{"id":7371,"date":"2026-09-10T13:22:56","date_gmt":"2026-09-10T18:22:56","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=7371"},"modified":"2026-09-10T13:22:56","modified_gmt":"2026-09-10T18:22:56","slug":"redman-week-3","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2026\/09\/10\/redman-week-3\/","title":{"rendered":"Redman week 3"},"content":{"rendered":"

Chapter 4:<\/span><\/h2>\n

Mapping density in GIS shows concentration of features. When a data set contains many individual points, looking at the map can become confusing. A density map helps this by calculating the features in one unit of an area, such as businesses per square mile. This allows you to compare density in areas of different sizes. Density maps allow for pattern recognition for concentration, which has many practical uses.<\/span><\/p>\n

The two main approaches to mapping density are defined areas and density surfaces:<\/span><\/p>\n

Mapping by defined areas uses established boundaries and uses the formula pop_density=total_pop\/(area?27878400). Defined areas can be mapped by either shaded fill maps or dot density maps. Shaded fill maps use a range of colors to display density ratios across an entire polygon. In dot density maps, each dot represents a specific number or amount of what you want to map. Dots are randomly placed within the defined area, not showing the true location. Closely packed dots show high density.<\/span><\/p>\n

Mapping by density surface provides a detailed, continuous representation of concentrations without relying on established borders. A density surface is created as a raster layer. GIS looks at the features within a specified neighborhood around each cell to calculate density value.<\/span><\/p>\n

The way that a density surface looks depends on many GIS calculations. Search radius is the parameters of the location examined. A smaller search radius yields a more local variation and more details, while a larger search yields more features. Cell size determines how fine or coarse the pattern will appear. Smaller cells make for a smoother pattern, but require more time and storage, while larger cells put less strain on the computer, but look coarser. In the calculation method, the simple method counts features of the search equally, making overlapping rings, and the weighted method uses a mathematical function to produce a more precise surface.<\/span><\/p>\n

After the calculation, density surfaces are displayed using graduated colors to distinguish contour lines, which connect points of equal density on top of the map using equal interval spacing to make for easier reading<\/span><\/p>\n

 <\/p>\n

Chapter 5:\u00a0<\/span><\/p>\n

This chapter focuses on identifying graphic features in a boundary line. Understanding what is inside an area helps people monitor local activities, predict outcomes, or compare multiple areas in a region. GIS can present this information in list, count, or summary form. It often uses tables, bar charts, or pie graphs to display statistics.\u00a0<\/span><\/p>\n

There are three methods for finding and mapping what is inside a geographic boundary: drawing areas and features, selecting features inside an area, and overlaying areas and features.\u00a0<\/span><\/p>\n

For drawing areas and features, you place a boundary over features to see what falls inside or outside the space. This method is fast, but lacks detailed calculations.<\/span><\/p>\n

Selecting features involves you specifying the area containing the features, then GIS selects a subset of the features inside the area. It is good for getting a list or summary of features inside a single area, and finding a distance from a feature, but does not tell you what is in each area, only all areas together.\u00a0<\/span><\/p>\n

Overlaying the areas and features is more detailed. It involves combining the area and features into a new layer. It is useful to find features in specific areas. It is good because it provides more detail, but it requires more time and effort.<\/span><\/p>\n

Choosing which of the three methods to use depends how much detail you have. All three methods have their pros and cons, and you get out of it what you put in, as with many other methods in GIS.<\/span><\/p>\n

 <\/p>\n

Chapter 6:\u00a0<\/span><\/p>\n

This chapter discusses mapping what is nearby. This allows for identification of features or areas affected by an event. Finding what is nearby is helpful when analyzing travel ranges and proximity surrounding a source feature. An example of this is when a fire department calculates how long it will take to get to a specific street to narrow down response time of calls.\u00a0<\/span><\/p>\n

Nearby measurements are calculated by using travel cost or physical distance. Cost represents the around or resources it takes to move between two points. Time, energy, money, and effort can all be calculated into this. Before you analyze nearby features, you have to choose if you want to apply the planar or geodesic measurement method and decide how to display the data. The planar method treats the earth as a flat surface, which can work for smaller areas, while the geodesic measurement takes into account the curvature of the earth, which is better for large regional areas. Once distances are calculated, GIS can summarize the attributes using inclusive rings, which show how feature counts accumulate as distance increases, or distinct bands, which display differences in feature amounts within separate distance intervals. Results can be displayed using buffers, which create defined zones at a specified distance to establish a service area.<\/span><\/p>\n

There are three ways to find what is nearby. The first is straight-line distance, which measures direct distance between two points without taking into account any obstacles. This is the simplest approach and it works well for establishing fixed boundaries, but is highly unrealistic for travel<\/span><\/p>\n

The second method, Distance or cost over a network, measures travel along established paths. This is more realistic as it provides an attainable path of travel and gives a more accurate travel time, such as increasing the time when the physical distance remains unchanged.\u00a0<\/span><\/p>\n

The third method is cost over a surface. This one measures overland travel across a continuous geographic surface rather than roads. This method assigns differing travel costs to different landscapes to account for terrain difficulty, which allows for more accurate wildlife tracking or planning off road rescue routes across landscaped with inconsistent terrain.<\/span><\/p>\n

Choosing a method depends on what data you need and whether you are measuring boundaries, road travel, or overland movement.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Chapter 4: Mapping density in GIS shows concentration of features. When a data set contains many individual points, looking at the map can become confusing. A density map helps this by calculating the features in one unit of an area, such as businesses per square mile. This allows you to compare density in areas of different sizes. Density maps allow for pattern recognition for concentration, which has many practical uses. The two main approaches to mapping density are defined areas and density surfaces: Mapping by defined areas uses established boundaries and uses the formula pop_density=total_pop\/(area?27878400). Defined areas can be mapped by either shaded fill maps or dot density maps. Shaded fill maps use a range of colors to display density ratios across an entire polygon. In dot density maps, each dot represents a specific number or amount of what you want to map. Dots are randomly placed within the defined area, not showing the true location. Closely packed dots show high density. Mapping by density surface provides a detailed, continuous representation of concentrations without relying on established borders. A density surface is created as a raster layer. GIS looks at the features within a specified neighborhood around each cell to calculate density value. The way that a density surface looks depends on many GIS calculations. Search radius is the parameters of the location examined. A smaller search radius yields a more local variation and more details, while a larger search yields more features. Cell size determines how fine or coarse the pattern will appear. Smaller cells make for a smoother pattern, but require more time and storage, while larger cells put less strain on the computer, but look coarser. In the calculation method, the simple method counts features of the search equally, making overlapping rings, and the weighted method uses a mathematical function to produce a more precise surface. After the calculation, density surfaces are displayed using graduated colors to distinguish contour lines, which connect points of equal density on top of the map using equal interval spacing to make for easier reading   Chapter 5:\u00a0 This chapter focuses on identifying graphic features in a boundary line. Understanding what is inside an area helps people monitor local activities, predict outcomes, or compare multiple areas in a region. GIS can present this information in list, count, or summary form. It often uses tables, bar charts, or pie graphs to display statistics.\u00a0 There are three methods for finding and mapping what is inside a geographic boundary: drawing areas and features, selecting features inside an area, and overlaying areas and features.\u00a0 For drawing areas and features, you place a boundary over features to see what falls inside or outside the space. This method is fast, but lacks detailed calculations. Selecting features involves you specifying the area containing the features, then GIS selects a subset of the features inside the area. It is good for getting a list or summary of features inside a single area, and finding a distance from a feature, but does not tell you what is in each area, only all areas together.\u00a0 Overlaying the areas and features is more detailed. It involves combining the area and features into a new layer. It is useful to find features in specific areas. It is good because it provides more detail, but it requires more time and effort. Choosing which of the three methods to use depends how much detail you have. All three methods have their pros and cons, and you get out of it what you put in, as with many other methods in GIS.   Chapter 6:\u00a0 This chapter discusses mapping what is nearby. This allows for identification of features or areas affected by an event. Finding what is nearby is helpful when analyzing travel ranges and proximity surrounding a source feature. An example of this is when a fire department calculates how long it will take to get to a specific street to narrow down response time of calls.\u00a0 Nearby measurements are calculated by using travel cost or physical distance. Cost represents the around or resources it takes to move between two points. Time, energy, money, and effort can all be calculated into this. Before you analyze nearby features, you have to choose if you want to apply the planar or geodesic measurement method and decide how to display the data. The planar method treats the earth as a flat surface, which can work for smaller areas, while the geodesic measurement takes into account the curvature of the earth, which is better for large regional areas. Once distances are calculated, GIS can summarize the attributes using inclusive rings, which show how feature counts accumulate as distance increases, or distinct bands, which display differences in feature amounts within separate distance intervals. Results can be displayed using buffers, which create defined zones at a specified distance to establish a service area. There are three ways to find what is nearby. The first is straight-line distance, which measures direct distance between two points without taking into account any obstacles. This is the simplest approach and it works well for establishing fixed boundaries, but is highly unrealistic for travel The second method, Distance or cost over a network, measures travel along established paths. This is more realistic as it provides an attainable path of travel and gives a more accurate travel time, such as increasing the time when the physical distance remains unchanged.\u00a0 The third method is cost over a surface. This one measures overland travel across a continuous geographic surface rather than roads. This method assigns differing travel costs to different landscapes to account for terrain difficulty, which allows for more accurate wildlife tracking or planning off road rescue routes across landscaped with inconsistent terrain. Choosing a method depends on what data you need and whether you are measuring boundaries, road travel, or overland movement.<\/p>\n","protected":false},"author":2433,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7371","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\/7371","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\/2433"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7371"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7371\/revisions"}],"predecessor-version":[{"id":7372,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7371\/revisions\/7372"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7371"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7371"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7371"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}