{"id":7382,"date":"2026-09-11T11:31:09","date_gmt":"2026-09-11T16:31:09","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=7382"},"modified":"2026-09-11T11:31:09","modified_gmt":"2026-09-11T16:31:09","slug":"sisler-week-3","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2026\/09\/11\/sisler-week-3\/","title":{"rendered":"Sisler Week 3"},"content":{"rendered":"

Mitchell Chapter 4-<\/span><\/p>\n

There are two ways to map density: by defined area and by density surface. You can map density by defined area using a dot map, where dots are randomly placed within the defined area to represent a certain number of features. These dot density maps show density graphically, rather than showing the actual density value. These kinds of maps are generally easier to read; however, the user has to make sure the dots are an appropriate size to not overlap and hide the pattern. Mapping density by defined area is normally displayed as a shaded map of the areas, with a range of colors. The density value applies to the entire area and may vary in that shaded region.\u00a0<\/span><\/p>\n

The second method of mapping density is by density surface. Density surface maps are created as a raster layer; each cell has a density value. This approach is more detailed but does require more effort. You can create a density surface map from locations, sample points of data, crimes, or even bird nests. A cool type of density surface map is a contour map, which compares all the density values within a certain defined radius. From that compared value, the point is then given the average and shaded to that average. The cell size of the map determines how coarse or fine the patterns are. If you want the map to be smooth, the cell size needs to be smaller, but the process will take a long time to process. To find the cell size, you have to convert density to cell units, divide by the number of cells, and take the square root. When displaying the density surface map, you have to be careful how you classify the values, whether that be natural breaks, quantile, equal intervals, or standard deviation. Each grouping shows a different pattern in the densities.\u00a0<\/span><\/p>\n

Mitchell Chapter 5-<\/span><\/p>\n

I found it interesting that people map things to know what’s inside the area to monitor things like crime, or to compare many areas based on what’s inside of them. The data can be of things in a single area, or multiple surrounding areas. There are two types of features that reside inside an area, discrete and continuous. Discrete features are unique, where you can count them. Where continuous features represent geographic phenomena, like vegetation type. The information that you want portrayed from the analysis can tell you a lot. For example if you want the features to be completely, or partially inside an area, or if you want the surrounding area to be highlighted. The different types of mapping these features can tell you different things.\u00a0<\/span><\/p>\n

There are three ways of finding what’s inside an area. The first is drawing areas and features, it’s quick and easy but only visual. This makes it harder to get information about the features inside. The second is selecting features inside the area, this is good for getting information inside a single area, but not several areas. The last method is overlaying the areas and features, which is good for showing what’s inside several areas, but takes more processing. After selecting the best method and creating a map, you can use the GIS to create a report of the features. A numeric attribute can be a sum, average, median, or standard deviation. When drawing features and highlighting them you can have the non highlighted parts be gray, or a lighter version of the features inside. The second option provides some information about what the features are. When overlaying areas, you can summarize the features by area. You can also summarize by category or value, it is important to account for variations in the areas. When making a map with overlaying areas on areas, it is important to look for slivers, and merge them with the adjacent larger areas.\u00a0<\/span><\/p>\n

Mitchell Chapter 6-<\/span><\/p>\n

This chapter focused on what’s nearby on the map. It discusses the importance of traveling ranges, and how to measure different ways to travel. A person would want to see what’s nearby for specific reasons, one of them could be to see how long it would take for police or firefighters to respond.\u00a0<\/span><\/p>\n

To find what’s nearby there are a few different ways to measure distance. One of the methods is measuring what’s nearby, this can be measured by cost or distance. When measuring response time, you would want to measure by time, not distance. When measuring distance there are two different ways, the planar method and the geodesic method. When measuring smaller areas its best to use the planar method, and when measuring large areas like a continent its best to use the geodesic method. When measuring using the straight line distance, it’s good for creating boundaries, or setting distances around a source. When mapping for distance travelled,\u00a0 its good for finding what’s within a travel distance of a fixed area\/network. Mapping cost is good for calculating travel cost over land like forest or roadways. When displaying the information from several sources, there may be areas of overlapping distances or features labeled. If a spider diagram is used it is easy to see the features (like customers) that are in a certain distance from the source (like stores). A network layer is a geometric network composed of edges, junctions, and turns. You can set travel parameters, and costs to each road, turn, or stop sign. This means that you could set costs to turns, intersections or individual segments.When setting a cost, you have to create a turntable. When you create a boundary, you can find what’s inside the boundary by using information from chapter 5. You can compare distances of rings surrounding a source. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Mitchell Chapter 4- There are two ways to map density: by defined area and by density surface. You can map density by defined area using a dot map, where dots are randomly placed within the defined area to represent a certain number of features. These dot density maps show density graphically, rather than showing the actual density value. These kinds of maps are generally easier to read; however, the user has to make sure the dots are an appropriate size to not overlap and hide the pattern. Mapping density by defined area is normally displayed as a shaded map of the areas, with a range of colors. The density value applies to the entire area and may vary in that shaded region.\u00a0 The second method of mapping density is by density surface. Density surface maps are created as a raster layer; each cell has a density value. This approach is more detailed but does require more effort. You can create a density surface map from locations, sample points of data, crimes, or even bird nests. A cool type of density surface map is a contour map, which compares all the density values within a certain defined radius. From that compared value, the point is then given the average and shaded to that average. The cell size of the map determines how coarse or fine the patterns are. If you want the map to be smooth, the cell size needs to be smaller, but the process will take a long time to process. To find the cell size, you have to convert density to cell units, divide by the number of cells, and take the square root. When displaying the density surface map, you have to be careful how you classify the values, whether that be natural breaks, quantile, equal intervals, or standard deviation. Each grouping shows a different pattern in the densities.\u00a0 Mitchell Chapter 5- I found it interesting that people map things to know what’s inside the area to monitor things like crime, or to compare many areas based on what’s inside of them. The data can be of things in a single area, or multiple surrounding areas. There are two types of features that reside inside an area, discrete and continuous. Discrete features are unique, where you can count them. Where continuous features represent geographic phenomena, like vegetation type. The information that you want portrayed from the analysis can tell you a lot. For example if you want the features to be completely, or partially inside an area, or if you want the surrounding area to be highlighted. The different types of mapping these features can tell you different things.\u00a0 There are three ways of finding what’s inside an area. The first is drawing areas and features, it’s quick and easy but only visual. This makes it harder to get information about the features inside. The second is selecting features inside the area, this is good for getting information inside a single area, but not several areas. The last method is overlaying the areas and features, which is good for showing what’s inside several areas, but takes more processing. After selecting the best method and creating a map, you can use the GIS to create a report of the features. A numeric attribute can be a sum, average, median, or standard deviation. When drawing features and highlighting them you can have the non highlighted parts be gray, or a lighter version of the features inside. The second option provides some information about what the features are. When overlaying areas, you can summarize the features by area. You can also summarize by category or value, it is important to account for variations in the areas. When making a map with overlaying areas on areas, it is important to look for slivers, and merge them with the adjacent larger areas.\u00a0 Mitchell Chapter 6- This chapter focused on what’s nearby on the map. It discusses the importance of traveling ranges, and how to measure different ways to travel. A person would want to see what’s nearby for specific reasons, one of them could be to see how long it would take for police or firefighters to respond.\u00a0 To find what’s nearby there are a few different ways to measure distance. One of the methods is measuring what’s nearby, this can be measured by cost or distance. When measuring response time, you would want to measure by time, not distance. When measuring distance there are two different ways, the planar method and the geodesic method. When measuring smaller areas its best to use the planar method, and when measuring large areas like a continent its best to use the geodesic method. When measuring using the straight line distance, it’s good for creating boundaries, or setting distances around a source. When mapping for distance travelled,\u00a0 its good for finding what’s within a travel distance of a fixed area\/network. Mapping cost is good for calculating travel cost over land like forest or roadways. When displaying the information from several sources, there may be areas of overlapping distances or features labeled. If a spider diagram is used it is easy to see the features (like customers) that are in a certain distance from the source (like stores). A network layer is a geometric network composed of edges, junctions, and turns. You can set travel parameters, and costs to each road, turn, or stop sign. This means that you could set costs to turns, intersections or individual segments.When setting a cost, you have to create a turntable. When you create a boundary, you can find what’s inside the boundary by using information from chapter 5. You can compare distances of rings surrounding a source.<\/p>\n","protected":false},"author":2415,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7382","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\/7382","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\/2415"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7382"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7382\/revisions"}],"predecessor-version":[{"id":7383,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7382\/revisions\/7383"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7382"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7382"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7382"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}