Geography 291: Geospatial Analysis with Desktop GIS

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

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.