quantile<\/b>– A classification method that places approximately the same number of features into each class.<\/span><\/p>\n Equal interval<\/b>– A classification method that divides the total range into classes of equal numerical size.<\/span><\/p>\n Mean-<\/b> The average value of a dataset.<\/span><\/p>\n Standard deviation<\/b>– A measurement describing how far values tend to vary from the mean.<\/span><\/p>\n Outlier<\/b>– An unusually high or low value compared with the rest of the dataset.<\/span><\/p>\n Histogram<\/b>– A graph showing the distribution of numerical values; it can help determine appropriate class breaks.<\/span><\/p>\n Graduated symbols<\/b>– Map symbols that increase or decrease in size according to the quantity being represented.<\/span><\/p>\n Graduated colors-<\/b> Colors or shades that change according to different ranges of values.<\/span><\/p>\n Contour<\/b>– A line connecting locations having the same value, such as equal elevation.<\/span><\/p>\n Data distribution-<\/b> The way numerical values are spread throughout a dataset.<\/span><\/p>\n Magnitude- <\/b>The size or amount of a value.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" Chapter 1– I learned about concepts and how to correctly read and identify locations and lines for example\u2026 Businesses, symbolized by the number of employees, are an example of individual locations. Streams are linear features. Parcels, color-coded by land value, are an example of discrete areas. Some new things I learned that I think are really important to remember are\u2026 Continuous phenomena such as precipitation or temperature can be found or measured anywhere. EX.) Temperature, precipitation, and elevation are examples. For instance, elevation changes continuously as you move across the landscape rather than existing at only one point.\u00a0 Also Continuous data which are areas enclosed by boundaries. Summarize data, which counts individual features within boundaries. The book then discussed the process of overlaying boundaries and businesses to be able to get a sense of how many business locations are within certain zip codes.\u00a0 Is cell size referring to pixel size? Also having a hard time understanding the concept of a raster and vector, although I can clearly see the difference within the quality of the map.\u00a0 What is a tract?? Other terms to keep in mind:\u00a0 Categories– Groups of similar things, helps organize data.\u00a0 Ranks– Feasturers in order from highest to lowest Counts and Amounts– Count is the total number of features in a map and amount is the measurable quantity of that feature. EX.) how many employees at a business.\u00a0 Ratios– Relationship between 2 quantities and are created by dividing one feather by another for each feature.\u00a0\u00a0 Continuous and noncontinuous values– Categories and ranks are not continuous values. Counts, amounts and ratios are continuous values.\u00a0 Polygon\/Area: A representation of a feature covering an area, such as a county, lake, parcel, or park. I have just come to the understanding that GIS is very much like a quantitative\/stats class that involves coding.\u00a0 Chapter 2– Goes over why location matters, deciding what to map, preparing the data, creating the map, and analyzing the patterns that appear. Mapping where things are can help people make decisions. Someone could map crimes to see where certain crimes occur so they can make a decision on where they might want to move, businesses to see consumer patterns, or environmental features to understand where habitats or populations are located. We need to figure out what we need to actually map. \u00a0We might not have to map every feature. Categories describe different types of features.\u00a0 EX.), crimes could be separated into assaults, burglaries, thefts, and auto thefts. Displaying too many categories on one map can make the map difficult to understand. It is recommended keeping a single map to around seven less categories to avoid confusion.\u00a0 We have to learn how to symbolize the features. Different symbols and colors can represent different categories. The symbols should make the map reading and\u00a0 patterns easy to understand. Features might be clustered in certain areas, spread evenly throughout an area, we can\u00a0 compare categories to determine whether different types of features seem to occur near each other. Distribution- The way geographic features are arranged across an area. Geographic pattern- A recognizable spatial arrangement of features. Category- A group of features that share the same characteristic or type. Categorical data- Information that places features into named groups instead of measuring an amount. Symbol-A visual representation of a geographic feature on a map. Symbology- The system of colors, shapes, lines, and other symbols used to represent map features. Map scale- The relationship between distance on a map and distance in the real world. Coordinate pair- Two coordinate values used together to identify a geographic location. Data preparation-Organizing and checking geographic information before performing GIS analysis. Attribute table- A table containing descriptive information associated with mapped features. Cluster- A group of features located relatively close together. Spatial distribution-The arrangement of features across geographic space. I need to figure out the definition and concept of a parcel. Chapter 3– Instead of only showing the location or category of a feature, GIS can represent the quantity of a feature. This makes it possible to compare places. EX.) Instead of only mapping businesses, you could show the number of employees at each business. Quantities can include counts\/ amounts, ratios, and ranks. A count might be the total number of people living in a county. A ratio compares one quantity with another. Population density, for example, compares population with land area. A rank puts features\/categories in an ordered position based on their values. Again showing every individual value may make the map too complicated. Instead, GIS can group values into ranges called classes. EX.)Counties could be divided into groups representing the range of low, medium, and high populations. Natural breaks looks for natural groupings and gaps in the dataset and places class boundaries around those groups. It is useful when values are unevenly distributed. Standard deviation groups features according to how far their values are above or below the dataset’s mean. Outliers are another important issue. An outlier is a value that is extremely high or low compared with the other values. There are also several ways of visually showing quantities EX.) graduated symbols, graduated colors, charts, contours, and 3D perspective views. Graduated symbols change size according to magnitude, while graduated colors use differences in shading to represent value ranges. Contours for continuous phenomena.\u00a0 Quantity- A numerical amount associated with a geographic feature. Class– A range of numerical values grouped together for mapping. Classification- The process of dividing numerical data into classes. Class break- The numerical boundary separating one class from another. Natural breaks – A classification method that creates classes based on natural groupings and gaps in the values. Quantile– A classification method that places approximately the same number of features into each class. Equal interval– A classification method that divides the total range into classes of equal numerical size. Mean- The average value of a dataset. Standard deviation– A measurement describing how far values tend to vary from the mean. Outlier– An unusually high or low value compared with the rest of the dataset. Histogram– A graph showing the distribution of numerical values; it can help determine appropriate class breaks. Graduated symbols– Map symbols that increase or decrease in size according to the quantity being represented. Graduated colors- Colors or shades that change according to different ranges of values. Contour– A line connecting locations having the same value, such as equal elevation. Data distribution- The way numerical values are spread throughout a dataset. Magnitude- The size or amount of a value.<\/p>\n","protected":false},"author":2426,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7273","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\/7273","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\/2426"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7273"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7273\/revisions"}],"predecessor-version":[{"id":7274,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7273\/revisions\/7274"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7273"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7273"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7273"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}