Month: January 2025

Chapter 4 focuses on the importance of file geodatabases (FGDBs) in GIS, particularly for efficient storage, organization, and querying of spatial data in ArcGIS Pro. Before reading this chapter, I didnt’ have a full grasp of the significance of FGDBs. However, I quickly learned that spatial data in GIS has geographic features, making it more complex than simple tables. FGDBs allow for fast queries and spatial relationships, which reminds me of databases in bioinformatics. One of the most valuable insights was that FGDBs allow the storage of multiple layers efficiently, and they provide better performance compared to traditional shapefiles. I now understand how much more flexible and organized FGDBs are, especially for large datasets. If I see a .gdb folder, I now know it holds many classes, raster datasets, and tables. The chapter also provided hands-on tutorials, such as how to create geodatabases and import shapefiles. One of the tutorials involved summarizing crime incidents by neighborhood, which I found particularly interesting from a Data Analytics perspective. Additionally, I found Python’s integration with ArcGIS valuable and would love to explore how it can automate geodatabase tasks. Some lingering questions include whether FGDBs have limitations for large environmental datasets, which I hope to explore further.

Chapter 5 deepens my understanding of how maps can reveal hidden patterns and serve as powerful tools for decision-making. Before this chapter, I hadn’t fully appreciated the significance of mapping areas and how it can uncover insights that raw data tables cannot easily reveal. For example, maps can show where the highest crime rates are in a city or identify hospitals within a 5-mile radius of schools. By condensing and summarizing complex data, GIS helps make sense of vast amounts of information. I was already familiar with latitude and longitude but had no prior knowledge about map projections and coordinate systems, which initially felt like learning a new language. I learned how selecting the wrong projection could misalign datasets and severely distort analysis, which made me realize the importance of choosing the right projection for accurate results. Additionally, the chapter introduced vector and raster data, which I found especially intriguing. I related raster data to microscope images, where grids and pixels have different intensities. The tutorial demonstrated how critical it is to align datasets properly using the correct coordinate system to avoid errors. Surprisingly, I enjoyed working with coordinate systems, even though I had struggled with geometry in high school. Real-world applications of GIS, like the ability to analyze geographic data for decision-making, made me appreciate its power even more. I also learned about spatial data interoperability, which refers to how different datasets can work together seamlessly. My lingering question is about how datum transformation might affect the precision of GIS analyses.

Chapter 6 emphasizes the importance of proximity and spatial relationships for decision-making, such as finding the nearest hospital or analyzing wildfire risks. I appreciated how practical the chapter was, showcasing how GIS tools can automate workflows and solve real-world problems efficiently. One of the key takeaways was learning about geoprocessing tools, such as dissolving features to merge school districts into larger regions, clipping data, and merging datasets—tasks that are essential for handling large environmental datasets. What stood out to me was spatial intersections, where two datasets are overlaid, and the affected area is extracted. This concept was mind-blowing and made me realize how powerful GIS is for analyzing spatial relationships. A significant real-world application I found particularly fascinating was how emergency services use geoprocessing to assign fire stations to fire zones while ensuring response times meet requirements. The chapter also touched on calculating straight-line distances, which represent the shortest possible path between two locations. While this is simple, it’s often unrealistic in real-world scenarios, as factors like driving routes, sidewalks, and crosswalks must be taken into account. GIS isn’t just about mapping; it’s about solving practical problems in everyday life. I also began to consider how GIS can handle large datasets during geoprocessing. I wonder if this process would slow down with larger data sets or how GIS can integrate road issues and other real-world factors to calculate more accurate analyses. Overall, this chapter gave me more confidence in using ArcGIS Pro. Initially, I struggled with understanding the purpose of GIS, but now I feel much more equipped to apply these tools for meaningful analysis and decision-making.

Just overall all that I have learned about GIS so far has been fascinating and the amount of detail that all of this entails has been super interesting for me to learn.

Chapter 4: 

Chapter four opens discussing density mapping. The purpose of this style of mapping is to more accurately portray clustered information who’s data would be hindered rather than elevated had it been individually mapped. It utilizes a standard unit of measurement, such as square miles, to provide a map with a clearer distribution. One of the main examples for when density mapping is useful is census tracts and counties. The book explains that due to their often arbitrary boundaries, these divisions of land can inaccurately represent data.

The chapter discusses the different ways that density mapping can be carried out. Using a dot map or calculating a density map for each area are the two ways given on page 110. A dot map is exactly what it sounds like, a map that you add dots to to represent the data. Rather than one dot representing each individual data point it represents a ‘specified number of features’  (e.g. 1 dot = 100 households). These dots are distributed randomly within each area meaning they don’t represent the data’s specific location either. The closer these dots are together the higher the density of the feature being represented is in that area. In order to calculate the density of an area, “you divide the total number of features, or total value of the features, by the polygon. Each area is then shaded based on its density value.” There are many map comparisons throughout the explanations that show how density surface becomes more effective when comparing mapped individual features.

The chapter also discusses the importance of your search radius. A larger search radius allows the GIS to consider more features when making calculations. While smaller search radiuses allow the GIS to represent more localized variation. It also makes an important emphasis on the point that the radius and density units do not have to be the same. 

 

Chapter 5:

The explanation and example of why to map what’s inside of an area made it much more interesting to read about. It took me a moment to comprehend what was different about “mapping inside” of an area vs creating another kind of map. However, the way in which this chapter explains this to be used as an ever-evolving tool puts it into a much better perspective. Mapping inside of a specified area allows us to monitor what’s occurring inside it, or to compare features from inside several areas. This can often shift the need for action or not. Distinguishing whether or not you need to map inside one or multiple areas will determine how much work is required to create these maps. Mapping inside single areas provides you with a lot, such as; A service area, a buffer, an administrative or natural boundary, a boundary you create, and other features. When mapping instead several areas, you are able to compare the findings, thus comparing these multiple areas. 

Discrete and continuous features were a topic that was very informative. Discrete features exist in well-defined boundaries. They represent things that occur at very specific locations. These would be things such as roads, buildings, etc. Continuous features exist over a broader scale. These are things that take large amounts of distance to change. These are things such as temperatures or elevation. 

I really appreciated the A summary of a numeric attribute section. There is nothing better than a book that can be explanation-dense just giving the reader a list of definitions. Thankfully all of the most common ones listed are basic concepts from the Statistics class I am also taking this semester. One of the very helpful aspects that has been very strong in this chapter is the What the GIS does headings. As i’m writing this I am going back over one of these sections that refers to overlaying areas with continuous values. Hearing the processes of the software helps me understand the actual uses for these actions.

 

Chapter 6:

Chapter six opens with discussing mapping ‘nearby.’ This is something that I guess I have never really considered to be as extensive as the chapter lays it out being. One part of mapping ‘nearby’ that intrigues me is how you define what is ‘nearby.’ As my interests usually lie in urban planning, this often looks different depending on the type of location you’re operating in. According to page 182, “Deciding how to measure ‘nearness’ and what information you need from the analysis will help you decide which method to use.” The term area of influence also caught my attention whilst reading. The idea of mapping a feature’s impact and scaling it is an extremely useful and exciting feature. Mapping the distances between places like schools and corner stores that sell nicotine products cannot always immediately point out the issues. 

I had a somewhat difficult time understanding what the text was referring to when the phrase costs were used to reference the measurement of something that’s nearby. For a minute I really thought we were talking about gas pricing or how much a subway card costs, when the discussion of time being a cost began on page 184 it started to make a lot more sense. This addresses one of my main concerns about ‘mapping nearby.’ As the United States has been bulldozed for vehicles, I was concerned that ‘nearby’ would be confined to a physical closeness.

Chapter 4

Chapter Four covered Map density and its purpose. By mapping density, we can see where the highest concentration of a feature is. Mapping density is important to show the number of people per census tract. Density maps are something I am familiar with and have used before in different classes. What I found interesting was that you can map the density of features or feature values. For example, you can map business locations or the number of employees at each business. You can also map density graphically, using a dot map, or calculate a density value for each area. The operation of creating a map by a defined area or by density surface varies greatly. The density surface method may provide better information however it requires more effort. The chapter concludes by reviewing the methods and features of each. It explains what GIS is doing when creating density surface maps, like how the software will total the number of features that fall in an area and divide it by the area of the neighborhood. The calculations GIS creates are determined by the parameters specified by the user. Such as cell size, search radius, calculation method, and units. After reading this chapter, I have learned a lot more about the niche parts of GIS. There are so many important factors that go into map making and it is important to be diligent.

Chapter 5

Chapter 5 started by discussing why we should map what’s inside an area. This is important to mapping because it tells us if we need to take action on a certain topic. Many organizations and people rely on this type of mapping to complete their jobs and make communities better. When mapping, you must determine if you are looking for something inside a single area or each of several areas. Single areas include a service area around a central facility and a buffer defines a distance around a certain feature. As well as a natural boundary, an area you draw manually, result of a model (ex.boundaries of a floodplain). When finding something in several areas you are able to compare them to each other. These areas include contiguous (ex. zipcodes), disjunct (ex. state parks), and nested (ex. 50-100 year old floodplains). You can either have discrete or continuous features in your chosen area. Discrete features are unique and identifiable like locations. Whereas continuous features represent seamless geographic phenomena like precipitation or elevations. There are three ways of finding what is inside a given area by drawing areas and features, selecting features inside the areas, and overlaying the areas and features. Each method explains what it is good for and what information you need for the method . I found it interesting that there were so many options when finding what is inside and what the tradeoffs are between them. They discuss how to choose the best method and how to complete each. Each method has its own criteria and process different from the others. Finally, the tools in GIS can help us create summaries for our results, including a count, frequency, and a summary of a  numeric attribute (most commonly a sum). A count is the total number of features in an area and the frequency is the number of features with a given value or within a range of values. You can display frequency in the form of a bar chart in order to read the data better. I found this helpful to know for the future, because it may be easier to read for me. The summary of a numeric attribute (sum) can just be the overall total of something in the map.

Chapter 6

This chapter begins with asking why map what’s nearby. When using GIS you can discover what is in traveling distance and find out what is occurring within a distance or feature.  I found the example of the wildlife biologist interesting because that is something I resonate with. It discussed that they may want to know what is in a certain traveling range around a stream. They would use the features in this area to determine prime deer habitat.  I found the following paragraphs to be interesting.  It discussed that distance is one way of defining how close something is but nearness does not always use distance. You can actually measure something nearby with cost, for example time, money, and effort expended. By mapping costs rather than  distance you are able to find a more precise measure of what’s nearby. This was interesting to me because I have noticed that sometimes somewhere that is 15 miles away is actually closer than somewhere that is 10 miles away. So seeing that being measured and explained through a GIS standpoint was intriguing. Next the chapter went over three ways of finding what’s nearby, straight line distance, distance or cost over a network, and cost over a surface. Each method is good for certain things and requires different data. I found it very helpful that this chapter gave a list of guidelines for choosing the best method. It helped me better understand why you would use different strategies for different problems. This chapter built on various previous concepts which made the question of why map what’s nearby much easier to understand.

Chapter 4: Mapping density explores techniques used in GIS to visualize data concentration and distribution. Mapping density shows where the highest concentration of features are, making it useful for identifying patterns. Areas with many features may be difficult to analyze visually, so density maps allow measurement using units like hectares or square miles to better understand distribution. This is useful in mapping things like census tracts or counties. The chapter highlights two main ways for density mapping, by defined area and by density surface.  Defined area mapping uses dot density maps, where each dot represents a specified quantity of a feature. A shaded density map can also be used, where polygons are colored based on density values. Density surface mapping is created in the GIS as a raster layer. Each cell in the raster layer receives a density value based on the number of features within the radius. When deciding how to map density, it is important to consider the features being mapped and the information needed. Density mapping can focus on features or feature values, which can lead to different interpretations. Displaying density surfaces effectively requires careful classification of data values. Common classification methods include: Natural breaks, quantile, equal interval, and standard deviation. Choosing the right number of classes is important- too many can make patterns hard to distinguish, and too little may oversimplify. Density surfaces are typically displayed using a single color gradient, with darker shades representing higher density.  Contours can also be combined with shaded density surfaces. Overall, chapter 4 provided a good understanding of density mapping and its significance in GIS analysis. 

 

Chapter 5: Finding what’s inside discusses how GIS allows users to analyze what is inside a specific area, which is important for monitoring and comparing multiple regions. Mapping what is inside an area helps identify patterns, summarize key features, and support decision-making processes. The chapter introduces three primary methods for determining what is inside a given area: drawing areas and features, selecting features inside an area, and overlaying the areas and features. Drawing areas and features is the simplest method, allowing you to create a visual boundary around an area and examine what features are inside or outside. However, this method is purely observational and lacks quantitative data. Selecting features inside an area is a more detailed approach, because GIS can generate lists, counts, or summaries of the features within a defined boundary. The most comprehensive method is overlaying areas and features, where GIS combines the area and its features into a new layer with attributes from both. The chapter also talks about discrete and continuous features. Discrete features are individually countable items, such as businesses or crimes, while continuous features represent measurements that vary over space, such as elevation or weather.  The choice between vector and raster overlay also impacts the accuracy of the analysis. Vector overlay provides precise areal measurements but requires more processing, while raster overlay automatically calculates areal extents but may be less accurate.

 

Chapter 6: finding what’s nearby focuses on what is near a specific feature. This type of analysis is essential for monitoring surrounding areas, measuring distances between features, and understanding spatial relationships. GIS allows for finding what is nearby by using three main methods: straight-line distance, distance or cost over a network, and cost over a surface. Each of these methods has its own practical applications and limitations, making it important to choose the right approach based on the type of data and analysis being conducted.

Straight-line distance is the simplest method and is commonly used to create a boundary around a feature. This technique is useful when a fixed range is required, such as identifying all homes within a 500-foot radius of a proposed construction site. However, it does not account for real-world barriers like roads, rivers, or elevation changes, which can affect actual accessibility. Distance or cost over a network is a more advanced method that considers travel constraints such as road networks or transit systems. This is particularly useful for measuring travel time to a location, such as determining emergency response times for a fire station. Unlike straight-line distance, this approach provides a more accurate representation of accessibility since it factors in infrastructure. Cost over a surface takes analysis a step further by incorporating the effects of terrain and environmental conditions. Instead of following fixed pathways like roads, it measures travel costs based on real-world conditions, such as steep slopes, water bodies, or different land covers. This method is commonly used for overland travel analysis, such as identifying suitable areas for hiking trails or wildlife movement. Overall, Chapter 6 builds on previous GIS concepts by shifting the focus from what is inside an area to what is nearby. By using different proximity analysis methods, GIS provides valuable insights for decision-making in urban planning, emergency response, environmental monitoring, and transportation analysis. Understanding how to find what is nearby is important for making informed spatial decisions.

Chapter 4: Identifying Clusters

Chapter 4 explains how to identify clusters, which happen when features are found in close proximity. By pinpointing, it can help to determine cause of clusters in that particular location. Statistics are used to determine if there are reasons for the clusters or if they happened by chance. Cluster of features with similar attribute values can be brought up using discrete features, spatially continuous data or data summarizing. They are interval or ratio values. Depending on what you are trying to pinpoint, you may have to put a specific period of time, or even a specific date. Clusters are almost always defined by straight-line distance. This could work, unless you are trying to find distance in travel time. The nearest Neighborhood hierarchical clustering specifies the distant features that can be found from each other in order to pbe part of a cluster. It also determined the minimum number to be able to consider it a cluster. It can also show the clustering at different geographical scales. To see the orientation of individual clusters, GIS may calculate the standard deviational ellipse for the points. To find the causes of clusters, you would want to compare clusters to a control group. To do this the control group and clusters can be mapped together, or you can creat clusters for the control group and compare them with the original clusters being analyzed. Clusters can also be identified on whether they are similar to their neighbors or not. Basically, if high values are surrounded by high values, they were similar, and vice versa. Using Moran’s I, means you are interested in local variation. A large positive value for Moran’s I indicates that the feature is surrounded by features with similar values, and a negative value means the feature is around dissimilar features. The G-statistic shows where cluster of high and low values are. There are two different methods. The Gi statistic helps you determine the effect of the target feature and what is going on around it. Gi* is the where you can find hot and cold spots.

Chapter 5: Analyzing Geographic Relationships

Chapter 5 begins with examples of how GIS is used in different fields. Some of the fields mentioned were transportation analysts, environmental lawyers, archeologists, state police, and wildlife biologists. In stats, attributes are the variables. Two analyze the relationships between attributes, you can use a defined area, sample point, or raster cell. The variables from different layers need to be associated with the same geographical unit. A ratio needs to be used if the locations are two different sizes. For different sets of features, they need to be combined somehow. To do this, you can either do a polygon layover or create rasters of the areas, making them the same size. Variables can also be created to represent spatial interaction between features. These features could be distance, travel time, or travel cost. Statistics is a huge thing when coming to analyzing geographic relationships. spatial autocorrelation is one. It violates the assumption that observations are independent. It brings the redundancy into analysis. To study the relationship between two variables as well as the nature, you measure the extent to which they vary together. Values have a direct relationship is the they both increase when one of the also increases. If one decreases while another increases, that is an inverse relationship. Other than that, there won’t be a relationship. There are also posiitive and negative correlations.

CHAPTER 4

density maps are something I have always taken an interest in, particularly in the ecological field. It’s always interesting to see the population density maps of local species. I also don’t usually think of density maps like the book demonstrated. They use business density instead of points. The book gives a great definition of when to use mapping density; ” mapping density is especially useful. Mapping density is especially useful when mapping areas such as census tracks or countries that vary greatly in size. As mentioned previously, this is how I viewed density maps. The color gradient makes density maps easy to follow and understand. The map Mitchell uses to show logging roads on page 108 does an efficient job at showing the density of the logging roads. However, it is confusing to follow. While reading, I was initially confused about the difference between map features or feature values. The book defines features as locations of businesses and feature values as the number of employees at each business. The visual figure on page 109 does a good job of demonstrating the differences, leaving me with no further questions on the differences. My previous understanding of density maps was strictly limited to the color gradient style that we are most familiar with. While reading Mitchell’s book, I was informed on the use of density maps as dot maps for this example. They still use businesses but one dot equals five businesses to show the density. One interesting point about dot maps was the dogs are distributed randomly within each area. They don’t represent an actual feature location however, the closer that together the dots are the higher density of features in that area.

CHAPTER 5

Chapter 5 is about why map what’s inside. Mitchell says on page 134 by monitoring what’s going on in an area, people know whether to take action. He uses the example of a district attorney who would monitor drug related to arrests to find out if an arrest within 1000 feet of a school if the arrest occurred within 1000 feet, stiffer penalties would be applied. The nice part about the density map that I learned about is you can find out what’s inside a single area or inside of several areas. When I originally read about surveying multiple areas, I was initially confused about the logistics behind it, but the book uses a great example, such as ZIP Codes or watersheds. The book has a good section on whether the features inside the map are discreet or continuous. It goes on to describe discreet features as unique, identifiable features you can list,  count, or summarize. A numeric attribute is associated with them. Continuous features represent seamless geographic phenomena. The example the book uses is especially continuous categories or classes such as vegetation type or elevation range *topographical maps. The section on page 147 under comparing methods was extremely insightful. While reading through chapter 5 i found myself wondering when to use which methods the three-layered tables lay out all three methods: drawing areas and features, selecting the features inside the area, and overlay the areas in features. Was extremely useful on what each method is good for the types of features that uses and the trade-offs associated with them. another thing I found interesting in Chapter 5 was the ability to take the data from the map and put it on paper. a count is the total number of features inside the area. this was taking was we collected and allows us to use it in a data set. 

CHAPTER 6

As I read in chapter 6, I think all of the learning objectives which were: Y map what’s nearby, defining your analysis, three ways of finding what’s nearby, using straight line distance, measuring distance, or cost over a network, calculating cost over a geographic surface were addressed fully and to the greatest extent. The first section wide map what’s nearby was extremely applicable to your daily life as they mentioned you can find out what’s occurring within the set distance. You could also find out what’s within traveling range. One thing I wouldn’t have thought to take into account about measuring distance was measuring flat plane, or the curvature of the earth. A recurring concept that we have seen through all six chapters now was needing to know the specific information you are looking for from the analysis i.e. do you need a list count or summary? Once again, I found the comparing methods table to be extremely useful a compared the three methods straight line distance, distance or cost over a network,  and cost over surface. The table gave the appropriate uses the rhyming features. You should look for the measures the pros and the cons and the very next section called choosing a method put the table into action and made it very straightforward. Much later in the chapter 6, it gets very in depth on making maps using distance using costs how arc GIS works specifying network layers stops, and turns travel parameters. It’s quite impressive. How much computing you can have arcGIS do.

Chapter 4:

Chapter 4 is all about mapping density, which helps us see where things are more concentrated instead of just plotting individual points on a map. This makes it easier to spot patterns and understand areas of high and low activity. One way to show density is by using different shades of color, where darker areas mean higher density. GIS has a few ways to do this, like graphs, dot density maps, or creating a density surface, which is the most detailed but also requires more data.

When making a density map, things like cell size, search radius, calculation method, and units of measurement matter a lot. A challenge is that data is often summarized by area, meaning it gets assigned to the center of a region, which might not always be accurate. The way we choose to display data can change how it looks, so different settings in GIS can give different results. The flexibility of GIS allows for different approaches, but it also means results can vary widely based on how data is processed. Another factor to consider is how data is collected, smaller datasets may not show accurate density trends, while too much data can lead to an overly complex representation.

Some questions I have: How do you decide the best search radius for a density map? How does interpolation affect the final results? How do different density visualization methods compare in terms of accuracy and clarity?

Chapter 5:

Chapter 5 talks about mapping what’s inside a certain area. This is useful for things like zoning laws or analyzing crime rates. GIS helps with this by letting you identify, count, and summarize features inside a set boundary. The ability to determine what falls within a boundary can help city planners, businesses, and law enforcement make better decisions.

There are three main ways to do this. First, you can just draw the boundaries and see what’s inside, which works well for simple visualizations. Second, GIS can select features that fall within the boundary and list them, which is useful for identifying all features within an area. Third, you can overlay the area and features to create a new layer that combines the data, which is the most flexible option and allows for deeper analysis.

Some things to keep in mind are whether the features you’re analyzing are continuous or discrete and whether they completely fall within an area. Some features might only partially exist within a boundary, which can lead to challenges in classification. GIS tools can help refine these classifications by weighting how much of a feature falls within a boundary or by assigning partial values based on overlap. These methods help summarize data across different regions, like neighborhoods or districts, allowing for deeper insights into how features interact with specific areas.

Some things I’m wondering: What are the limitations of overlay analysis? How does GIS handle features that only partially fall within an area? How could boundary analysis be improved to ensure more accurate data representation?

Chapter 6:

Chapter 6 focuses on figuring out what’s nearby. This is important for things like emergency planning, business locations, and public services. But “nearby” can mean different things, it could be a straight-line distance, a route along roads, or even the time it takes to get there. Understanding the right way to define proximity is key to making GIS analysis useful.

GIS offers several ways to analyze proximity. You can create buffers around a feature to set a specific distance, which is useful for defining areas of influence. Another approach is making spider diagrams that show connections between locations. Road networks can be used to measure real travel distances, while cost-based distance analysis helps measure things like travel time or terrain difficulty. These different methods allow for flexible applications, whether determining emergency response times or measuring accessibility to public spaces.

Choosing the right distance threshold is key. A 10-minute drive and a 10-mile radius might give completely different results. That’s why understanding how distance works in GIS is important. Road networks can change over time, and factors like traffic congestion can affect how “nearby” something actually is. GIS allows for adjustments based on real-world conditions, making its insights more practical.

Some questions I have: When is it better to use straight-line distance versus road networks? How does GIS factor in things like traffic when measuring distance? What are the best ways to incorporate real-time data into proximity analysis?

Chapter 4

Map densities are very useful for finding patterns, which is very relevant in terms of public health and potential outbreaks that need to be tracked and have surveillance on them. I found the section on deciding what to map based on features and information you need to map to be very useful. I also thought that the business and employees per square mile density map examples on page 109 are very interesting and are a good example of being able to use density maps for finding patterns. I found the dot density maps to be very useful and something that I do not think I have really seen before. I liked how it uses the color key and dots to indicate the values of density, which I find to not only be visually appealing but also useful when interpreting the map. It was also interesting to learn about when dot maps are most effective, like when the dots are too small or too far apart to convey a true pattern to the viewer. I had no clue what a density surface was until learning that it is created by raster layers. I was still slightly confused by what that really meant but the section about what GIS actually does was insightful to learn that it “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.” This made even more sense after reading the section about using graduated colors and how you have to assign values to each layer in order for them to build on top of each other. In addition, the contour feature seems like it will be useful to better define these boundaries of densities in some particular maps that have a rate of change or rapid change.

 

Chapter 5

I liked how Mitchell discussed why mapping inside areas is so important because “by monitoring what’s going on in an area, people know whether to take action.” The example of the district attorney and crimes near schools is not an example that immediately came to mind but is an issue that might not be as easily identified without mapping. The differentiation between single areas versus multiple areas was useful because they have distinctly different purposes in terms of monitoring. Again, some knowledge from statistics courses seemed to be useful when talking about discrete versus continuous features because these definitions are very similar to their statistical counterparts. However, it was good to review that discrete features can be listed or counted or can be summarized by a numeric attribute. Continuous features are defined as features that represent seamless geographic phenomena. Continuous feature examples are categories or classes. The section on information needed for analysis was also very useful to know what type of map you could make with the given example features for a flood plain. The section on “Drawing areas and features” gives a good overview of the importance of what you are trying to portray in terms of features and what you need in terms of data sets. The table on page 147 comparing drawing areas and features, selecting the features of the area, and overlaying the areas and features was useful to understand how each type can be used for different features and their pros and cons. The overlapping areas and features help define discrete features inside continuous areas. I really enjoyed the maps under the “Overlaying areas with continuous categories or classes” on page 167, I think that these were both very useful as an example of how useful this feature can be when comparing different data sets.

Chapter 6 

I think that this section was very interesting because it mentioned how GIS can be used to look at what is happening within a traveling range and not just a fixed spot. I found the example of this to be very useful in terms of understanding what the author meant by looking at what is happening within a traveling range. I think that another good example of this would be notifying people within 500 feet of a health hazard, or maybe in extreme cases or a hypothetical situation – there would be an outbreak of measles. Due to the severity and how contagious measles is and also how long it stays on surfaces and in the air of a room, this technique could be useful in an outbreak scenario because then all of the people within a certain traveling distance in the area could be notified and tested to prevent more cases or severe cases. The section on street segments was also something that seemed very familiar and maybe even “simple” but then when I started reading more about it, it started to become more complicated due to their complexity of networks, distances, and especially costs. Learning about the per-unit cost put things into perspective to me in terms of understanding why our infrastructure is not always in good condition because when you think about this cost per unit and how many units there are in so many places, I have started to understand why everything is not kept up with completely, especially in weather climates that roads will experience freezing and thawing to create potholes (thanks, Ohio!). Learning about cost turntables and how important they are in terms of calculating costs. As much as I really hate to think about money sometimes, I feel like this was a good section to have towards the end of the book because it helped me make sense of how important cost is in terms of GIS.

Chapter 4

In chapter four we go over mapping density. This chapter teaches you how mapping density lets you see patterns of where things can be concentrated. Mapping density shows where the highest concentration of features is, the individual features, and areas of different sizes. A density map lets you measure the number of features using a uniform areal unit like square miles on a map, it can let you determine where to place what you might want in a dense area. When deciding what to map it is important to think of the features you’re mapping. You need information to go on the map to decide the density value on the map. This chapter goes over deciding if you want to map features or feature values. Density of features are like the amount of locations in a place, and feature values are like the number of people populating these locations. They can give very different results and shift the density of your map, and your results. There are two ways to define mapping density, by the defined area, or by density surface. When mapping density by the defined area you define it graphically using a dot on the map. When using a density surface you use the GIS raster layer and it usually requires more effort but it provides a more detailed map. I like the visual aspect of dot maps, it gives a quick sense of density on the map without really needing to look at the legend. This chapter teaches us about cell size, it determines how coarse or fine the patterns will appear. The smaller the cell size the smoother the surface, but the more cells when using a large cell size it will take longer to process and take more storage space. With units in GIS you need to choose a value for the units you’re mapping, and the results on your map will depend on how you created the density surface. 

 

Chapter 5

In chapter five we went over mapping what’s inside, drawing areas and features, overlaying areas and features, and defining our analysis. When mapping what’s inside the map you can compare and monitor what is going on inside each area and take action when needed. When mapping it is important to get the right data and collect the correct information in order to monitor the activity. When mapping multiple areas you have to make sure to identify each area uniquely using a name or numbers. The features can be discrete or continuous. When features are discrete they are unique and identifiable, they can be listed or counted. Continuous features represent seamless geographic phenomena, you can summarize the features for each area. When using GIS you can use lists, counts or summaries inside an area to find out information. There are three ways of finding what’s inside the area, drawing the area, selecting features inside the area, and overlapping areas and features. There are three ways of finding out what’s inside the map. You can draw the areas and features by creating a map and showing the boundary of the area and the features, it is good for a visual approach. Selecting features inside the area is good for getting a list or summary inside an area, it specifies the area and layer containing the features. The last way is overlaying the areas and features, it combines the area and the features to create a new layer comparing the two layers. It’s good for finding which features are in each of several areas. In order to choose the right method for your project you need to decide if you only need to see the features inside, if you want to see the summary of features fully or partially inside, or need a summary of continuous values. 

 

Chapter 6

In chapter six we find out why we map what’s nearby, mapping what’s nearby can let you monitor events in an area, or the features affected by an activity. When using GIS you can find out what’s happening within a set distance of a feature, it helps you identify the features inside the area that are affected by an event or activity. Traveling range can be measured using distance, time, or cost. This can help define the area served by a facility. Knowing what’s within traveling range can help delineate areas that are suitable for specific use. In GIS you can also take into account the curvature of the earth when mapping larger areas, you can use output layers to correctly display the curved surface of the globe. You can get three things once you’ve identified which features are near your source, a list of features, a count, or a summary. The count can be a total or a count by category, a summary statistic can be a total amount, an amount by category, or a statistical summary. To find the range of what you want, you can choose a single range or several, when specifying more than one range you can create inclusive rings or distinct bands. Inclusive rings are useful for finding out how the total amount increases as the distance increases. Distinct bands are useful if you want to compare distance to other characteristics. To find out what’s nearby you can use straight-line distance, you specify the source feature and the distance, and GIS finds the area or surrounding features within the distance. This is good for creating a boundary or selecting features at a set distance around a source. When You can use two other methods, distance or cost over a network, and cost over a surface. Distance or cost over a network can help you specify the source locations and distance or travel cost along each linear feature, GIS is able to find which segments of the network are within the distance or cost. When using cost over a surface you specify the location of the source features and a travel cost. GIS creates a new layer showing the travel cost from each source feature