Category: Course Student Work

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

Chapter 1: Introducing GIS Analysis

 

This chapter begins with an overview of GIS analysis and its crucial role in understanding different geographic patterns. Since this is an introductory chapter, many concepts are introduced. GIS Analysis is the most prominent term introduced, and it explains how GIS is used to analyze spatial relationships and patterns in geographic information. He also introduces spatial patterns, which are the layout of shapes and features in a space, and spatial relationships, which are how certain geographic features interact with each other. These techniques significantly impact decision-making, as they can help us visualize certain things and significantly influence our choices. Another set of terms this chapter goes over are different types of data.  I was interested in this concept because they can all be used differently for other purposes and data sets. For instance, point data can represent specific locations, such as cities or landmarks, while line data can represent linear features like roads or rivers. The different data vector points (points, lines, and polygons) help to show specific features or regions, while raster data (grid-based) helps to show a surface and how it changes. Vector points were what I associated with GIS, but it was interesting to learn about raster data since I hadn’t thought of the different types of data that a person could input. The attributes linked to data were also interesting to me, and they are used to help us and the computer understand the data better. The steps outlined in this chapter for GIS analysis reminded me much of the scientific method, with the various steps of testing, retesting, and analyzing while looking at an issue from multiple angles. The most significant takeaway from this chapter is the pivotal role of GIS analysis in decision-making, as it can help us visualize certain things and significantly impact our choices, thereby underlining its importance and influence. Statistics, a crucial component of GIS analysis, provide the tools to quantify and analyze patterns in spatial data, making them an indispensable part of the process. 

 

Chapter 2: Mapping Where Things Are

 

This chapter also places a strong emphasis on statistics, highlighting that a robust understanding of statistics is instrumental in interpreting spatial data. Statistics play a pivotal role in GIS analysis, providing the tools to quantify and analyze patterns in spatial data. One of the significant terms in this chapter is spatial statistics, which applies statistical techniques to spatial data to quantify and analyze patterns. The next term is descriptive statistics, which are just basic statistics. These include mean, the average of a set of data; median, the middle value in a set of ordered data; and standard deviation, the distance from the mean that a large percentage of the data is. This can help compare outliers and find where similar values are located. The chapter also highlights the process of creating a map. In making a map, the person will provide each location’s coordinates and a category value. Then, the person must specify how they want the information displayed. Too many categories can be overwhelming, while too few will show some patterns and can leave out specific details. Visual information and statistical information can be used to locate these patterns. The most significant part of mapping is deciding what, where, and how to map things. Using the correct map is essential because if not, the data can be confusing and lead to misleading results. Just like when writing, the audience is significant as well. The map could be more complex if you have scientists with a lot of background information. If the map is intended for the general public, it must be more straightforward and contain more information to give context. 

 

Chapter 3: Mapping the Most and Least

 

This chapter, which focuses on techniques for analyzing patterns, introduces the concept of spatial pattern analysis. This technique examines geographic arrangements to find patterns, enhancing our understanding of the distribution of certain items and the factors influencing them. Visualization plays a key role in this process, as it allows us to see high- and low-density points. When mapping, three different quantities of features will be given: discreet features, like locations or regions; continuous phenomena that show a constant value in 3D; and data summarized by area, which separates areas through shading, usually with a gradient of colors or contours. When creating maps, it is essential to use specific analysis techniques to give appropriate and helpful results. After being given a quantity, values can be given a symbol to make the map more visually appealing and understandable. This visualization aspect is so important to help see patterns, but if that isn’t enough, statistics can also be looked at. Classes can be added to separate higher and lower values, making the map more understandable. Classification schemes are used to create classes. I like having black-and-white categories because I tend to overthink things in grey areas and which category they should go in. I found these common schemes very helpful: quantile, equal interval, standard deviation, and natural breaks. For quantile, the number of features in each class is the same. The space between high and low values is equal for each class in equal intervals. In standard deviation, the classes are based on how far away from the mean they are. Lastly, in natural breaks, the classes are created based on groups in the data, close values. Overall, the type of map used is essential, and choosing an excellent way to analyze the data can make finding patterns tenfold easier. Selecting a map or analysis method that is less effective can lead to a lack of finding a pattern, which could have lots of impacts on society.  

Chapter 1:Introduction to GIS Analysis

GIS analysis is similar to something that I am sure many of us have been doing, but now we are putting it into a geographic context. Especially when looking for both patterns and relationships, sometimes these maps can be self explanatory, however the more interesting problems are finding ways to explain both the correlation and causation of what occurs and what data is shown with the maps. The GIS analysis process that the first chapter outlines reminds me of the scientific method if we were not to make a hypothesis, finding a question to ask, finding a way to frame the question while also being able to measure said data, understanding the data that is presented and gained, choosing a method of measuring the data and method to present the data for further analysis, processing the data, and looking at the results of the data. One thing that I had not considered before was the many ways of looking at the results that are gained, such as displaying them on a map, looking at a data, or within a chart. Measuring geographic features can be extremely useful, but I was unaware at first of how many different types of features there are, there are things easier to measure such as mountains, city limits, and where rivers are located, those things can be pinpointed and are considered discrete features.
Discrete features seem to answe the question of ‘is this feature here or not?’. Continuous phenomena is something that is a value that can change during the average day, such as precipitation or temperature. Continuous data can be represented in areas enclosed by boundaries assuming that everything within the data is the same type.
Features summarized by data can represent a simple count or density of various features within a certan enclosed area.Within GIS, there are 2 different ways to represent data, through vectors and rasters. With vectors, the system uses a table with shapes & points to represent data at certain sets of coordinates and bounds, this is most similar to a graph or shapes. With a raster model, it is similar to a large excel sheet where you paint the different types of cells to represent different numbers & data points. The book states that both types of representations of the data are good, discrete features are usually represented with vectors, continuous are represented as either vector or raster, and continuous numeric values are represented with raster models.
With attributes, every feature has some attributes that are able to be used to identify whatever we are trying to represent. These attributes are the following: categories, ranks, counts, amounts, and ratios. A category is an overarching topic that contains a group of similar stuff. One example is that if I were to be mapping a city, i would put office buildings, restaurants, and shops in under the category of ‘Businesses’. With ranks, I would put something in order from high to low, or Excellent -> Poor. With ratios, I would put different colors for the amount of features in a map, if I were to try to map something similar to population density, a lighter color would reflect a lower amount of people living there, and a darker color would represent a higher population. If I were to use a count, I woult count something such as the amount of customers going into a business and make a larger circle around the business if there were more customers.

Chapter 2: Mapping where Things Are:
Looking for patterns can be key for helping me understand the are that I am mapping. Something such as a crime map can help me understand what the biggest issues of the area can be in terms of crime, maybe see what parts of the city meet more crimes vs lesser crimes, which could explain where police usually are, or if crimes get reported in general. The main decision is deciding what needs mapped, what to display and how to display them. There are different purposes for different types of maps, such as the example with the police department, a business needing to know its demographics, and other considerations need to be made. How the map gets used is something that becomes a key issue when thinking about how to create the map, while a city zoning map would be useful for bringing up to a committee meeting, it may not be as useful for other purposes such as the case of where crime occurs. The level of detail is needed to be put into consideration for what type of audience will be seeing my map, will it be for a general audience, or some seasoned professionals? Some key considerations for my maps to make sure that I know I have geographic coordinates, and hopefully have both a category & value for every main part of my map. When I assign a map feature with a type, I must have a code within the feature. These types should be stored prior to adding them to the map so I do not have to go back and add them later. Some categories can be hierarchical, and will have a ubtype, such as general zoning vs mixed usage. When I make my nmap, I need to know what features I would like to display and figure out what the symbols I will be wanting to use. With a single type, I will represent all features with the same symbol, like If I wanted to represent sales by delivery, I would represent each sale with a dot. I can also separate data and map only certain types of data, such as amazon deliveries vs uspc deliveries. I am also able to map by category, this is typically done with different colors, but I will only want to do up to 7 different categories at one time as most people wouldn’t be able to distinguish more than those 7. If I were to display more then 7, it would be more wise to group those categories together on a secondary map that is easier to digest such as the one on Page 40. If my map does its job and presents the information pretty clearly, there should hopefully be a pattern that is able to be more easily seen and understood.

Chapter 3: Mapping the Most and Least

The reason people map the most and least of something is to find a pattern or find qualities of features such as those within real estate. Some things I am able to map with Most -> least is any feature associated with discrete, continuous,m or data summarized by an area. The discrete features are typically represented by graduated symbols while areas are often shaded to represent quantities. Continuous phenomena are typically displayed using graduated colors while even a 3d perspective view can be used to represent the continuous surfaces. Data summarized by data is typically displayed with shading the area via the value while using a chart to use it as a representation of the data. While forming the map, I need to keep the indented audience & purpose both in mind. If I were to just use the map for presentation, I will want to explain what the data points mean, however if I were to be trying to explore the data, the map would provide a good baseline of a direction to go in when it comes to patterns and ideas. There are many numerical ideas that I will want to keep in mind, such as amounts, counts, ratios, or rankings of various areas. I will want to find the best way(s) to represent these through the data. This is typically done in the method of using gradients or small -> large shapes. Once I figure out the quantities and type of quantities, I will want to figure out how to classify the data. If they were to be individually presented, I will not need to group them. If I were to group points of data together, I will want to use classes by assigning them the same symbol. I would want to use standard classification schemes if I were to want to group similar values in order to look for patterns. I’ll be able to figure out the best scheme for creating the class break by looking at the distribution of the values of data. One of the best ways I am able to do this is by creating different graphs or charts using the data. But that being said, it is incredibly important to keep things concise and understandable.

 

Chapter 1: Introducing GIS Analysis

The first step in the GIS analysis process is framing a specific question, which will help decide how to approach the analysis, what methods to use, and how to display the results. The next step is to understand the data and features related to the question to determine the specific method, which is usually narrowed down based on what the results need to look like (either a quick process with limited results or a detailed analysis with precise results). Once selecting a method, the next step is to perform the necessary actions in GIS and analyze the results, whether they are displayed as a map, table, or chart. The geographical features include discrete and continuous phenomena, summarized by area. Discrete locations and lines can be pinpointed, and the feature can be present (e.g., Bodies of water, such as streams, are linear features). Continuous phenomena, such as precipitation or temperature, can be measured and recorded anywhere over the mapped area (e.g., annual precipitation/average monthly temperature values can be determined at any location). Summarized data represents the specific features within an area’s boundaries (e.g., the number of businesses in each zip code), which applies to the entire area, not a particular location. GIS uses two models, vector and raster, to represent geographical features. In the vector model, the features can be discrete locations or events, lines, and areas defined by x,y locations in space. A series of coordinate pairs represent lines demonstrating roads, streams, or pipelines. Areas (parcels of land, counties, or watersheds) are defined by borders and are represented by closed polygons. In the raster model, different features are represented by a matrix of cells (each layer representing one attribute) within continuous space. Cell size should be based on the original map scale/the minimum mapping unit to avoid using too large/too small a cell size (both of which can impact the precision of the map). The geographic attributes include categories, ranks, counts, amounts, and ratios. Categories are groups of similar characteristics (e.g., roads can be categorized as highways, freeways, or local roads). Ranks put features in order from high to low and are based on another feature attribute, such as a type or category. Counts and amounts demonstrate total numbers, with a count being the actual number of features on a map and amounts being any measurable quantity associated with a specific feature. Ratios illustrate the relationship between two quantities and show the differences between large and small areas. 

 

Chapter 2: Mapping Where Things Are

GIS uses mapping to demonstrate the distribution of features on a map rather than at individual features, which helps the user better understand the patterns of the area they are viewing. Mapping can help explain causes for patterns and allow the user to focus their efforts on specific distributions of features. When deciding what to map, the user needs to look for geographic patterns in the data, then use different layers and symbols to represent various features based on the information and results they seek. The map used should be appropriate for the audience and the issue that is trying to be solved; smaller maps should only have the information needed to demonstrate patterns, whereas larger maps will need to present more detailed data/information while remaining readable. To create a map, the user must prepare their data by assigning each feature a location in geographic coordinates and category values. Then, the user will tell GIS if they want their features displayed in a layer as a single type or by category values. When mapping a single type, all of the features demonstrated on the map use the same symbols; although these are basic maps, they can still reveal patterns. Mapping features by category involves using a different symbol for each category value; this gives an idea of how an area functions. The user can also display a subset of categories to uncover patterns and relations between various features (if a map has more than seven categories, it can make the area clustered, so grouping some will help). When choosing symbols to display categories, using different colors for each feature will help distinguish patterns better than other shapes. Including recognizable landmarks (roads, highways, buildings) in a map is beneficial to help people connect meaning to the patterns/results found. Patterns can be seen by looking at the map, or hidden patterns need statistics to measure and quantify the relationship between features. 

 

Chapter 3: Mapping the Most and Least

Mapping the most and least allows users to understand what areas meet their criteria, require action, or highlight relationships between places. Including features based on quantities adds another level of information beyond simple location features and brings a more in-depth understanding of the patterns/information seen. Users can map the features based on three quantities: discrete features, continuous phenomena, and data summarized by area. Discrete features are locations, linear features, or regions (e.g., line thickness determines river fish habitat). Continuous phenomena are areas/surfaces of continuous values using graduated colors, contours, or 3D views (e.g., soil fertility in an area is measured by a color gradient). Data summarized by area is demonstrated by separating different areas/features with various shading (e.g., the number of businesses in each zip code is represented by lighter/darker shading). After determining the quantities, the user must assign a symbol or group of values to each individual value into classes. Mapping individual values allows the user to see an accurate picture of the data and search for patterns within the raw data. Classes are features with similar values assigned the same symbol; users should make the differences in values between classes as great as possible to make the results as straightforward as possible. Users should create classes manually to ensure that their features meet specific requirements/compare values to meaningful values (they should specify upper and lower limits and symbols for each class). A standard classification scheme should be used if the user wants to group similar values to look for patterns in the data; the four most common schemes include natural breaks, quantile, equal interval, and standard deviation. In natural breaks or Jenks, the classes are based on natural groups of the data values. In quantile, each class has an equal number of features. In equal intervals, the difference between the high and low values is the same for each class. Standard deviation features are placed into classes based on the value variance from the mean. When making a map in GIS, it is easy to add more information than needed; remember to keep the information simple, clear, and concise.

Me in Hocking Hills! 

 

Hello! My name is Rylea Gist, and I am a sophomore at OWU. I am majoring in environmental science and biology.

 

Before reading Chapter One of Nadine Schuurman’s GIS: A Short Introduction, I only vaguely knew what GIS was and how widespread it was. I thought it was used primarily by farmers and scientists to study the geographical makeup of specific areas. After reading the chapter, I realized how much of my daily life is rooted in GIS, from deciding the route to specific locations to visualizing consumer data to determine the price of goods. I was super interested in the origins of GIS as well. I would never have imagined that the roots of GIS came from meticulously drawn maps layered together over a lightbox. It is crazy how quickly GIS has grown, from having to be hand-drawn and layered to now being able to put data into a computer to create the maps for you. Logically, it makes sense how and why GIS has developed, considering that humans are such visual learners, as well as the need to consider a variety of different topics at the same time. Combining several maps and seeing a pattern is much easier than looking strictly at numerical data. I also found it interesting that some scientists entirely relied on the maps created by GIS to be complete facts.

In contrast, others were more hesitant and had to ask more scientific questions about the data, its origin, and its effectiveness in different situations. This idea reminded me of my statistics class in high school, where we had to be highly cautious in reading and interpreting statistics because so many areas could be flawed. I wonder how using biased data could create a biased result and what the implications are for society. Overall, this chapter is exciting, and it excites me to keep learning about GIS and see what I can do with it in the future.

The first GIS application I found was an American scientist using GIS to track feral cats on New Zealand’s Auckland Island. These cats are not native to the island, and therefore, they are destroying the biodiversity. The goal is to locate and eradicate the feral cats, restore the island to its natural state, and let the native flora grow. She uses GIS to map the location of cats in conjunction with bird populations to develop a strategy to remove the cats. She also uses GIS to determine population density and the best places for helicopters to place bait. 

Keywords: GIS Application Stray Cats

https://www.esri.com/about/newsroom/blog/gis-analyst-maps-feral-cats-solution/?srsltid=AfmBOopRdZXqEInheDGzv8O7Ae6S6-f0I3WkqjVH8zXjq1Y9RMR5SBCh

The following GIS application is to save monarch butterflies. The Rights-of-Way as Habitat Working Group combines data from various organizations with GIS. This data will help to visualize habitat locations and support these organizations in determining where to plant more milkweed to help the monarch butterfly population. This is so important because it is estimated that monarch butterflies are going to need 5x more milkweed to survive, so this data helps to show exactly where the milkweed should be planted to best impact the butterfly population

Keywords: GIS Applications Monarch Butterflies

https://www.esri.com/about/newsroom/blog/mapping-to-save-monarchs/?srsltid=AfmBOopJemnVkuWwbQzY5SH8FJVfcrcPdR_C1OoTkAOMpKuulrwndwbI

 

 

Hi, my name is Rene Villanueva-Henkle, and I am a triple major in Junior Environmental Studies, Biology, and Philosophy. I spend a lot of my time being outside, staying active, and working on building/fixing computers.

I found it interesting that after the introduction pushes this idea that all disciplines, City Planning, Construction, Conservation, and Social Work use GIS, the initial software was created by two men with an ENVS Background. While I know anyone can use this software, it makes it feel that much more special to use it within this discipline. I found the story of the creator of the initial concept of GIS pretty interesting, in that it preceded its own technology. I was having trouble understanding the difference between GISystems and GIScience, but it became clear to me after the example of John Snow. It was easier for me to see how his work mapping and tracking cholera deaths was GISystems, and him going out into the field and asking questions to the Workhouse inmates and investigating himself was GIScience. 

I was also surprised to read that GIS is the program used in Traffic distribution and disruption calculations. I honestly think at times that Delaware City Planners don’t care about traffic, especially with the booming population coming in from Columbus workers, but I fear I will have to give them more credit. Or perhaps lenience would be the better word. 

I also found the usage of GIS is E-commerce, specifically for sites like Amazon to be particularly interesting. I have noticed in the past few years that there could be as many as 5 different delivery drivers for Amazon on my block wide stretch of Sandusky within one week, all of them being regulars as well. I now realize that it is possible there are people (or automated programs) using GIS to find the most efficient route for each individual driver based on their packages each day, which is fascinating to me. I can’t imagine the processing power it would take to do that for every driver in central Ohio, let alone the U.S. and internationally.

 

First Search GIS+MOSS+POPULATION

This is a pretty primitive version of GIS, but this map shows concentration of lead in moss across Norway over a 15 year span. There are also other maps showing cadmium and mercury concentration in moss, as well as the concentration of all three of these metals in the surface soil in the same places. This study revealed that the Surface soil was soaking up much more of these heavy metals than the moss was. https://doi.org/10.1016/j.atmosenv.2014.09.059

 

Second Search: GIS+COMPUTER+INCOME

This also did not provide what I was looking for (A map of household income compared to how many computers in each household) but proved to be equally interesting. This is a 3d model of the town of Innsbruck, Austria, that is imported into GIS, and shows the locations that would be able to use solar most effectively. The group did computations to show irradiation for 183 simulated days.

https://doi.org/10.1016/j.compenvurbsys.2016.02.007

Hello everyone! My name is Samuel Baer. I am a sophomore majoring in both Environmental Science and Geography. I am a part of the honors program, the symphonic wind ensemble, and Cru. I’m from Mt. Gilead, Ohio which is about 30 minutes from here. I am a commuter so it feels like I’m always driving.

To me this chapter was really interesting. One of the first things that really stood out to me was the fact that GIS has been around since the early sixties. To me it just doesn’t feel like something that would have been made that long ago. I find it interesting that Canada was one of the first nations to develop it, but I don’t find it surprising due to the amount of unpopulated area in Canada. I find the idea of GIS starting in the quantitative revolution is interesting, and it makes sense to me, even though it contains a visual component. It’s really fascinating how philosophical a mapping program really can be. Whether it’s talking about whether it’s more quantitative or not. Also The idea of GIScience is really interesting to me. Specifically studying how to interpret GIS. I had heard of GIS before the course but I didn’t realize how big of a topic it was. I think studying GIS would be more interesting to me rather than GIScience just because the idea of practice is more appealing to me. This chapter made me really excited for the course because the author is very passionate and thorough.

First Search: GIS in Subway Systems

GIS can be applied to public transportation systems such as subways, bus routes, trains etc. Mainly it is used for the navigation between stops. GIS can also be used to track patterns in traffic and even track it live. With GIS, city planners are able to plan and analyze data with more precision. The maps can be automated to plan routes and determine schedules. It also can allow planners to pick more efficient stations and maintenance facilities. https://www.iunera.com/kraken/public-transport/geographic-information-system-gis-public-transit/#:~:text=A%20GIS%20gives%20the%20transit,Useful%20in%20map%20production

Second Search: GIS uses with Watersheds
https://www.hazenandsawyer.com/projects/using-gis-to-visualize-watershed-priorities-in-real-time
Hazen has applied GIS in Gwinnett County, Georgia. By adding layers of land use, septic parcels, and sanitation, they created an interactive map that allows them to determine where their priorities should be. They are able to slide the data, and the map will change to show where the priorities lie.