https:\/\/websoilsurvey.nrcs.usda.gov\/app\/WebSoilSurvey.aspx<\/span><\/a><\/p>\n\u00a0After the hour-long talk the person ended it by saying soil doesn\u2019t matter and I wanted to cry. anyways.) Using recognizable features in maps are extremely important to audiences, especially those who live there that are looking for specific rivers, landmarks, maybe a weird statue. Patterns can be seen just by the human eye, but sometimes maps have \u201ceaster eggs\u201d for those who may see different patterns, this varies on how meaningful certain details are to people.\u00a0<\/span><\/p>\nChapter 3<\/b><\/p>\n
So why are we actually mapping all details? Shouldn\u2019t we just focus on mapping the most important features out there? NOPE. Everything is significant in some sort of way, and may actually be beneficial to someone. (typically businesses.) You can include and exclude things from maps just by a degree of relevance, because honestly it may be weird to have \u201chow many slugs are in each yard of Delaware, OH\u201d on a map that shows who all has a subscription to oriental trading company (I bet I just unlocked a memory for you, you\u2019re welcome.) In legends, specific things can be shown by having bigger or smaller, or thinner\/thicker lines. (Expressed by having big circles as 2501-8000 employees, or very thin lines as unknown fish habitats in streams.) Mitchell describes continuous phenomena as more of a colorful part of the map. Where areas are displayed as graduated colors and surfaces are contoured, or a 3D perspective, but can also be graduated. (Remember my palette comment in chapter 2? I think this is what I meant. My heart and little GIS mind was in the right place.) Typically lighter colors are expressed as little or a lack of in a map, while darker more shaded in colors are seen as plentiful, lots of. I feel you can heavily argue the point made on page 56 of the Mitchell textbook based simply on perspective. Maps can be a way to express data or even trends especially if used in a long term fashion. (Long term deforestation, crop rotation, droughts, maybe long term crimes? Sue me.)\u00a0<\/span><\/p>\nCounts and amounts: <\/b>\u00a0total numbers. (I actually don\u2019t know what I truly meant by this, but I\u2019m going with it.)\u00a0<\/span><\/p>\n\n- Counts: actual numbers<\/span><\/li>\n
- Amounts: total value associated with each feature<\/span><\/li>\n
- Can be used for discrete features<\/span><\/li>\n<\/ul>\n
Summarizing by area is a bit harder, because counts and amounts throw a wrench in the patterns if areas are different in size (use ratios so this is accurately represented)\u00a0<\/span><\/p>\nRatios:<\/b> Relationship between two quantities and are created by dividing one quantity by another.\u00a0<\/span><\/p>\n\n- This evens out differences in areas that differ in size, but this again is the most significant for summarizing by data<\/span><\/li>\n<\/ul>\n
\n- Averages are also good, because it helps compare places that have little features to places that have a variety of features <\/span>\u00a0<\/b><\/li>\n<\/ul>\n
Standard classification schemes:\u00a0<\/b><\/p>\n\n- Helps group similar values to look for similarities in data (I saw some statistics, gagged, and then moved onto chapter 4.)<\/span><\/li>\n
- I liked this chapter towards the end because it showed a lot of ways to plot things on a map (I think I saw a pie chart on one page? I saw topography \u201ccontour lines\u201d and was very happy about that)\u00a0<\/span><\/li>\n<\/ul>\n
Chapter 4<\/b><\/p>\n
Mapping density is very important when it comes to looking at concentrations of features in an area.\u00a0<\/span><\/p>\n\n- Useful for census, counties. I think this may also be used on occasion for political party concentrations in certain states?<\/span><\/li>\n
- Dot maps represent density of individuals locations and is summarized by defined areas<\/span><\/li>\n
- Divide total number of features or total value\u00a0 by the area of the polygon=density value<\/span><\/li>\n<\/ul>\n
The Dense surface is created via a raster layer and is more blobby (I think of this as when you look at a map when a storm is coming. Is it a cluster of dots coming at your town or is it a huge blob?<\/span><\/p>\nBe careful with dot patterns, they should be an appropriate size for the areas on the map. Too small or too large can obscure patterns which messes up the point the map was trying to make.\u00a0<\/span><\/p>\n\n- Search radius: larger it is, more generalized patterns in density surface will be<\/span>\n
\n- Consideration of more features when calculating value of each cell\u00a0<\/span><\/li>\n
- Number of features is divided by a larger area<\/span><\/li>\n
- Smaller search shows more localized variation, but broader patterns in the data may be shadowed because of small radius<\/span><\/li>\n<\/ul>\n<\/li>\n
- Calculation method: Uses one of two methods\u00a0<\/span>\n
\n- Simple: counts features within radius of each cell, which forms ring like structures that overlap each other\u00a0<\/span><\/li>\n
- Weighted: Mathematical function to give more relevance to features closest to the center (No edge effect here) Smoother and more generalized density surface. Maybe easier to interpret.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
Units allow you to specify areal units you wish to use for density values.\u00a0<\/span><\/p>\n\n- \u00a0If areal units are different, values in legends may be extrapolated. (may predict hypothetical values that fall outside the specific data set.)\u00a0<\/span><\/li>\n<\/ul>\n
Main takeaway from this chapter: A map is so much more complicated than I ever anticipated in my lifetime, there are so many factors that go into this and I didn\u2019t even know you could incorporate standard deviation into a map. That\u2019s insane, mapmakers are insane.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Chapter 1 \u00a0I actually enjoyed this chapter a lot because it expressed different types of analysis in numerous parts of daily life (%forest, burglaries, parcels near a liquor store, etc) I cannot stress how cool it is that GIS is around us more than just using it for maps and navigation, which was my initial thought prior starting this course.\u00a0 Keeping in mind what different types of geographical features are and how they are represented, a discrete feature is a feature that has definite boundaries. An example of this is a lake or a building. Continuous phenomena is something that can be measured regardless of location, so in the book it mentions temperature or precipitation. (no gaps, starts off as sample points.) Lastly, summarized data is the density of individual features within a boundary, the data applies to a whole area, but isn’t really a specified location. (Example: 740 area code is typically in Southeastern Ohio, but it isn’t specific to what county it is in. It could be Guernsey, Belmont, Noble, or even Marion county. It’s more than those counties. I’m just using the example that it isn’t limited to one county.) There are two ways to represent GIS model wise: These are called vector and raster.\u00a0 Vector: This is defined using x,y locations in an area which does not have boundaries, GIS then connects these dot-like coordinates to\u00a0 draw lines and outlines. These dots can be areas, lines, events and of course locations. Main takeaway: Vectors utilize lines as a way to create almost an outline for locations, streams, and areas. ->discrete and summarized data typically Raster: These are seen as more of a matrix of cells with continuous space. Used in layers, but layers can be added on top of one another and analysis is then done by combining all of the layers to make a new layer that contains cell values. This seems to rely more on scale of the cell, because it changes the layer being analyzed and also the presentation of the map. Main takeaway: cell size should be close to the original scale of the map, because using too large or small of a cell size can cause conflict with information and lack of precision on the map. ->continuous values Both Vector and Raster: Continuous categories.\u00a0 Map projections: locations on the globe, but are translated onto a flat surface like a map (Flat earth vibes, not liking that but okay GIS.)\u00a0 Distorts features and shapes, also measurements of distance, areas that are already established (counties, villages.) Coordinate system: Uses specific units to target features in a 2D space, as well as the origin of those units.\u00a0 Good gravy, this chapter is packed with information\u00a0 I liked that it mentioned land use in this chapter, we learned about that in GEOG 347, so that\u2019s rad.\u00a0 Chapter 2 Now there is some degree of significance to plotting and mapping things, mainly because when we look at a map, we are looking for something specific, whether it is a similar structure or a pattern that is familiar to us. (When I drive home, sometimes on Apple maps I look specifically for the Y bridge on Zanesville because it is what I use to get into Cambridge, I can see this on a map and in person.) Maps also can be used to determine trends in areas (ex: Police officers investigating crime activity and seeing whether or not it is in the same area or different parts of the city.)\u00a0 Maps are dependent on audiences and what issue is being addressed. (I really liked the color palette for the keys, sorry that\u2019s random but it\u2019s very pretty.) Features require locations, and locations require coordinates prior to being typed into a geographical database (so latitude, longitude, and addresses.)\u00a0 It\u2019s important to use specific colors that draw attention to different categories on maps, oftentimes soil maps utilize different colors and codes because soil types are fairly wordy sometimes, but they put the full name in the legend\/key. (I think back to a time when I was in a soil microbial lab where this person showed us all the soil types in Kalamazoo county. Here\u2019s the website if anyone wishes to take a gander at it, https:\/\/websoilsurvey.nrcs.usda.gov\/app\/WebSoilSurvey.aspx \u00a0After the hour-long talk the person ended it by saying soil doesn\u2019t matter and I wanted to cry. anyways.) Using recognizable features in maps are extremely important to audiences, especially those who live there that are looking for specific rivers, landmarks, maybe a weird statue. Patterns can be seen just by the human eye, but sometimes maps have \u201ceaster eggs\u201d for those who may see different patterns, this varies on how meaningful certain details are to people.\u00a0 Chapter 3 So why are we actually mapping all details? Shouldn\u2019t we just focus on mapping the most important features out there? NOPE. Everything is significant in some sort of way, and may actually be beneficial to someone. (typically businesses.) You can include and exclude things from maps just by a degree of relevance, because honestly it may be weird to have \u201chow many slugs are in each yard of Delaware, OH\u201d on a map that shows who all has a subscription to oriental trading company (I bet I just unlocked a memory for you, you\u2019re welcome.) In legends, specific things can be shown by having bigger or smaller, or thinner\/thicker lines. (Expressed by having big circles as 2501-8000 employees, or very thin lines as unknown fish habitats in streams.) Mitchell describes continuous phenomena as more of a colorful part of the map. Where areas are displayed as graduated colors and surfaces are contoured, or a 3D perspective, but can also be graduated. (Remember my palette comment in chapter 2? I think this is what I meant. My heart and little GIS mind was in the right place.) Typically lighter colors are expressed as little or a lack of in a map, while darker more shaded in colors are seen as plentiful, lots of. I feel you can heavily argue the point made on page 56 of the Mitchell textbook based simply on perspective. Maps can be a way to express data or even trends especially if used in a long term fashion. (Long term deforestation, crop rotation, droughts, maybe long term crimes? Sue me.)\u00a0 Counts and amounts: \u00a0total numbers. (I actually don\u2019t know what I truly meant by this, but I\u2019m going with it.)\u00a0 Counts: actual numbers Amounts: total value associated with each feature Can be used for discrete features Summarizing by area is a bit harder, because counts and amounts throw a wrench in the patterns if areas are different in size (use ratios so this is accurately represented)\u00a0 Ratios: Relationship between two quantities and are created by dividing one quantity by another.\u00a0 This evens out differences in areas that differ in size, but this again is the most significant for summarizing by data Averages are also good, because it helps compare places that have little features to places that have a variety of features \u00a0 Standard classification schemes:\u00a0 Helps group similar values to look for similarities in data (I saw some statistics, gagged, and then moved onto chapter 4.) I liked this chapter towards the end because it showed a lot of ways to plot things on a map (I think I saw a pie chart on one page? I saw topography \u201ccontour lines\u201d and was very happy about that)\u00a0 Chapter 4 Mapping density is very important when it comes to looking at concentrations of features in an area.\u00a0 Useful for census, counties. I think this may also be used on occasion for political party concentrations in certain states? Dot maps represent density of individuals locations and is summarized by defined areas Divide total number of features or total value\u00a0 by the area of the polygon=density value The Dense surface is created via a raster layer and is more blobby (I think of this as when you look at a map when a storm is coming. Is it a cluster of dots coming at your town or is it a huge blob? Be careful with dot patterns, they should be an appropriate size for the areas on the map. Too small or too large can obscure patterns which messes up the point the map was trying to make.\u00a0 Search radius: larger it is, more generalized patterns in density surface will be Consideration of more features when calculating value of each cell\u00a0 Number of features is divided by a larger area Smaller search shows more localized variation, but broader patterns in the data may be shadowed because of small radius Calculation method: Uses one of two methods\u00a0 Simple: counts features within radius of each cell, which forms ring like structures that overlap each other\u00a0 Weighted: Mathematical function to give more relevance to features closest to the center (No edge effect here) Smoother and more generalized density surface. Maybe easier to interpret. Units allow you to specify areal units you wish to use for density values.\u00a0 \u00a0If areal units are different, values in legends may be extrapolated. (may predict hypothetical values that fall outside the specific data set.)\u00a0 Main takeaway from this chapter: A map is so much more complicated than I ever anticipated in my lifetime, there are so many factors that go into this and I didn\u2019t even know you could incorporate standard deviation into a map. That\u2019s insane, mapmakers are insane.<\/p>\n","protected":false},"author":2163,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-256","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\/256","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\/2163"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=256"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/256\/revisions"}],"predecessor-version":[{"id":257,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/256\/revisions\/257"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=256"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=256"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=256"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}