Geography 291: Geospatial Analysis with Desktop GIS

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

Mitchell Chapter 4

Mapping density shows where the highest concentration of features is. Density maps are most useful for looking at patterns and large collections of data. A density map lets you measure the number of features using a uniform aerial unit, such as hectares or square miles, so you can clearly see the distribution. Density maps are useful for mapping areas that vary in size, such as census tracts or counties. You can map the density of features or of feature values. You can create a density map based on features summarized by defined area or by creating a density surface. You can map density graphically, using a dot map, or calculate a density value for each area. To calculate a density value for each area, you divide the total number of features, or total value of the features, by the area of the polygon. A density surface is usually created in the GIS as a raster layer with each cell in the layer getting a density value. This approach provides the most detailed information but requires more effort. You can create a density surface from individual locations, or linear features. 

To create a density surface, the GIS 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. The GIS does this for every cell and creates a running average of features per area. Several parameters that you specify affect how the GIS calculates the density surface, and thus what the patterns will look like. These include cell size, search radius, calculation method, and units. The cell size determines how coarse or fine the patterns will appear. The smaller the cell size, the smoother the surface. A larger cell size will process faster but will result in a coarser surface. 

Mitchell Chapter 5

People map what’s inside an area to monitor what’s occurring inside it, or to compare several areas based on what’s inside each. By monitoring what’s going on in an area, people know whether to take action. Summarizing what’s inside each of several areas lets people compare areas to see where there’s more and less of something. To find what’s inside, you can draw an area boundary on top of the features, use an area boundary to select the features inside and list or summarize them, or combine the area boundary and features to create summary data. Finding what’s inside a single area, lets you monitor activity or summarize information about the area. Finding how much of something is inside each of several areas lets you compare the areas. 

Discrete features are unique, identifiable features. You can list or count them or summarize a numeric attribute associated with them. They are either locations or discrete areas such as parcels. Continuous features represent seamless geographic phenomena, you can summarize the features for each area. 

Drawing areas on top of features is a quick and easy way to see what’s inside. You create a map showing the boundary of the area and the features, you can then see which features are inside and outside the area. Selecting the features inside the area includes specifying the area and the layer containing the features, and the GIS selects a subset of the features inside the area. Another way is to overlay the areas and features. The GIS 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 area. This approach 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. 

Mitchell Chapter 6

Using GIS, you can find out what’s occurring within a set distance of a feature and what’s within traveling range. Finding what’s within a set distance identifies the area affected by an event or activity. It also lets you monitor activity in the area. Traveling range is measured using distance, time, or cost. Finding what’s within the traveling range of a feature can help define the area served by a facility. Knowing what’s within traveling range can also help delineate areas that are suitable for, or capable of supporting, a specific use. To do this, you can measure straight-line distance, measure distance, or cost over a network. For some analyses, you have the option of calculating distance assuming the surface of the Earth is flat (planar method) or taking into account the curvature of the Earth (geodesic method). The planar method is appropriate when your area of interest is relatively small, the results of your analysis will appear as the correct shape when displayed on a flat map. Geodesic method should be used when your area of interest covers a large region. 

Straight-line distance is the easiest way of finding out what’s nearby. With this, you specify the source feature and the distance, and the GIS finds the area or the surrounding features within the distance. This approach is good for creating a boundary or selecting features at a set distance around a source. With the distance or cost over a network approach, you specify the source locations and a distance or travel cost along each linear feature. The GIS finds which segments of the network are within the distance or cost. You can then use the area covered by these segments to find the surrounding features near each source. This approach is good for finding what’s within a travel distance or cost of a location over a fixed network. With the cost over a surface approach, you specify the location of the source features and a travel cost. The GIS creates a new layer showing the travel cost from each source feature. This approach is good for calculating overland travel cost. 

Chapter 4: I found chapter 4 interesting because it discusses density mapping and its various applications. This type of mapping is very common because of how useful it is to find patterns in data, and is versatile for this purpose. Density maps can be made from all sorts of data, and can even be made from both features and feature data. In terms of visualization, they can be displayed as dot maps where a dot represents a single instance (or a set number of instances) of the phenomenon being recorded, or as shaded areas where each area has a different shade indicative of the frequency of the phenomenon being recorded in that area. Shaded areas can also be more abstract shapes not defined by manmade borders, such as in a map showing something like rainfall which is not confined by human borders. Dot maps must be considered more carefully than shaded area maps because it is important to choose a value for each dot that allows for the distribution of the data to be clear. For example, you would not want to choose a value so low that the dots appear as one singular mass, but you would not want to choose a value so large that it is unclear which area is being represented. Additionally, depending on the size of the dots, the thickness and detail of borders must be taken into consideration. Because the dots can be obscured by border lines, they should not be too thick and you may want to consider if the borders are useful to the viewer and not include them if they would not be useful in understanding the data. In conclusion, density maps are not very complicated but have various applications for all sorts of data, making them useful in a variety of situations.

 

Chapter 5: Mapping what’s inside seems to have applications in determining whether action needs to be taken based off of the data that has been collected, and can be used to find patterns in data. Similar to other types of mapping, the data can either be discrete or continuous, which is an important factor to consider when making a map of this type. There are three ways of mapping what’s inside – drawing areas and features, selecting the features inside an area, and overlaying the areas and features. Drawing areas and features involves creating a map showing the boundary of an area and the features that are inside the area. It is a simple method useful for determining if features are inside or outside an area. If the areas being used are discrete, the data can simply be overlaid on top of the area to show the information. For continuous data, a map of the data is first made and a map of the defined area is overlaid onto it. Selecting the features inside an area uses GIS to summarize the features inside of an area. The results that have been collected from a map can be made more concise and understandable by the GIS, using tools like statistical summaries to display the information from the map in a way that is quickly discernable. Overlaying the areas and features uses GIS to combine layers on a map, displaying the overlap between the areas and the features (or lack thereof). This method is useful when dealing with discrete features, however it can also be used when dealing with continuous values. In conclusion, this type of mapping is somewhat complex but can be useful in more specific situations. Each type of mapping what’s inside has its own situations where it is more viable than other forms of mapping.

 

Chapter 6: Finding what’s nearby is about making maps in order to determine if something is in an area around a selected point, or how much of something is near the point. An important aspect of this type of mapping is whether or not travel is involved. If it is, factors like travel cost and distance may need to be taken into account in the case of something like a tourism guide. Something I found interesting about this chapter is that in some cases it is necessary to take into consideration the curvature of the earth. I had never considered that this would be something map makers had to work around, but it makes sense. As with all maps, finding what’s nearby requires you to know what sort of information needs to be gathered from the map. This will influence how the map is made, for example the mapping of taxi routes in a city would require a different type of map than intercontinental airplane maps which would need to consider the aforementioned curvature of the earth and would result in a different type of map. There are three ways to find what’s nearby. Straight-line distance creates an area around a source point of interest using the distance being measured as the radius of a circle. Distance/cost over a network is slightly more complicated, requiring a source location and a distance or travel cost to be specified. Then, the GIS determines which areas are within the distance or cost. Cost over a surface also requires a source location and a travel cost, and shows the cost proportionate to the distance away from the source location. This type is useful when considering how the distance being traveled will affect the target that is travelling. Finding what’s nearby is similar to mapping what’s inside, but has a greater focus on distance and travel cost. 

Ch 4: Chapter four  was very specific when it came to creating density maps, which are maps that show the level of concentration in different areas. Reading patterns on these maps are easy and useful to understand. Additionally, these maps can map density of features or feature values. Furthermore, these maps can map defined areas but not specific centers of density. One type of this map includes  dot density maps where each area is based on how much each dot represents. There are two specific versions of creating this type of map. One way is by representing the data graphically and the other way is when the analyst would individually control the amount of dots represented.  The other type of defined area map is a shade density area map, which is similar to mapping ratios, class ranges and colors. These types of map styles were mentioned in chapter one. 

In order to map specific places, using a  density surface map can identify individual locations. This type of map is good for point or line features that are concentrated. Although, with this type of mapping the GIS would choose a radius to identify the features within the area in order to map the data. Another important aspect of density maps are the density values. One way of creating density values was to use cell size which can determine the  clarity of patterns. Typically the  smaller the cell the  smoother the pattern, and the larger the cell the pattern is more coarse. At the end of the chapter, I realized there is a lot of work when it comes to creating GIS maps. It is also important to remember previous topics. Terms such as equal intervals or  quantiles were brought up from chapter three when using graduated colors to map density surfaces. It is important to  keep up with the readings and not forget previous subjects. 

Ch 5: Chapter five was very specific when it came to choosing the best method of selecting areas within a map. I learned that it is  important to map  areas to monitor current events within it and it is good for comparing any patterns or important information. In order to compare areas it is important to analyze the type of data used to describe each area. For example, if it was a single area, the researcher would monitor the  activity or summarize the information. Contrary, if it is multiple areas, the research would see how much of something is inside each area to compare. Another important aspect of mapping areas is to consider what kind of feature is inside each area. This can help determine which method to use to organize the data. The researcher could use a list feature which is listing all the features inside an area. One could use a count feature which totals the number of features inside an area. Summarizing the number of features within an area is a final method of collecting information.  

However, each type of method comes with its own challenges when it comes to including the total amount in an area. For example, using a list feature would require an inclusion of areas partially inside a boundary. Meanwhile, other features may have to be fully included within a boundary.  I believe that this is another example of the difficulties of interpreting data that the GIS system has. Furthermore, most of the data comes down to whether it is discrete or continuous. This means that the rest of the analysis is based on the type of data. Despite all the particularities of creating area maps, and interpreting data, it will make conclusions easier in the end. Especially when it comes to creating data tables or charts to represent data statistics. 

Ch 6: In chapter six I learned about mapping data information nearby the feature or original data. Gathering this information could help map and discover ongoing events nearby or plan for future projects. One of the methods used to gather nearby data is using distance, where time, money or distance is measured using straight lines within featured areas of influence. This method is efficient to estimate travel range. One way of achieving this is by using buffers which are extended lines to measure area within the features. Although, this data can use time or money to measure the straight line distance as well. Assuming that GIS measures the same distance, I wonder if the results would be the same even if the straight line distance was measured in different units. 

Additionally, it is also important to figure out how to interpret the values corresponding to the spatial area. There are some maps that would prefer to model the data on a flat surface while others prefer a round surface. The planar method is when the area is small enough to be represented and shaped as a flat map. Contrary, the geodesic map is when the area is large enough to appear correctly on a curved surface of a globe to present accurate results. Furthermore, the chapter also mentioned the importance of using count summary as previously mentioned in chapter five. This seemed a bit repetitive and redundant,  but it may also serve as a reference for the researcher to build on previous skills and enhance what they already know.  In this second reading assignment I was able to learn a lot about mapping densities and areas. As well as the importance of paying attention to nearby surroundings. It is also interesting to notice how previous skills and details build onto each other as the book progresses. Sometimes it enhances a previously mentioned topic, but other times it does feel repetitive. Nonetheless, I was intrigued by the amount of GIS knowledge I gathered and can’t wait to try it out!

 

Chapter 4: Mapping Density

This chapter focuses on density mapping and the different ways it can be used to show where features are most concentrated. Density mapping is important because it helps reveal patterns that are not always clear when looking at raw totals alone. Examples discussed in the chapter include mapping workers in a business district or households with children within a specific ZIP code, which helps highlight areas of higher intensity or activity.

The chapter describes two main approaches to mapping density: defined areas and density surfaces. Defined area maps, such as dot maps, are often used with structured data like census information because the boundaries and values are already established. Density surface maps use raster layers or contour maps to create a smoother and more detailed representation of density. Although density surface maps require more effort to create, they are especially useful when identifying subtle spatial patterns that might be missed with defined areas.

Later in the chapter, Mitchell explains how density maps are created by adjusting factors such as cell size, search radius, and units of measurement. The chapter also emphasizes the importance of color gradients and thoughtful design choices to prevent misleading interpretations. Overall, this chapter showed that density mapping is a powerful analytical tool, but its effectiveness depends heavily on the choices made by the analyst.

Key Concepts: Density mapping, defined areas, density surfaces, cell size, search radius, normalization

Questions: How do analysts decide which type of density map is most appropriate for a dataset? How can map design choices unintentionally influence how density patterns are interpreted?

Chapter 5: Finding What’s Inside

Chapter 5 focuses on determining what features are located within specific areas, which is a central function of spatial analysis. The chapter begins by explaining how this approach can be used to identify patterns such as crime hotspots or areas of high conservation value. By analyzing which features fall within defined boundaries, GIS allows for more meaningful comparisons between regions.

The chapter outlines several methods for finding what is inside an area, including drawing areas manually, selecting features within boundaries, and overlapping multiple layers. These techniques allow analysts to examine how different features relate to one another spatially. I found the conservation example especially effective in showing how GIS can be used to prioritize areas based on the features they contain.

Another important idea in this chapter is how areas are visually represented on maps. Showing only an area’s boundary emphasizes borders, while shading or screening an area highlights the space as a whole. These visual decisions can significantly affect interpretation and should match the goal of the analysis. This chapter emphasized how containment analysis supports real-world decision-making.

Key Concepts: Containment, overlay, selection, spatial analysis, boundaries

Questions: How precise do boundaries need to be for containment analysis to be reliable? How can analysts communicate uncertainty when boundaries affect real-world decisions?

Chapter 6: What’s Nearby

Chapter 6 explores how GIS can be used to analyze proximity and determine what is located near a specific feature. The chapter introduces three main methods for measuring proximity: straight-line distance, distance or cost over a network, and cost over a surface. Each method serves a different purpose depending on whether the focus is simple distance, travel time, or accessibility.

One concept that stood out to me was the use of distinct distance bands to show areas of influence around a feature. This reminded me of the Chicago School model of urban structure, which also uses distance to explain spatial organization. While the comparison is not exact, both approaches rely on distance as a way to interpret spatial patterns.

The chapter also discusses spider diagrams, which visually connect a single feature to multiple locations. This makes it easier to see whether a feature is within range of several important points. Toward the end of the chapter, Mitchell explains the importance of limiting the number of mapped features or using clear symbolization to avoid confusion. Overall, this chapter showed how proximity analysis helps turn spatial data into practical insights for planning and analysis.

Key Concepts: Proximity analysis, straight-line distance, network distance, cost surface, spider diagrams

Questions: When is straight-line distance insufficient for proximity analysis? How do analysts decide which proximity method best fits a real-world problem?

Chapter 4

Chapter 4 further describes the different types of maps one can use when creating maps on GIS. One map listed is the Density map, which is useful for people who want to map areas that vary in size, considering the presence of differing concentrations. On the subject of density maps, the reading makes it clear that there are different types of ways to use a density map, such as mapping specific features or the variables associated with those features. One question that arises from this topic is: Is it possible to combine feature maps and feature value maps to create a larger picture of data? This chapter feels quite relevant to my mapping plans for the class, which is the distribution of a specific insect species, for which I could use a dot map. Dot maps represent density based on how close or far the dots are from each other. GIS seems to make it relatively user-friendly to understand how to read and create density maps, as when it comes to the defined area maps, the higher density areas are within darker colored boundaries, while the lighter areas are lower density locations. Another question that this chapter prompts me to wonder is whether one can toggle between dot maps and shaded maps, as they are both typically pictured together when presented in the textbook. I found it interesting that the application gives the user so much freedom with customization, as you can even determine how many features a single dot represents, which could definitely help simplify a map if the data is immense but congregated. A common theme I have noticed throughout the chapter is that GIS provides a lot of support when it comes to mathematical calculations, as it can aid in the numerical calculation of density for any given feature. One other feature of notability is the choice of cell size, which is how big a plot is on the map, which can help broaden the options between a detailed and broad map. 

Chapter 5

Chapter 5 appears to be delving into the boundaries of a map and the relevance of what occurs within those boundaries. I find it important to note the different types of boundaries one can use, such as a single area, a buffer that surrounds a specific zone or feature, and a natural boundary. In consideration of my own future map, if I choose to map an insect with aquatic larvae, then I could use the buffer map type, so that I can observe how far an aquatic insect may migrate from its watershed of origin. Two types of boundaries can be made: discrete, which are clearly defined zones, and continuous, which are more loosely defined and are typically natural structures. The boundaries are frequently determined by the purpose of the data, like the mapping of a floodplain in an urban area, or the distribution of types of trees in protected areas. There are three ways one can go about creating borders: freehand drawing, selecting the features within the boundaries, and overlaying the features. Chapter 5 references previous chapters by bringing up the statistical analysis strategies of count, frequency, as well as bar charts and piecharts. I also find it interesting that it develops on the content of chapter 4 by describing how you can combine overlay and drawn maps for a more complex map. One question that arose when reading this was: Should one use different data analysis techniques for different types of bordered maps? One topic to note is the usage of the raster method, as it can create calculations that deduct what the areal extent is on the map. The vector method is similar, but more precise, which can lead to more effort required on the user’s end. These methods can also aid in analysing data. 

Chapter 6

An important factor to consider when collecting or presenting data for a GIS map is what qualities or factors are present within a boundary. A term to monitor this is the travelling range, which can be measured through three different metrics: cost, time, and distance. The travelling distance can be used in conjunction with multiple metrics, such as the time it would take to travel a particular distance within a particular boundary. One question that comes to mind when considering this is: Is this type of GIS method used to track the distance at which particular species migrate, or would the distance and method of travel be too complex to map in this way? Based on the reading, money seems to also be a frequent use of urban mapping, as people calculate how long it will take to travel from one place to another. When calculating the travel distance, two methods can be used: the planar method, which is for smaller, flat parcels of land, and the geodesic method, which is for larger, less linear pieces of land. The chapter specifies that different geometric features within a boundary prompt different statistical methods for data analysis, such as a list, a count, and a summary statistic, also known as a total count. I find it relevant to note that there are also different scales at which you can produce a border to show a range of travelling distances. These can include inclusive rings and district bands, which perform similar functions with just a different viewing option. I think it is interesting that GIS, to some degree, can even calculate the travel costs for the user, according to the textbook. One can also create a feature called a buffer, meaning that they can create an expanse of area around a particular variable on the map.

Chapter 1:

“GIS analysis is a process for looking at geographic patterns in your data and at relationships between features.”

Chapter one goes through a lot of the basics and general definitions about the methods, systems and steps of GIS analysis. I found it informative but lacking anything beyond the simple overview on the topics, explaining a further “deep dive” in the following chapters.

The key ideas/ notes I gathered are: Paying attention to the geographic features are important to figuring out how they’re represented within a map as each feature can vary greatly between one another. Discrete features are specific, pinpointed locations. Continuous features are things that can be found everywhere, all of the time- like the weather. Summarized area explains the density of individual features that are within a boundary and can be categorized through totals and percentages of demographic data. Another important basic explanation was how to represent these geographic features, through vector (specific locations in space) and raster models (continuous space). A major note to remember is that the cell size of the raster layers can change how the image comes out- for example, too large of a cell size can cause the image to lose details. Finally, at the end of the chapter it explains the geographic attributes the features would have. This section was very straightforward and easier to understand, providing a good example of what/how exactly GIS is used to find and convey information.

I will add after reading this chapter that it is a bit confusing to simply read the textbook without putting what it is stating into practice. I think I will probably understand more once we begin working on these systems/ maps through the software. Then I would really be able to come back and connect the dots from the textbook to the software.

 

Chapter 2:

The second chapter repeats heavily the importance of paying attention to patterns within the maps and data. Stating that identifying patterns are meaningful and can be helpful in deducting why things are located a certain way, or finding the correlation between two features. Chapter two gives a lot of information on the key words and ideas that were discussed in a general sense from chapter one and in the beginning of this chapter. It very clearly explains what GIS does for each map- from storing the location of a geographic feature through coordinates to drawing images/ symbols from those coordinates, creating patterns between them. This chapter also contains a few addditional guidelines for what the created maps should look like, and how to make them. With a key point of making sure the maps are easy for the audience to comprehend, appropriate for the issue being discussed, and contain no unnecessary information. Occasionally including a landmark or reference location depending on the type of map/ data, the audience and reason for being made- the map presentation should vary considering the information being processed within it. How people are directed to perceive a map is a significant detail to keep in mind when creating it. A very important note on the creation of the map to first ensure that all the geographic features have coordinates and are categorized accordingly. That being said another important mapping note is when categorizing and laying features, to keep the map clean and comprehensible there should be no more than 7 categories (aka colors) per map! Finally, it is overall important to understand what the data represents in order to know how to group and display the data on a map when creating it.

I did make a personal connection between the mapping systems and photography while reading. I can envision that the editing and development of layers within the map are similar to the layers in Photoshop. You can remove, alter, swap and hide certain layers to view the end photo/ map in a specific way, creating different patterns on the map with certain overlays. It is also similar to art in the sense that you need to edit the visuals of the map/ project in a particular way in order to make it clear and  comprehensive to the viewers.

 

Chapter 3:

In chapter three, the reading expands upon what the earlier chapters were building up concept wise. It returns to creating maps in further detail, explaining how to show the quantity of features rather than only where they are located. It describes that when mapping quantities, they can be categorized through different types. Those are, counts/ amounts, ratios (one quantity/another) or rankings. Simply defining them: counts/ amounts are the standard quantity number of a feature, ratios represent the relationship between quantities like an average, and ranks are the relative ranking of features from high to low. The text then goes on to explain and guide how to create and use more strategies of grouping (these types are grouped in classes), similar to the many forms of grouping and sub-classing represented in the past chapters. I will add that I found the comparisons of the classification schemes to be very informative, with how it included disadvantages and “how it works” for each classification. Chapter 3 also includes outliers, which is a very normal aspect to scientific research and data. How the outliers are dealt with depends on if it’s an anomaly, error, or valid data point, so it’s imperative that it’s observed carefully.

Chapter three once again emphasizes the importance of interpreting patterns within the map, evolving from the patterns of location into the quantity of a feature- leading to the speculation and discovery of why something has a certain quantity. This chapter also describes the beginning basics of the analysis process of the class/program. Where the previous chapters were basic guides and definitions, chapter three shows readers how to think analytically about all the information learned until this point. It also demonstrates how GIS is actually this analytical process, stating that every decision made when working through a map can impact the final result. Your intention when developing the map matters! Through this point, although chapter two began to show how to create a map, this chapter finishes the job in high detail, along with a ton of helpful example maps/ charts.

I appreciate how all of the chapters continue to include many more examples- through very specific representations of how GIS is used in everyday life. The fact that the examples didn’t stop after the intro of the first chapter is a good decision teaching-wise. Specific examples of certain maps in relation to the section of the chapter it’s talking about. Although the technical definitions and explanations were confusing, this kind of connected everything back to real world use and made it easy to understand what GIS is. The visuals paired with the examples and descriptions are also very helpful when beginning to understand the information.

The first thing Chapter 4 introduces is mapping densities. Mapping densities is what allows you to look at patterns over areas rather than individual features. In the examples, it includes an area map and uses different shades of red to show each density and the space it covers. Another example used the same shades of red but put the colors within county lines to measure a census track. It shows examples of how you can either map density in defined areas or by density surface. For a defined area, you would use a dot map; and for density surface you utilize the GIS raster layer described in the previous chapters. While continuing learning about GIS, one thing that interested me was how GIS is similar to a choose your own adventure. There are so many different types of maps, and values to show that each requires their own process and customization. Each GIS is completely unique depending on what can best show your information. This was especially true for densities when the chapter included a chart of what to do depending on what density you are graphing. Another interesting piece of this chapter was the description of what the software does when graphing density. I enjoyed the inclusion of the image showing GIS creating a radius around the specific cell center to then create a smooth surface of the density amount. The cell size changes based on how detailed the pattern will appear on the map. It also showed how you can use contour lines to specifically show the rate of change within the data. Another interesting feature that surprised me was how much math GIS uses. I imagined it to be more similar to coding, rather than having different equations that it has shown in these first few chapters. 

The focus of Chapter 5 is to map inside an area to monitor what is occurring inside of it. To do this you have to either draw a boundary line on top, use an area boundary (selects or summarizes features inside), or combine the boundary and features. With this, it explains you can include multiple areas so you need to determine how many you have. The chapter gives lots of examples of both single areas and multiple areas and shows what the final product could look like. The inside data can be categorized as either continuous or discrete features. The continuous features being seamless, geographic phenomena, and discrete being unique, identifiable features. You can also graph the information in either a list, count, or summary. It also lets you use lines to show specific features within the boundary lines. Similarly to the last chapter, I think it is fascinating to see all the options and examples of how to customize the map being created. This chapter also explains that there are three ways to specifically find what is inside. The first way is through drawing areas and features which is good for seeing one of few features inside or outside a single area and is made through a dataset containing boundaries and the features. The next method is selecting features inside the area which is good for getting a summary of features within an area and is created through a dataset of the areas and specific attributes. The final way is by overlaying the areas and features which is good for finding how much of something is in one or more areas, and is created by having data containing the areas, a dataset for features, and any specific attributes. From what I read, the third option is basically a combination of the previous two. This chapter also goes into detail about the calculations behind GIS and how you can find the mean, median, and standard deviation of the data being graphed. It also allows you to overlay the areas and features explained to create more complex models. 

Rather than finding what is inside, Chapter 6 is all about finding what is nearby. The main purpose of mapping what is nearby is to find out what is occurring within a set distance of a feature and what is in traveling range. To start, you have to measure what you consider near through distance or travel cost. While distance measures specifically how close something is, cost is a way to measure without distance. One common cost is time, money, or effort expended, which all together equal travel cost. When measuring distance, you have to be specific about whether it is over a flat plain or using the curvature of the Earth. This also is started by determining whether you need a list, count, or summary. One thing I am noticing after reading up to 6 chapters is the patterns in GIS. While each different type of map and feature appears to be confusing and different, oftentimes the processes are similar and involve the same steps and decisions. When determining how many distance or cost ranges to include, you can include inclusive rings or distance bands. Inclusive rings show how the total amount increases as the distance increases. The example it used to help clarify was that you can use the rings to see the number of customers within 1,000 feet, 2,000 feet, and 3,000 feet, and note how the number increases. Distance bands compare distance to other characteristics. This example was that you could find the number of customers within 1,000 feet, the number between 1,000 feet and 2,000 feet, and the number between 2,000 feet and 3,000 feet. Distance bands can basically split up inclusive rings to find more specific data. Similar to finding what is inside, there are also three ways of finding what is nearby! These include straight line distance, finding the area of surrounding features within your selected distance; distance/cost over a network, specifying distance or travel cost along the linear feature; and cost over a surface, a new layer showing the travel cost from each source feature. Like the last chapter, I found it helpful that this one also includes a graph explaining what each one is used for and the pros and cons. This reading  goes into specifics over each method and includes lots of images and examples to make it easily understood.