Month: September 2024

I’m Jenna and I’m a senior Economics/Politics and Government double major and an Environmental Studies minor. I am in Kappa Alpha Theta and on the OWU cheer team. Previously to reading this introduction, I had a vague understanding of GIS and what it does as well as its applications. However, this went very in depth and taught me a lot about the science of GIS itself. I generally thought of GIS as being more of a software rather than a discipline of science. The reading made this distinction and talked about the differences between the science and systems. It makes clear that you need to understand the reasoning and history behind the systems holistically in order to properly utilize and practice GIS. However, it discusses that GIS does not necessarily have one singular and comprehensive identity because it can mean many different things. I like this concept because it leaves room for growth and adaptability. I also enjoyed the discussion of the history because I thought of GIS as a solely modern development, but learning about its roots. The different applications of GIS are also very interesting because I understood how prolific it was, but didn’t realize that it touched so many different aspects of everyday life as well as different sectors of work.
I looked at GIS applications for economics and there are some obvious examples that I had studied before or talked about in class, but one concept I found new and interesting was its application in tourism economics. GIS is used to look at movement patterns of tourists to help plan infrastructure as well as marketing and can help look at the economic impacts on specific communities.
 
Another application I looked at was GIS applications in restaurants just because I work in a restaurant and thought it would be interesting to see how they can connect. One thing I found was site selection analysis which can allow businesses such as restaurants to look at traffic patterns and competition density among other factors to determine the best location for the restaurant\

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Mitchell

Chapter 1 Introducing GIS Analysis

This chapter starts with a definition of GIS Analysis, it states ” GIS Analysis is a process for looking at geographic patterns in your data and relationships between features.”. This first paragraph also gives examples of how you may do this could be a very simple like by creating a map or a complex process “involving models that mimic the real world by combining many data layers”. To begin an analysis you must before anything else figure out what information you need, this is often formatted as a question. Other factors you must consider is how the analysis will be used and by who. To determine what method you will use you must know the type of data and features you are working with, you must also factor in your original question and what the analysis will be used for. Results of the analysis can be displayed in maps, values in a table, or a chart. The next section of this chapter discusses different types of geographic features. Discrete features, continuous phenomena, and features summarized by area. There are two ways of mapping geological features: Vector and raster. In the vector model feature shapes are determined by X,Y locations in space, whereas the raster model features are represented as a matrix of cells in a continuous space. The book details that while any type feature. can be represented in either model is it most common for discrete features and data summarized by area to be represented in vector, and for continuous  numeric values to be represented using the raster model. Continuous categories can be represented as vector or raster. There are 5 types of geographic attribute values: categories, ranks, counts, amounts, and ratios. You can identify these values in a geographic feature to help you determine what the feature is, to describe it, or to understand the represented magnitude associated with the feature. Categories and ranks are not continuous values. Counts, amounts, and ratios are continuous values.

Chapter 2 Mapping Where Things Are

The first paragraphs of this chapter explain the importance of investigating the patterns of multiple features on a given map. It gives the example of police using GIS in this way to track crime and decide where to assign patrols.  Which features to display and how to display them is determined by the information you need and how the map will be used. Before creating your map you must have geographic coordinates assigned to the features you wish to map. The brunt of this work is done by the GIS. An optional step before creating your map is assigning a category attribute with a value to each feature. To map single type features you simply draw all features using the same symbol. This chapter explains that GIS stores the location of each feature as a pair of geographic coordinates or as a set of coordinate pairs that define its shape whether that be a line or an area. Using a subset when mapping your features can help to reveal patterns that are less apparent when all features are mapped. Mapping by category by allocating different symbols for each category can help you to understand how a place functions. You can also display features by type to further reveal different patterns since features could belong to more than one category. When mapping using categories sometimes it is helpful to create separate maps for each category as the features may be too close together and make them hard to distinguish one from another. When mapping multiple categories it is important to map no more than 7 on a single map. The amount of categories reasonable to show on a single map can also be affected by map scale, and the features being mapped. If you have more than 7 categories sometimes you can make generalized groups for the categories to make the patterns easier to see. It is helpful to the people who will be looking at your map if you map recognizable landmarks for example: major roads or highways, administrative or political boundaries, locations of towns or cities, or major rivers.

Chapter 3 Mapping the Most and Least

This chapter begins by discussing how mapping the most and least is helpful to find places that meet ones criteria or to see the relationships between places. Mapping features based on quantities rather than just the location of the features adds another level of information to the map you are making. Quantities associated with discrete features, continuous phenomena or data summarized by area can be mapped. Count: The actual number of features on the map. Amount: the total of a value associated with each feature. Counts and amounts can be mapped for discreet features or continuous phenomena. Using counts or amounts is not suitable if you are summarizing by area as it can skew the pattern. It is recommended to use ratios to represent the distribution of features. The most common ratios are averages, proportions, and densities. Ratios show you the relationship between two quantities. Ranks order features form high to low, and depict relative values, this is useful when the direct measurement is difficult. Counts, amounts, and ratios are usually grouped into classes whereas ranks must be mapped individually. If you are looking for features that meet specific criteria or are comparing features to a specific meaningful value  you should create classes manually. But if you want to group similar values to look for patterns in the data you should use standard classification schemes. There are four standard classification schemes Natural Breaks, Quantile, Equal Interval, and standard division. To determine which scheme to use you must create a bar chart with the horizontal axis showing the attribute values and the vertical axis showing the number of features having a particular value. There are 5 given options in GIS to create maps to show quantities graduated symbols, graduated colors, chart, contours, and 3D perspective views.

Hi guys! My name is Devyn Deal. I am a junior majoring in Environmental Studies. I have a great love for the earth and enjoy doing outdoor activities. I love to keep house plants and have quite the collection at home. Above is a photo of me and my sweet girl Nora having a little sit after a hike.

My first connection to Schuurman Chapter 1 is that I too had no clue what GIS was before coming to college. I found it very interesting to see the example of an early version of maps produced by GIS. It is extraordinary to see how far GIS mapping capabilities have come. I had never considered how game changing GIS technologies are in that humans can come to a vastly different conclusion based on the same data depending on weather they are reviewing the data via a numerical output, or through a visual output. It was interesting to learn there are two different definitions of what the GIS acronym stands for. In reading this chapter I learned the word sphericity. This chapter enlightened me on just how many uses there are for GIS. I had no clue there was so many questions and so many different types of research GIS was helpful in, I had only considered the ways presented to me in previous geography classes where we utilized GIS. For example I had never considered how valuable GIS could be in the economic world, or in public health.  I am curious to learn more about how nonprofit groups use GIS to represent themselves. I would also love to learn more about how GIS and feminism intersects. I am curious what some proposed solutions would be in the discussion of how to represent barriers on a map that are fuzzy and not do not necessarily have a precise line, like the black bear and grizzly bear example.

The first GIS application I looked into was the conservation of loggerhead sea turtles as they are an endangered species. In the example that I found satellite transmitters were attached to female loggerhead sea turtles to track their movements. This data was used to better protect the species by having a better idea of where they most frequent. Before the use of GIS it was unknown where sea turtles went after laying their eggs on shore. This study found most adult female loggerheads from Georgia, North Carolina, and South Carolina migrate to the Mid-Atlantic Bight. This study was the first to track the movement of a large amount of sea turtles rather than just one in order to get a good idea of where their home base was.

Chapter 1

GIS is defined as a “process for looking at geographic patterns in your data and at relationships between features” (Mitchell). All GIS analysis has to start but knowing what information it is you need to gather, so you have to start with a question. Once you have gathered the necessary information you need there are multiple ways to display it such as a map, charts, or a table with values. On the maps there are various geographic features. They are; discrete, continuous phenomena, and summarized by area. Discrete features are features that can be pinpointed. I think personally a better word is definite features, since the feature is either there or it isn’t. Continuous phenomena are features that are identified everywhere such as temperature and precipitation. They are features that have no defined borders, and often start out as sample points for certain areas. Then the GIS will assign values for the in between area (interpolation. This is honestly so cool to me. The idea that it will create a blended feature just sounds like a futuristic thing. Finally summarized by area are features that represent the density or counts of features in boundaries like business profits or population density. In all honesty this one looks the least cool (Hey you asked for comments). Features can be represented in GIS by using two models. The first model is a vector which has each feature in a table and the shapes are defined by “x,y” locations (like a coordinate plane). Vector areas are represented by polygons on the map. The second model is raster. Raster is a matrix of cells in a continuous space. The cell size that’s used for the raster will affect the results of the map. A small cell size will have more cells which leads to more detail, similar to that of a screen. When making GIS layers they should be on the same map projection and coordinate system. This way you avoid issues in distortion between layers. Each feature has one or more attributes that identify what it is or how big it is. The first attribute is ranks, which put features in order from high to low. Ranks are relative, so you only know the order, but you wouldn’t know how much higher or lower the values are from each other. The next two are counts and amounts. Counts show you the actual number of features on a map and amounts are any measurable quantity associated with any feature. And the final attribute is ratios, which show how two quantities are related. 

Chapter 2

Maps can be used for a lot of different reasons, whether it’s where a feature is or what the feature is. They can also be used to look at the distribution of features. By looking at this you can see patterns that form and they can help you better understand the area you are trying to study or map. Mapping where things are can better show you where you need to focus or where all of your criteria are met. One example that came to my mind was their watershed GIS that was able to articulate which watershed needed to be prioritized. One really important thing to do with GIS is to tailor the map to who you are marketing to. If I was showing a map to the sheriff’s office, I’m not going to include a layer for commercial sales. To me, this seems kind of obvious but at the same time, I wouldn’t have thought about this. The amount of detail also should be tailored, specifically to the format at which you are presenting the data. If you are presenting on a screen during a meeting, small details might not be the best. But, if you are working on a poster that will hang up in a hallway for an extended period of time, you can add many more details. Each feature in GIS needs geographic coordinates. They can be determined by street names or addresses. Also they can be identified by using longitude and latitude. GIS also requires you to have a code for each feature (i.e. robberies). I didn’t really understand why at first, but then it explains that a numbering system could allow your data to be broken down into subsets. The book says you should not use any more than seven categories for a map. However, in my opinion I think five is a better number because it’s less crowded. The book does come with a solution to complicated data sets. The solution is to have separate maps, one with the generalized data and the other with the subset data. I honestly really like this idea because it would allow one map to be simple, then IF you need more specific data you can look in the subset map. GIS can help you recognize patterns such as population density, characteristics, and relative location. However, you have to be careful because being too zoomed in could reveal different patterns than if you were to zoom out. This really reminds me of observer bias. But honestly it made a lot of sense. I really liked this chapter, I found it very interesting.

Chapter 3

When people map the most and least places for their subject. They are able to see the relationship between the areas. This allows more information to be processed. Not to mention, if you only map out the most frequent places for your feature, you’re not collecting any real data because you are not collecting any control. There’s nothing to compare it to. That’s like looking at a map of the U.S and the only feature is “The United States,” what would be the point? In the section “What type of feature are you mapping?” they resay what they say in chapter 1. They talk about the 3 types of features (discrete features, continuous phenomena, or data summarized by area.). They also reinstate that you have to remember who your audience is. This way you are not representing data that doesn’t pertain to the group who is receiving the map. Quantities are also an extremely important part of mapping most and least. They can be; counts, amounts, rations, or ranks. The chapter goes over counts and amounts again, however it does add some advice. Mapping counts and amounts are used in each of the three types of mappings. It can also be helpful to present quantities differently. Sometimes it is better to present exact locations rather than by generalized areas such as area codes. This also goes into proportions, which can show how much of that area represents the whole. This reminds me a lot of things like political maps, when they show who has voted per county. Creating classes for certain features is also a helpful tool. This allows you to see different features more easily. Using different symbols is the best way to do this in my opinion. Sometimes it is best for the classes to be percentages, this is because it would show you the dense areas vs. the not so dense. But sometimes there are outliers in the data that can skew these percentages. With these you should use a number for the classes, which would allow you to add needed breaks.

 

Chapter 1: Introducing GIS Analysis

 

This chapter begins by briefly introducing the uses of GIS and defining GIS Analysis, which is looking for patterns between geographic features and the relationships between them. This can be done by creating/ using a map or overlapping multiple layers to see differences that may not be readily apparent.

The next section describes the first step in the process, which is Framing a Question. Knowing what information you want from an analysis is key to creating this question, and you need to determine the audience (yourself, your peers, a professor) to successfully set up your methods and frame the question more accurately. The book also describes the different features that you will encounter while doing GIS Analysis, those being Discrete features, Continuous phenomena, and features summarized by Area. Each has it’s own specific use case, with Discrete features being single points on a map, Continuous being variables that are present across the whole area but change (such as temperature or Elevation), and summarized features show counts within a boundary, such as population within county lines. 

The book then shows us two different ways of visualizing data using ArcGIS, those being vector and raster modeling. Vector is good for showing discrete features and summarized features, as they typically use one layer, and Raster is good for showing continuous categories or numbers. However, either render can be used to show any feature. I found this section fairly interesting, as it seemed for continuous data, there was not too much of a difference between vector and raster models, however, there were large differences when discrete and summarized data were done on both models. The next section, dealing with Attribute values, seemed fairly easy to understand, especially after doing work on Rstudio Cloud for the past few semesters, and the final section seems to be nearly identical to R. 

 

Chapter 2: Introducing GIS Analysis

 

The main focus from this chapter, in my opinion, is to make your maps as accurate and as easy to read as possible. The chapter starts with numerous examples of how and why you may need to show others your maps, emphasizing the fact that you will need to know this information. I appreciated that each mapping strategy had its pros and cons described, as it showed that none of these methods are truly useless, just have trade offs.

Every one of these strategies was a different variation on creating different layers to show information, by subsetting the data in different ways. With discrete or continuous data, you can highlight a certain subset by making it a strong, striking color, and the other subsets different background colors.  If you have one map that has every data value on it, it can become clustered and hard to look at. Because of this, it can be very helpful to make multiple maps that each show a different subset, as well as one map which combines them all. 

Another important piece of information that the book emphasizes is to use no more than 7 categories at once, as this can also be overwhelming. If you have more than seven features, you can group certain categories together that have similar traits. Your use of symbols is also important when designing your map. Colors are much more distinguishable than symbols, so they should have a higher priority. However, when using Linear features, you should use different widths rather than colors as that is more easy to see.  Text labels can also be used to label your different categories. The last section of the chapter talks about how you can use different features to understand more about the feature you are looking at. For example, if looking at patterns of growth over an area, elevation can be key to finding the origin of these patterns. 

 

Chapter 3: Mapping the most and the Least

 

This chapter starts out by explaining why mapping minimum and maximum values in data is important. This is because it can show weak points in current systems, and where we might need to improve. There are multiple ways of recording these values, those being “Counts and Amounts,” which are the number of features, “Ratios,” which show the relationship between quantities, and “Ranks,” which order quantities from high to low (and assigns a value). These features can then be grouped into classes, which simplify and group amounts to prevent your data from getting too cluttered. To create these classes, you can either do it manually or use a classification scheme. You only need to do it manually if you are trying to find features suiting a specific criteria, such as a specific percentage or something specific to your area of study. If not doing it manually, you can use the aforementioned classification schemes. Natural Breaks (Jenks) find large jumps in data values and group the data between those lines. Using a Quantile divides groups so that each one has an equal number of features (Essentially having small amounts of large data and large amounts of small data). Equal Intervals makes the difference in groupings equal across the data (Regardless of size or quantity). Finally, there is standard deviation, which groups data by its distance from the mean. When choosing between these schemes, you have to take into account the distribution of your data and if you are trying to find a difference or similarity between. The book also discusses what to do with Outliers if you find them, as some schemes cause these outliers to heavily skew your results. Next, we are taught how to visualize our data on a map. We have five options; Graduated Symbols, which are good for discrete data but can be hard to read if too abundant, Graduated Colors, which are good for continuous and area data, but do not always accurately represent the difference in data, Charts, which essentially have the same pros and cons as the symbols, Contours, which are good for continuous phenomena but does not show individual features well, and 3D perspective views, which have the same pros and cons as Contours. You need to know which schemes to use to make your maps statistically accurate and how to use these map types in order to effectively display our data.

Chapter 1:

• GIS has been growing enormously and the use of it is also increasing. It started as a database but now has many more applications. The first step of GIS is examining geographical patterns and the relationship between their features. This can be done by making a map of these patterns. The next step is to formulate a question to better understand what information you need, the more specific the better. You still may not have all the information you need after this which is why choosing the correct method for your analysis is important. Then comes the GIS to process the data. Finally, the last step is to display the results as a table, map, graph, etc.. Being able to see your processed data is important as it allows for patterns to be noticed more easily than looking at raw data. 
• There are a couple of different features in GIS that affect the analysis process. 

• • Discrete Features: When there are discrete locations or lines the actual location can be pinpointed. 

• Continuous Phenomena: Two examples of this are temperature and precipitation. Continuous phenomena can determine a value at any given location. 

• Interpolation: A process in which GIS assigns values to the area between the points, using the data points. 

• Summarized Data: Data representing the counts or density of individual features within area boundaries. 

• Map Projections: Translates locations on the globe onto the flat surface of your map. The map projections distort the features being displayed on the map and this can be a concern if you are mapping larger areas. 

• Categories: A process that lets you organize your data by grouping similar things. 

• Ranks: This puts features in order from high to low and is used when direct measurements are difficult or there is a combination of factors. 

• Counts and Amounts: Shows you the total numbers, and the number of features on a map. 

• Ratios: Show you the relationship between two quantities. These are created by dividing one quantity by another for each feature. This is used to even out the difference between large and small areas. 

 

Chapter 2:

• This chapter is set up similarly to the first chapter in which it explains the step-by-step details about figuring out what to map and how to use it. It also focuses solely on what is on the map and the presentation of it. To properly use a map one must figure out what map is appropriate for the issue addressed. You have to think about from the perspective of someone who knows nothing about the data, what would they need to see on the map to properly interpret the data. Just like in the last chapter with making categories, these features that were categorized need to have their code of identification. Codes can indicate the major type and subtype of each feature. 
• Originally I had no idea how to start making these maps but I understand a bit better that each process is step by step and not all at once. Such as in making a single map type, you add features by drawing symbols on the map. Mapping by category can show patterns of that specific data. 
• There seem to be a lot of different ways to present the data on the map such as mapping by category as stated before. Displaying the features by type allows you to use different categories to display different patterns instead of just using category information. However, with any feature, you do not want to display too much on a map as it can make patterns difficult to follow. To fix this problem you can always group the categories. 
• I kept reading about symbols and wasn’t sure if it was as direct as it seems but it is. Choosing a symbol is as simple as picking one, but it can also help show the pattern of the data. Symbols usually use a combination of shape and color. 

Chapter 3:

• The start of the chapter seems to be a small refresher to the last chapter about what you need to map. Once again it is important to remember who is going to be seeing the map, as you may be able to present the data differently depending on the person. In the past chapters there was a lot of discussion about mapping categories but mapping individual data is just as important. While it may take more effort it does create a more accurate representation of the data. 

• Classes: Groups features with similar values by assigning them the same symbol and allows you to see features with similar values. This does change how the map looks. 
• Natural Breaks: This is done by using classes based on natural grouping data values, separating them from highest to lowest. 
• Quantile: Block groups with similar values are forced into adjacent classes. The block groups at the high end are put into one class. 
• Equal interval: The difference between high and low is the same for every class. In this example, it allows for the blocks with the highest median income to be identified. 
• Standard deviation: In this case, the classes are based on how much their values vary from the mean.
• Natural Breaks: Values within a class are likely to be similar and values between classes are different. Due to the natural break finding groupings and patterns inherent in the data. 

• There are multiple formats to make a map such as graduated symbols, graduated colors, charts, contours, and 3D perspective views. Understanding which features you are using is important to making the map. If I were to have discrete locations or lines I would use graduated symbols to show value ranges,  charts to show both categories and quantities, or a 3D view to show relative magnitude. The chart starting on 154 will probably be useful later down the course. 

Mitchell

Ch. 1

Creating a map might not initially seem like a deep analytical task, but it involves several layers of analysis. Mitchell categorizes data into different types to better understand and represent geographic information. Discrete features refer to specific locations that can be precisely pinpointed, such as linear paths or individual spots. In contrast, continuous phenomena can be measured anywhere within a given space. Interpolation is used to estimate values for areas between measurement points. Although parcels provide a broad area of data, their non-legal definition can introduce some margin for error.

Boundaries help group data into similar types or categories and are usually legally defined, creating a structured way to summarize data by area, like demographic or business information. When features are tagged with codes that assign them to specific areas, statistical analysis on the data table is required to prepare it for mapping. GIS technology allows for overlaying features on areas without predefined codes to determine what belongs where.

Geographic features can be represented in two main ways: vector and raster. Vector representation involves defining features by specific x,y coordinates and tables, which requires precise location data. Analysis with vectors typically involves summarizing attributes in a data table, though sometimes raster data is used for combining layers. Raster data represents features as a matrix of cells in a continuous space, with each layer representing a different attribute. The accuracy of raster data depends on cell size—the smaller the cell, the more precise the information.

Map projections and coordinate systems are crucial when mapping large areas, as they account for the Earth’s curvature. Attribute values can be categorized into several types, including categories (groups of similar things), ranks (ordering features by relative importance), amounts and counts (total numbers showing magnitude), and ratios (relationships between quantities to better reflect feature distribution).

Ch. 2