{"id":158,"date":"2022-09-02T14:47:07","date_gmt":"2022-09-02T19:47:07","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-191\/?p=158"},"modified":"2022-10-02T08:19:02","modified_gmt":"2022-10-02T13:19:02","slug":"aj-lashway-week-2","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2022\/09\/02\/aj-lashway-week-2\/","title":{"rendered":"AJ Lashway Week 2"},"content":{"rendered":"<p><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Chapter 1<\/span><\/span><\/p>\n<p><span style=\"font-weight: 400\">Notes:<\/span><\/p>\n<p><span style=\"font-weight: 400\">Map projection will be dependent on the scale of data, level of precision required, and where the data is located.<\/span><\/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\">Discrete data<\/span><\/span><span style=\"font-weight: 400\">\u2013 points or lines in space where a given feature is either there, or isn\u2019t; there are \u2018gaps\u2019 in the map. Typically uses a <\/span><b>vector<\/b><span style=\"font-weight: 400\"> model.<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; streams, parcels of land, businesses<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Continuous data<\/span><\/span><span style=\"font-weight: 400\">\u2013 data covers the entire map, and you can determine the value for any given point. These are typically <\/span><b>numeric values<\/b><span style=\"font-weight: 400\"> in <\/span><b>raster<\/b><span style=\"font-weight: 400\">, but can also be mapped using <\/span><b>vector<\/b><span style=\"font-weight: 400\">.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; temperature\/heat maps, precipitation, soil type<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Summarized data<\/span><\/span><span style=\"font-weight: 400\">\u2013\u00a0 a given value applies to an entire area, not a specific location. Typically uses a <\/span><b>vector<\/b><span style=\"font-weight: 400\"> model.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; number of businesses in a zip code, total length of streams 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\">Vector model<\/span><\/span><span style=\"font-weight: 400\">\u2013 features are shapes defined by \u201cx, y\u201d locations in space.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Can be <\/span><b>discrete<\/b><span style=\"font-weight: 400\"> locations, events, lines, or areas.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Uses geographic coordinates (x, y).<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Lines are a series of coordinate pairs.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Areas are closed polygons.<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Raster model<\/span><\/span><span style=\"font-weight: 400\">\u2013 features are a matrix of cells in <\/span><b>continuous<\/b><span style=\"font-weight: 400\"> space.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Consists of multiple layers (typically), with each layer representing one <\/span><b>attribute<\/b><span style=\"font-weight: 400\">.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Can use varying <\/span><b>cell size<\/b><span style=\"font-weight: 400\"> (examples on page 11).<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Small cell sizes result in a more defined map, but requires more storage space. Large cell sizes will show patterns, but they lose the level of detail achieved with smaller sizes.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Attribute values<\/span><\/span><span style=\"font-weight: 400\">\u2013 identify what the feature is, describe it, or represent some magnitude associated with the feature.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Types: categories, ranks, counts, amounts, ratios<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Categories<\/span><\/span><span style=\"font-weight: 400\">\u2013 groups of similar things<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; roads: freeways, highways, local roads<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; crimes: burglaries, thefts, assaults<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Ranks<\/span><\/span><span style=\"font-weight: 400\">\u2013 put features in order from <\/span><span style=\"font-weight: 400\">high<\/span><span style=\"font-weight: 400\"> to <\/span><span style=\"font-weight: 400\">low<\/span><span style=\"font-weight: 400\">. Most often used when direct measurements are difficult, or if the quantity represents a <\/span><span style=\"font-weight: 400\">combination of features<\/span><span style=\"font-weight: 400\">.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; \u201cscenic value\u201d of rivers; area in mountain gorge ranks higher than area near a dairy farm<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">You can rank based on different <\/span><b>attribute values<\/b>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; soils of a certain type ranked the same in relation to suitability for growing a particular crop.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Counts &amp; Amounts<\/span><\/span><span style=\"font-weight: 400\">\u2013 shows you total numbers. <\/span><b>Count<\/b><span style=\"font-weight: 400\"> is the actual number of features. <\/span><b>Amount<\/b><span style=\"font-weight: 400\"> can be any quantity associated with the feature.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; amount: number of employees at a given business<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">They let you see the actual value of each feature <\/span><i><span style=\"font-weight: 400\">as well as<\/span><\/i><span style=\"font-weight: 400\"> its magnitude compared with other features.<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Ratios<\/span><\/span><span style=\"font-weight: 400\">\u2013 shows the relationship between 2 quantities, created by dividing 1 quantity by another for each feature. They more accurately show the distribution of features.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; dividing (# of people in each tract)\/(# of households)=(average # of people\/household)<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Proportions<\/span><\/span><span style=\"font-weight: 400\">\u2013 show what part of a total each value is.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; number of 18-30 year olds\/total population<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">They are often shown as percentages<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Densities<\/span><\/span><span style=\"font-weight: 400\">\u2013 show the distribution of features or values per unit area<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; population of county\/land area in miles squared= people\/square mile<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Selecting<\/span><\/span><span style=\"font-weight: 400\">\u2013 used to specify features to work with, or to assign new <\/span><b>attribute values<\/b><span style=\"font-weight: 400\"> to specific features.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">select ATTRIBUTE = VALUE<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Can also use (&gt;), (&lt;), and unequal (&lt;&gt;)<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Calculating<\/span><\/span><span style=\"font-weight: 400\">\u2013 used to assign NEW values to features in the data table.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">select FIELD = VALUE \u2192 calculate ATTRIBUTE = VALUE<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Summarizing<\/span><\/span><span style=\"font-weight: 400\">\u2013 [summarize] the values for specific attributes to get statistics.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; create a new table \u2192 list a value for each type \u2192 add count of features<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Questions:<\/span><\/p>\n<p><span style=\"font-weight: 400\">Would the census population data from GEOG 112 be considered summarized data?<\/span><\/p>\n<p><span style=\"font-weight: 400\">Why would you use a rank based on an attribute rather than just using the secondary attribute?<\/span><\/p>\n<p><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Chapter 2<\/span><\/span><\/p>\n<p><span style=\"font-weight: 400\">Notes:<\/span><\/p>\n<p><span style=\"font-weight: 400\">The amount of information shown on a particular map depends on what the map will be used for. You need to know the intended audience for the map and its purpose before starting, and plan accordingly.<\/span><\/p>\n<p><span style=\"font-weight: 400\">The category values discussed in the previous chapter may have <\/span><b>subtypes<\/b><span style=\"font-weight: 400\"> that add varying levels of detail. The same base map can then be expanded upon, depending on its purpose and the intended audience at the moment.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Even if you\u2019re intending on focusing on a certain set of data, having surrounded data can help to contextualize the information and resulting patterns. If the data is discrete, showing these data sets on separate maps may make information more digestible. If the data is continuous, displaying all or a couple of categories on the same map is favorable in many cases. When it comes to categories and how many should be displayed, 7 is a good rule of thumb for a <\/span><i><span style=\"font-weight: 400\">maximum<\/span><\/i><span style=\"font-weight: 400\">. However, the distribution of features and scale of the map can affect this. You can display more features if they\u2019re scattered than if they\u2019re clustered together.<\/span><\/p>\n<p><span style=\"font-weight: 400\">So, it\u2019s good to experiment with how many categories are being displayed. Getting another set of eyes that aren\u2019t familiar with the data set is probably crucial to ensure the map is understandable. This is also a good way of figuring out how the data is being perceived by the reader. Depending on how categories are grouped, that perception can change dramatically.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Definitions:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Single type<\/span><span style=\"font-weight: 400\">\u2013 when the same symbol is used for all features.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Reference features<\/span><span style=\"font-weight: 400\">\u2013 landmarks\/locations that can be used to ground a map in a certain area, and convey more meaning to the reader.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ex; major roads, locations of cities\/towns, stores<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">They should be mapped in light colors or greys to avoid dominating the map.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Chapter 3<\/span><\/p>\n<p><span style=\"font-weight: 400\">Notes:<\/span><\/p>\n<p><span style=\"font-weight: 400\">Mapping based on quantities can give additional context that can give a better picture of what\u2019s being represented. Again, knowing the purpose of the map being created will tell you how to make it and whether quantities will be beneficial.<\/span><\/p>\n<p><b>Discrete<\/b><span style=\"font-weight: 400\"> data uses graduated symbols or shaded areas, while <\/span><b>continuous<\/b><span style=\"font-weight: 400\"> data uses graduated colors, contours, or 3D perspective views.<\/span><\/p>\n<p><span style=\"font-weight: 400\">When mapping based on quantities, you will want to start off with the basic data set and figure out what patterns are present. Then, make a map that helps highlight these patterns. Each feature included in the data set should only be incorporated in a way that best represents the data.<\/span><\/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\">Quantities<\/span><\/span><span style=\"font-weight: 400\">\u2013 a data set\/set of points that have variation amongst the features.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">These can be counts or amounts, ratios, or ranks<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Class<\/span><\/span><span style=\"font-weight: 400\">\u2013 a grouping of a range of similar data, typically used when features all (or mostly) have different values, and the data range is large. Classes will make it easier to identify patterns.<\/span>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Natural Breaks<\/span><\/span><span style=\"font-weight: 400\">\u2013 natural groupings of data values present in the individual sets.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Quantile<\/span><\/span><span style=\"font-weight: 400\">\u2013 each class contains an equal number of features.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Equal Interval<\/span><\/span><span style=\"font-weight: 400\">\u2013 the difference between the high and low values of each class is the same.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Standard Deviation<\/span><\/span><span style=\"font-weight: 400\">\u2013 features are broken into classes based on how much their values vary from the mean.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Chapter 4<\/span><\/p>\n<p><span style=\"font-weight: 400\">Notes:<\/span><\/p>\n<p><span style=\"font-weight: 400\">Density mapping is helpful in cases where there are many features. It will be easier to read in some cases than individual points representing each feature. You\u2019ll have to decide two major things: 1) whether to shade defined areas, or create a continuous density surface and 2) decide if you\u2019re focusing on features themselves or on values associated with features.<\/span><\/p>\n<p><span style=\"font-weight: 400\">In general, summarizing data with map density can make patterns more general, but easier to look at and identify specific numbers for overall areas. Map density should be used for already summarized data with defined borders. Density <\/span><i><span style=\"font-weight: 400\">surfaces<\/span><\/i><span style=\"font-weight: 400\"> provide the most detail, but require the most effort by far to put together. These are best for concentrated data.<\/span><\/p>\n<p><span style=\"font-weight: 400\">The level of specificity in a data set\/range of area can greatly affect what the resulting map looks like. In density surface mapping, areas between features are estimated through interpolation. Interpolation can cause extreme highs and lows to vanish. So, while patterns are easier to see, there should be another map that shows locations of features to provide context.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Definitions:<\/span><\/p>\n<ul>\n<li><span style=\"text-decoration: underline\"><span style=\"font-weight: 400\">Density<\/span><\/span><span style=\"font-weight: 400\">\u2013 used to show where the highest concentration of features is.<\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Chapter 1 Notes: Map projection will be dependent on the scale of data, level of precision required, and where the data is located. Definitions: Discrete data\u2013 points or lines in space where a given feature is either there, or isn\u2019t; there are \u2018gaps\u2019 in the map. Typically uses a vector model. ex; streams, parcels of land, businesses Continuous data\u2013 data covers the entire map, and you can determine the value for any given point. These are typically numeric values in raster, but can also be mapped using vector. ex; temperature\/heat maps, precipitation, soil type Summarized data\u2013\u00a0 a given value applies to an entire area, not a specific location. Typically uses a vector model. ex; number of businesses in a zip code, total length of streams in a watershed. Vector model\u2013 features are shapes defined by \u201cx, y\u201d locations in space. Can be discrete locations, events, lines, or areas. Uses geographic coordinates (x, y). Lines are a series of coordinate pairs. Areas are closed polygons. Raster model\u2013 features are a matrix of cells in continuous space. Consists of multiple layers (typically), with each layer representing one attribute. Can use varying cell size (examples on page 11). Small cell sizes result in a more defined map, but requires more storage space. Large cell sizes will show patterns, but they lose the level of detail achieved with smaller sizes. Attribute values\u2013 identify what the feature is, describe it, or represent some magnitude associated with the feature. Types: categories, ranks, counts, amounts, ratios Categories\u2013 groups of similar things ex; roads: freeways, highways, local roads ex; crimes: burglaries, thefts, assaults Ranks\u2013 put features in order from high to low. Most often used when direct measurements are difficult, or if the quantity represents a combination of features. ex; \u201cscenic value\u201d of rivers; area in mountain gorge ranks higher than area near a dairy farm You can rank based on different attribute values ex; soils of a certain type ranked the same in relation to suitability for growing a particular crop. Counts &amp; Amounts\u2013 shows you total numbers. Count is the actual number of features. Amount can be any quantity associated with the feature. ex; amount: number of employees at a given business They let you see the actual value of each feature as well as its magnitude compared with other features. Ratios\u2013 shows the relationship between 2 quantities, created by dividing 1 quantity by another for each feature. They more accurately show the distribution of features. ex; dividing (# of people in each tract)\/(# of households)=(average # of people\/household) Proportions\u2013 show what part of a total each value is. ex; number of 18-30 year olds\/total population They are often shown as percentages Densities\u2013 show the distribution of features or values per unit area ex; population of county\/land area in miles squared= people\/square mile Selecting\u2013 used to specify features to work with, or to assign new attribute values to specific features. select ATTRIBUTE = VALUE Can also use (&gt;), (&lt;), and unequal (&lt;&gt;) Calculating\u2013 used to assign NEW values to features in the data table. select FIELD = VALUE \u2192 calculate ATTRIBUTE = VALUE Summarizing\u2013 [summarize] the values for specific attributes to get statistics. ex; create a new table \u2192 list a value for each type \u2192 add count of features Questions: Would the census population data from GEOG 112 be considered summarized data? Why would you use a rank based on an attribute rather than just using the secondary attribute? Chapter 2 Notes: The amount of information shown on a particular map depends on what the map will be used for. You need to know the intended audience for the map and its purpose before starting, and plan accordingly. The category values discussed in the previous chapter may have subtypes that add varying levels of detail. The same base map can then be expanded upon, depending on its purpose and the intended audience at the moment. Even if you\u2019re intending on focusing on a certain set of data, having surrounded data can help to contextualize the information and resulting patterns. If the data is discrete, showing these data sets on separate maps may make information more digestible. If the data is continuous, displaying all or a couple of categories on the same map is favorable in many cases. When it comes to categories and how many should be displayed, 7 is a good rule of thumb for a maximum. However, the distribution of features and scale of the map can affect this. You can display more features if they\u2019re scattered than if they\u2019re clustered together. So, it\u2019s good to experiment with how many categories are being displayed. Getting another set of eyes that aren\u2019t familiar with the data set is probably crucial to ensure the map is understandable. This is also a good way of figuring out how the data is being perceived by the reader. Depending on how categories are grouped, that perception can change dramatically. Definitions:\u00a0 Single type\u2013 when the same symbol is used for all features. Reference features\u2013 landmarks\/locations that can be used to ground a map in a certain area, and convey more meaning to the reader. ex; major roads, locations of cities\/towns, stores They should be mapped in light colors or greys to avoid dominating the map. Chapter 3 Notes: Mapping based on quantities can give additional context that can give a better picture of what\u2019s being represented. Again, knowing the purpose of the map being created will tell you how to make it and whether quantities will be beneficial. Discrete data uses graduated symbols or shaded areas, while continuous data uses graduated colors, contours, or 3D perspective views. When mapping based on quantities, you will want to start off with the basic data set and figure out what patterns are present. Then, make a map that helps highlight these patterns. Each feature included in the data set should only be incorporated in a way that best represents the data. Definitions: Quantities\u2013 a data set\/set of points that have variation amongst the features. These can be counts or amounts, ratios, or ranks Class\u2013 a grouping of a range of similar data, typically used when features all (or mostly) have different values, and the data range is large. Classes will make it easier to identify patterns. Natural Breaks\u2013 natural groupings of data values present in the individual sets. Quantile\u2013 each class contains an equal number of features. Equal Interval\u2013 the difference between the high and low values of each class is the same. Standard Deviation\u2013 features are broken into classes based on how much their values vary from the mean. Chapter 4 Notes: Density mapping is helpful in cases where there are many features. It will be easier to read in some cases than individual points representing each feature. You\u2019ll have to decide two major things: 1) whether to shade defined areas, or create a continuous density surface and 2) decide if you\u2019re focusing on features themselves or on values associated with features. In general, summarizing data with map density can make patterns more general, but easier to look at and identify specific numbers for overall areas. Map density should be used for already summarized data with defined borders. Density surfaces provide the most detail, but require the most effort by far to put together. These are best for concentrated data. The level of specificity in a data set\/range of area can greatly affect what the resulting map looks like. In density surface mapping, areas between features are estimated through interpolation. Interpolation can cause extreme highs and lows to vanish. So, while patterns are easier to see, there should be another map that shows locations of features to provide context. Definitions: Density\u2013 used to show where the highest concentration of features is.<\/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-158","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\/158","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=158"}],"version-history":[{"count":2,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/158\/revisions"}],"predecessor-version":[{"id":163,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/158\/revisions\/163"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=158"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=158"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=158"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}