Chapter 4: Mapping Density<\/b><\/p>\n
Mapping density helps see patterns of where things are concentrated when mapping areas vary greatly in size. It allows you to measure the number of features using a uniform areal unit (hectares, sq miles). You can map density features( ie locations of businesses) or feature values (# of employees at each business).<\/span><\/p>\n Compare methods:<\/b><\/p>\n Map density by area:\u00a0<\/span><\/p>\n Create density surface:<\/span><\/p>\n Mapping density for defined areas<\/b><\/p>\n This section goes through the steps to calculate density for a defined area. It gives example calculations. The GIS can calculate the density for you and shade each area it needs. Then this goes into how to create a dot density map in detail. THe GIS divides the value of the polygon by the amount represented by a dot to find out how many dots to draw in each area. Dot maps are used to get a quick sense of density in a place and represent density graphically. You can compare areas by using GIS to summarize features or feature values for each polygon.\u00a0<\/span><\/p>\n Creating a density surface:<\/b><\/p>\n Density surfaces are created in a GIS as raster layers. They are used to show where point or line features are concentrated. GIS defines a neighborhood around each cell center and then totals the # of features that fall within that neighborhood and then divides that # by the area of the neighborhood. It then creates a running average of features per area which then creates a smooth surface. Parameters such as cell size, search radius, calculation methods and units are used to specify how GIS calculates the density surface.\u00a0<\/span><\/p>\n Density surfaces can be displayed using graduated colors or contours and are displayed using shades of a single color. Higher values are shown using darker colors. Contour lines connect points of equal density value on the surface, and show the rate of change across the surface. The textbook then goes into detail about how to look at the results depending on how you created the density surface.\u00a0\u00a0<\/span><\/p>\n <\/p>\n Chapter 5: Finding What\u2019s Inside<\/b><\/p>\n This chapter focuses on whether an activity occurs inside an area or summarizes info for each of several areas to compare them. It is important to map what’s inside so you can know where or not action is needed. To define what is inside you have to draw an area boundary on top of the features. Single areas allow you to monitor activity about one place and can include a service area around a central facility, a buffer around a feature, a natural boundary, or a manually drawn area. Multiple areas include contiguous, disjunct, or nested. Features can be discrete ( unique and identifiable) or continuous ( represent seamless geographic phenomena). These were both terms learned in chapter one. By now the chapters are starting to connect and make more sense. There is some information needed to form your analysis which can include a list, count, summary, or sum of the data. This chapter outlines three ways to find out what is inside and lists the best way to choose each method:<\/span><\/p>\n The chapter then outlines the best way to make the map for each of the three methods. The last part of this chapter discusses using your results to display and analyze what you have looked at using tables that list the areal extent of each category in that area of study. When using a raster, GIS takes care of the table for you but if you are using vectors, you have to summarize the category values for each area. This section discusses the methods to help do that. It then talks about looking at single vs multiple areas and single vs multiple categories.\u00a0<\/span><\/p>\n <\/p>\n Chapter 6: Finding What\u2019s Nearby<\/b><\/p>\n This chapter focused on seeing stuff within a set distance of a feature in order to monitor events or activity. It is important to map what is nearby to figure out what is happening with a certain distance that you measured. This chapter then goes into detail on defining your analysis which means figuring out what is nearby by measuring a starting line distance, measuring distance or cost over a network, or measuring a cost over a surface. Similar to chapter 5, when you identify the nearby features, you must then determine what info you need including a list, count, or summary . This chapter then highlights the three ways you can find out what is nearby and they include:<\/span><\/p>\n The chapter then discusses choosing the correct method for what you are trying to achieve, how to make a map. It discusses creating a buffer using the straight line distance method where GIS will draw a line around a feature at the distance you tell it to and see what information is within it. GIS can use boundaries either manually which is more flexible, or having the GIS do it which can draw either a compact or general boundary. It talks about what a cost is (can be time, money, or other measured source) which is calculated by specifying the layer containing the source features and a second layer that has the cost value of each cell. For each method it discusses the processes of making a map and the features that are important to the process. Chapter 6 felt like it tied a lot of the concepts we learned in earlier chapters together.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" Chapter 4: Mapping Density Mapping density helps see patterns of where things are concentrated when mapping areas vary greatly in size. It allows you to measure the number of features using a uniform areal unit (hectares, sq miles). You can map density features( ie locations of businesses) or feature values (# of employees at each business). Compare methods: Map density by area:\u00a0 Use if you have data already summarized by area, or lines or points you can summarize by area Output Shaded fill map or dot density map Trade offs Easy but won\u2019t pinpoint exact centers of density, especially in larger areas May require some attribute processing Create density surface: Use if you have individual locations, sample points, or lines Output: Shaded density surface or contour map Trade offs Gives a more precise point of view Requires more data processing Mapping density for defined areas This section goes through the steps to calculate density for a defined area. It gives example calculations. The GIS can calculate the density for you and shade each area it needs. Then this goes into how to create a dot density map in detail. THe GIS divides the value of the polygon by the amount represented by a dot to find out how many dots to draw in each area. Dot maps are used to get a quick sense of density in a place and represent density graphically. You can compare areas by using GIS to summarize features or feature values for each polygon.\u00a0 Creating a density surface: Density surfaces are created in a GIS as raster layers. They are used to show where point or line features are concentrated. GIS defines a neighborhood around each cell center and then totals the # of features that fall within that neighborhood and then divides that # by the area of the neighborhood. It then creates a running average of features per area which then creates a smooth surface. Parameters such as cell size, search radius, calculation methods and units are used to specify how GIS calculates the density surface.\u00a0 Density surfaces can be displayed using graduated colors or contours and are displayed using shades of a single color. Higher values are shown using darker colors. Contour lines connect points of equal density value on the surface, and show the rate of change across the surface. The textbook then goes into detail about how to look at the results depending on how you created the density surface.\u00a0\u00a0 Chapter 5: Finding What\u2019s Inside This chapter focuses on whether an activity occurs inside an area or summarizes info for each of several areas to compare them. It is important to map what’s inside so you can know where or not action is needed. To define what is inside you have to draw an area boundary on top of the features. Single areas allow you to monitor activity about one place and can include a service area around a central facility, a buffer around a feature, a natural boundary, or a manually drawn area. Multiple areas include contiguous, disjunct, or nested. Features can be discrete ( unique and identifiable) or continuous ( represent seamless geographic phenomena). These were both terms learned in chapter one. By now the chapters are starting to connect and make more sense. There is some information needed to form your analysis which can include a list, count, summary, or sum of the data. This chapter outlines three ways to find out what is inside and lists the best way to choose each method: Drawing areas and features See which features are inside or outside of the area Selecting the features inside the area You specify the area the the layer with the features and then GIS will select a\u00a0 subset of the features inside the area Overlaying the areas and features GIS combines area and features to create a new layer with attributes from both and then compares them The chapter then outlines the best way to make the map for each of the three methods. The last part of this chapter discusses using your results to display and analyze what you have looked at using tables that list the areal extent of each category in that area of study. When using a raster, GIS takes care of the table for you but if you are using vectors, you have to summarize the category values for each area. This section discusses the methods to help do that. It then talks about looking at single vs multiple areas and single vs multiple categories.\u00a0 Chapter 6: Finding What\u2019s Nearby This chapter focused on seeing stuff within a set distance of a feature in order to monitor events or activity. It is important to map what is nearby to figure out what is happening with a certain distance that you measured. This chapter then goes into detail on defining your analysis which means figuring out what is nearby by measuring a starting line distance, measuring distance or cost over a network, or measuring a cost over a surface. Similar to chapter 5, when you identify the nearby features, you must then determine what info you need including a list, count, or summary . This chapter then highlights the three ways you can find out what is nearby and they include: Straight line distance Used to define an area of influence around a feature and creating a boundary or selecting features within the distance Distance or cost over a network Used for measuring travel over a fixed infrastructure Cost over a surface Used for measuring overland travel and calculating how much area is within the travel range The chapter then discusses choosing the correct method for what you are trying to achieve, how to make a map. It discusses creating a buffer using the straight line distance method where GIS will draw a line around a feature at the distance you tell it to and see what information is within it. GIS can use boundaries either manually which is more flexible, or having the GIS do it which can draw either a compact or general boundary. It talks about what a cost is (can be time, money, or other measured source) which is calculated by specifying the layer containing the source features and a second layer that has the cost value of each cell. For each method it discusses the processes of making a map and the features that are important to the process. Chapter 6 felt like it tied a lot of the concepts we learned in earlier chapters together.<\/p>\n","protected":false},"author":2320,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-5149","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\/5149","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\/2320"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=5149"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/5149\/revisions"}],"predecessor-version":[{"id":5150,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/5149\/revisions\/5150"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=5149"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=5149"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=5149"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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