{"id":177,"date":"2022-09-09T13:46:34","date_gmt":"2022-09-09T18:46:34","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-191\/?p=177"},"modified":"2022-10-02T08:20:34","modified_gmt":"2022-10-02T13:20:34","slug":"aj-lashway-week-3","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2022\/09\/09\/aj-lashway-week-3\/","title":{"rendered":"AJ Lashway Week 3"},"content":{"rendered":"<p><span style=\"font-weight: 400\">Chapter 5<\/span><\/p>\n<p><span style=\"font-weight: 400\">Notes:<\/span><\/p>\n<p><span style=\"font-weight: 400\">You can use an <\/span><b>area boundary<\/b><span style=\"font-weight: 400\"> to define the features inside. These can be created on top of features, can be used to select features inside the area\/summarize selected features, and <\/span><i><span style=\"font-weight: 400\">combine<\/span><\/i><span style=\"font-weight: 400\"> the area boundary and features in order to create summary data.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Single areas can be sectioned off to let you monitor activity or summarize information. For example, a stream buffer that is off-limits for logging. Then there are multiple areas, that can compare what\u2019s within several different areas in a contiguous fashion. Examples of these contiguous areas are zip codes and watersheds.<\/span><\/p>\n<p><span style=\"font-weight: 400\">You can change what you\u2019re analyzing using different feature attributes (as discussed in previous chapters). Sometimes features will bleed out of the area; there are a couple different ways to deal with this. You can only include features fully contained, include features that partially extend outside (would use <\/span><b>counts<\/b><span style=\"font-weight: 400\">), or include only portions that are inside of the area (would use <\/span><b>amounts<\/b><span style=\"font-weight: 400\">). This decision all depends on what you\u2019re mapping and the level of precision required.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400\">Vectors are typically used with continuous data and can result in <\/span><b>slivers<\/b><span style=\"font-weight: 400\">, which can be smoothed out with the GIS. You need to keep in mind the <\/span><i><span style=\"font-weight: 400\">extent<\/span><\/i><span style=\"font-weight: 400\"> of the data, the degree of <\/span><i><span style=\"font-weight: 400\">accuracy<\/span><\/i><span style=\"font-weight: 400\"> you\u2019re dealing with, and only have <\/span><i><span style=\"font-weight: 400\">very small<\/span><\/i><span style=\"font-weight: 400\"> slivers removed automatically. Anything slightly bigger should be removed manually to ensure that important data isn\u2019t lost.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Vector is more precise, but requires more time and processing power; it requires the summarization of category values in the final table. Raster is more efficient, but can be less accurate. The accuracy will depend on the cell size, and slivers can still be created using raster.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400\">Definitions:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Frequency<\/span><\/span><span style=\"font-weight: 400\">\u2013 the number of features with a given value or within a range of values inside the area.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Represented with a bar chart or pie chart.<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Sum<\/span><\/span><span style=\"font-weight: 400\">\u2013 overall total or total by category.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400\">Chapter 6<\/span><\/p>\n<p><span style=\"font-weight: 400\">Notes:<\/span><\/p>\n<p><span style=\"font-weight: 400\">You can use GIS to find out what\u2019s nearby and how that\u2019s relevant to the data set and audience you\u2019re creating a map for. When dealing with distance, you must define \u201ccloseness,\u201d as it\u2019s very subjective. You need to quantify what is \u201cnear\u201d and what is \u201cfar.\u201d.<\/span><\/p>\n<p><b>Buffers<\/b><span style=\"font-weight: 400\"> can be used to give features more definition. They can be used to add a literal buffer along stream banks to forbid logging, or just to simplify complicated data sets. Network layers connect edges through the GIS to allow different usages of <\/span><b>distance<\/b><span style=\"font-weight: 400\"> and <\/span><b>cost<\/b><span style=\"font-weight: 400\">, and can be used in conjunction with buffers.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400\">Definitions:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Travel costs<\/span><\/span><span style=\"font-weight: 400\">\u2013 the effort or other detriment associated with one path\/area over another.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Planar method<\/span><\/span><span style=\"font-weight: 400\">\u2013 calculating distance assuming the surface of the earth is flat.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Used for short distances or small areas (county, city).<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Geodesic method<\/span><\/span><span style=\"font-weight: 400\">\u2013 taking into account the curvature of the earth.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Used for long distances (continent, earth as a whole).<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Inclusive rings<\/span><\/span><span style=\"font-weight: 400\">\u2013 bands of data ranges used to see relative changes at varying scales.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><span style=\"text-decoration: underline\">Distinct bands<\/span><\/span><span style=\"font-weight: 400\">\u2013 for comparing distance with other characteristics.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Straight-line Distance<\/span><\/span><span style=\"font-weight: 400\">\u2013 specify the source feature and distance, then uthe GIS finds the area or surrounding features.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Primarily used to create boundaries.<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Distance or Cost Over Network<\/span><\/span><span style=\"font-weight: 400\">\u2013 specify source locations and a distance or travel cost along each linear feature.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Used to find what\u2019s within travel distance or cost over a fixed network.<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Cost Over a Surface<\/span><\/span><span style=\"font-weight: 400\">\u2013 specify location of source features and travel cost, and creates a new layer showing the travel cost from each feature.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It calculates the overland travel cost.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400\">Chapter 7<\/span><\/p>\n<p><span style=\"font-weight: 400\">Notes:<\/span><\/p>\n<p><span style=\"font-weight: 400\">Maps can also be made to change in order to document past conditions and\/or predict future events. You can go date by date, or hop between a certain\/set period of time in a pattern (every two days, every other month, every 3 hours). Make sure to keep note of how <\/span><i><span style=\"font-weight: 400\">exactly<\/span><\/i><span style=\"font-weight: 400\"> time is changing and its relationship with the feature(s).<\/span><\/p>\n<p><span style=\"font-weight: 400\">Time patterns can be used to track movements over time. You can use lines between points to better emphasize findings as well. The distance between points can represent various speeds. For example, two dots that are closer together show a slower amount of movement of a hurricane over a 3-hour period than dots that are further apart after the same amount of time has passed.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Coloration and shading to emphasize change with continuous features. Equal time intervals being used for each feature is critical to seeing an accurate rate of change. Events mapped over time typically use color grades that represent different (but equal in length) time periods. If there are several events reoccurring at the same locations, you can use pie chart markers in place of simple dots.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400\">Definitions:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Change in Location<\/span><\/span><span style=\"font-weight: 400\">\u2013 see how features behave so you can predict where they\u2019ll go.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Ex; bird migrations, hurricanes<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Change in Character or Magnitude<\/span><\/span><span style=\"font-weight: 400\">\u2013 shows how conditions in a given location have changed.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Ex; land cover change in a watershed<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Travel<\/span><\/span><span style=\"font-weight: 400\">\u2013 change between two or more dates or times.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Before &amp; After<\/span><\/span><span style=\"font-weight: 400\">\u2013 conditions preceding and following an event.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Cycle<\/span><\/span><span style=\"font-weight: 400\">\u2013 change over a reoccurring time period.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Ex; day, month, year<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Chapter 5 Notes: You can use an area boundary to define the features inside. These can be created on top of features, can be used to select features inside the area\/summarize selected features, and combine the area boundary and features in order to create summary data. Single areas can be sectioned off to let you monitor activity or summarize information. For example, a stream buffer that is off-limits for logging. Then there are multiple areas, that can compare what\u2019s within several different areas in a contiguous fashion. Examples of these contiguous areas are zip codes and watersheds. You can change what you\u2019re analyzing using different feature attributes (as discussed in previous chapters). Sometimes features will bleed out of the area; there are a couple different ways to deal with this. You can only include features fully contained, include features that partially extend outside (would use counts), or include only portions that are inside of the area (would use amounts). This decision all depends on what you\u2019re mapping and the level of precision required. &nbsp; Vectors are typically used with continuous data and can result in slivers, which can be smoothed out with the GIS. You need to keep in mind the extent of the data, the degree of accuracy you\u2019re dealing with, and only have very small slivers removed automatically. Anything slightly bigger should be removed manually to ensure that important data isn\u2019t lost. Vector is more precise, but requires more time and processing power; it requires the summarization of category values in the final table. Raster is more efficient, but can be less accurate. The accuracy will depend on the cell size, and slivers can still be created using raster. &nbsp; Definitions: Frequency\u2013 the number of features with a given value or within a range of values inside the area. Represented with a bar chart or pie chart. Sum\u2013 overall total or total by category. &nbsp; Chapter 6 Notes: You can use GIS to find out what\u2019s nearby and how that\u2019s relevant to the data set and audience you\u2019re creating a map for. When dealing with distance, you must define \u201ccloseness,\u201d as it\u2019s very subjective. You need to quantify what is \u201cnear\u201d and what is \u201cfar.\u201d. Buffers can be used to give features more definition. They can be used to add a literal buffer along stream banks to forbid logging, or just to simplify complicated data sets. Network layers connect edges through the GIS to allow different usages of distance and cost, and can be used in conjunction with buffers. &nbsp; Definitions: Travel costs\u2013 the effort or other detriment associated with one path\/area over another. Planar method\u2013 calculating distance assuming the surface of the earth is flat. Used for short distances or small areas (county, city). Geodesic method\u2013 taking into account the curvature of the earth. Used for long distances (continent, earth as a whole). Inclusive rings\u2013 bands of data ranges used to see relative changes at varying scales. Distinct bands\u2013 for comparing distance with other characteristics. Straight-line Distance\u2013 specify the source feature and distance, then uthe GIS finds the area or surrounding features. Primarily used to create boundaries. Distance or Cost Over Network\u2013 specify source locations and a distance or travel cost along each linear feature. Used to find what\u2019s within travel distance or cost over a fixed network. Cost Over a Surface\u2013 specify location of source features and travel cost, and creates a new layer showing the travel cost from each feature. It calculates the overland travel cost. &nbsp; Chapter 7 Notes: Maps can also be made to change in order to document past conditions and\/or predict future events. You can go date by date, or hop between a certain\/set period of time in a pattern (every two days, every other month, every 3 hours). Make sure to keep note of how exactly time is changing and its relationship with the feature(s). Time patterns can be used to track movements over time. You can use lines between points to better emphasize findings as well. The distance between points can represent various speeds. For example, two dots that are closer together show a slower amount of movement of a hurricane over a 3-hour period than dots that are further apart after the same amount of time has passed. Coloration and shading to emphasize change with continuous features. Equal time intervals being used for each feature is critical to seeing an accurate rate of change. Events mapped over time typically use color grades that represent different (but equal in length) time periods. If there are several events reoccurring at the same locations, you can use pie chart markers in place of simple dots. &nbsp; Definitions: Change in Location\u2013 see how features behave so you can predict where they\u2019ll go. Ex; bird migrations, hurricanes Change in Character or Magnitude\u2013 shows how conditions in a given location have changed. Ex; land cover change in a watershed Travel\u2013 change between two or more dates or times. Before &amp; After\u2013 conditions preceding and following an event. Cycle\u2013 change over a reoccurring time period. Ex; day, month, year<\/p>\n","protected":false},"author":2159,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-177","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\/177","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\/2159"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=177"}],"version-history":[{"count":2,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/177\/revisions"}],"predecessor-version":[{"id":186,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/177\/revisions\/186"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=177"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=177"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=177"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}