Geography 291: Geospatial Analysis with Desktop GIS

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.

Mitchell Chapter 4

Chapter 4  is focused on what density is, and how to map density via GIS. Mapping density is important as allows you to see patterns of where features are concentrated. This is relatively important as this helps you find areas that require action or or monitor changing conditions. In my opinion, Mitchell’s example of a crime analyst helped me understand why density maps are important. A crime analyst may map the density of burglaries occurring over a year, per square mile, to compare different parts of the city. This aligns with the goals of mapping density, as a crime analyst may then take this data to let policymakers know where intervention is necessary.

The author discusses two ways of mapping density, which is by defined area and by density surface. Mapping density by defined area involves mapping density geographically using a dot map, or calculating the density value for each area. The closer together the dots are, this means that the higher the density of features are within that specified area. Mapping density by density surface involves the creation of a density surface in GIS as a raster layer. Each cell in the layer gets a density value, such as a number of businesses per square mile, based on the number of features within the radius of the cell. Creating a density surface seems like the easiest possibility to me as this technique is used if you have individual locations, sample points, or lines– which is great for raw data that has yet to be analyzed or summarized by another analyst/researcher. In order to map density for defined areas, you may calculate a density value for each defined area or create a dot density map. A density value requires the user to calculate density based on the areal extent of each polygon, while the creation of a dot density map allows for the user to map each area based on a total count or amount and specify how much each dot represents. I find this chapter very useful to understand how to plot features on a map that reflect density, and I see this how this may be applied to the field of public health or epidemiology in the form of disease clusters.

Mitchell Chapter 5

Chapter 5 discusses why it is important to find what is inside a location– which lets you see whether an activity occurs inside an area or summarize information for each of several areas so you can compare them. In order to find what’s inside, you can draw an area boundary on top of the features, use an area or boundary to select the inside and list or summarize them, or combine the area boundary and features to create summary data. It is important to find what is inside a single area, as this allows for intervention. Single areas include: a buffer that defines a distance around some features,  an administrative or natural boundary, an area that you draw manually, or a service area around a central facility. Furthermore, finding how much of something is inside each of several areas lets you compare the areas, which can include state parks, zip codes, watersheds, stores, or floodplains.

Mitchell discusses three ways of finding what’s inside: drawing areas and features, selecting the features inside the area, and overlaying the areas and features. Drawing areas and features are good for seeing whether one or a few features are inside or outside a single area, and all you need is a dataset containing the boundary of the area or areas and a dataset containing the features. Selecting the features inside the area is a good approach for getting a list or summary of features inside a single area, or a group of areas you’re treating as one. For this, you need the dataset containing the areas and a dataset with the features, including any attributes you want to summarize. Finally, overlapping the areas and features 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. For this approach you need the data containing the areas and a dataset with the features, including any attributes you want to summarize.

Mitchell Chapter 6

Chapter 6 discusses how to find what is nearby, which lets you see what’s within a set distance or travel range of a feature. This lets you monitor events in an area, or find the area served by a facility or the features affected by an activity. It is important to map what’s nearby, as you can find out what’s occurring within a set distance of a feature, and find features inside an area that is affected by an event or activity. I personally see this being applied for natural disasters, as you may be able to see areas that have been affected or areas that are in more of a need.

The author then discusses three ways of finding what’s nearby, which includes straight-line distance, distance or cost over a network, and cost over a surface. Straight-line distance can specify the source feature and the distance, and the GIS finds the area or the surrounding features within the distance. Straight-line distance is good for creating a boundary or selecting features at a set distance around the source. For this, you need a layer containing the source feature and a layer containing the surrounding features. The second way of finding what’s nearby is distance or cost over a network, in which the user specifies the source locations or travel cost along each linear feature, then the GIS finds which segments of the network are within the distance or cost. This technique is useful for finding what’s within a travel distance or cost of a location, over a fixed network. In order to use distance or cost over a network, locations of the source features, a network layer, and in most cases, a layer containing the surrounding features are required. The last technique is cost over a surface, in which the user specifies the location of the source features and a travel cost, then the GIS creates a new layer showing the travel cost from each source feature. This is good for calculating overland travel cost, and you need a layer containing the source features and a raster layer.

Chapter 1 

Chapter 1 helped me to better understand what GIS really is and what it’s supposed to do. I thought that GIS was pretty technical and computer-based, but I realized through reading Chapter 1 that GIS analysis really begins with thinking through a question. GIS is a system to store, manage, analyze, and display spatial data, but the value of GIS depends on how well the analyst frames the problem. The author, Mitchell, points to important concepts that include geographic features, attributes, and layers, and shows how they all contribute to answering a question. What I found interesting was that it’s not accidental or arbitrary to do a GIS analysis; you make a decision about what you want to find out and then use appropriate data to do it. I think I now see it as a process of reasoning rather than a technical process. I also appreciated that I could see how maps were used to make important decisions, including public policy, and that it really makes you think about your decisions, even small ones, that you make while working with your data. Reading this chapter really made me think about how important it is to be clear about what you want to do before you start to do it. It also made me realize the level of responsibility that comes with conducting GIS analysis, as the choices you make at the beginning can affect how other people will interpret a place or a pattern. It also made me ask questions such as: How do analysts ensure that they frame their questions correctly? How do they deal with incomplete data? How does uncertainty play a role when maps are being used to make decisions?

Chapter 2 

In Chapter 2, Mitchell discusses how before analyzing why something is happening, one should first understand where it is happening. He goes on to say that mapping locations is the first step in GIS analysis and helps one understand basic spatial patterns such as clustering, dispersion, and gaps. This may sound like a very basic concept, but as I read through this chapter and the rest of the book, I understand why this concept is important. Sometimes it can be difficult to understand patterns when using a table or list, but with a map, it can instantly become clear. Another important point that Mitchell makes is how scale affects a map. What may look significant at one scale may not look significant at another scale, and this shows how easy it is to come to a false conclusion when one does not think about scale. Another important point that was discussed in this chapter was how mapping involves making decisions about what to map and how to map it. This shows how mapping can sometimes be more interpretive than I initially thought. I found this to be interesting because sometimes when one looks at a map, it seems to be a very factual piece of work, but with this reading, I can see how it involves interpretation just as any other type of analysis does. This chapter has made me think about how mapping can sometimes mislead people unintentionally if one does not make these decisions carefully enough. Some questions that I had while reading this chapter were: What is the right way to go about choosing a scale when mapping? What is the right way to go about choosing a classification method to avoid misleading patterns? 

Chapter 3 

Chapter 3 introduces the reader to the importance of mapping quantities in providing more depth in the analysis, rather than just the location of the data. Mitchell introduces the reader to the difference between mapping totals and mapping ratios or density. It was clear that the importance of normalization cannot be overstated, especially when dealing with data that is not normalized. A larger area is likely to have higher totals despite the density being low. Another important aspect of mapping quantities that the author introduces is the different ways in which data is classified. I found the chapter interesting in that the author shows the reader that the choice of classification is likely to affect the general look of the map. Another important aspect that the author introduces is the issue of dealing with outliers. In some cases, the outlier is likely to distort the map if not handled with care. This reminded me that the importance of understanding the data cannot be overstated before mapping the data. The author also introduces the reader to the importance of density mapping in highlighting the data. In my view, this chapter reminded me that mapping quantities is a powerful tool that requires careful consideration in order to avoid misinterpretation. After going through this chapter, I had some questions in my mind. When is the use of totals more important compared to the use of ratios? How does one deal with the issue of outliers in classification? How does density mapping affect the interpretation of the data?

My name is Kainaat Aslam, and I am a senior at Ohio Wesleyan University. I decided to take GEOG 291 to make myself more comfortable working with spatial data and to gain knowledge of how GIS is actually used in life. I have not really worked much with GIS before, and I am excited to see how mapping and visualization all come together. I like to travel, go out with my friends and explore different places of interest so learning more about geography tools actually feels pretty connected to my interests. I am working and going to school at the moment; hence, I like that this class is flexible and allows me to stay on top of my work with weekly due dates. I have also completed the GEOG 291 Quiz.

Schuurman Chapter 1 Response

In Chapter 1, I learned about the importance and widespread usage of GIS. I have come to realize that GIS is everywhere and that it influences almost every field of study. Schuurman starts off by demonstrating that GIS is involved in numerous fields that people would not associate with GIS. She presents examples such as navigation systems, policing, organ donations, detecting clusters of diseases, agriculture, archaeology, and even the placement of businesses. I was struck by the rapid development of the technology to the point that many people now rely on it on a daily basis without being aware of the degree to which it influences their decisions in life. Schuurman observes that many geographers have a kind of ‘love-hate’ relationship with GIS technology because of its strengths and weaknesses.

Another important concept that stood out in my mind during the reading was the “identity problem” of GIS. What I mean by that is that GIS does not mean one single thing; its definition varies depending on the user’s perspective. A city planner might think of a GIS as a computer program that finds boundaries or locates a property, while a researcher might think of a GIS as a science that concerns itself with the way spatial phenomena are represented in computers.

Another concept that came to mind while reading this chapter is that GIS has both technical and philosophical roots. She mentions some research done in the 1960s that would later influence computerized GIS systems such as the early Canada GIS. The chapter also makes the point that GIS isn’t neutral. The choices people make about things like boundaries, categories, scale, and how the map is displayed all shape the way the information ends up being interpreted. There is always some decision-making involved with GIS that will affect what people interpret from their use of the program. This chapter helped me understand that GIS is not just software but rather an approach to thinking about places and what they mean.

GIS Application 1: Public Health and Disease Mapping

GIS technology is commonly used in the field of public health. GIS can track disease outbreaks and the spread of disease. An outbreak can often be represented as clusters or groups. Using the technology, public health officials can easily pinpoint the areas that are most affected by a disease and plan the most appropriate course of action. Outbreaks tend to occur in clusters on a map, and GIS helps health departments visualize where these clusters are occurring and how they are changing over time. By adding environmental information, population density, or transportation routes on top of the case information, health officials can begin to identify what might be driving the spread of the disease. This makes GIS useful not only for identifying where the disease is occurring, but also for beginning to form hypotheses about why the disease is occurring in a particular place.

https://online.utpb.edu/about-us/articles/gis-geospatial/how-public-health-experts-use-gis-to-fight-disease-and-save-lives/

https://www.cdc.gov/field-epi-manual/php/chapters/gis-data.html

GIS Application 2: Crime hotspot Analysis

Another important application of GIS is the study of crime patterns. Police departments create maps to represent the areas in which various types of crime are occurring and use GIS to identify “hotspots,” or areas that experience repeated or high volumes of crime. This helps to identify the areas that need to be targeted by police patrols or crime prevention programs. Crime mapping also enables the study of the relationship of various environmental factors to crime patterns. Hotspot analysis aids in the identification of crime trends and the evaluation of the effectiveness of crime prevention strategies. 

https://www.esri.com/en-us/industries/blog/articles/crime-analysis-with-arcgis-pro-video-blog-series-part-3

https://www.esri.com/en-us/industries/law-enforcement/strategies/crime-analysis

Mitchell Chapter 4:

This chapter begins with talking about map density and why it is important. Using a density map will allow you to see different features and high/low concentrations of features. You can use the GIS map in order to graph different points of data. There are two different types of mapping: 1) the density of features (like the locations of businesses) and 2) feature values (like the number of employees at each business). There are also two ways to map density, the first one being by the defined area, and the second being by the density surface.

The next section of the chapter compares the two methods of mapping, and allows people to see which method would be better for their data. I found this comparison very useful and something I will come back to! The next step of the mapping process is to calculate the actual density of the area being mapped. A dot map in particular gives people a really quick glance at the density and is very visual. I liked how the graph was portrayed in this. Specifically for a dot density map, they made sure to tell us that we have dots based on smaller areas, but the bigger boundaries would have to be defined. 

I think it was interesting how they discussed calculating density values. It seems to me like they are just based on the cells and what values are in them. But I liked how they included the conversion and equations that would be helpful! The search radius allows for larger and more general patterns within the map. It lets you specifically put what data points you want to be calculated for density in there. Assigning the number of classes and using contours helps with creating the map, as discussed in Chapter 3. The chapter ends with learning about how to discuss your results!

Mitchell Chapter 5: 

As the chapters have discussed, everyone uses GIS differently depending on their job. For example, the authors provided an example of how many drug-related arrests take place within a certain distance of a school, because that will enforce a bigger penalty. There are different types of boundaries you can draw as well. They also discuss how features can be discrete: unique, identifiable features; or continuous: seamless geographic phenomena. You also need to discover if you need a list, count, or summary based on your data. I think it’s cool that you can only choose features that are inside or outside your boundaries and not both, it makes it easier to comprehend. 

However, Mitchell does go on to say that if you need information about what is beyond the area, then you can include that as well. There are three ways of finding out what is inside, and those three ways are: 1) drawing features and areas, 2) selecting the features inside the area, and 3) overlaying the areas and features. Similar to Chapter 4, I liked how they put in a little table of comparison on how to choose the different methods! This is very helpful to me. Making a map is the first step, and after that you have to follow the instructions based on what data you have. Discrete areas are important to note because the parcels inside can affect what is outside. 

You can also specify features using geographic selection, which is finding out what features are in a particular distance of another feature. You can summarize using categories or quantities. The GIS will then select what features are inside the area and then flag the ones that are inside to let us know that they are inside. To read the results you can use the sum, the average/mean, the median, or the standard deviation. The final step is overlaying areas and features, and that seems like the coolest way to me! It will read the boundaries and make a map for us to examine the continuous categories or classes. 

Mitchell Chapter 6:

While using GIS, you can find out what locations and features are nearby. Travelling ranges can include measuring distance, time, and cost, and that can be specific to a certain facility. However, to define a certain range, you can use one of three methods: 1) straight-line distance, 2) measure distance or cost over a network, or 3) the measure of cost over a surface. However, cost does not have to be an amount of money, it can also be measured in time. I thought that was interesting because I didn’t think about it that way! Like in Chapter 5, they talk about a list, count, and summary, and that is how it is defined. 

The authors discuss how the different methods work, and what each method is specific for. Again, they provide a table that allows people to compare the best ways to use each method. If you have features that are within a given distance of a source feature, you should use a straight-line distance method. If you have to create other features in which you have to specify the buffer and source feature, then you create a buffer. It tells you how you can select and tag different features within the map. They also make different diagrams based on the data and also create a distance surface. 

Summarizing discrete and continuous features depends on what data you have, and ArcGIS creates default displays that will do their own colors and features. I think it’s cool that you can specify the colors and boundaries that you want. The GIS will identify the lines in a network, and that will also help define the boundaries. Centers: “they usually represent centers that people, goods, or services travel to or from. You can then find the surrounding features along, or within, the area covered by those lines”. The map will start measuring at the center of your feature, then it will create the map around that and based on your boundaries. You can always make multiple centers too. The chapter ends with more ways to calculate data! It seems pretty easy as long as you’re thorough.