This week I read Geographic Patterns and Relationships 2nd edition by Andy Mitchell<\/p>\n
Mitchell Chapter – 1 is a general introduction to GIS and its applications. The beginning\u00a0 lists a few applications for maps and GIS, for instance It is possible to map where the most or least amount of something is. It is also possible to map density as well as change. A definition\/description for GIS is also provided. In this chapter we are given guidance on asking questions using GIS, and we are instructed to be as specific as possible, which leads me to wonder where the line of specificity is? Meaning how general or broad can I be about asking a question so that it still is effective at providing an answer without providing a misleading or useless answer.\u00a0<\/span><\/p>\n The rest of chapter 1 delves into how to frame a question, and how you can use data generated by your map. For instance you can summarize data generated from the map. It is also possible to use satellite imaging to create continuous data which is good for visualizing patterns such as precipitation, soil characteristics, and temperature. When using continuous data it is sometimes good to use raster data which works well since raster data is a grouping of cells, whereas vector data is based on individual points.\u00a0\u00a0\u00a0<\/span><\/p>\n <\/p>\n Key Words: <\/span>Discrete features <\/b>(pinpoint data), <\/span>Continuous phenomena<\/b> (data that can be found anywhere), <\/span>Features summarized by area<\/b> (data found within set boundaries), <\/span>Vector data<\/b> (areas defined by points and set polygons), <\/span>Raster data<\/b> (data represented as a matrix of cells)<\/span><\/p>\n <\/p>\n Mitchell Chapter – 2 explains how to create data before using it as well as data storage to display either detailed or general information. It also covers different ways to represent or draw data on a map, either through lines or in a given area designated by points. It also discusses best practices when it comes to data visualization when using maps and spatial data. For instance when using spatial data, it is necessary to have coordinates present so that you are able to plot sites with the GIS, it is also beneficial to have a character or attribute associated with the coordinate data so it is easy to group them together. Then when grouping similar types of data you just graph them all as either the same color or with a distinct shape. Similarly to statistical programs like Rstudios, you can subset data and have only specific values shown. When assigning categories it is best to have as few as possible as this makes it easiest to distinguish patterns (a good amount would be 5-7), however having a larger amount displays more detailed patterns.\u00a0<\/span><\/p>\n <\/p>\n Mitchell Chapter – 3 explains how it can be helpful to use spatial data to represent numerical data, such as how many people work at a specific location by using graduated symbols (using larger points to indicate more people). This type of visualization can help explain where something is as well as give context to what is being displayed. Data can be organized by rations (percentages), or by ranks which order data from least to greatest. When creating all this data it can sometimes be too unwieldy to use and hard to interpret as a viewer so it is best to create classes, which groups data into designated categories. There are many different ways to create data classes and there are also a number of types of classes that can be used, and each one is useful for different things. Some of them are good for seeing generalized patterns, while others are useful for making sure the data is properly displayed if it is not evenly distributed.\u00a0<\/span><\/p>\n Key words: <\/span>Ratio <\/b>(a proportion or percentage), <\/span>Rank <\/b>(ordered from greatest to least), <\/span>Class <\/b>(Data is grouped into a class representing certain ranges to make a map more concise), <\/span>Natural breaks <\/b>(data in a given class are similar, represents natural groupings found in your data), <\/span>Quantile <\/b>(each class has an equal number of features), <\/span>Equal interval <\/b>(classes are made by grouping a set amount of creatures into each class), <\/span>Standard deviation <\/b>(classes are generated by how many standard deviations away from the mean they are), <\/span>Graduated symbols <\/b>(points representing counts), <\/span>Graduated colors <\/b>(colors used to represent rations and ranks), <\/span>Charts <\/b>(graphs generated in the areas they represent), <\/span>Contours <\/b>(lines representing counts or rations, most noticeably used for precipitation and barometric pressure), <\/span>3D perspective views <\/b>(3D images used to display magnitude of data). <\/span><\/p>\n","protected":false},"excerpt":{"rendered":" This week I read Geographic Patterns and Relationships 2nd edition by Andy Mitchell Mitchell Chapter – 1 is a general introduction to GIS and its applications. The beginning\u00a0 lists a few applications for maps and GIS, for instance It is possible to map where the most or least amount of something is. It is also possible to map density as well as change. A definition\/description for GIS is also provided. In this chapter we are given guidance on asking questions using GIS, and we are instructed to be as specific as possible, which leads me to wonder where the line of specificity is? Meaning how general or broad can I be about asking a question so that it still is effective at providing an answer without providing a misleading or useless answer.\u00a0 The rest of chapter 1 delves into how to frame a question, and how you can use data generated by your map. For instance you can summarize data generated from the map. It is also possible to use satellite imaging to create continuous data which is good for visualizing patterns such as precipitation, soil characteristics, and temperature. When using continuous data it is sometimes good to use raster data which works well since raster data is a grouping of cells, whereas vector data is based on individual points.\u00a0\u00a0\u00a0 Key Words: Discrete features (pinpoint data), Continuous phenomena (data that can be found anywhere), Features summarized by area (data found within set boundaries), Vector data (areas defined by points and set polygons), Raster data (data represented as a matrix of cells) Mitchell Chapter – 2 explains how to create data before using it as well as data storage to display either detailed or general information. It also covers different ways to represent or draw data on a map, either through lines or in a given area designated by points. It also discusses best practices when it comes to data visualization when using maps and spatial data. For instance when using spatial data, it is necessary to have coordinates present so that you are able to plot sites with the GIS, it is also beneficial to have a character or attribute associated with the coordinate data so it is easy to group them together. Then when grouping similar types of data you just graph them all as either the same color or with a distinct shape. Similarly to statistical programs like Rstudios, you can subset data and have only specific values shown. When assigning categories it is best to have as few as possible as this makes it easiest to distinguish patterns (a good amount would be 5-7), however having a larger amount displays more detailed patterns.\u00a0 Mitchell Chapter – 3 explains how it can be helpful to use spatial data to represent numerical data, such as how many people work at a specific location by using graduated symbols (using larger points to indicate more people). This type of visualization can help explain where something is as well as give context to what is being displayed. Data can be organized by rations (percentages), or by ranks which order data from least to greatest. When creating all this data it can sometimes be too unwieldy to use and hard to interpret as a viewer so it is best to create classes, which groups data into designated categories. There are many different ways to create data classes and there are also a number of types of classes that can be used, and each one is useful for different things. Some of them are good for seeing generalized patterns, while others are useful for making sure the data is properly displayed if it is not evenly distributed.\u00a0 Key words: Ratio (a proportion or percentage), Rank (ordered from greatest to least), Class (Data is grouped into a class representing certain ranges to make a map more concise), Natural breaks (data in a given class are similar, represents natural groupings found in your data), Quantile (each class has an equal number of features), Equal interval (classes are made by grouping a set amount of creatures into each class), Standard deviation (classes are generated by how many standard deviations away from the mean they are), Graduated symbols (points representing counts), Graduated colors (colors used to represent rations and ranks), Charts (graphs generated in the areas they represent), Contours (lines representing counts or rations, most noticeably used for precipitation and barometric pressure), 3D perspective views (3D images used to display magnitude of data).<\/p>\n","protected":false},"author":2318,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-4979","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\/4979","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\/2318"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=4979"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/4979\/revisions"}],"predecessor-version":[{"id":4980,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/4979\/revisions\/4980"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=4979"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=4979"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=4979"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}