Month: September 2025

Chapter 4- 

• Density maps are used for identifying patterns rather than showing the precise location of something. They are more useful for mapping areas.
• For a density map, densities for a specific area can be represented by different shading or a density surface. You can map the densities of points or lines. You can also map data that is summarized by area. 
• Density of features (number of businesses in an area) or feature values (like how many employees are at each business) can be mapped. 
• Density by defined area can be graphed using dots or density value. Calculating density value- divide the number of features by the area of the polygon.
• Density graphed by a density surface- Created as a raster layer. Every cell in that layer is assigned a density value, which can be found using the number of features within a radius of the cell. This type of graphing gives the most detail, but is the most difficult to make. 
• Which method should you use? 
  • Defined area- when you already have data already summarized by area or when you want to compare administrative or natural areas with defined borders.
  • Density surface- Shows the concentration of points or lines.
• Calculating a density value for defined areas-
  • “Add a new field to the feature data table to hold the density value.” (Mitchell, 2020)
  • “Then, assign the density values by dividing the value you’re mapping by the area of the polygon.” (Mitchell, 2020)
  • “If the density units are different from the area units, you’ll need to use a conversion factor in the calculation to change the area units to the density units.” (Mitchell, 2020)
• Creating a dot density map- 
  • “The GIS divides the value of the polygon by the amount represented by a dot to find out how many dots to draw in each area.” (Mitchell, 2020)
  • GIS places dots randomly within the area. The dots do not actually represent locations. 
• Cell size- determines how coarse patterns on your map will be. 
  • 1. Convert density units to cell units (1 sq. km = 1,000 m * 1,000 m = 1,000,000 sq. m)
  • 2. Divide by the number of cells(1,000,000 sq. meters / 100 cells = 10,000 sq. meters per cell)
  • 3. Take the square root to get the cell size (one side)
• Search Radius- the larger the search radius, the more generalized the patterns in the density surface will be. The smaller it is, the more local variation shown. 
• Contour lines- Contour lines connect points of equal density value on the surface.

Chapter 5-

• Finding what’s inside-
  • Single area- ex. Customers within a proposed sales territory or a service area around a central facility.
  • Several areas- ex. the number of businesses within a group of zip codes.
• Multiple areas- 
  • Contiguous- such as zip codes or water sheds.
  • Disjunct- such as state parks.
  • Nested- such as 50- and 100-year floodplains, or the area within 1, 2, and 3 miles of a store
• In your map, linear features and discrete areas may not all fall inside the set boundary of a map. You can choose to include features only fully in a designated area, only the parts that lie within the boundary, or what partially lies in a designated area. 
• 3 ways of finding what’s inside-
  • One- drawing areas and features. This method is good for seeing whether one or a few features are inside or outside a single area.
  • Two- Selecting the features inside the area. This method is good for getting a list or summary of features inside a single area or areas. It’s also good for finding what is in a certain radius of another feature. 
  • Three- Overlaying the areas and features. This method is good for finding which features are in each of several areas or finding out how much of something is in one or more areas.
• Drawing areas and features:
  • Locations and lines- draw them using a single symbol or symbolize them by category or quantity. After, draw the boundary of the area on top.
  • Discrete areas- (1) Shade the outer area with a light color and draw the boundaries of the area features on top. (2) Fill the outer area with a translucent color or a pattern on top of the discrete area boundaries. (3) Draw the outer area boundary with a thick line, and the discrete area boundaries with a thin line in a lighter shade or different color.
  • Continuous features- Draw the areas symbolized by category or quantity (as a class range), and then draw the boundary of the area or areas on top.
• Selecting features inside an area- 
  • specify the features and the area. 
  • GIS checks the location of each feature to see if it’s inside the area and flags the ones that are.
  • It then highlights the selected features on the map and selects the corresponding rows in the feature set’s data table. 
• The vector method- GIS splits category or class boundaries where they cross areas and creates a new dataset with the areas that result. Each new area has the attributes of both input layers.
• The rastor method- GIS compares each cell on the area layer to the corresponding cell on the layer containing the categories. It counts the number of cells of each category within each area, calculates the areal extent by multiplying the number of cells by the area of a cell, and presents the results in a table.

Chapter 6-

• Finding what’s nearby-
  • An area of influence is measured by a straight line distance.
  • Travel to or from a feature is measured using distance or travel cost.
  • Travel can be measured over a geometric network, such as streets or deer walking to a stream.
  • Can also measure nearness using time it takes to travel there (ie. through heavy traffic would warrant more time).
  • You can measure distance as the Earth being flat (planar method) or you can use the curvature of the Earth (geodesic method).
  • You can get distance information on several features, not just one. 
  • 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.
• Using straight line distance-
  • (1) Create a buffer to define a boundary and find what’s inside it.
  • (2) Select features to find features within a given distance.
  • (3) Calculate feature-to-feature distance to find and assign distance to locations near a source.
  • (4) Create a distance surface to calculate continuous distance from a source.
• Centers- Source locations in networks. Usually represent centers that people, goods, or services travel to or from.
• Geometric network- composed of edges, junctions (points where edges meet), and turns. To get accurate costs to travel through a junction, make sure that (1) edges are in the right place, (2) edges actually exist, (3) edges connect to other segments accurately, and (4) there are correct attributes for each edge.
• Impedance value- the cost to travel between the center and surrounding locations for a street segment.
• Creating a boundary-
  • List all individual locations.
  • Get a count of locations in the area covered by the selected segments.
  • Have data summarized by area. ie. you want to total the number of households per census block to find out how many households are within a 15-minute drive of a recycling center.
  • Get a list, count, or amount for linear features or areas, ie. the total length of salmon streams within a half-hour drive of the town.
• “You can limit the area for which the GIS calculates cost distance values by specifying a maximum cost. The GIS stops calculating cost distance when all cells within the specified cost have been assigned a value. Any remaining cells are not assigned a value on the output layer. If you don’t specify a maximum cost, the GIS calculates a value for all cells in the study area” (Mitchell, 2020). 

Chapter 1: This chapter helped inform me on the basic principles of GIS. It opened my eyes to how specific and precise some data sets must be in order to achieve the expected outcome. Initially, when using GIS, the chapter discusses the importance of framing the question that you would like to answer. Mitchell discusses the different methods that you can use to achieve your results, as well as different ways the results can be presented to suit your needs. He discusses that data can be presented very specifically or on a broader scale. Mitchell goes over how continuous data may be processed differently from other data. This was something that intrigued me because it can be related to weather maps showing precipitation and wind patterns. It was also interesting to learn that this data is not as exact as other data presented by GIS because the data is processed as it varies on the landscape, and creates models using data that are similar to each other to create groupings on the map. Further in the chapter, Mitchell also discussed the differences between vector and raster models. While I can identify the use for each model, I personally prefer the vector models because they are more exact and are displayed both in a map and within a table. Raster models are used to process continuous data. However, I think the continuous data is a little more difficult to understand and process. For example, issues can be run into when presenting raster data because of the pixel size, which can impact how easy the data is to interpret. Mitchel also discusses the process of overlaying certain parts of a map and its difficulties. I never would have thought about the difficulties that you might run into based on the size of the area you are trying to examine and the curvature of the globe.

Chapter 2: This chapter helped me to understand visual displays of a map and how important these displays can be when using GIS. Mitchell talks about the importance of certain patterns in maps and how they can apply differently to each map. This let me know that when planning to map something out, I have to be specific and very conscious of how I represent different data points. This also let me know that mapping can be a process of trial and error. Sometimes, if symbols or colors on a map are too simple than it can be confusing for the audience viewing that map. Additionally, the chapter taught me the importance of breaking a map down into subsets. Using subsets can help me break down all of the data into smaller groups and notice patterns among the smaller groups of data, rather than all of the data at once. On the other hand, something that I also have to keep in mind when creating these maps is that I don’t want them to be too clustered, so I need to keep in mind the scale of the map. For example, if I have a larger map, I can create more categories and boundaries without them being too clustered. However, on a smaller-scale map, the same number of boundaries or categories might be too complex and make the map difficult to read and decipher. In order to avoid this, I can group the categories together in a table to differentiate between a general and a specific category. I think that this can be very useful because it allows me to see both the complex data and more specific categories if I need to. It also allows me to see the categories grouped together, which makes them easier to read on a map. I think one of the easiest ways to do this, however, is to group the data together by symbol rather than code. This way, there is a distinct visual difference that I can identify with having to read all of the codes in the data. 

Chapter 3: This chapter went further into depth about the different types of visual displays for maps and charts. The chapter discussed the different types of maps and ways to analyze data, and how they can be suited based on the way the data is skewed. While this chapter went into a lot of detail about counts, amounts, and ways to display data, it was a little overwhelming. I think that all the data provided by the chapter was very useful, and something that I can look back on to help me in the future. It was a lot to look at all at once. I think that after being able to apply the different methods that the chapter discussed, they will become more memorable. However, trying to differentiate between them after reading about them is a challenge for me. Mitchell discusses the importance of ranks, ratios, and densities and how they can be applied to certain maps. He goes further into this discussion by talking about how this data can be grouped into classes. This is something that I think is super important because it highly influences how the data is presented and how easy it is to understand. If the data is too exact, it can make it harder to read; however, by grouping it into classes, the data is simplified for the viewer. Additionally, Mitchel goes over the different ways that data can be displayed on a map. While I understand the importance of the variety of displays, I think that some of them make it confusing to analyze the data. For example, while I think the 3D models are cool, I also think that they make it difficult to label and analyze data.

1. I have completed the GEOG291 quiz
2. My name is Elaina Massaro. I am currently a freshman and plan to double major in Environmental Science and Zoology. I enjoy working with animals, reading, and doing ceramics.
3. While reading the first chapter about GIS, I was very intrigued to learn about the depth of the program. The chapter informed me on the progression of GIS and opened my eyes to how complex it has become over the years. It was interesting to follow the process of different people and scientists collaborating to create a huge program. Originally, going into this class, I did not have any prior knowledge of GIS or how it worked, so following the history of the program was something new for me. I thought that it was super cool to see how so many data sets can be overlapped and interact with each other. I think that GIS is an amazing tool that can be used to study interactions of both biotic and abiotic factors. Seeing the variety of ways that GIS is used was very eye-opening. Some of the applications are things that I would have never thought to use the program for. For example, Schuurman discusses how GIS can be used to predict future events such as city expansion. This is not something I would ever think to use this program for, nor would I think it to be possible. I also thought that it was interesting to see the data described by the author in the form of maps. This makes the data much easier to understand and allows me to comprehend the extent of the work that the program is doing and how many factors go into it. One of my sources talks about the application of GIS in animal rescues. It talks about how they use GIS to estimate where more animals are regularly dumped, and what they need to do in order to accommodate that. 

https://www.aspcapro.org/resource/using-geographic-information-systems-gis-map-animal-data

Another one of my sources uses GIS for animal tracking, specifically wolves. They use a variety of maps to show the wolves’ movements throughout the span of multiple days.

https://storymaps.arcgis.com/stories/32412cf13731440582fe051cd360b009 

Mitchell Chapter – 4 is about mapping density. This was mainly shown as population per county/census tract, or businesses per area. Mapping density is useful because it can be a way to display ratio data using either graduated colors, points or contour lines to visualize patterns. As an ecologist it could be useful to look at density maps showing percent logging or percent population in a given area so that I can find good study sites. The chapter covers different ways to display density on a map, and the main two methods are through points or density surface. When planning to map density it is important to think about what it is you are planning to use your map for. If you are planning to just try and visualize a trend and aren’t doing a lot of analysis from the map otherwise, it is best suited to use a large cell size. This is because a large cell generalizes your data, however it also processes a lot faster. It can be refined further to have a smaller cell size if further analysis is required. 

 

Key words: Defined area density (density based on area of a polygon), Dot density (area mapped by count/amount and each dot is given an amount to represent), Cell size (amount of space that the GIS uses to represent data, smaller is more detailed but takes longer to process).

 

Mitchell Chapter – 5 is about what is actually represented by the map, and what it can be used to interpret. This can be done by designating an area surrounding a central point or by layering data on top of one another. This chapter also includes different ways to highlight different details about the map, such as by using just an outline of an area, outlining and shading an area, or by screening out the space around the area. Some applications for this that I can see, is by using census data to find out the amount of people living in poverty within a given area, this can be done by defining an area and than using graduated colors or symbols to show the count for the feature data within the defined space. A scientific application that I could use this for would be to map the land use types for a study area, say Delaware county, then I could define my specific study sites within the county and then use the land use map as a sort of base layer to determine what the land use type is for all of my given field sites. 

 

Key words: Single area (area surrounding a central point), Count (total number of features in an area), Frequency (number of features within a given value or range of values inside the area).

 

Mitchell Chapter – 6 is about determining distance from a source. This is often expressed as a cost, whether that cost be time, money, or physical distance. To do so you need to define your area, by selecting either a line, network, or a surface. Measuring distance can sometimes be difficult as the earth is curved and depending on the scale of your area it is sometimes necessary to use the geodesic method to account for the earth’s curvature. A few things that can be done by measuring distance or cost induce: generating lists of customers near a given business to give advertisements to, counting the amount of properties near a fire station, or generating summary statistics around your area. A good use of concepts from this chapter would be buffering an area surrounding a tributary with vegetation and planning this out by selecting the tributary and then creating a buffer a set distance away from the tributary. 

 

Key words: Inclusive rings (creates an area that is a specified distance away from a given point), Distance bands (similar to inclusive rings, but spanning distances incrementally), Straight line distance (the GIS defines an area based on a source feature and a given distance), Cost by Network (a travel cost is designated per each linear feature), Cost over a surface (Surface features of a specified area are given travel costs), Buffer (a zone is marked out a set distance away from a designated area), Spider diagram (lines are drawn connecting features back to a certain point they are close to)