Geography 291: Geospatial Analysis with Desktop GIS

CHAPTER 4

density maps are something I have always taken an interest in, particularly in the ecological field. It’s always interesting to see the population density maps of local species. I also don’t usually think of density maps like the book demonstrated. They use business density instead of points. The book gives a great definition of when to use mapping density; ” mapping density is especially useful. Mapping density is especially useful when mapping areas such as census tracks or countries that vary greatly in size. As mentioned previously, this is how I viewed density maps. The color gradient makes density maps easy to follow and understand. The map Mitchell uses to show logging roads on page 108 does an efficient job at showing the density of the logging roads. However, it is confusing to follow. While reading, I was initially confused about the difference between map features or feature values. The book defines features as locations of businesses and feature values as the number of employees at each business. The visual figure on page 109 does a good job of demonstrating the differences, leaving me with no further questions on the differences. My previous understanding of density maps was strictly limited to the color gradient style that we are most familiar with. While reading Mitchell’s book, I was informed on the use of density maps as dot maps for this example. They still use businesses but one dot equals five businesses to show the density. One interesting point about dot maps was the dogs are distributed randomly within each area. They don’t represent an actual feature location however, the closer that together the dots are the higher density of features in that area.

CHAPTER 5

Chapter 5 is about why map what’s inside. Mitchell says on page 134 by monitoring what’s going on in an area, people know whether to take action. He uses the example of a district attorney who would monitor drug related to arrests to find out if an arrest within 1000 feet of a school if the arrest occurred within 1000 feet, stiffer penalties would be applied. The nice part about the density map that I learned about is you can find out what’s inside a single area or inside of several areas. When I originally read about surveying multiple areas, I was initially confused about the logistics behind it, but the book uses a great example, such as ZIP Codes or watersheds. The book has a good section on whether the features inside the map are discreet or continuous. It goes on to describe discreet features as unique, identifiable features you can list,  count, or summarize. A numeric attribute is associated with them. Continuous features represent seamless geographic phenomena. The example the book uses is especially continuous categories or classes such as vegetation type or elevation range *topographical maps. The section on page 147 under comparing methods was extremely insightful. While reading through chapter 5 i found myself wondering when to use which methods the three-layered tables lay out all three methods: drawing areas and features, selecting the features inside the area, and overlay the areas in features. Was extremely useful on what each method is good for the types of features that uses and the trade-offs associated with them. another thing I found interesting in Chapter 5 was the ability to take the data from the map and put it on paper. a count is the total number of features inside the area. this was taking was we collected and allows us to use it in a data set. 

CHAPTER 6

As I read in chapter 6, I think all of the learning objectives which were: Y map what’s nearby, defining your analysis, three ways of finding what’s nearby, using straight line distance, measuring distance, or cost over a network, calculating cost over a geographic surface were addressed fully and to the greatest extent. The first section wide map what’s nearby was extremely applicable to your daily life as they mentioned you can find out what’s occurring within the set distance. You could also find out what’s within traveling range. One thing I wouldn’t have thought to take into account about measuring distance was measuring flat plane, or the curvature of the earth. A recurring concept that we have seen through all six chapters now was needing to know the specific information you are looking for from the analysis i.e. do you need a list count or summary? Once again, I found the comparing methods table to be extremely useful a compared the three methods straight line distance, distance or cost over a network,  and cost over surface. The table gave the appropriate uses the rhyming features. You should look for the measures the pros and the cons and the very next section called choosing a method put the table into action and made it very straightforward. Much later in the chapter 6, it gets very in depth on making maps using distance using costs how arc GIS works specifying network layers stops, and turns travel parameters. It’s quite impressive. How much computing you can have arcGIS do.

Chapter 4:

Chapter 4 is all about mapping density, which helps us see where things are more concentrated instead of just plotting individual points on a map. This makes it easier to spot patterns and understand areas of high and low activity. One way to show density is by using different shades of color, where darker areas mean higher density. GIS has a few ways to do this, like graphs, dot density maps, or creating a density surface, which is the most detailed but also requires more data.

When making a density map, things like cell size, search radius, calculation method, and units of measurement matter a lot. A challenge is that data is often summarized by area, meaning it gets assigned to the center of a region, which might not always be accurate. The way we choose to display data can change how it looks, so different settings in GIS can give different results. The flexibility of GIS allows for different approaches, but it also means results can vary widely based on how data is processed. Another factor to consider is how data is collected, smaller datasets may not show accurate density trends, while too much data can lead to an overly complex representation.

Some questions I have: How do you decide the best search radius for a density map? How does interpolation affect the final results? How do different density visualization methods compare in terms of accuracy and clarity?

Chapter 5:

Chapter 5 talks about mapping what’s inside a certain area. This is useful for things like zoning laws or analyzing crime rates. GIS helps with this by letting you identify, count, and summarize features inside a set boundary. The ability to determine what falls within a boundary can help city planners, businesses, and law enforcement make better decisions.

There are three main ways to do this. First, you can just draw the boundaries and see what’s inside, which works well for simple visualizations. Second, GIS can select features that fall within the boundary and list them, which is useful for identifying all features within an area. Third, you can overlay the area and features to create a new layer that combines the data, which is the most flexible option and allows for deeper analysis.

Some things to keep in mind are whether the features you’re analyzing are continuous or discrete and whether they completely fall within an area. Some features might only partially exist within a boundary, which can lead to challenges in classification. GIS tools can help refine these classifications by weighting how much of a feature falls within a boundary or by assigning partial values based on overlap. These methods help summarize data across different regions, like neighborhoods or districts, allowing for deeper insights into how features interact with specific areas.

Some things I’m wondering: What are the limitations of overlay analysis? How does GIS handle features that only partially fall within an area? How could boundary analysis be improved to ensure more accurate data representation?

Chapter 6:

Chapter 6 focuses on figuring out what’s nearby. This is important for things like emergency planning, business locations, and public services. But “nearby” can mean different things, it could be a straight-line distance, a route along roads, or even the time it takes to get there. Understanding the right way to define proximity is key to making GIS analysis useful.

GIS offers several ways to analyze proximity. You can create buffers around a feature to set a specific distance, which is useful for defining areas of influence. Another approach is making spider diagrams that show connections between locations. Road networks can be used to measure real travel distances, while cost-based distance analysis helps measure things like travel time or terrain difficulty. These different methods allow for flexible applications, whether determining emergency response times or measuring accessibility to public spaces.

Choosing the right distance threshold is key. A 10-minute drive and a 10-mile radius might give completely different results. That’s why understanding how distance works in GIS is important. Road networks can change over time, and factors like traffic congestion can affect how “nearby” something actually is. GIS allows for adjustments based on real-world conditions, making its insights more practical.

Some questions I have: When is it better to use straight-line distance versus road networks? How does GIS factor in things like traffic when measuring distance? What are the best ways to incorporate real-time data into proximity analysis?

Chapter 4

Map densities are very useful for finding patterns, which is very relevant in terms of public health and potential outbreaks that need to be tracked and have surveillance on them. I found the section on deciding what to map based on features and information you need to map to be very useful. I also thought that the business and employees per square mile density map examples on page 109 are very interesting and are a good example of being able to use density maps for finding patterns. I found the dot density maps to be very useful and something that I do not think I have really seen before. I liked how it uses the color key and dots to indicate the values of density, which I find to not only be visually appealing but also useful when interpreting the map. It was also interesting to learn about when dot maps are most effective, like when the dots are too small or too far apart to convey a true pattern to the viewer. I had no clue what a density surface was until learning that it is created by raster layers. I was still slightly confused by what that really meant but the section about what GIS actually does was insightful to learn that it “defines a neighborhood around each cell center. It totals the number of features that fall within that neighborhood and divides that number by the area of the neighborhood.” This made even more sense after reading the section about using graduated colors and how you have to assign values to each layer in order for them to build on top of each other. In addition, the contour feature seems like it will be useful to better define these boundaries of densities in some particular maps that have a rate of change or rapid change.

 

Chapter 5

I liked how Mitchell discussed why mapping inside areas is so important because “by monitoring what’s going on in an area, people know whether to take action.” The example of the district attorney and crimes near schools is not an example that immediately came to mind but is an issue that might not be as easily identified without mapping. The differentiation between single areas versus multiple areas was useful because they have distinctly different purposes in terms of monitoring. Again, some knowledge from statistics courses seemed to be useful when talking about discrete versus continuous features because these definitions are very similar to their statistical counterparts. However, it was good to review that discrete features can be listed or counted or can be summarized by a numeric attribute. Continuous features are defined as features that represent seamless geographic phenomena. Continuous feature examples are categories or classes. The section on information needed for analysis was also very useful to know what type of map you could make with the given example features for a flood plain. The section on “Drawing areas and features” gives a good overview of the importance of what you are trying to portray in terms of features and what you need in terms of data sets. The table on page 147 comparing drawing areas and features, selecting the features of the area, and overlaying the areas and features was useful to understand how each type can be used for different features and their pros and cons. The overlapping areas and features help define discrete features inside continuous areas. I really enjoyed the maps under the “Overlaying areas with continuous categories or classes” on page 167, I think that these were both very useful as an example of how useful this feature can be when comparing different data sets.

Chapter 6 

I think that this section was very interesting because it mentioned how GIS can be used to look at what is happening within a traveling range and not just a fixed spot. I found the example of this to be very useful in terms of understanding what the author meant by looking at what is happening within a traveling range. I think that another good example of this would be notifying people within 500 feet of a health hazard, or maybe in extreme cases or a hypothetical situation – there would be an outbreak of measles. Due to the severity and how contagious measles is and also how long it stays on surfaces and in the air of a room, this technique could be useful in an outbreak scenario because then all of the people within a certain traveling distance in the area could be notified and tested to prevent more cases or severe cases. The section on street segments was also something that seemed very familiar and maybe even “simple” but then when I started reading more about it, it started to become more complicated due to their complexity of networks, distances, and especially costs. Learning about the per-unit cost put things into perspective to me in terms of understanding why our infrastructure is not always in good condition because when you think about this cost per unit and how many units there are in so many places, I have started to understand why everything is not kept up with completely, especially in weather climates that roads will experience freezing and thawing to create potholes (thanks, Ohio!). Learning about cost turntables and how important they are in terms of calculating costs. As much as I really hate to think about money sometimes, I feel like this was a good section to have towards the end of the book because it helped me make sense of how important cost is in terms of GIS.

Chapter 4

In chapter four we go over mapping density. This chapter teaches you how mapping density lets you see patterns of where things can be concentrated. Mapping density shows where the highest concentration of features is, the individual features, and areas of different sizes. A density map lets you measure the number of features using a uniform areal unit like square miles on a map, it can let you determine where to place what you might want in a dense area. When deciding what to map it is important to think of the features you’re mapping. You need information to go on the map to decide the density value on the map. This chapter goes over deciding if you want to map features or feature values. Density of features are like the amount of locations in a place, and feature values are like the number of people populating these locations. They can give very different results and shift the density of your map, and your results. There are two ways to define mapping density, by the defined area, or by density surface. When mapping density by the defined area you define it graphically using a dot on the map. When using a density surface you use the GIS raster layer and it usually requires more effort but it provides a more detailed map. I like the visual aspect of dot maps, it gives a quick sense of density on the map without really needing to look at the legend. This chapter teaches us about cell size, it determines how coarse or fine the patterns will appear. The smaller the cell size the smoother the surface, but the more cells when using a large cell size it will take longer to process and take more storage space. With units in GIS you need to choose a value for the units you’re mapping, and the results on your map will depend on how you created the density surface. 

 

Chapter 5

In chapter five we went over mapping what’s inside, drawing areas and features, overlaying areas and features, and defining our analysis. When mapping what’s inside the map you can compare and monitor what is going on inside each area and take action when needed. When mapping it is important to get the right data and collect the correct information in order to monitor the activity. When mapping multiple areas you have to make sure to identify each area uniquely using a name or numbers. The features can be discrete or continuous. When features are discrete they are unique and identifiable, they can be listed or counted. Continuous features represent seamless geographic phenomena, you can summarize the features for each area. When using GIS you can use lists, counts or summaries inside an area to find out information. There are three ways of finding what’s inside the area, drawing the area, selecting features inside the area, and overlapping areas and features. There are three ways of finding out what’s inside the map. You can draw the areas and features by creating a map and showing the boundary of the area and the features, it is good for a visual approach. Selecting features inside the area is good for getting a list or summary inside an area, it specifies the area and layer containing the features. The last way is overlaying the areas and features, it combines the area and the features to create a new layer comparing the two layers. It’s good for finding which features are in each of several areas. In order to choose the right method for your project you need to decide if you only need to see the features inside, if you want to see the summary of features fully or partially inside, or need a summary of continuous values. 

 

Chapter 6

In chapter six we find out why we map what’s nearby, mapping what’s nearby can let you monitor events in an area, or the features affected by an activity. When using GIS you can find out what’s happening within a set distance of a feature, it helps you identify the features inside the area that are affected by an event or activity. Traveling range can be measured using distance, time, or cost. This can help define the area served by a facility. Knowing what’s within traveling range can help delineate areas that are suitable for specific use. In GIS you can also take into account the curvature of the earth when mapping larger areas, you can use output layers to correctly display the curved surface of the globe. You can get three things once you’ve identified which features are near your source, a list of features, a count, or a summary. The count can be a total or a count by category, a summary statistic can be a total amount, an amount by category, or a statistical summary. To find the range of what you want, you can choose a single range or several, when specifying more than one range you can create inclusive rings or distinct bands. Inclusive rings are useful for finding out how the total amount increases as the distance increases. Distinct bands are useful if you want to compare distance to other characteristics. To find out what’s nearby you can use straight-line distance, you specify the source feature and the distance, and GIS finds the area or surrounding features within the distance. This is good for creating a boundary or selecting features at a set distance around a source. When You can use two other methods, distance or cost over a network, and cost over a surface. Distance or cost over a network can help you specify the source locations and distance or travel cost along each linear feature, GIS is able to find which segments of the network are within the distance or cost. When using cost over a surface you specify the location of the source features and a travel cost. GIS creates a new layer showing the travel cost from each source feature 

Chapter 4:

This chapter talked about file geodatabases (FGDBs), which allow a large storage capacity and better performance in ArcGIS Pro. Before I read this chapter, I hadn’t fully understood the importance of FDGBs. Coming from a Biology background (before I changed my major to Computer Science), I initially thought of spatial data like large Excel datasets. Here spatial databases have geographic features, which makes them more complex than a simple table. Some cool things that I learned were that FGDBs allow fast queries and spatial relationships. This reminds me of databases in bioinformatics. In addition, I learned that FGDBs store multiple layers efficiently. Before, I thought shapefiles were enough. I now understand how limited FGDBs can be. They allow better organization, which makes them much needed for GIS. If I see a .gdb folder I now know that it means that it holds many classes, raster datasets, and tables. This chapter provided us with hands-on tutorials, showing us how to create geodatabases and how to import shapefiles. One of the tutorials showed us how to collect spatial data, like when it summarized crime incidents by neighborhood. As a Data Analytics major, I found it interesting how similar GIS queries are to data analysis tasks that I have done in Excel. This tool seemed more powerful, as you can filter data based on more stuff. Python is very valuable for ArcGIS and I would love to explore its use more. Some questions that were raised was if FGDBs have limits for large environmental datasets. Another question that I had was how can Python automate file geodatabase tasks in ArcGIS.

 

Chapter 5:

 

Before starting to learn GIS, I never thought about how maps can reveal hidden patterns. After reading this chapter I now get their importance and how powerful tools they are. This chapter talks about mapping an area. This is necessary as we can understand what is going on inside the mapped area. For example, where the highest crimes happen in a city, or which hospitals are within 5 miles of schools, etc. Instead of looking at raw data tables– which can be overwhelming most of the time- GIS successfully condenses and summarizes this data. I was familiar with latitude and longitude, but I had now idea about map projections and coordinate systems. I feel like I am learning a new language when I try to understand spatial data. I understood how easily things can go wrong if I don’t choose the right projection. I finally understood why some maps deform distances. Another interesting thing I learned was vector and raster data. I related raster data to a microscope image, as there are grids and pixels that have different intensities. From the tutorial, I saw that if I choose the wrong coordinate system the datasets will be completely misaligned, and that would ruin the analysis. As someone who hated high school geometry, I never thought that I would enjoy working with coordinate systems. The real-world applications made me appreciate GIS even more, as it showed me how important it can be for making decisions. I also learned that spatial data interoperability is how different data get to work together. Some questions I have are how does datum transformation affect GIS precision?

 

Chapter 6:

 

This chapter showed us that knowing what is near you is important for making decisions. Whether it’s finding the closest hospital or analyzing wildfire risks. GIS plays a fundamental role in spatial decision-making. I really liked how practical this chapter was. Geoprocessing tools allow you to repeat and automate GIS workflows without having to click through the menu. Some key takeaways from this chapter were the dissolving features, like merging school districts into larger administrative regions, clipping data, and merging datasets. The last one is essential when we have to deal with large environmental datasets. Lastly, spatial intersections, which for me was mind blowing. I liked how you can overlay two datasets and extract the affected area. What really stood out to me was geoprocessing. It is used by emergency services to assign fire stations to fire zones while making sure that the response times are the desired ones.  In addition, straight-line distances are the shortest way possible between two locations. It can be simple, but can also be considered unrealistic because there are a lot of factors that should be taken into consideration, like how people are actually driving, sidewalks, crosswalks, etc. GIS isn’t just about mapping, it is about solving real-world problems. A question that I had was how geoprocessing scales with large data sets, like would it slow down?  Another one was how GIS combines road issues and other things that must be taken into account in order to calculate and analyze locations more accurately. This chapter made me think about how much GIS impacts our everyday life without even knowing it. From finding what is the fastest way to go somewhere, to emergency responses. A final thought that I had when finishing this chapter reading was that I felt more confident about ArcGIS Pro. In the beginning, I struggled with what GIS is and what is its use, but now I am getting it

Chapter 4

The first thing that really grabbed my interest when reading this chapter is how the mapping of density looks visually. That might sound kinda dumb cause that is the whole point of GIS, but I believe that the gradient looking mapping of density plays exceptionally well, at least to my, towards our sense of what the data represents. I find it quite easy to interpret the difference between the darkly shaded areas of density compared to the lighter shaded areas that surround it, representing less density. To contrast this though, I’m not the biggest fan of how the use of dots to represent density appear on maps. In the section about dots it shows a map before and after using them for density and to be honest the one that JUST represented each individual business was far easier to read. Funnily, the dot system (not for density) seems to be very helpful to be but beside and used with a density surface map, showing the finder details of whatever data you’re projecting.  Within all of this and the idea of density I feel like I’m reading a lot about computer calculations and stuff which is confusing, just really wondering how much of this stuff I only kind of understand needs to be known well. I know I’ve absolutely said this before a few times in my last journal, but there is definitely a disconnect between me and the specific knowledge that I need to be gaining in order to complete this course when we go to the Application. I will note though that I’ve just heard that part week 5 is hard and I’m probably overthinking the severity of this little issue.

Chapter 5

(What I’m writing here was wrote before I finished the chapter, but isn’t this kind of just everything overexplained. Like if there was nothing inside your map it just wouldn’t be there.) I hate to go back to the old classic immediately, but this chapter feels like it’s not really conveying any new information to me without using GIS along with it to see what its talking about. Like it still feels weirdly obvious that you should be able to locate and see specific data in a specific area, I just still don’t exactly know how to do that myself. It feels like someone is trying to teach me how to shoot a basketball, but he wont show me how to do it and just tells me all the ways to do it right. Yes, that is helpful, but at least for me it would be a lot better to actually see and work on the shot.

Chapter 6

From what I understand this is just chapter 5 but about what surrounds specific things on the map instead of what specific things surround this are of the map. At this point in the reading I’m struggling very hard to figure out how to write more about any of this. I’m very much so at a point where there has been so many things that only kind of make sense to me piled up that its all kind of not making sense. However its weird because it’s also almost impossible to not understand it because a lot of what I am reading feels like its just saying the same basic idea of “you can see how far this is away from that” over and over again in slightly different ways. Like I know that I’ve read literally hundreds of pages about this, but the more I read the less it feels like I’m reading anything. I’m sorry if I may be being kinda picky about the reading or whatever, but I seriously don’t know how to write about any of this anymore. I’m at a point where it feels like there isn’t any more starter information to soak up through this book and that I am only getting more confused by the repetition of almost identical ideas. I know that especially for these last two chapters I really didn’t write much, but I cant figure out how to keep writing it. Big props to Andy Mitchel for being able to write all that!

Chapter 4 is all about mapping density. It discusses why it is important, the different ways to map density, how to map density for specific areas, and how to decide what to map. The definition of mapping density is where the highest concentration of features is. One thing that was very helpful was when the book explained that you should look at your features and the information needed to complete the map before making a decision on what kind of map to create. Density maps are also more helpful for comparing areas. There are two ways you can map density. The first is by defined the area using a dot map. Dot maps are beneficial for representing the density of individual things in large areas. The other use of dot maps is when you have many features in one space and don’t want to map all of them individually. The second way to map density is through density surface. This is created as a raster layer as opposed to a vector. It is more detailed than a dot map but requires more time and effort. The book goes on to lay out the comparisons and explain how to decide which mapping method to use. Then, it explains what GIS does and how that applies to mapping density, which I found very helpful. There are multiple factors that affect a density map, including cell size, which determines how “fine or coarse” the patterns will be; search radius, which determines how generalized the area the map is showing will be; calculation method, which will determine how clear/easy it is to interpret the map, and units which help make sure the map is accurate.  Something I had not considered important was the colors used on the map. It was interesting to read that while “more” on most maps is darker, the contrast of making “more” lighter colors can help grab the reader’s attention.

Chapter 5, titled “Finding What’s Inside,” is all about why it is important to map inside an area. The first examples the book gives are about whether or not one should take action based on the map’s findings. There are numerous questions to answer when analyzing your data before mapping. The first is how many areas you are planning to map, which can be determined by what it is that you are mapping. The second question is whether or not the features inside are discrete, meaning they are identifiable or continuous, which are seamless geographic features. There is also important information you must get when analyzing your data. You must determine how you get your information (count, list, or summary) and also determine whether to include features partially inside of the area. The book then highlights the three ways to determine what is inside an area. You can draw what’s inside the area, select the features inside the areas, and overlay the features and areas. The first is best for seeing if one particular feature is in an area. Selecting the features is best for getting lists or summaries, and overlaying is best for finding how much of something is in an area or seeing if something is in more than one area. One important thing this chapter highlights is how to make this kind of map easy to read. It talks about lines, locations, and discrete and continuous features. You can use the results from the map to make lists, and show statistics of features in that area. Overlaying areas is a useful tool to help find features and summarize them. Throughout the chapter, it highlights what GIS does in each feature and method listed which was helpful for me because it allowed me to understand the different uses of GIS better.

Chapter 6 is about finding something that is near a feature you are mapping. This is useful for finding out what is in an area’s traveling range or finding the specific distance between two features.  Similar to when finding what’s inside an area, you need to first define your analysis. A lot of the steps when finding things nearby are similar to the steps needed to find what’s inside. You should ask yourself many of the same questions and collect the same data. There are three ways to find what is nearby: straight line distance, where you identify the source and distance and then GIS finds everything surrounding it; distance over a network, which is when you explain the source and distance; and GIS finds things that meet those parameters, and cost over a surface which is when you declare what each source feature is and a cost and GIS creates a new layer on the map with each features cost. Another similarity between chapters five and six is that they both compare each method and explain when it is best to use each one. A term that comes up a lot in this chapter is buffer. A buffer is a zone a certain distance from the designated area in every direction. Buffers are important because yu can specify multiple sources and see the distance from each one of them and how they overlap.  There are different ways you can set up a map once you have a buffer depending on what you want the readers to understand from it. Instead of a buffer you can also select features at “x” distance. This is similar to a buffer but without the physical boundary/circle shown on the map. Another option when finding things nearby is “feature to feature” which will show you the distance between two features. The last option the book discusses in this chapter is creating a distance surface. This means creating a raster layer of all of the continuous distances.

 

The ESRI Guide to GIS Analysis, vol. 1  (second edition, 2020) by Andy Mitchell

Chapter 4

Density maps show where the highest concentration of features is and are particularly useful when looking for patterns in areas that vary in size, such as census tracts and counties. Before creating a map, the kind of data being used and whether the density of features or feature values will be mapped should be considered. Density can either be mapped graphically (calculating density values for each area or dot mapping) or by density surface. If the data have already been summarized by area or the objective of the map is to compare administrative or natural areas with defined borders, density should be mapped by defined area. If the objective of the map is to see the concentration of point or line features, a density surface should be created. When mapping density for defined areas, calculating a density value for each area involves dividing the total number of features/total value of the features by the area of each polygon while creating a dot density map involves specifying how many features each dot represents and how big the dots are. Density surfaces, however, are created in GIS using raster layers. The specified cell size, search radius, calculation method, and units affect how the GIS calculates the density surface and, ultimately, what the patterns will look like. Density surfaces can either be displayed using graduated colors (usually displayed using shades of a single color with common classification schemes including natural breaks, quantile, equal interval, or standard deviation) or contours (connecting points of equal density). While the patterns on a density map are partially dependent on how the density surface was created, it is important to remember that there may not actually be any features where the highest density is. To see a better picture of what is going in a place, the locations of features from which the density surface was calculated should also be mapped with the density surface or on a separate map.

Chapter 5

Mapping what is inside an area allows for the monitoring of what is occurring inside it or the comparison of several areas based on what is inside each. Depending on the number of areas, what type of features are inside the areas (discrete or continuous), and the information needed from the analysis (list, count, or summary), an area boundary can be drawn on top of the features, the features inside can be selected using a area boundary, or the area boundary and features can be combined to create summary data in order to find what’s inside. A map that shows the boundary of the area and the features is good for seeing whether one or a few features are inside or outside a single area. This method requires a dataset containing the boundary of the area or areas and a dataset containing the features. Creating a map by selecting the features inside an area is good for getting a list or summary of features inside a single area or group of areas being treated as one. A dataset containing the areas and a dataset with the features are needed for this method which involves specifying the area and the layer containing the features so that the GIS may select a subset of the features inside the area. To overlay the areas and features, the GIS either combines the area and the features to create a new layer with the attributes of both or compares the two layers to calculate summary statistics for each. Overlaying the areas and features is good for finding the features that are in each of several areas or finding out how much of something is in one or more areas. Data containing the areas and a dataset with the features are needed for this method. When selecting features inside an area, GIS can be used to create a report of the selected features (count, frequency, sum, average, median, standard deviation). There are also key differences between overlaying areas with discrete features, continuous categories or classes, and continuous values.

Chapter 6

What is occurring within a set distance or traveling range of a feature is understood through finding what is nearby. There are also three methods to find what is nearby: measuring straight-line distance, measuring distance or cost over a network, or measuring cost over a surface. Selecting a method entails determining the information needed from the analysis (list, count, or summary) and defining and measuring “near” which can be based on a set distance or on travel to or from a feature. Using straight-line distance means specifying the source feature and the distance before the GIS finds the area or surrounding features within the distance. This method is good for creating a boundary or selecting features at a set distance around the source. A layer containing the source feature and a layer containing the surrounding features are required to find what is nearby using straight-line distance. Using the area covered by segments of the network  within the distance or cost to find the surrounding features near each source is known as measuring distance or cost over a network. If the objective is to find what is within a travel distance or cost of a location using the locations of the source features, a network layer, and a layer containing the surrounding features, this is a suitable method. Measuring cost over a surface begins by specifying the location of the source features and a travel cost. Then, the GIS creates a new layer showing the travel cost from each source feature. This approach is good for calculating overland travel cost and it requires a layer containing the source features and a raster layer representing the cost surface. When calculating cost over a geographic surface, it is important to acknowledge that cost refers not only to monetary value but also to factors like time, effort, or resource expenditure required to traverse a landscape.

Chapter 4 centers around the concept of density and mapping it. Mapping density makes it easier to understand which areas are the most concentrated in some type of resource or landmark, for example in the book it references small businesses. Instead of simply plotting each location on a map where they could become overlapped and difficult to understand, you map with darker colors in areas of high density and include a key so that density can be better visualized. Mapping density rather than simply the location of features on a map gives you a measure of their density per area. Density can be mapped using a graph, a dot map, or calculating density for each designated area. Creating a density surface in GIS is usually preferable but it requires the most data input and more individual data on locations rather than data separated by region or county lines. GIS can also take a map density by area map and use the data to construct a dot density map to represent density graphically.
When using GIS to create a density surface there are many factors to consider including cell size, search radius, calculation method, and units of measurement. Another thing to consider when creating a density surface is that data that is summarized by defined areas can be used to make these types of maps but it must be generalized by the centroid of each defined area. This means that the summarized data is assigned to the point at the center of the defined area for which it is summarized. Additionally, for these maps a graduation of colors is assigned to each value so that the density can be visualized. The results of creating these types of maps in GIS are almost entirely dependent on the choices made with the many variables that can be manipulated in the program, meaning the same data can look different in final products where different visualization choices were made.

Chapter 5 discusses the need to map what is inside an area. This is necessary because the bonds for these areas can be “within 1000 feet of a school” or something like that to impose stiffer punishments on crime. This is an example of finding what is inside one area but you can also use mapping to find what is inside several areas such as each district in a city. In either case, you first have to know the boundaries of your area(s). Then, the discrete or continuous nature of the features you are measuring has to be taken into account. You can also use GIS to list features, count them, or get a summary. You also have to consider if the features being measured are completely in the area because discrete features can easily be partially in or out of a defined boundary.
There are three ways to find what is inside the area. First, drawing both the areas and the features, this way you can see the boundaries of your area and what features are inside it. This is specifically good if you only need to know which features are inside and outside of an area. Second, you can specify the area and the layer that contains your features so that GIS selects a subset of the features which is inside the area. This is best for getting a list of the features inside an area. Finally, you can overlay the areas and the features to create a new layer which compares the two layers and summarizes the statistics for each area. Which is best for doing both at the same time, as it is the most flexible.
Using the results of these summaries can be tricky. Some ways it can be used include: the count of a total number of features in an area, the frequency of a number of features with similar values in an area, or to summarize a specific numeric attribute such as the sum of certain features. These similar principles can be applied to much more complicated data, overlaying layers onto each other and creating understandable visualizations of complicated data over a range of areas.

Chapter 6 focuses on mapping what is nearby to a feature. This is important because some features may require notifying anyone living within a set distance. However, “nearby” is a concept that ranges in distance, within GIS you must define the distance which is being considered as nearby. Sometimes it is just straight distance away, or in some cases distance has to be measured using networks of transportation such as roads. Distance and cost can both be used to measure what is nearby, cost can include the amount of time it takes to get somewhere from your location. You also must consider the information you require from your analysis, sometimes it may be a list of everything “nearby”, a count of the total number of restaurants nearby, or a summary statistic for the area.
To determine “nearby” you can use GIS to set an inclusive ring based on straight-line distance, which is best for defining the area of influence around the feature. To do this you have to create a buffer in GIS at a certain distance from the feature you are discussing. Additionally, using this method you can use GIS to find the distance between two features, or to create a spider diagram with your chosen feature at the center. Another option is using distance or cost over a network (such as roadways), which is best used when measuring travel over a fixed infrastructure is necessary. GIS includes a ready-to-use street network which can be used to find whats nearby in terms of distance; however, this is not the only possible network you may want to use so custom networks can be built in the program. GIS will start at your feature and check the distance to the nearest junction in relation to your specified distance, and it will repeat this until a definition of everything “nearby” has been reached. You can also specify more than one center in this type of mapping. Finally, measuring cost (of time or another variable) over a surface is most helpful when you need to measure overland travel and calculate how much area is within your range. This has to be done using a raster layer of continuous distance from your feature.