Geography 291: Geospatial Analysis with Desktop GIS

I read the chapters 1-3 in The ESRI guide. Here are my takeaways and notes:

CHAPTER 1:

• GIS can be used to effectively analyze geographic information; to me, this seems useful for large scale disciplines like ecology and sociology
• Discrete features: features which are location locked. I think this would be stuff like a river.
• Continuous features: features which could be measured anywhere. The textbook uses the example of temperature for this.
  • Can be mapped as areas enclosed in boundaries, where the points within a boundary are all the same (or are not significantly different)
• Data can also be linked to places, for example a US map color-coded by number of cows. I think the data in these maps might not be as specific this way as its averaged in a larger area, but they seem easier to read than the alternative, which would be useful for communicating data to someone who isn’t as good at reading maps.
• Vector: Each feature mapped by X and Y coordinates located within a table.
• Raster: a collection of cells. This is typically continuous, whereas vectors are discrete.
• Categories: Groups of similar things
• Ranks: Features put in a high-low order.
• Counts, Amounts: show total numbers
• Ratios: Relationship between 2 quantities of 2 things
• Ranks and Categories are discrete, Counts amounts and ratios are continuous
• Tables can be messed with similarly to how tables in a spreadsheet are messed with.

  Chapter 1 questions:

• The textbook seems to differentiate between vector’s x/y axes and general coordinates. If these are not the same, how do you obtain vector points within a map?
• GIS so far has been mapped to 2d maps; the textbook briefly mentions Lidar, a 3d mapping technique. How does that function with GIS?
   

CHAPTER 2:

• Maps can be used to determine the patterns within a geographic region
• Categories should be tailored to the audience of the analysis; for example, a map in a research paper could be more detailed than one for newspaper.
• A map should also be readable; a small map in the corner of a report fits less detail than a map for a poster.
• GIS uses either street addresses and latitude-longitude to assign geographic coordinates
• Most categories are hierarchical with subcategories
• In some cases, 1 code defines both category and subcategory. In others, these are separated in the code
• Each type of data will be drawn by one “symbol” (presumably, this will make sense when I start doing GIS work) each, and assigned a category value.
• GIS will, after the previous note’s step is taken, draw the features you specified in the program.
• Subsets of data are used for individual locations more often than linear or continuous data, because subsets of those would result in incomplete seeming data, and/or context-less data.
• You do not want to showcase more than 7 categories visually on a larger map, because people can usually only visually understand up to 7 points of data.
• The above statement is less true for a smaller map, and in fact keeping too little within the map would be too little information.
• How you group categories can very easily change how a reader interprets your work.
• There are three ways to group categories: one is to put 2 columns in a table for specialized and generalized categories and to group each category individually, second is to code all specialized categories into certain generalized codes, and third is to assign the same symbol to various specialized code, which you can label however you wish.
• Linear categories shouldnt be separated by color, instead by a textural difference which is easier to read.
• Colors also need to be distinguishable to each other, and text labels are often used.
• Reference features shouldn’t be too clashy with the rest of the map.

Chapter 2 questions:

• Presenting data simplified in a certain way can cause bias; but not simplifying it at all can lead to overwhelming my audience. What is the best course of action to create an as-unbiased-as-possible report?

 

CHAPTER 3:

• Mapping most/least allows for an understanding of where to take action from, as well as to understand relationships between the two extremes
• Mapping numerical values also allows us to more easily figure out the answers to the questions we ask with GIS
• Discrete numerical features can represent singular points, linear features, or areas; the former two represented with graduating symbols and the latter with color coding.
• Continuous values are defined either by a specific area (like a county or a state) or by certain value (“region where value = x”)
• When focused on exploring the data, you’ll want to keep your data specific. When you’re more focused on presentation, generalizing becomes a better idea.
• COUNT: actual number of features on a map (“there are 12 bananas on this map”)
• AMOUNT: value associated which each feature (“This feature has 14 bananas within it”)
• Ratios divides one quantity with another, which can help evening out skewed data if one area is larger than the other.
• Proportions are what part of the whole your data represents
• Densities show concentrations within the data
• Ranks compare data relatively in ways assigned by the GIS operator and not by math
• Classes are groups of numerical values, such as ranges in which your specialized data would sit.
• Individual value mapping is better for interpreting raw data
• Classes can be made manually or with a classification scheme
• Classification schemes: Natural breaks/jenks (naturally grouped), Quantile (equal amounts of data sets in each class), Equal Interval (equal ranges in each class), and standard deviation
• Natural breaks is best if the data is uneven, st. dev and equal interval is best if its even and you want to emphasize difference between features, and quantile is best for relative differences
• Outliers can be put into their own class, grouped together in an outlier class, or shoved into the nearest class.
• Data should be simple enough for readers to understand.
• Graduated symbols and colors tend to make the largest/most complicated and the darkest colors the “most” value.
• Charts can summarize data in an area and show a little more data
• Contour lines show change in values across spatially continuous data
• 3D commonly used with continuous views as well (doesn’t feel like it’ll be used for the class?)

Chapter 3 Questions:

• How might numerical value be classed as significant or insignificant beyond a “yeah that looks important”? Does GIS allow for usage of statistical tests?

Chapter 1: 

Chapter 1 begins with a brief introduction which describes how the GIS industry has grown and evolved since the original edition of the book was published in 1999. Additionally, there is a short section on the structure of the book and what one can hope to learn by reading it. The author then describes what GIS analysis is and how each analysis begins with a question that you are hoping to answer, and is influenced by factors like how your research will be used and who will use it. These questions, along with the format and form of your data, the methods by which you process it, and how precisely you are attempting to answer your questions all have additional effects on your analysis methods, and ultimately how your results are created. After this, the next section deals with the various geographical features and how they can be displayed. There is then a comparison of discrete vs continuous features and a description of what data summarization is, with examples on where it can be applied, such as number of features or average altitude for some region. Following this, the author compares 2 ways of representing features on the map, that being raster graphics, which displays features as sets of cells in a grid, and vector graphics that defines objects by sets of points making up its border. Finally, the chapter concludes with a description of various attributes that features can have, including rank, which can be used to categorize objects from highest to lowest value, and ratios between attributes, like population and land area that objects also have, followed by a brief section about summarizing and working with data tables.

Key Concepts:

Discrete Features: Features that either are or are not present at any given location, such as property lines, roads or county lines. 

Continuous Phenomena: Factors that are found across an entire region and can exist at any value in some range, such as amount of rain, altitude, or soil type.

Raster Graphics: Displays objects as cells in a grid which displays features as sets of cells in a grid

Vector Graphics: Features are formed by sets of points in specific points on the map.

 

Chapter 2:

Chapter 2 begins with a section outlining the purposes of mapping, and how to choose what features you would like to map. By mapping the locations of events or features, the text explains, you can find trends in where they occur. For each feature on the map, it must have a location and any additional information associated with it, such as speed limit if the feature was a road, or median housing price if the feature was a certain region of zip codes. Within each category a feature may fit in, like houses in a city, additional subcategories can be added, such as single vs multiresidence housing. Categories can also be grouped to simplify the map and make overarching patterns easier to understand. However, grouping categories must be done with care, as depending on the groupings chosen, trends may vary greatly.  Symbols also play an important role in the representation of objects with a specific location, like the locations of houses, or traffic lights. Shading can be used to represent features like zoning districts, while lines can represent features such as rivers or roads, with attributes such as color and width being used to further show differences between features. By analyzing the patterns formed by the features we map, we can find patterns that ultimately allow us to draw conclusions about the data we represent. For example, by mapping soil types and rainfall patterns, we could make determinations about which land in an area would be most suitable for farming, or by mapping house fires in a town, we could determine which areas would most benefit from the construction of a new fire station.

Key Concepts:

Category: A specific value representing a characteristic that some data object has, usually out of a set of possible values.

Grouping categories: The practice of grouping a set of objects with similar characteristics to make visualization easier

Symbol: A marker used to denote the location of individual objects of some specific feature, often with different symbols used to represent different feature types

 

Chapter 3:

Mapping the most and least gives us information about where features are and are not found, allowing us to understand the relationships between location and feature distribution. Shading, varying feature size and color can all be used to show how quantities of features vary across maps, with some methods, like shading being more applicable to areas, while others like size are better applied to individual objects like markers. While it is important to keep in mind the distinction between exploring the data and presenting a map to display a specific pattern, you often begin with exploring the data, followed by mapping to show the patterns you find. Ratios can also be a useful feature for summarizing data, since they can often display patterns better than raw numbers allow for. For example, the ratio between housing and businesses for a city can give a more accurate representation of land use than simple counts would. Ranking is also a process used for displaying relationships between features, in which a set of objects is listed from highest to lowest, such as ranking regions from greatest to least rainfall, or ranking streets from highest to lowest traffic flows. Classes can also be used to generalize data, and are usually formed by grouping features by the value of some attribute, like household income or soil type. Classes can be manually determined to best fit the data, or in some cases by using standard classification schemes, the 3 most common being standard deviation, equal intervals, quantile, and natural breaks, each of which has its specific advantages and disadvantages. In the process of making a map, the goal is to display the patterns as accurately and clearly as is possible, which can be done by focusing on the patterns you are trying to convey information about and by choosing a map styling that fits the data you are displaying. There exists a variety of map stylings, each applicable to a different scenario. Graduated symbols easily show the rank or relative size of features, while graduated colors can be applied to maps showing data by area, like population by township, or forested land in each census tract. Charts can be used to show ratios between a set of features in each area on the map, but can become cluttered if too many are used too close together, or if too many categories are used. Contour lines show the rate of change for continuous features, like showing changes in elevation for a mountain range, or clay content in soil for a county. A 3D view can be used to show continuous phenomena, with height usually representing the magnitude of the value at that point. Ultimately, if the map is made correctly, it should be able to convey the data it is trying to display in a clear and understandable way, allowing its audience to understand and gain insight into the trends that are present.

 

Key Concepts:

Ratios: Using averages, proportions and densities to better understand and display patterns, showing the relationship between two different quantities

Ranks: Putting features in order from greatest to least, showing quality relationships rather than quantitative values.

Classes: Groupings of features based on values in order to make generalizations to data.

Mitchell Chapter 1-3 Summaries

 

Chapter 1

 

The book starts off with a question how important GIS is and what it’s used for/applications. The book additionally tells you what tasks in GIS are common such as mapping where things are, mapping the most and least, mapping density, finding what’s inside, finding what’s nearby, and mapping change. The most important things to consider when performing a GIS task are how it will be used and who will use it according to the book and personally I agree. Essentially when you conduct a GIS survey you should choose how much effort would be appropriate for the task. If its for a court case on ENVS policy on deer hunting you should find all the data to who and in what county someone killed more deer than is allowed but if it’s a survey then maybe you list the total for the state in general. Additionally for GIS there is a 5 step method to analysis processing framing the question, understanding the data, choosing the method, processing the data, and evaluating the results.These steps ensure that analysis is systematic and produces reliable outcomes. Another key point in the chapter is the distinction between different types of geographic features: discrete features such as businesses, parcels, or rivers; continuous phenomena such as temperature or rainfall; and data summarized by area like population totals within a county. Each of these can be represented using either a vector model, which stores features as coordinate-based points, lines, or polygons, or a raster model, which represents space as a grid of cells, each with its own value. Attributes linked to features are also critical in analysis, and they can be categorized as nominal (categories), ordinal (ranks), interval/ratio (counts or amounts), or ratios that standardize values like population density. Finally, the chapter emphasizes basic operations like selecting, calculating, and summarizing attributes in tables, which allow analysts to extract new information from raw data.”

 

Chapter 2

 

The chapter talks about deciding what to map and what to include in a GIS map. According to the book when mapping a layer you designate a symbol to each type of data. A layer is a collection of geographic data pertaining to one type of information about the place you are mapping. For example one layer could be a street, another could be houses, and finally one can be the cars if you’re mapping traffic data for neighborhoods. The book emphasizes that the map should be tailored to the audience you’re presenting it to. Additionally for every point you plot you should have the cords and optionally the data corresponding to it if necessary. When you map features by types you should include a code that identifies its type of info. Additionally when creating a category you need to specify the layer’s data on the data table and assign the appropriate value to the feature. When mapping a layer you can include multiple different types of info into one layer with a symbol for each or one type per layer. You can additionally categorize information if you choose or hierarchically rank each data under a symbol/shade. Sometimes if a category is complex you can create different maps per category. The book says displaying types of categories may make it easier to see how different categories are related. The book sets a limit to categories as well and emphasizes that if you’re writing multiple categories onto one map then 7 is enough. Additionally the distribution of features affects the data. “When a map shows many small, scattered features instead of large continuous areas, it becomes harder for readers to distinguish between categories. In cases where features are spread out, it is possible to display more categories, but if the features are densely packed, fewer categories should be used for clarity.

 

Chapter 3

 

Chapter 3 of the book focuses on how to interpret data after it has been gathered. It offers practical guidance on what the data can reveal and how to present it effectively. For example, mapping patterns with similar features and categorizing them can help in selecting the most appropriate data for an assignment. The chapter also introduces the concept of continuous phenomena and area. In GIS, an area is defined as the amount of space inside a boundary on a map, typically measured in square units. The book explains how areas can be displayed using graduated colors, contours, or 3D perspective views. Interpreting data in terms of area can involve shading each region based on its value or using charts to show the amount of each category within that area. GIS professionals often summarize individual locations and linear features within areas to help communicate patterns more clearly. A key part of data interpretation involves understanding quantities, where features are symbolized based on their attributes. Chapter 3 discusses the use of counts and amounts, which show the value of each feature and allow for comparison across features. These can apply to both discrete features and continuous phenomena. However, the book notes that using counts alone can skew results if the areas differ in size. To address this, it recommends using ratios-created by dividing one quantity by another-to reveal relationships between values more accurately. The chapter also introduces ranks, which help by assigning a hierarchy to combinations of attributes. Finally, it explains how to set up classes to group counts, amounts, or ratios. Classifying data helps determine the types of quantities being dealt with and allows for clearer, more consistent analysis and presentation.

This week, I read chapters 1-3 of the Esri Guide to GIS Analysis by Andy Mitchell. Here are some of the key takeaways from each chapter:

Chapter 1-

-GIS analysis looks at geographic patterns and relationships between features.
-Start analysis by deciding what information you need. How will the analysis be used and by who?
-The type of data and features present determines what method of analysis you use. You may need to create more data depending on what information you already possess.
-By looking at the results of your analysis, you may decide that you need different parameters than the ones you started with because the information they provide may or may not be useful.
-Discrete features- “For discrete locations and lines, the actual location can be pinpointed. At any given spot, the feature is either present or not.”
-Continuous phenomena- like precipitation or temperature. Can be measured anywhere. They happen across the entire area being observed.
-Features summarized by area- “represents the counts or density of individual features within area boundaries.” Examples include the number of businesses in each zip code, the total length of streams in each watershed, or the number of households in each county (obtained by summing the number of households in each census tract). The data applies to an area, but with no specific location.
-Geographic features can be represented by either vectors or rasters. A row in a table with x and y locations demonstrates a vector, while a raster is represented by a matrix of cells in a continuous space.
-Discrete features and features summarized by area are typically represented using vectors and continuous phenomena are usually represented by both, and continuous numeric values by raster.
-Types of attribute values- categories, ranks, counts, amounts, ratios.
-Categories are types of similar things like roads or crimes. Not continuous
-Ranks put features in order, like when direct measures are hard to quantify (when several things factor in), such as scenic value. Not continuous
-Counts and amounts show total numbers. A count shows the actual number of features and amount can be any measurable quantity within the represented feature. For example, a count would be the number of businesses and the amount would be the number of employees each business has. continuous
-Ratios show the relationship between two quantities, such as the average number of people per household. Continuous
-When working with data tables, you are commonly summarizing values of attributes, calculating attribute values, and selecting features.

Chapter 2-

-Preparing data- make sure that coordinates have been given to each feature you are mapping and make sure that you have a category attribute with a value for each feature as well.
-If you have data that is already in the GIS database, then you do not need to assign coordinates because they are already given to you. However, if you have brought in data from another program or if you are entering it by hand, then you need to assign geographic coordinates by filling in information such as a street address, or latitude–longitude values, which will allow the system to assign geographic coordinates.
-You may need to add information (code) to each feature that identifies its type.
-In order to add a category, a new attribute must be created in the layer’s data table. Then, you have to assign values to each feature.
-“In some cases, a single code indicates both the major type and subtype. For example, all crimes with a value between 500 and 599 are burglaries, but the type of burglary is indicated by the specific value.”
-Mapping a single type- draw all features as the same symbol.
-Using a subset of features- Book uses the example of mapping all crimes, as well as specifically all burglaries, or only commercial burglaries.
-Mapping by category- draw features using different symbols. The book gives an example of mapping all major roads by road type.
-Displaying features by type- Book uses the example of mapping burglaries by the type of building entered (residential or commercial) or by the type of entry (forced or non-forced).
-Do not display more than 7 categories. Most people can distinguish up to seven colors or patterns on a map. Displaying any more than 7 makes a map harder to read and patterns harder to find.
-Grouping categories- If you have more than 7 categories, group them.
-When using symbols, colors are easier to distinguish than shapes.
-Mapping reference features also make a map easier to understand and read.
-Zooming out may help you distinguish patterns.

Chapter 3-

-Mapping patterns of features with similar values shows where the most and least are. This is important for finding places that are in critical need of action.
-Continuous phenomena are typically mapped as shaded areas to show quantity.
-A count is the actual number of features on the map.
-An amount is the total of a value associated with each feature.
-Using counts and amounts when summarizing areas can mess up patterns that you’re trying to discover.
-Ratios show the relationship between quantities. They are created by dividing one quantity over another. (most common are densities, proportions, and averages).
-Ranks put features in order (high to low). They give relative values instead of precise ones.
-Grouping values into classes helps audiences to compare features quickly.
-To create classes, you can do so manually or by using a standard classification scheme. These methods assign the same symbol to different features to group them.
-A class break is where there is a jump in values (between bars).
-Understand quantiles, standard deviation, and natural breaks, and equal intervals.
-Using natural breaks can isolate outliers and avoid skewing your patterns.
-GIS gives these methods for creating maps to show quantities: Graduated symbols, graduated colors, charts, contours, and 3D perspective views.
-Discrete locations or lines, use- Graduated colors or symbols to show value ranges, charts to show both categories and quantities, and a 3D view to show relative magnitude.
-Discrete areas or data summarized by area, use: Graduated colors or symbols to show value ranges, charts to show both categories and quantities, and a 3D view to show relative magnitude.
-Spatially continuous phenomenon, use: Graduated colors to show value ranges, contours to show the rate of change, and a 3D view to show relative magnitude.

Hello! My name is Haruno Tadokoro. I am from Tokyo, Japan.  I am a junior, majoring in Environmental Science and minoring in Politics & Governance. My mother got COVID the day before my departure to the U.S., so I missed the first week and just got back yesterday. 

 

I did the quiz for this class Geography 291. While reading the first Chapter by Schuurman I was surprised to learn that Geographic Information Systems (GIS), which are instrumental to technologies like car navigation and Google Maps that we use almost every day, actually began to be developed back in the 1960s. I had always assumed that these systems were more recent innovations because they certainly seem so new and high-tech. Reading about where it all came from helped me appreciate how long ago humans started developing digital mapping and spatial analysis, even when computers were far more rudimentary than they are now.
Personally, I have utilized GIS-related tools in my travels. For example, I like to research where I am headed, how high it is, or use web-based maps to explore land and distance. Previously, I imagined that GIS was mostly for personal convenience, like taking the best route or discovering new places. But I learned GIS is far more than that and it’s used differently depending on whom is using it and why. It’s used by urban planners to design cities, environmental scientists to research climate change, and emergency responders to use in times of crisis.
This made me think of the way that technology itself is value-neutral, but we can use it in ways that give it meaning and purpose. GIS can be used to create communities that are safer, more efficient, and more sustainable, but it also raises concerns of data control and privacy. As a user who has been mostly at the surface level, I find myself now curious about learning more about what goes on behind the scenes with GIS and how it might further develop in the future. It is incredible to think that something created over 60 years ago still forms the basis for so many of the tools we utilize today.

I found Female researchers are using GIS to visualize gender inequality geographically. In Image 1, disparities and burdens based on gender are layered onto a map, visually representing factors such as the degree of impact on women across different regions.

Hi! My name is Sky. I’m from right here in Delaware and I’m a junior majoring in Environmental Studies.

Yes, I know my submission is late.. I was having major technical difficulties :/

 

After going over both the quiz and reading, it made me realize how much about GIS I really didn’t know. I’m coming into this class not knowing much at all about GIS systems and what they are capable of. One thing I thought was really interesting was how widespread GIS is. It isn’t something that is specific to one major or one group of people, GIS is open to really anyone (like how it can be used for police officers, students, and even starbucks shops). The reading goes over mostly history (dating back all the way to the 1960s) and the understanding/role of GIS systems. The reading was intended to demonstrate how GIS can fit into normal day tasks for a wide range of people. It’s really intriguing that coincidentally, the start of GIS actually didn’t involve computers. This is weird to think about because our world pretty much revolves around technology. The two different “faces” of GIS I think is something important to think about and that GIScience is basically the underlying theory of GISystems. GIS has more to it than just data and maps so I’m excited to get to learn more about it and what I could use it for. 

One of the GIS applications that I found and thought was really interesting was the use of GIS to track animal movements in marine environments. The reading goes over a bit about how GIS tracking isn’t used within water ecosystems as much as terrestrial areas. They kind of combined general GIS software with analysis tools that are specific to animal movement. There are a bunch of different functions that the paper goes over as well to show what types of animal movements and other things you can get out of using the software. The reason that studying animal movement is so important is because it can help with a lot of different research including fishing management, migration, and habitat management. The figure goes over location of the halibut being studied between different areas like pacific locations, rocky locations, and just randomly selected areas. I liked reading over this study and think that any of them having to do with tracking animals and their habitats is fun and interesting. 

 

Source: Hooge, P. N., Eichenlaub, W. M., & Solomon, E. K. (2001). Using GIS to analyze animal movements in the marine environment(pp. 37–51). http://www.bio.davidson.edu/people/midorcas/GISclass/GISprojects/hartman/Anim_Mov_UseMe.pdf

This week I read Geographic Patterns and Relationships 2nd edition by Andy Mitchell

Mitchell Chapter – 1 is a general introduction to GIS and its applications. The beginning  lists a few applications for maps and GIS, for instance It is possible to map where the most or least amount of something is. It is also possible to map density as well as change. A definition/description for GIS is also provided. In this chapter we are given guidance on asking questions using GIS, and we are instructed to be as specific as possible, which leads me to wonder where the line of specificity is? Meaning how general or broad can I be about asking a question so that it still is effective at providing an answer without providing a misleading or useless answer. 

The rest of chapter 1 delves into how to frame a question, and how you can use data generated by your map. For instance you can summarize data generated from the map. It is also possible to use satellite imaging to create continuous data which is good for visualizing patterns such as precipitation, soil characteristics, and temperature. When using continuous data it is sometimes good to use raster data which works well since raster data is a grouping of cells, whereas vector data is based on individual points.   

 

Key Words: Discrete features (pinpoint data), Continuous phenomena (data that can be found anywhere), Features summarized by area (data found within set boundaries), Vector data (areas defined by points and set polygons), Raster data (data represented as a matrix of cells)

 

Mitchell Chapter – 2 explains how to create data before using it as well as data storage to display either detailed or general information. It also covers different ways to represent or draw data on a map, either through lines or in a given area designated by points. It also discusses best practices when it comes to data visualization when using maps and spatial data. For instance when using spatial data, it is necessary to have coordinates present so that you are able to plot sites with the GIS, it is also beneficial to have a character or attribute associated with the coordinate data so it is easy to group them together. Then when grouping similar types of data you just graph them all as either the same color or with a distinct shape. Similarly to statistical programs like Rstudios, you can subset data and have only specific values shown. When assigning categories it is best to have as few as possible as this makes it easiest to distinguish patterns (a good amount would be 5-7), however having a larger amount displays more detailed patterns. 

 

Mitchell Chapter – 3 explains how it can be helpful to use spatial data to represent numerical data, such as how many people work at a specific location by using graduated symbols (using larger points to indicate more people). This type of visualization can help explain where something is as well as give context to what is being displayed. Data can be organized by rations (percentages), or by ranks which order data from least to greatest. When creating all this data it can sometimes be too unwieldy to use and hard to interpret as a viewer so it is best to create classes, which groups data into designated categories. There are many different ways to create data classes and there are also a number of types of classes that can be used, and each one is useful for different things. Some of them are good for seeing generalized patterns, while others are useful for making sure the data is properly displayed if it is not evenly distributed. 

Key words: Ratio (a proportion or percentage), Rank (ordered from greatest to least), Class (Data is grouped into a class representing certain ranges to make a map more concise), Natural breaks (data in a given class are similar, represents natural groupings found in your data), Quantile (each class has an equal number of features), Equal interval (classes are made by grouping a set amount of creatures into each class), Standard deviation (classes are generated by how many standard deviations away from the mean they are), Graduated symbols (points representing counts), Graduated colors (colors used to represent rations and ranks), Charts (graphs generated in the areas they represent), Contours (lines representing counts or rations, most noticeably used for precipitation and barometric pressure), 3D perspective views (3D images used to display magnitude of data).

Hi, my name is Parker Tomlin, and I am a senior this year. I’m majoring in Exercise Science,

I did the quiz for GEO 291. While reading Chapter One of Schuurman’s text, I gained a deeper understanding of the history of Geographic Information Systems (GIS) and how they have evolved over time. I was especially intrigued by the wide range of applications GIS has today and the ongoing debates within the field of geography about its use and implications. Schuurman draws an important distinction between understanding how GIS is applied (GISystems) and the deeper theoretical understanding of how and why these systems work (GIScience). This differentiation helps frame the conversation around GIS not just as a tool, but as a field of study in itself. The origins of GIS date back to the 1960s, when Ian McHarg used spatial analysis to determine the best possible route for a highway. His work laid the foundation for computerized spatial analysis, which at the time was largely undervalued. However, pioneers like Harold McCarty and William Garrison began to recognize its potential, followed by Roger Tomlinson and Lee Pratt, who were instrumental in developing computerized cartography systems in Canada. These early contributions helped GIS become what it is today—a powerful and evolving field. As GIS has advanced, it has been categorized into two branches: GISystems, which focuses on the tools and technology, and GIScience, which explores the methods, ethics, and implications behind those tools. While GIS has practical applications in areas like agriculture, urban planning, and even e-commerce, GIScience plays a crucial role in ensuring these systems are accurate, ethical, and free from bias. It also raises important questions about who controls GIS data, how it is collected, and how it might impact individual privacy.

Application 1

For my first application, I wanted to look at grizzly bear populations and compare them to salmon populations in the same area.

The yellow, orange, and red areas on the map indicate an area of concern for grizzly bear populations. The map of the salmon population made it easy to see that the areas where the grizzly bear population was healthy were also where the salmon population was the most dense.

Sources:

https://www.arcgis.com/apps/mapviewer/index.html?layers=856c6b542ede4815a14be63bd5e261cc

https://www.arcgis.com/apps/mapviewer/index.html?layers=fbe6f9687c90440a9aef0194c8f0f2e6

Chapter 1 

Comments/Notes

Since the time Esri Press first published the book in 1999, GIS has become more popular. There have been major advances in GIS software, like incorporating intuitive interfaces as well as more advanced mapping and analytical tools. Some of the most common geographic analysis tasks people do while doing their jobs are mapping where things are. One common task involves mapping the locations of the most and least common objects. Mapping the density. Finding what’s inside and nearby. Mapping changes.  GIS analysis is a process for looking at the geographic patterns in the data you collect and at the relationships between features. To do this, you start by framing the question, and an example of this is, “How many people moved to Delaware, Ohio, in the past 5 years?” Some factors that influence the data are how it will be used and who will use it. When working with GIS, the type of data and features you are working with will help determine what method you will use. When collecting data, there are almost always two or three ways that you can collect the data. Some of the types of features that you can see on GIS are discrete features, continuous phenomena, and features summarized by area. Discrete features are when the actual location can be pinpointed. At any given spot, whether you can see the feature or not. Continuous phenomena like precipitation or temperature can be found or measured anywhere. Features summarized by area represent the counts of individual features within a certain area’s boundaries. There are two ways that the GIS can be represented: vector and raster. Vector is when each feature is in a row or table, and feature shapes are defined by x,y locations in space. Raster is when features are represented as closed polygons. There are types of attribute values: categories, ranks, counts, amounts, and ratios. 

Questions:

How did spatial data scientists discover that GIS can be used for much more than building geodatabases and making maps?

 

 

Chapter 2

Comments/Notes:

People use maps to see where and what an individual’s feature is located. When looking at the map, you can see the distribution of features on the map. Rather than at individual features, you can see different patterns that can help you better understand the area you are mapping. Mapping is important because mapping where certain things are can allow you to see where you need to take action or different areas that meet your criteria. Also, by looking at the different location features, you can begin to explore causes for the patterns you are seeing. Before you create the map, you need to make sure the features you are mapping have geographical coordinates assigned and optionally have a category attribute with a value for each feature. When assigning location, you need to make sure each feature has a location in the geographic location. When assigning category values, you need to make sure that each feature has a code that can identify its type. Many categories are hierarchical, with major types divided into subtypes. The GIS stores the location of each feature as a pair of geographic coordinates or a certain set of coordinate pairs that define its shape, like a line or area. When you make a map, the GIS uses the coordinates you input to draw the features using symbols you specify. Most of the time, mapping a subset is more commonly done for individual locations. When you are mapping an area that is large relative to the size of the features, using more than categories can make patterns harder to see. But when smaller areas are mapped, individual features are easier to see, so more categories will also be easier to see. 

Questions:

When you are making maps or looking at a map, can you overlay two maps of similar areas to compare them both if made by two different people?

 

 

Chapter 3 

Comments/Notes:

When mapping, people map the most and least because it is to find places that meet their criteria and take action, or it is to see the relationships between the different places. To be able to map the most and the least, you need to map features based on a quantity associated with each other. Mapping features based on the quantities adds an additional level of information that is beyond simply mapping the locations of features. When mapping, you need to know the type of features you are mapping as well as the purpose of your map, which will help you decide how best to present the quantities to see the patterns on your map. When mapping discrete features, they can be individual locations, linear features, or areas. Locations and linear features are most likely to be represented with graduated symbols, while areas are often shaded to represent quantities. Continuous phenomena can be defined as areas or surfaces of continuous values. Areas are often displayed using graduated colors, while surfaces are displayed using graduated colors, contours, or a 3D perspective view. Data summaries by area are usually displayed by shading each area based on its value or using charts to show the amount of each category in each of the areas. Once you have decided on how to classify the data values of your map, you will want to create a map that presents the information you have found to the map readers as easily or clearly as possible. Since GIS makes it easy to create maps and the database often has so much information, the temptation is to show more information than you actually need on your map. 

Questions:

N/A

Hi my name is Braidy Duncan and I am a sophomore this year, and am majoring in Environmental Science.

I did the quiz for this class Geography 291. While reading the first Chapter by Schuurman, I learned some things about GIS that I have never really thought about before today. For instance I never thought about the applications of GIS in anything other than an agricultural sense. So seeing that GIS is used in super niche ways was something very interesting to me. The optimization portion where Ian Mcharg was trying to find the best way to route a highway was something that really peaked my interest as optimization is something that has always intrigued me whether it be the optimization of space or time if you can optimize something it is an amazing feeling. I really like the idea of the first waves of GIS being done without computers and it is something that really baffles me that it can be done in the first place. The map of the Cholera outbreak is really cool to see. Seeing the correlations between the number of cases of Cholera and the water pumps associated to those areas was really cool, and in application it probably allowed the researchers to help put a stop to the outbreak way faster than it would have been stopped without the GIS systems. Something else that I have never thought about was that GIS systems are used everyday.  The different ways in which geographic information can be utilized to help world processes is something that I really look forward to exploring within this class. So having read this chapter I am really excited to see all of the different maps that are out there and really see how far GIS imaging can go and personally I can’t wait.

For my interest in soils I wanted to get to know where I am on a deeper level so I looked for a GIS image of Delaware Ohio.

Source:Delaware County Ohio. (2024, June 27). Cities, Villages & Townships – Delaware County. Delaware County. https://co.delaware.oh.us/cities