Chapter 4: Chapter four discusses mapping density, which is useful for displaying where the data is most concentrated. Displaying the density can help identify specific areas with the highest values, such as population, businesses, or crimes within square miles. You can map features which are the number of businesses or people in an area or you can map feature values which refer to the quantity of something for each feature such as the number of workers at each business. One way to display density is by using dot maps where dots represent a specific number of a feature and how close the dots are together represents a higher density and dots that are distributed further apart represent lower density areas. Surface density can be shown using a raster layer where lower-density areas are shaded in a lighter color and higher-density areas are shaded in a darker color. You should only map using surface density if the data is specific locations, sample points, or lines. However, only map density should be used if the data is summarized by area. ArcGIS allows you to map density without needing to do prior calculations, which is very useful and efficient. ArcGIS also makes it easy to make a dot density map by automatically adjusting the number of dots when the amount each dot represents is defined. It’s important to pay attention to cell size, search radius, calculation method, and units. Cell size determines how smooth the appearance of the pattern is. Smaller cell size allows for smoother appearances but can lead to longer processing times and take up storage space. A cell size between 10 and 100 cells/ density unit is best. Search radius shows broader patterns when it is larger and more localized patterns when smaller. Calculation method is how the cell values are calculated. Simple calculation includes features in the search radius of each cell. Weighted calculation shows features closest to the center of the cell. Units are important because the appropriate unit should be chosen based on what is being mapped. Using larger units is better for showing features further away while smaller units are better for more localized features.
Chapter 5: Chapter 5 explains how to tell if certain features or activities occur within a certain area or not. The reasoning behind this was fascinating. Mapping what is happening within an area can help understand what issues certain areas may struggle with and potential reasons that certain areas may have these issues. I’m still a little confused about what continuous features are referring to but based on the explanation in Chapter 5 I think it refers to features that can change over time such as elevation and vegetation type. Discrete features are countable and unchanging over time such as locations of crimes or streams and roads. To find out what is inside a certain area or boundary, you can use three different methods. One option is drawing the boundary and identifying what features are within those boundaries. Another option is making a boundary and then selecting a layer that has specific features, which GIS can use to find subsets of those features in that specific area. This is most useful when you want to summarize the features inside an area. The last option is a little confusing to me. I interpreted this method as GIS using the area and specified features to make a new layer that displays only the features in that area. I think that is how it works, but without doing it in GIS I’m not completely sure that is how it works. It seems that using GIS to make a new layer allows you to find features within an area and information about the features. Drawing and counting the features tells the number of features but doesn’t give additional information about the features. Choosing the features within the area gives a summary of what’s inside the area but doesn’t provide specific details about what’s inside the area. However, making a new layer with GIS is best for multiple areas while the other options are best for when you are analyzing only one area. GIS allows you to get a report on certain features which you can use to make a statistical summary that can be used to compare features within areas.
Chapter 6: Chapter 6 explores how to map what is nearby and why this might be useful. Mapping certain things near a certain feature can be useful, especially for gaining info and preparation. For example, knowing how many families are within 20 minutes of the hospital allows the hospital to prepare by knowing how many employees should be staffed to help the nearby population. You can measure what is nearby based on distance, such as what is within a 50-mile radius, or by cost, such as time or gas money per mile. Cost is more useful when basing what is nearby on travel, which makes sense because one house may be a mile from the hospital but can get there within 4 minutes while a house that is ¾ of a mile is 6 minutes from the hospital due to traffic and roads. You can use different ranges to see what is nearby in different distances or costs from the feature. Inclusive rings can be used to see how an amount of something nearby changes as the distance increases. Distinct bands on the other hand compare how the range closest to the feature, 500-1000ft compares to the range furthest from the feature, 1000-2000 ft. This could give information about if the location of the feature is in the best location based on what is nearby. For example, more crimes may occur in the further range than the closer range, which would mean that it would take longer for the police to get to the scene. This could help the police prepare by sending more patrol cars into the further range to reach the scene faster. Different methods to use to measure what is nearby are straight line distance, distance or cost over a network, and cost over a surface. Straight line distance makes a boundary a certain distance from the feature and IDs what is near the feature within the boundary. Distance or cost over a network uses a certain distance or cost, like time, from the feature to see what is nearby the feature in that range. I don’t understand what cost over a surface is referring to, however. I think it might be referring to the amount of time it takes to get to a feature based on the surface, such as hills or flat surfaces that could affect how long it takes to get to the feature.