Category: Course Student Work

Chapter4-Chapter 4 talks about the importance of using. Density maps: how they’re used, what they should be used for, and how the GIS System is used to make these kinds of maps. With your data, you can decide what kind of way you want to map out density. It tells you exactly which density map to use. Is specifically why you should use them. The two ways you can map out density are defined area and density surface. Both use completely different ways of mapping density and have many benefits and some downsides. When mapping by a defined area, you are either using mapping dots or calculating the Density value for each area. Using dot density mapping, maps show density graphically rather than the density value. A density surface is usually created in the GIS as a raster layer. This approach provides the most detailed information but requires more effort. When using a density surface, you usually use colored layers to indicate how Many of one thing are in that area. When using it, use specific colors to better interpret the dot-density map. It is important that you don’t make your dots either too big or too small or place them in a way that makes the pattern unrecognizable. If you do this, it might be very hard for a person to understand your map and the main points you are trying to highlight. Make sure to always double-check your units because this may affect how your map is displayed and the information on it. If you are using. If you have a density layer map and use a different unit, it may calculate and display your information differently from how you want. 

 

Chapter 5- When trying to figure out what’s inside your map, an important thing the chapter says is that you might want to circle the area that you are trying to understand. When trying to figure out what is in your area, there are many ways to do so, and the methods you use depend on the information and data you have. Ways that may determine your data include whether you are looking into multiple areas or one specific area. If you are looking to explore multiple areas, show how much of something is in each area. If you’re looking at multiple areas, you’ll want to be able to identify each area by name. If you, for example, were using fire stations across multiple areas, you might want to list each fire station as fire station alpha, fire station delta. It lets you compare the areas together. If you’re looking at a single area, you can monitor and summarize information about it. When looking at features inside an area, can we list them as discrete or continuous?  Discrete features are unique to that area. Count them or put a numeric value on them. Continuous features represent seamless geographic phenomena. When reviewing your Analysis, you will need to determine whether to list, count, or summarize all your attributes. You can choose to include only features that fall completely inside, features that fall inside but extend beyond the boundary, or include only the portion of the features that falls inside the area boundary. When trying to find what’s inside an area, there are multiple ways to draw the area and its features, select the features within the area, and overlay the area and its features. Each of these has its own benefits and trade-offs. Drawing areas and features helps figure out whether things are inside or outside an area. The trade-off is that it is quick and easy, but it’s only visual. A summary of what’s directly inside the area. The trade-off is that it is good for getting information within a single specific area. Overlaying the areas and features. Identify the features within multiple areas and summarize them by area. The tradeoff is More time consuming and requires more processing from GIS

 

Chapter 6: When trying to figure out where something is in relation to something else in GIS, we will have to decide whether to measure it by distance or cost. Distance is one way to define and measure how close something is. The other would be by the coast, depending on how long it would take to get there. Once you find nearby items by distance or cost, you will need to determine how many items to list, count, or summarize that are near what you are measuring. When looking for this, a thing that might be Inclusive rings. Which rings would help you find what’s in the distance or within your budget? Another helpful kind of ring is a Distinct band. These bands are useful for comparing distance with other characteristics. Whenever you’re trying to figure out what’s inside, there are many different ways to determine the distance between each thing. There are three ways to determine whether they are straight-line distance, distance, or cost over a network, or cost over a surface. For using the straight line distance. Its prose is relatively quick and easy, and it measures distance. The cons of it. It only gives you a rough approximation of travel distance. When using distance or cost over a network, it measures the distance or cost. It gives a more precise travel distance. But the downside is that it requires an accurate network layer, which can be challenging. Cost over a surface measures cost. Its prose lets you continue combining several layers to measure the overall travel cost. It requires some data preparation to build a cost surface, which may cost you extra time. After determining which one you’ll use, you’ll have to create a buffer. Buffers draw lines around a feature at a specific distance. 

CH 4-

Chapter four of the book goes over density in relation to GIS mapping; its uses, formats, and when it’s a good choice to utilize. Density mapping can be a great tool when graphing visuals on populations of various things (i.e., people, businesses, animals, plants, etc.). It can be done by a defined area or by loose markings, which can in turn be symbolized by shading or pinpoint markers. The style chosen is based on the information you are trying to represent. If you are mapping population by county of a certain state, defined barriers would work well. However, if you are attempting to establish a coffee shop in a town, pinpoint markers would be best.

CH 5-

Chapter five goes over “mapping what’s inside”, where information is mapped within a certain defined area, either for data on that region or for comparing regions to one another. This form of mapping focused on one area, but on many things within that area. Chapter five also goes over the type of information and analysis needed, depending on the goal of mapping. For example, do you want everything outside of your desires mapping area to be shown with only the desired area highlighted, or do you want only information to be shown within the desired region? This is once again dependent on the intention of the map. It also goes into how to decide what features to show on your map; are roads important features? rivers?

CH 6-

Chapter six talks about distance and how far out to map from your desired location. It also speaks on what specific features outside of your zone to focus on, depending on the map’s intent. The unit of “distance” you use is also an important factor to weigh in. For something within the natural sciences, this distance can be “meters from a water source”, but for something more anthropocentric, distance might be better measured as “walking time from campus”.

 

Chapter 4:

This chapter focuses on mapping density as a way to move beyond where things are to populations. Instead of counting features per location, density maps show how concentrated those features are across space, which is often more useful for understanding patterns and making decisions. Mitchell walks through two main approaches: calculating density for predefined areas (like people per square mile) and creating a continuous density surface from point data. He emphasizes how choices like area size, classification method, and search radius affect the patterns you see and the story the map tells. The chapter also ties density mapping to practical questions like identifying hotspots, comparing demand across regions, or planning services based on intensity rather than raw counts. Overall, it frames density as a way to reveal underlying structure that other maps might miss. Most of this chapter seemed fairly straight forward like the density when looking at a map. I feel like when you are given a key for a map it is hard to misinterpret density. However, some points made by Mitchell made me think a little. For example, the search radius and how much area a spot represents on a map. This is important to know and I didn’t previously think a about it. A term used a lot in the chapter was density surface. I learned that density surface is basically just a smooth map that shows concentration smoothly on a map rather than just points. Overall, I found the chapter pretty interesting because I was familiar with most of it but also learned a few new things.

Chapter 5:

This chapter focuses on using GIS to figure out what features or values exist inside a given area. Mitchell frames this as a basic but essential spatial analysis . Once you define a boundary like a neighborhood, watershed, service zone, or habitat you often need to know what’s contained within it. The chapter walks through several approaches, starting with simple counting and moving to summarizing attributes, such as total population, average income, or total length of roads within an area. He also covers how to handle situations where features only partially fall inside a boundary, which leads to splitting features and proportionally allocating values. Throughout, the emphasis is on using these techniques to support real decisions, like estimating demand, assessing environmental impact, or comparing regions fairly. Overall, the chapter shows how the what’s inside analysis turns data into meaningful, area‑based summaries that help interpret what is really going on in that area. I think of this chapter as just taking a deeper look into points. Something that I previously did not think about was the idea of handling features that are only partially inside of a region. Mitchell says that you can cut the area to better fit a split region or allocate it. The chapter also shows real world ways GIS and taking a deeper look can be useful. Things like estimating population inside a hazard zone, calculating how much habitat falls inside a proposed development, figuring out how many customers live inside a store’s trade area, or measuring road miles inside a district.

Chapter 6:

This chapter focuses on how GIS helps you analyze proximity, which is one of the most common spatial questions. Mitchell breaks this into several techniques. The simplest is identifying features within a set distance, like schools within a mile of a highway or wells near a contamination site. He then expands to buffering, where you create zones around points, lines, or areas to see what falls inside those zones. The chapter also covers measuring actual distance rather than straight‑line distance, which matters when movement follows roads, rivers, or terrain. Mitchell shows how proximity analysis can compare distances between features, rank locations by closeness, or find the nearest facility. The chapter emphasizes that proximity isn’t just about distance and that its more about understanding how closeness influences interaction, accessibility, and potential impact. Something I found interesting in this chapter was the inclusive rings and the distinct bands. These tools make it easy to find how many points, like customers for example, are within a circle of a given radius. You can also seem how that number changes as you increase or decrease the size of the radius. Another thing I saw that would have many real world applications is the distance or cost over a distance. I can see how this would be used in GPS for maybe emergency vehicles and others. Another interesting thing in the chapter was using distance as a proxy. You could measure distances of households from a store to project sales. I also thought that how you could create a distance surface in maps was cool. Overall, I thought the chapter was decently straight forward but interesting seeing all the different maps you can create using distances and its many applications.

 

Chapter IV

 

Chapter four is entirely focused on density based mapping. At first I believed this chapter was going to feel like a tedious slog, given that density based maps do away with precise location in favor of a more relative form (such as population per square mile). Effectively mapping density is a trade off from point based data to quantitative data which I believe to be somewhat redundant given that point based data typically tend to show density anyways.

However, upon reading the chapter I do believe that density mapping has its uses, especially when cross examined with more point driven data. An example the textbook gave for this was comparing sights of crimes with regional information on average income in the areas or reported gang activity. 

The GIS software does plenty of work as far as plotting and interpreting the density based data goes, including totalling the number of values within a designated area and dividing it equally across the size of the area in question as well as both general averages of the data and weighted averages.

Much of the general advice from the previous chapters apply here as well. One which I feel is a little bit redundant at this point is the specification on the classes of value a density map should have to be easily readable. This is something that was discussed in the previous chapters relative to the different classes of point based data. (More point classes makes the map harder to read, applies the same to value classes in density mapping)

 

Chapter V

 

Finally I am getting to exciting things! Mapping within a certain radius and interpreting data within that is the kind of geographical analysis I took this class to learn about.

The chapter designated several types of area based maps: Single areas (Within a single area, it is in the name) Buffers (an area surrounding a certain feature or features) and boundaries. (the boring one)

There is also a distinction to be made between the maps I just discussed and mapping multiple areas, such as contiguous areas which typically are located right next to each other, and Disjunct areas which have “buffer land”.

Then the chapter discusses discrete and continuous features. We have been over this.

The GIS software is capable of several methods of analyzing limited maps. The book has the example of a flood map and shows the GIS being able to find specific designated areas that may fall within or without the flood path, as well as being able to count and/or list the designated areas within the flood path. There is even the ability to create distinctions between the areas and categorize them by value or type based on whether they fall within the designated focus area or create visual data representations based on the data within the areas.

 

Chapter VI

 

I feel the subject matter of this chapter overlaps considerably with the previous chapter. Once again, this is based nearly entirely on mapping within a certain area. The primary difference being the discussions on how the GIS software can calculate the distance (or cost) and possible travel routes. 

Of course, nothing fun is ever easy and frankly a wrench was thrown into my ideas on applying this when the book discussed the differences between miles based on a certain geographical projection (effectively assuming the earth is flat) and based on the curvature of the earth. I hope the difference between these values will not be too big of an issue given the rather limited area I intend to map, but I know for a fact if I were mapping a larger scale project that this would potentially derail my analysis completely. 

The book describes the differences between different methods of mapping using proximity as the primary considerations as well as discussing the measure these methods record and which situations these can be applied in.

I actually really enjoyed reading about the cost over surface method. I am currently reading about the construction of the first trans-continental railroad which used a great deal of cost over surface mapping when surveying the land and even using that to modify the land to be better suited for rail transportation. (The TV show Hell on Wheels famously depicts a “cut crew” who would dig up large amounts of earth to construct a level railbed.)

This leads into the next thing I thought was interesting: Networks. Using lines that can represent road, railroads, or airways the GIS can automatically calculate the distance or cost by only following the network to the intended destination. I feel like that is one of those things that is so obvious to the common man that it effectively vanishes from our consciousness.

Chapter 4: 

Chapter Four’s focus is on density mapping, which is a way to understand how specific features or events are distributed across space. Rather than simply showing where things are located, density mapping highlights the concentrations, variations, and spatial relationships within data. By calculating the values per unit area, density maps allow users to see where features are clustered, sparse, or unusually high or low, which adds important context that simple point maps cannot provide. 

The chapter explains that there are two main approaches to mapping density. The first is mapping density within defined areas, such as counties. This method relies on existing boundaries and calculates density by dividing the number of features by the area of each region. These maps are often displayed using shaded polygons and are useful for comparing one area to another.

The second approach is density surface mapping, which produces a continuous surface using raster data. Instead of fixed boundaries, density values are calculated for each cell based on nearby features within a given radius. This method is more detailed and visually expressive, making it better suited for identifying spatial patterns, gradients, and hotspots. However, it also requires more processing time, storage, and careful design choices on the users end. Before creating a density map, the user must decide what they want to analyze such as raw counts, normalized values, or interpolated surfaces. Raw counts will show simple distributions, normalized values will allow fair comparisons across areas of different sizes, and interpolated surfaces will reveal patterns and relationships. The chapter also discusses practical GIS considerations to take into account, such as choosing cell sizes, classification methods, and effective color schemes to represent the data.

Overall, the chapter demonstrates how density mapping can be applied across many fields from environmental science and public health to business and urban planning by clearly showing how values vary across a region and where concentrations are highest or lowest.

 

Chapter 5: 

Chapter Five shifts the focus of GIS analysis from broad spatial patterns to narrowing in on specific areas and features. Rather than viewing the entire dataset at once this chapter emphasizes how GIS can be used to isolate only the information that is relevant to a particular research question. This targeted approach will allow users to answer questions about what exists within a defined boundary by making spatial analysis more precise and meaningful. A major theme of the chapter is the importance of clearly defining the area of interest before beginning any sort of analysis. GIS provides several ways to make these boundaries, including service areas around facilities, buffers that represent distance limits, and natural or administrative boundaries such as watersheds or political regions. You may work with a single area or multiple areas, which can be contiguous, disjointed, or nested. Choosing the correct type of boundary depends on both the question and the nature of the data being examined.

The chapter outlines three primary methods for determining what lies within an area. The first involves manually drawing areas or features, which can be useful for quick visual checks but may lack some precision. The second method uses GIS tools to automatically select features that fall within a specified boundary, producing more accurate lists or summaries of those features. The third and most powerful method is overlay analysis, where layers are combined so their attributes intersect. This approach allows users to calculate how much of a feature exists within an area or to create new datasets that merge information from multiple layers.

Chapter Five also revisits the distinction between discrete and continuous data, reminding us that feature type plays a key role in selecting the appropriate analytical methods. This chapter highlights how effective GIS analysis depends not just on technical tools, but on thoughtful decision making about data types, boundaries, and analytical goals to accurately represent your data. 

 

Chapter 6: 

Chapter Six focuses on the concept of proximity which involves determining what is close to a particular location or feature. Proximity analysis is essential in many real world situations because distance strongly influences access, risk, and decision making. Whether planners are deciding where to locate public facilities or scientists are studying environmental impacts, understanding what is nearby provides critical insight. The chapter emphasizes that proximity analysis must begin with careful definition. Analysts must decide what “near” actually means in the context of their study and how it should be measured. GIS offers several ways to do this, each suited to their different situations. 

The most basic method is straight-line distance, which measures the shortest path between two locations. This method is simple and useful for creating boundaries, it does not account for realworld obstacles such as roads, rivers, or terrain. To address these limitations, the chapter introduces network based distance or cost, which measures travel along actual paths like streets or sidewalks. This method is commonly used in navigation systems and is more realistic when movement is restricted to established routes. A third approach, cost over a surface, incorporates barriers and varying levels of difficulty across a landscape. This method is particularly valuable in environmental and ecological studies where movement is affected by natural features. The chapter also explains how proximity can be measured across the Earth’s surface using either a flat plane approach for small areas or a geodesic approach for larger regions. In addition to this proximity ranges can be specified using inclusive rings, which show how effects accumulate over distance, or distinct bands, which allow comparisons between distance zones.

Chapter Six demonstrates how GIS based proximity analysis accurately helps translate spatial data into practical information. By selecting appropriate distance measures and methods, we can better understand how an event or data can affect its surrounding area, allowing for more accurate data representation in our maps. 

 

Chapter 4

 

Chapter Four is all about mapping density, in other words, looking at how particular criteria are disturbed. Density doesn’t just show us locations of the criteria we are searching, but instead helps us see concentration and relationships among the data. We are able to find per unit area with density. This helps in many different fields and areas. There are two ways to approach density maps. The first way looks at density in defined areas. Existing boundaries are used to calculate how many of a particular criteria fall in a particular square mile. Maps of this nature are displayed with shaded areas. The other method is density surface mapping. Continuous areas can be used for this instead of predefined boundaries. The raster type layering is used for these maps. This helps show patterns as well. When using density mapping you first have to decide if you are looking at how many, called raw counts, unit area amounts, called normalized values, or density gradients, called interpolated surfaces. If you want a simple distribution, use raw counts. If you want to make comparisons, use normalized values, and if you want to see patterns, use interpolated surfaces. I like that density mapping is more than just seeing dots on a map. We can see where things occur the most and when they thin out. We can also easily see outliers. This all helps to add context to points. For another class I have selected a project idea about hellbenders and the effect the PFAs have on them. I would use density mapping to determine where populations of hellbenders are highest and lowest. I would do the same for PFA levels. These layers together would help me draw relationships and make educated guesses about the effects of PFAs on the hellbender population. My point is, this can be used to make sorts of comparisons and relationships. 

 

Chapter 5

 

Chapter Five is focused on isolating the relevant data needed, instead of looking at the big picture. GIS helps isolate the features and boundaries that the researcher is interested in looking into and filters out the rest. There are three methods to use to do this. The first method is drawing areas and features by using either the boundaries already existing in the software or creating new ones to look at the area of interest. This method is limited however because manual drawing can be less precise. The second method is selecting features within an area. This has more precision. The program selects features inside specific boundaries. The last method is using overlaying areas and features where layers are stacked so that datasets intersect. This chapter helps pull some of the broad concepts of earlier chapters together and focuses on methods using spatial reasoning. This chapter helped to understand more of the “science” behind mapping. I like that in each chapter there are multiple methods to do things and the book helps outline the reasoning behind choosing one over the other. This brings back the point from earlier chapters, where the researcher needs to know what they are looking for and how they want to convey that message, in order to choose the correct method. This chapter also reviews the idea of discrete vs continuous data. This is important to remember when selecting methodology, and I like that the chapter did a reminder of that. 

 

Chapter Six

 

Chapter six focuses on the concept of proximity. This means finding what is nearby a particular place or feature. Proximity is important in real-world applications. Distance plays a role in assessing risks, distribution of goods, and access to different things. For example, if you are planning a park, you may need to map how close the park is to a school or other parks in order to determine if a park is needed or if there are likely to be people to go to a park. You also wouldn’t want to put a park next to a prison. So looking at proximity to different features is really important. Just like in previous chapters, this chapter mentions that it is important to define what you are looking for, before you begin. The proximity question starts with defining. You need to define what nearby means, what metrics are you using? One way to find nearby is to use the straight line distance. This is measurement of a direct like between two features. This is simple, but doesn’t account for obstacles in the way. My mom would call this “straight as the crow flies” measurement. We have all been told that something is just a mile down the road, but it took 10 minutes to drive there due to stop lights and cross walks, and having to keep the car on the road. None of those things are accounted for in the straight-line method. A second method is measuring distance or cost over a network. Network distance is more meaningful when a straight path can’t be taken. This accounts for the time and distance along actual paths. This is what you get when you put your destination into Google Maps. Lastly, there is the calculated cost across a geographic surface. This takes barriers into account. Environmental analysis uses this method. It is important to account for the method needed for what you are analyzing. If we think back to the park example, if there is a river running through town and a railroad track, there are barriers to kids accessing the park. You can’t just pick a point and assume that everyone has access that is inside that circle of proximity.

 

 

Chapter 4:

This chapter shows us how to map densities using the GIS, and why this can be useful to many people, from business owners to the average person looking to find out what’s within 1,000 feet of them. To put it simply, mapping densities shows you exactly how values vary across a region as well as where the highest/lowest concentration of a feature is. It is very useful in analyzing patterns and measuring the number of features using a uniform area unit such as square miles or hectares. A common use for density mapping is census tracking of population densities. Other common uses include locations of different businesses, and the number of employees in each business.

There are 2 common ways of mapping densities;

By defined area: This method is typically used if you want to compare areas with defined borders. It can be done with geographic mapping, dot mapping or by calculating a density value for each location. Dot maps are usually used to represent individual locations (eg. trees) and density values can be calculated by dividing the total number of features by the area of the polygon.

By density surface: A density surface is typically created by the GIS as a raster layer. Each cell in the layer is a density value (eg. no. of businesses within a square mile) based on the number of features within the radius of the cell. This provides very detailed information, but requires a lot of time, effort and storage. This method can be useful if you have individual locations, sample points or lines. One practical bit of advice in regards to this method was that you can use the GIS to change the sizes of the dots based on their densities in a map.

The chapter also goes over how you can use the GIS to create a density surface; convert density units into cell units, divide this value by the number of cells and take the square root of this value to get the cell size. It also gives more information about different data categories (eg. natural breaks, standard deviation) and gives advice on how best to use colors and contours to display your data. Overall, I think the chapter explained the process and intricacies of mapping densities in GIS very well. It did make me wonder if the mapping process changes when you are mapping over different surfaces, eg. land and water.

 

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Chapter 5:

This chapter focuses on mapping what is within a certain region or border. People can want to do this for many reasons, but one of the more common ones is to compare different areas based on what is within them, eg. monitoring drug arrests within 1,000 feet from a school.

One of the key steps in this process is defining your analysis. To do this with the GIS, you must draw an area boundary on top of the features and use the boundary to select the features within it and list/summarize them. The area boundary and feature data can also be combined to create summarized data. In order to effectively carry this out in GIS, you need to consider 1) how many areas you have at your disposal and 2) what types of features are inside the area. One type of area boundary is a single area. Types of service areas include;

• A service area around a central facility (eg. a library district)
• A buffer that defines a distance around some features (eg. a stream off limits to logging)
• An administrative/natural boundary

You could also choose to work with multiple areas. Types of these include;

• Contiguous (eg. zip codes, watersheds)
• Disjuncts (eg. state parks)
• Nested (eg. 50- and 100- year floodplains)

Similarly to previous chapters, chapter 5 also briefly touches on the different kinds of features; discrete features (unique, identifiable features such as student addresses or locations of eagle nests) and continuous features (features that represent seamless geographic phenomena). I found this section to be very useful because it reminded me of the different feature types you can deal with in GIS, and encouraged me to do my own research into how they can appear and be displayed in GIS.

Finally, the chapter covers the three ways of finding what is inside:

• Drawing areas and features: You can use the GIS to create maps to see whether one or a few features are inside or outside a boundary
• Selecting the features within the area: Specifying the area and layer containing the features you want can help you get a list of features within one or multiple groups.
• Overlaying the areas and features: Combining the area and features to create a new layer with attributes of both can help you find out how much of a specific feature is in one or more area

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Chapter 6:

This chapter went over how you can utilize GIS to find out what is nearby you or another location altogether. Finding what is within a set distance can help identify an area, as well as the features inside the area that have been affected by an event or activity. One example of the GIS being used in this way is notifying residents within 1,000 feet of an accident. It can also be used to define the area served by a facility (eg. a library) and delineate areas suitable for a specific purpose. One example of this would be a wildlife biologist mapping an area within a half mile of a stream.

The textbook states that in order to find what is inside a set distance, we need to define and measure the concept of “near”. It can be defined by a set distance or the travel to/from a feature. And it can be measured by distance and cost. When analyzing the surface of the earth, we can either look at it with the flat plane method (typically used with small areas of interest) or the geodesic method (normally used with larger areas of interest such as continents). We also got an explanation of the 2 ways we can specify a range; inclusive rings (useful in finding out how total amounts increase as distances increase) and distinct bands (useful in comparing distances to other characteristics, such as the number of customers within a 1000 vs 2000 year range).

And finally, in the section I thought was the most interesting, the textbook covers the 3 ways of finding out what is nearby;

• Straight-line distance: By specifying the source feature and the distance, the GIS can find the area or the surrounding features within the distance. This method can be good for creating a boundary.
• Distance/cost over a network: Specifying the source locations and a distance/travel cost along a linear feature can help you find what is within a travel distance/cost of a certain location.
• Cost over a surface: Specifies locations of source features and a travel cost.

Overall I thought this chapter was intriguing because of how relatable it is to the lives of many people. The average person on most days most likely uses GPS technology everyday to find locations nearby them (eg. supermarkets, restaurants) to learn more about what is in their area. The information in this chapter can also be useful to those who wish to conduct scientific research and analyze data (eg. a wildlife conservationists who want to look at residential areas near a riverbank). This chapter also made me anticipate working with the GIS software later in the semester and doing my own investigations with the data provided.

Mitchell Chapter 4

Chapter 4, Mapping Density, shows the mapmaker where the targeted feature is concentrated. Density itself is a ratio, measuring counts (OR amounts) per unit area. Density can be valuable when working with boundaries creating areas of different sizes, like counties or census tracts. Two distinct areas might have the same number of features, like businesses or population, but their difference in size is what determines their densities.

Mapping density is a good way to summarize discrete data. You can plot density graphically as discrete data to get a “bird’s eye view” of feature distribution, then code each area on the map based on the number of features per unit area. This is helpful for understanding overall trends but does not show specific densities within each area boundary. I don’t think this method of mapping is particularly useful for planning; it may be helpful for general trends but not much else. In my opinion, an alternative that seems more ideal is the creation of a density surface with a raster layer. This creates an appearance of continuous shading that transcends boundary lines. Additionally, mapping by features and mapping by feature values can show trends differently. Mapping by features tells you where things are, but feature values (like number of employees) can show trends within the density of the feature.

One thing I didn’t really understand in this chapter was “you often display the dots based on smaller areas but draw the boundaries of larger areas.” In that case, are the dots are not 100% accurately transposed onto the area boundaries? I suppose it doesn’t have to be perfect because the purpose of density maps is just for noticing general trends, not worrying about exact locations.

Finally, this chapter circles back onto topics discussed in previous chapters, like determining the best cell size, ways to separate graduated colors, and contours. I bet that the best method of determining graduated colors depends on each individual map, but in the book’s examples, the natural breaks method seems the most effective.

 

Mitchell Chapter 5

Chapter 5, Finding What’s Inside, describes ways to look at what is happening inside of a certain area. This area can be on the map boundary already, like a census tract or county, or it can be a natural feature like a watershed, state park, or protected area superimposed onto a layer of preexisting map boundaries.

Density, as discussed in Chapter 4, is a frequent example of “finding what’s inside.” I found it really cool that the GIS can clip out the target area on a map to simplify our view of the continuous data inside of those boundaries, especially when those areas are disjunct. AND it can calculate amounts of land use/type within these specific areas? Sick!

There are three ways to show what mapped boundaries are inside a particular area. You can 1.) superimpose the target area on top of the map’s preexisting features, 2.) highlight parcels with any portion inside of the target area, or 3.) view the target area alone divided into the parcels that make it up. The entire target area is full, and no mapped boundaries beyond the target area are shown. As with most map-related topics, there are merits and drawbacks to each style of mapping here. Drawing the target area on top is a good basic visual, highlighting all included parcels shows a potentially larger scope of effect from the target area, and overlaying the features within the area can help summarize characteristics within the area.

This chapter gives several methods for drawing the target area on top of the map of parcels; the best method of which once again depends on how specific you want your map to be. I like comparing the different methods of color and shading, but all of this study of maps has led me to realize that in many cases, the simpler the map, the better. Using fewer colors and focusing on specific areas typically gives enough surface-level information for a general audience. Then, when more specialized information is needed, conclusions from the more general maps can be used to create the most relevant specific maps. Additionally, GIS software itself can take some of the manual labor out of category-making. One example that seems particularly useful is when one feature on one map splits itself between two features on another layer—the GIS will create two subcategories to split that feature in two.

 

Mitchell Chapter 6

Chapter 6, Finding What’s Nearby, seeks to help the mapper answer questions like, “What areas will a facility serve?” and “What should the facility expect in terms of service volume?” These questions are affected by “costs” such as distance and time, or literal monetary quantities like gas mileage.

There are three main ways to define analysis of finding what’s nearby: using straight-line distances, finding the distance or cost over a network, or measuring the cost over a surface. As with any other type of map analysis with multiple options, there are times and places for each method.

A straight-line method finds any features within a certain radius of the center. This method provides a quick, simple estimate of features within a spatial constraint, and is often used when determining buffer areas. One type of straight-line mapping that I found particularly interesting was the spider mapping method. This involves drawing straight lines from the center to features within the designated radius. These maps show if there is any skew in location of likely consumers, or if some consumers are in radii of multiple centers and can incite competition. However, this method fails to consider geographical obstacles. A feature may be within the specified distance of one center, but when travel costs are accounted for, another center may be in a more efficient location. 

These instances can be mapped by a method considering distance or cost over a network. This type of analysis is typically more considerate of real-world application, and considers the impedance value, or cost to travel from the center to surrounding locations. Some locations may be nearer than others but have higher impedance values, and a cost over a network method takes this into account. An example I found fascinating was taking different kinds of road turns and junctions into account when planning travel costs in terms of time. For example, a turn at a stop sign takes less time than one at a traffic light. A feature may be outside of a straight-line distance radius but have a lower travel cost than another feature within that radius.

Finally, mapping cost over surface is most commonly used for travel over terrain. It’s sort of a mix of the previous two methods: there’s not really an established infrastructure, but geographical land features are accounted for in travel costs. This method uses a raster layer to display continuous data, and the shading can illustrate differences in rates of change across terrain, showing where travel cost increases rapidly or slowly.

Chapter 4: 

          In this chapter I learned more about Mapping Density from different aspects starting with why we map density in the first place. Mapping density helps you look at patterns rather than locations in individual features which in turn can be used when mapping areas of different sizes. When we work with areas that contain many features, it can be harder to see which areas have a higher concentration when compared to others, this is when uniform area units are needed which allows for you to clearly see distribution. There are two different ways of mapping density-  By defined area, or by density surface with both being comparable. By using a dot density map, you can get a quick sense of density in a place, with the dots representing density graphically with dots being displayed based on smaller areas and drawn boundaries of larger areas. When creating a density surface, GIS calculates density for each cell in layers thus having GIS create a density surface. Calculating density values through cell sizes helps determine how coarse or fine the pattern will be. Larger cells process faster but also have a coarser surface with size equating to the length of a side. I also learned more about search radius with a larger radius meaning more generalized patterns in density surface with GIS considering more features when calculating, and a smaller radius meaning more location variation. Adding to this, if a search radius is too small, the data patterns might not show up when mapping. With units,  GIS lets you specify areal units where you want density calculated, if the areal units are different from the cell units, the values in the legend will be extrapolated. Graduated colors allow for classification of values allowing for you to see the pattern. There are different ranges including-  natural breaks, quantile, equal interval, and standard deviation.

 

chapter 5: 

          In this section, I learned more about why and how people map in order to find what’s inside an area by trying to monitor what’s inside it. This allows for people to compare areas based on findings while summarizing lets people compare areas to see where more or less is. By defining Analysis, you are able to use area bonding which lets you summarize and combine in order to make summary data. You need to consider how many areas you have and what information you need. With this, you can find what’s inside either a single area or several areas through your work. When wondering about discrete or continuous work, discrete is equal to unique identifiable features while continuous is used for seamless geographic phenomena. This both give you the information you need to form a summary, connected with lists, counts, or summaries. There are three different ways of finding out what’s inside- by drawing areas and features, selecting the features inside of an area, or by overlaying areas and features in order to create a new layer with the attributes that you would want to summarize. This is useful for again finding out how much of something there is, with this you will need new data containing areas of a data set with these features. GIS is useful by checking to see which area each feature is in while also assigning the areas Identification and attributes to the features that area read on the data table. When making a map, you are mapping individual locations, similar to mapping location using geographic selection. 

 

 

Chapter 6:

          In chapter 6, I learned more about finding what’s nearby. This helps you see within a distance or travel range of a feature while also letting you monitor events in an area or find the area that is surveyed by a community. This can be connected to features affected by a setting or activity. By mapping nearby, you are finding what’s within a set distance that identifies with the area including a tracing range being measured using distance, time, or cost- this can help define the area surveyed by a facility. When defining your analysis, you are deciding how to advertently measure “realness”. There  are different subsections including straight line distance, the measure of distance or cost over a network, and the measure of cost over a surface. When defining and measuring near, you are basing it off of a set distance you specify, and the travel to or from a feature (measuring using distance or travel cost). When creating a buffer, you specify the source feature as well as the buffer distance, you can save the lines as a permanent boundary or use it temporarily when you are finding out how much or something is inside of an area. When selecting features within a distance, you use selection to find what’s nearby- like creating a buffer. GIS helps you out by selecting the surrounding features that are within the distance after you specify the distance from the source. Selecting features can be useful if you were to need a summary of features that are near a source while you don’t need to display or even create a buffer boundary. GIS can also help you with feature to feature if you are finding individual locations that are near a source feature. When calculating cost over a geographic surface, you are able to find out what’s nearby when traveling overland. GIS helps by creating a raster layer where the value of each cell is the total travel cost from the closest source cell. 

 

Chapter 4 

This chapter explains map density and how it can help with seeing concentrated patterns on given areas. Methods such as using a uniform areal unit can allow the distribution to be seen clearly. When it comes to deciding on what to map, it is very useful to think about the features being mapped and the information needed in order to create the map. There is also a difference between mapping the density of features and mapping feature values, such as the difference between mapping the locations of a business versus the density of its employees. Very different data sets.

There are two ways to map density: either based on features summarized by a defined area or by creating a density surface. Mapping by a defined area is recommended if you already have data summarized by area or lines that indicate this. It usually includes the use of dot density maps, helpful for representing the density of individual things such as people, trees, crimes, etc. It also explains further about the different layers for density values and different approaches when it comes to how detailed you may want your map. Its also noted as relatively easier than mapping by density surface. For mapping a density surface, it’s usually made in GIS as a raster layer. Meaning each cell gets a specific density value instead of being grouped into one. This approach is very helpful to use if you have many individual locations or sample points. This precision does require more effort as a result. 

I also learned that some GIS software, such as ArcGIS, lets you calculate density on the fly, or do things like summarize features or feature values for each polygon to make it easier. When reading about how to create a density surface, I did get a little lost, honestly. Especially on cells and their size, plus converting density into their units. However, their different sizes and their important roles in mapping is really interesting. Theres a good balance between big and small to get the smoothest results. 

 

Chapter 5 

This chapter is mainly about whats inside the map and monitoring it. By doing this, it can be known when action needs to be taken in certain areas or not. For example, how close a crime was committed to a school, and therefore requiring harsher consequences. It is important to define one’s analysis as well, and there are several methods to fit into different types of data sets. Consider how many areas there are, and what type of features are inside the areas. This will help determine finding whats inside a single or several areas. It defines what is found inside a single area, and it allows you to monitor activity or summarize info about the area. For example, the number of calls to 911 within a 1.5-mile radius of a fire station. As for multiple areas, you can compare the data. Such as zipcodes being contiguous (borders touching).

Another aspect mentioned is if the features inside are discrete or continuous. Discrete features would be defined as unique and identifiable. They can beput in a list or summarized numerically, such as crimes, pipelines, streams, etc. Continuous features are a seamless category, and don’t have an easily defined amount; they are more of a summarization. Things such as vegetation or elevation range. They have continuous numeric values that vary across a surface. There are even more different methods for features, such as: Drawing areas and features, quick and easy, but visual only. Selecting features in the area is good for a list or summary, but only for info on single areas. Overlaying the areas and features is very good for displaying what’s within several areas and summarizing by area, just takes more processing. 

Chapter 6 

For the last chapter, on finding what’s nearby, it explains how you can use mapping to see what’s within a set distance of a feature. It emphasises that it allows you to monitor activity in an area. Such as finding the traveling range of a feature, which can be done by distance, time, or cost. When finding things nearby, you have to decide how you want to measure the closeness of a location or feature, and what info you need to find a method. Methods could be straight-line distance, measure distance or cost over a network, or measure cost over a surface. Straight-line distance should be  if you’re defining an area of influence or want a quick estimate of travel range. It is simple, a rough approximation. Cost or distance over a network should be if you’re measuring travel over a fixed infrastructure to or from a source. It is more precise; it just needs more accurate network layering.  Cost over a surface should be if you’re measuring overland travel. It gives an area within travel range, allows several combined layers, and just requires some data preparation.

Key terminology, such as network layersare also introduced. Network layers are a geometric network composed of edges,  junctions, and turns. Junctions are the points where edges meet, and turns are used to specify the cost to travel through a junction. The GIS can tell where edges are connected. When creating things like this, you can also use buffers. Buffers are used to define a boundary and find what’s inside it. To make a buffer, you have to specify the source feature and the buffer distance. The GIS draws a line around the feature at the specified distance. The line can be kept as either a permanent boundary or temporarily. More important details like knowing if it is a flat plane or follows the curvature of the Earth, whether its necessary to have a list, count, or summary, etc. There are many repetitive concepts near the end of the chapter, which do help with remembering their functions, but it is difficult to separate the new;y obtained info from the old.

Theres so much information in these chapters I have no idea how to keep it down to 300 words tbh