Month: August 2025

Chapter 1: Introducing GIS Analysis

GIS is a powerful tool for analyzing and visualizing data. It is used to map where things are, show concentrations (most/least), analyze density, finding what’s inside a specific area, and track changes over time. At the core of GIS analysis is the process of asking questions, selecting appropriate methods based on available and required data, processing that data, and interpreting the results in the form of maps, tables, or charts. 

GIS data comes in several forms. Features can be discrete, meaning their locations can be pinpointed, or continuous, which can be measured anywhere. Features can also be summarized by area. These features are represented using either vector (coordinates) or raster (layers) data. The accuracy of representation depends on map projections (globally) and coordinate systems (specified area). 

Each geographic feature in GIS has attribute values, which describe its characteristics. These values are classified into types such as categories, ranks, counts, amounts, and ratios (proportions and densities). This classification helps in selecting the appropriate analysis technique. Ultimately, GIS allows users to reveal spatial patterns and relationships that may not be obvious, making it an essential tool for decision-making in a wide range of fields. 

Chapter 2: Mapping Where Things Are

Mapping where things are is a foundational function of GIS that helps identify geographic patterns and relationships. Before creating a map, it is crucial to decide what information needs to be shown and why. GIS analysis allows users to pinpoint where features exist or where they don’t, identify their types, and determine their distribution. However, map design should balance detail and clarity, where too much detail can overwhelm viewers, while too little might leave out crucial data. 

The first step in preparing data is assigning geographic coordinates or addresses to features. Each feature must also be assigned a category value that identifies its type. When making maps, there are many different approaches. You can map a single type using the same symbol, focus on a subset of features, or map by category, using distinct symbols for different types. If features belong to multiple categories, it’s important to visually distinguish each group, but it is suggested not to use more than 7 categories on one map. If more than 7 categories are needed, they should be grouped to avoid clutter. 

Choosing symbols is essential for clear communication. Individual locations can be shown using color coded markers, linear features can vary in width or pattern, and areas may be differentiated using raster layers or shading. Text labels can also help in identifying areas. Including reference features like roads, rivers, or landmarks adds context, which can make a map more meaningful to the audience. 

When analyzing geographic patterns, zooming out can help identify broader trends. Combining spatial patternswith background knowledge often reveals why features are arranged in a certain way. Well designed maps, supported by prepared and categorized data, allow GIS users to communicate spatial relationships effectively to an audience.

Chapter 3: Mapping the Most and Least

Mapping the most and least is a method in GIS used to explore how quantities vary across locations. This approach allows users to see relationships between places, revealing patterns not visible in raw data. This technique is especially useful for comparing counts, amounts, ratios, ranks, and densities across geographical areas. When mapping quantities, it is crucial to consider the audience, whether the map is exploratory or intended for presentation, influences the choice of using data or visual maps.

Understanding quantities is important. Counts represent the actual number of features, while amounts are total values associated with features. Ratios compare two values, while proportions and averages divide values to show relationships. Densities show distribution over space. Ranks order features from high to low, either through text (high, medium, low) or scales (1-10). 

These quantities are often grouped into classes to make patterns easier to interpret. Creating classes of data helps readers compare areas more quickly, though this can reduce the precision of the data. There are several classification methods:

• Natural breaks (Jenks): classes are based on natural groupings of data values
• Quantile: Each class contains an equal number of features
• Equal interval: the difference between high and low values is the same
• Standard deviation: features are placed in classes based on how far away from the mean they are

When making a map, various visualization techniques can be used:

• Graduated symbols

• • • Features: locations, lines, areas
    • Values: counts/amounts, ratios, ranks

• Graduated colors

• • • Features: areas, continuous phenomena
    • Values: ratios, ranks

• Charts

• • • Features: locations, areas
    • Values: counts/amounts, ratios

• Contours

• • • Features: continuous phenomena
    • Values: amounts, ratios

• 3D perspective views

• • Features: continuous phenomena, locations, areas
  • Values: counts/amounts, ratios

Using these tools, GIS helps reveal spatial patterns in quantitative data.

After reading Chapters 1-3 here are my notes and major takeaways

Chapter 1: Is the brief intro to the book and explains some of the major things that you can do with GIS systems. It explains a little bit further as to what GIS is and its uses, both the niche and the most popular uses. This chapter tells us about the most common geographic analysis uses such as Mapping where things are, mapping the most and least, mapping density, finding what is inside of an area, finding what is nearby an area, and mapping change of areas over time. This chapter explains how to implement five crucial steps when performing an analysis, starting with “Framing the Question,” figuring out what you need in terms of the information you are using, and how that information will help your analysis. the second step is “Understanding your Data,” In order for your map to be useful you have to understand the data in which you are plotting out. The third step is “Choosing a Method,” this step is crucial in getting the information you need as there is often more ways than one to do obtain your specific information. So depending on if you need more precise results or if you are just looking for an over arching view of the data your method  in which you get that data varies.  After you have your data, the next step is “Process the Data,” which just means you run your data through GIS. After that you arrive to the last step which is “Look at the Results,” where your data will be displayed as a map, values in a table, or a chart. This chapter explains the differences in many different types of features and how to tell them apart whilst using the GIS program. One of the most important parts of this chapter is that it tells us to make sure our data is all in the same map projection and coordinate systems because if they are not your map will not be shown properly.

Chapter 2: This chapter talks a little bit more about the importance of having specific data and making sure that that specific data is put into the programs correctly. Having geographic coordinates is helpful because it allows for the GIS system to work rather quickly. However if you don’t have geographic coordinates you can use other forms of location info. So stuff like latitude and longitude lines are helpful additionally sometimes you can use street or road names as a form of locational information. It explains in detail the actual workings of making the map, including deciding which features are going to be included in your map. A question that I would have at this point is it keeps talking about layers and the way in which you program information on those layers, my question is what exactly are layers and how are you supposed to program those layers, I do feel like this will be answered once I start looking at the program but that is a question I have. This chapter also explains what exactly GIS does and how it creates the maps we use by marking the coordinates and connecting them to other coordinates to show the features of the map. Additionally it shows the importance of giving context of your maps when mapping subsets, because without context the subsets are not helpful to the overall map.

Chapter 3: This chapter explains the importance of mapping comparisons of the least and most parts of the data in which you present to the GIS system. This chapter talks about showing the densities of data as well, which show information in terms of how concentrated info is on the map. Density on maps is highly effective when you are summarizing data the has a vast variety of data points. Another thing this chapter talks about is ranks, putting ranks into your GIS map orders information from high to low or vice versa depending on what you are trying to map out. It explains how even when using ranks you do not really know where each rank should really fall as the ranking system is subjective to the person making the ma and the person reading it. Another topic this chapter discusses is classes and how it is optimal to use classes when the map is going to be used in the public eye as it allows for information to be set beside each other and contrasted easier than if the data was scattered. This is opposed to mapping individual values, which present more accurate values but is harder to actually gauge differences off of. However, mapping individual values allows you to search more for patterns in the raw data. Whereas in classes you are looking more for comparison between things, individual values help you find similarities.

 

 

Chapter one gives a general overview of GIS. According to the book “GIS analysis is a process for looking at geographic patterns in your data and at relationships between features.” GIS does tasks including, mapping where things are, mapping the most and least, mapping density, finding what’s inside, finding what’s nearby, and mapping change. There are different types of methods for getting the information that you need. The type of data that you work with can determine what kind of method you will want to use. Results are shown on a map, values in a table, or a chart. When you look at the results it can help you decide whether the information is valid or useful. The type of geographic features will affect the analysis process. It is important to be aware of the different types of geographic features. GIS also can measure continuous phenomena such as temperature or precipitation. Continuous data often starts out as a series of sample points. It is also important to understand different types of geographic attributes. The different types of geographic attribute values include categories, ranks, counts, amounts, and ratio. Categories help with organizing your data. Everything with the same category are alike in some way. Category values can be represented using numeric codes or texts. Ranks order the different features from high to low. Ranks are used when it is hard to use direct measurement to quantify certain things. Counts and amounts show you total numbers. They can let you see the actual value of each feature. Ratios show the relationship between two different values. For example, dividing the number of people in each tract by the number of households gives you the average number of people per household. Categories and ranks are not continuous values whereas counts, amounts, and ratios are.

Chapter two talks specifically about mapping with GIS. It is important to use maps to see where things are. When you map out where things are it can show you where action needs to be taken. For example police can use GIS to map where crimes occur each month. In order to look for geographic patterns in your data you map the features in a layer using different kinds of symbols. It is important that the map is appropriate for the audience and the issue being addressed. Maps should display detailed categorical values. Many categories are divided into subtypes. In order to create your map you need to tell the GIS which features you want displayed. You can also show them as category values. To map features as a single type you need to draw using the same symbol which might suggest differences in the features you could explore further. You can also map by using categories by using different symbols for each category value which can provide an understanding of how a place functions. For example mapping crimes by type shows you which types of crimes occur where. If you have more than seven categories you will want to group them in order to make it easier to see the patterns. Another important thing to know is that if you reassign something from one category to another it can create two different maps.

Chapter three talks about mapping the most and least. People map where the most and least are to find places that meet their criteria. Mapping features based on quantities adds an additional level of information. To map the most and least it needs to contain a quantity. You can map quantities associated with discrete features, continuous phenomena, or data summarized by area. Ratios show the relationship between two quantities and are created by dividing one quantity by another. The most common ratios are averages, proportions, and densities. Proportions show you what part of a whole each quantity represents. They are often presented as percentages. You create ratios by adding a new field to the layer’s data table and calculating the new values by dividing the two fields containing counts or amounts. There are also statistics used with GIS, specifically standard deviation. The GIS first finds the mean value by adding all data values and dividing by the number of features. It then subtracts the mean from each value to calculate the standard deviation. When making a map you’ll want to make sure it is presented as clearly as possible. You’ll want to present information that is necessary to show patterns in the data. There are advantages and disadvantages for different types of values. Contour lines can be used to show the rate of change.

Chapter 1:

Concepts & Definitions

• GIS Analysis: looking at spatial data to identify patterns and relationships
• Geographic Features: Discrete feature = exact (roads); Continuous phenomena = measurable everywhere (temp); Summarized by area = counts or an aggregation (population per country)
• Data Models: Vector = points, lines, x,y coords in tables; Raster = grid/cells, each has a value (continuous data)
• Map projections & coord systems: projection = going from curved surface to flat map; coord system = defines measurement units and origin for locations
• Geographic Attributes = descriptive info tied to features; Categories = groups features (crime type); Ranks = order features by value; Counts = number of features; Amounts = measurable quantity; Ratios = relationships between quantities
• Continuous and Noncontinuous values: noncontinuous = fixed set values; continuous = any value in a range
• For Data tables: Select by using queries to filter data; use =,<,>; calculating by adding new fields or computing values; summarizing by getting totals, averages, and frequencies

Notes

• GIS can be used for data exploration and is not just cartography
• Framing the right questions is highly important, along with the analysis
• Data tables seem to be the backbone of GIS analysis
• How specific you need to be depends on what data you are trying to collect
• Reading this text, while illuminating, doesn’t fully give me an idea of how to map, sadly
• Chapter lays the foundations: features, attributes, models, projections
• Need good questions, data, and choices for a good GIS map
• Fundamentals of GIS have remained the same despite technology advancing rapidly
• Knowing your audience is important, casual versus scientific versus legal contexts
• Two similar maps can answer completely different questions depending on the data used
• GIS can be used for infrastructure planning

Questions

• What does GIS actually look like?
• How do you factor error into your data on GIS?
• How many layers can you add to a map?
• How friendly are the tools to a newcomer?

 

Chapter 2:

Concepts & Definitions

• Category values = feature that has a code that identifies its type, like whether a crime is a homicide or theft
• General code for attributes is the major type and detailed code is the sub type
• Single type map = all features use same symbol (very basic)
• Grouping categories = multiple categories grouped together to make patterns easier to view; instead of 1. Heavy industrial, 2. Light industrial, 3. Medium industrial, group to just Industrial

Notes

• Maps used to see where or what an individual feature is
• Patterns help to better understand an area while mapping
• Locations and features can allow you to see patterns
• For geographic patterns in data, mapping features in a layer using different kinds of symbols is ideal
• If an audience is unfamiliar with an area/data shown on map, use information that will provide reference locations, like roads or lakes
• GIS reads location information or latitude and longitude values and assigns geographic coordinates
• Many categories are hierarchical, state highways into how heavy traffic is on them
• GIS can use coordinate pairs to define the location of an address (4 points of a square)
• GIS can be used to map a subset of the data; all crimes into just selecting only jaywalking, which can reveal patterns
• Mapping subsets most common for individual locations
• Map showing only subsets of features could be incomplete
• Can change the color and symbols/characteristics of categories
• Features might belong to more than one category
• If patterns complex or features close together, creating a separate map for each category can make patterns easier to view
• If showing several categories on one map, display no more than seven categories
• When smaller areas mapped, individual features easier to see so using not enough categories can leave information out
• The way categories grouped or changed influence the perception of information
• Can group categories by using a general code to ‘combine’ them or by using two tables with the detailed codes corresponding to a general code
• Text labels can help identify categories
• Landmarks always helpful for people
• Zooming in and out can reveal patterns, like clusters
• Patterns may be the result of a multitude of factors, so statistics to measure the relationship between these features is important

Questions

• Can you use any shape or symbol for categories?
• How hard is it to specify a location using points?

 

Chapter 3:

Concepts & Definitions

• Continuous phenomena = defined areas or a surface of continuous values
• Data summarized = amount of category in each area
• Counts = actual number of features on the map
• Amount = total value associated with each feature
• Ratios = relationship between two quantities; averages, proportions (%), densities
• Densities = where features concentrated; ex: population of a city / land area (Sq Mi), people per square mile
• Ranks = putting features in order from highest to lowest
• Classification schemes = grouping similar values to look for patterns in data; may want map to focus more on highest income households or focus more on the number of classes; four common schemes = natural breaks, quantile, equal interval, and standard deviation
• Z-factor =  a value that increases variation in the surface for 3D

Notes

• Mapping features based on quantities can add additional levels of information beyond just a location, like amount of customers at a shop instead of shops with customers
• Make sure to keep the purpose of your map and audience in mind; exploring data versus showing a map
• Knowing the type of quantities being mapped is the best way to showcase the data
• Counts and amounts can skew patterns if areas vary in size, using ratios or percentages can be more accurate to represent features
• Proportions great to show what part of a whole you want a quantity to represent
• Ratio = 1/10 versus percent = 1/10 * 100
• Can create ratios by adding an extra field in the layer’s data table
• ArcGIS lets you create them by setting up the calculation
• Ranks useful for direct measurement; may rank suitability for growing crops; 1-10
• Block groups can show off data values using shades
• Mapping individual values may give an accurate showcase of the data but is more time consuming, so ranks may be better for your sanity
• Each classification scheme has pro’s and con’s, just depending on what you want the map to showcase, creating a bar chart can help
• If outliers, using natural breaks can help isolate them
• If trying to use shades to showcase different percent’s, use up to seven colors on a map
• Page 93 of chapter 3 good resource for what map you wanna make
• Can create pie charts on graduate symbols

Chapter 1). 

GIS analysis is a process for investigating geographic patterns in data and interpreting the relationships between associated features. At the core of GIS analysis is a similar starting point to analysis in all fields. The book says the first step is to frame a question. In scientific research, we have research questions. Really when doing any sort of report, essay, project, we have a research question. In politics and government I learned a lot about framing which is the gathering or presentation of information under a specific context in an effort to dictate how it is understood. I think the critical point about this step is to develop your question in as much specificity as possible, that way you have a direct approach to the analysis, a concrete method to go about, and a particular plan for presenting the results. This is something we have learned generally here at OWU overall in terms of limiting the generality of a research question and instead making it as precise as possible. Another super important thing to consider at this stage is the target audience and the context of usage. For the understanding your data stage, I think that it is important to recognize that this goes beyond just the header of knowing your features but also includes the capacity to build new data from existing sets. This taps into the fact that GIS can be used not only to analyze current geographic patterns and data, but also to construct new ones. For the methods, a key point is that how the data will be used fundamentally influences how you obtain and formulate that data. Once a method is chosen, I think it is super helpful to know that you can compare and contrast different analyses in order to proceed with the information that is most fitting in terms of presentation and accuracy at large. I think these preliminary steps and considerations will be exceptionally useful in making the process overall more straightforward. Moving on to geographic features I think that the distinction between continuous and discrete features is significant in that discrete features represent an actual value and a specific location like businesses represented by the number of employees. On the other hand, continuous variables have a range and can be measured at any given location and encompass the entire mapping space like temperature. In context, there may be a business with a large number of employees and then an area with no business at all. And so with these discrete features there are gaps involved. Something important to remember with continuous information is that it can be spaced regularly or irregularly. For example atmospheric pressure readings for environmental monitoring are recorded at the same time every hour per se and so there are these set intervals at play. Continuous data can also be irregularly spaced which essentially means there is no uniform interval of spacing/measurement involved. I was a bit confused by the term interpolation but from my understanding it uses discrete data and known points to approximate values for potentially unknown locations involved with continuous data. The point then is to formulate a continuous mapping space which can be essential for mapping some continuous phenomena. Another main distinction I took away from the discussion of continuous and discrete data is that boundaries are modeled and interpreted differently showing degrees of similarity for continuous data and showing legal borders if you will for discrete data. While features can also be summarized by area, oftentimes data comes summarized by area (data found within set boundaries). I think it’s cool that we can perform basic statistics to summarize any additional data by area, then merging the data tables and mapping to identify connections. Moving on to representing geographic features, in my head I aim to think of the vector model as the x, y coordinate model. This is based on the rows of data tables. Locations get coordinates, lines get coordinate pairs and areas get borders. The raster model contains features that are shown by cells spaced or layered across the map in a continuous space. Discrete features and data summarized by are are generally modeled by vector. Continuous variables are modeled by vector and raster but continuous numerical values are shown using raster modeling. Due to the map projection translation process, the distortion of features is something to consider when mapping larger areas. For the types of attribute values a cool thing I learned is that we can assign ranks based off of other attribute features. Rasta modeling comes into play here for this multi criteria ranking and multi layer data mapping. Ranks put features in order when values are hard to quantity like if I want to look at the scenic or recreational value of a body of water through a city. The main point for counts and amounts is that a count is the total number of forests on a map whereas an amount can be the number of trees within a forest. Ratios are good for showing evenness in terms of the distribution of features. The number of people in an area divided by the number of households is the average number of people per household. Categories and ranks are discrete whereas counts, amounts, and ratios are continuous. For doing the selecting, calculating, and summarizing components of working with data tables, I think I get most of it, I will just definitely need some hands-on practice to make sure I do.  

Question:

Is there a way to manage the distortion of features when mapping larger areas or is it just something to consider when evaluating the map and when presenting?

Chapter 2).

Mapping helps us understand where things are but also much more. Through the patterns of placement that can be devised, we gain insight on why things are where they are. In this sense, it’s more beneficial to look at the distribution of features, the full story rather than individual features or the single story. Like we learned from Schuurman, GIS is used by many different types of people for a vast range of purposes and mapping where things are can serve a totally different role for a police officer than for an ecologist. When thinking about what to map, it is helpful to use symbol types based on what features you are looking at and how the map of those features will be utilized. We can map different types of features to see if there is any overlap. Information depth can vary based on the audience the map is being shown to and the medium through which the map will be presented. When preparing the data to be mapped the assigning of geographic coordinates is essential. Data from any GIS database already has assigned coordinates but if we bring data in from any outside source then we must include a street name or latiutude/longitude to register with GIS to internalize and dispel coordinates for us. Major types and subtypes of features can be obtained from already stored information or created by adding a category in the data table. When actually making the map we can map single types of features or show multiple features by category values. Single type features get the same symbol when mapped which often does still reveal patterns. We can map all features or a subset of features to seek more intricate patterns for individual locations. A main point I got here is that it is good to show all types but if you want to do a subset then just highlight that and make the other types a lighter color shade. Another tip I took away is that using different colors or symbols for each category value of the feature is good for displaying the hierarchy of features and being able to distinguish the types of features. Features can also belong to more than one category and we can show that. There are burglaries overall, then types of burglaries, but also things like the type of buildings entered for a burglary. NO MORE than 7 categories, break it up and do a side by side evaluation. When grouping categories we can assign one record a code for its general category and a code for its detailed category in the database. For locations, use colors to distinguish categories and for linear features use different widths or patterns or lines. Displaying reference features like landmarks or major waterways can be helpful for serving a representative audience in terms of being able to recognize and relate to the map. I learned that a useful tool is to choose simple monochrome base maps of ArcGIS for this mapping reference features component. In terms of analyzing geographic patterns, scale has a big impact and so zooming in and out may be needed. Clustered, uniform, and random are three core types of distributions to look out for. 

Question:

For our work in this class, will base maps be used most of the time, sometimes, or will we always have to include reference features?

Will there be cases where we are obligated to bring in data from outside sources, not GIS data bases, then having to give GIS a basis to formulate coordinates for us? Or will we mostly be dealing with data from ArcGIS?

Chapter 3).

Mapping features based on a quantity associated with each feature adds an additional layer of helpful information. This is essential for thinking about these overarching goals of finding places that align with what we are looking for or identifying relationships between places. Similar to the example the books describes, I thought of one in that mapping crime based on where crime has occurred gives us an understanding of crime overall but mapping crime based on the number of crimes committed at each location give a much more accurate depiction of the levels and frequency of crime. If crime has occurred once or twice in one area, but has occurred 100 times in another area, those details on where crime is concentrated is much more useful.  To represent quantity, location and linear features are represented with graduated symbols while areas are typically shaded to show quantities. Continuous features as defined areas can be shown through graduated colors while continuous surfaces are shown using graduated colors, contours, or a 3-D view. Examples of areas can be zipcodes or watersheds. In terms of further understanding quantities a count is the number of people in a census tract whereas an amount is the number of 30-45 year olds per se. When summarizing an area, it is best to use ratios as areas differ in size and using ratios will best represent the distribution of features. We also need to be aware that we are working with the right ratio. Average is the most common type of ratio and best fitted when comparing areas with a disproportionate amount of features. A little reminder is that when calculating averages we divide quantities that use different measures and when doing proportions we use the same measures. Density as another type of ratio is used to show concentration of features calculated by dicing a value by the area of the feature to get a value per unit of area. When mapping quantities there is this overarching tradeoff that exists in between displaying the data values most accurately and generalizing them to visualize patterns. Counts, amounts, and ratios are typically generalized into classes. The four most common standard classification schemes are natural breaks, quantile, equal interval, and standard deviation. A good tool to use is to plot the values on a chart to understand the distribution and then select a classification scheme. I am a bit confused on how to do these classification schemes and the making of the charts to figure out distributions but I think with some practice that will be fine. I get the general idea of each classification scheme but it will definitely take some practice to be able to work through these. When working through this, it is good to remember to use natural breaks for uneven data and for even data use equal interval or standard deviation. Use of quantile shows relative differences between features. Something I took away as a reminder when juggling all of this is that ArcGIS allows us to easily and quickly change classes, symbols, and so forth. This is helpful when trying to explore the data and seek out patterns. Another pointer is to be aware of outliers that can either be eros is the data set or abnormalities from a small data size. Outliers can be marked as insufficient data as a last resort. When managing the number of classes, changing this will bring out patterns more or make them less clear. In order to make the map most understandable and readable, we can work with the legend and round out min/max values. We may also have to manually go in and edit the class values once the GIS has defined them for us. This goes especially for natural breaks classifications. We can also change the numbed values to high or low if there are meaningless decimals making the map harder to read. When making maps, keep them simple and show only information that effectively displays the patterns. When using graduated colors use darker shades to indicate higher class values. When using graduated symbols the main takeaway is to use symbols that show patterns without obscuring feature locations. I think using charts is hard to read and graduated colors are easier to read and show the details. Employing graduate charts makes this a bit easier to read and shows the relative sizes of each feature. I like this a bit more. For 3D perspective views, I am a bit confused on how to combine the z factor with light source. There is a lot of description on how this is done but I think it will be helpful to see it done or try to actually do it. 

Question:

Do we need to know the internal operations of GIS when performing certain processes that give us the classifications schemes or values that we are looking for? There is some discussion on what the GIS is doing in detail and so I am wondering if that is something we need to understand or pay attention to.

ESRI Ch. 1-3

The first chapter begins with a basic introduction to the advancements in GIS technology and how it’s used for world problems. GIS allows for analysis of data as a tool to answer questions, learn new information, and most likely raise a few new questions too. It’s pretty clear right off the bat how many applications the program has for everything from fighting for equity, to social control, to environmental restoration. I understand the basic aspects of discrete features from just kind of being around maps my whole life. The rest of the chapter introduces the more statistical concepts, which I may need a refresher on.

Chapter 2 goes into more of the visual aspects of map making and begins to truly explain how much math the program does once you do the more “choosing” of what you want to present, how you’re going to present it, and what symbols to use. I found it interesting that there’s both art and psychology around making a palatable, comprehensible map for your intended audience. It was surprising to learn how much of GIS and making maps is just regular graphic design.

Chapter 3 discusses more of the statistical parts of making a map. While the program seems to do the calculations, you have to decide what calculations to make based on what you want to explore or present. As for the applications of different ratios I’m probably going to have to figure that out by messing with it as I go along. The chapter then discusses charts and how data in them can be grouped and I found that genuinely a little scary, because changing the way the data was grouped made the income maps look completely different which could used be all sorts of harm. Finally, I don’t really understand the 3d perspective view or how that could be better than any other map, because I feel like I couldn’t get much information from the examples.

 

Key Concepts: 

-Chapter 1-

Analysis

Features

Attributes

-Chapter 2-

Categories

Grouping

Patterns

-Chapter 3-

Quantities

Ranks

Ratios

This week, I read chapters 1, 2, and 3 of the Esri Guide to GIS Analysis. Here are some of my key takeaways from each chapter:

Chapter 1: The general idea of GIS is to look at geographical patterns in your data and their relationships, and that can involve just a few steps or very many. When starting your process, you first must ask a question. The more specific your question is, the easier it will be to decide how to continue with your process. This chapter dives into the different ways to represent geographic features, discrete, continuous phenomena, and is summarized by area, along with when to use each feature with appropriate examples. We also learn the 2 different ways of representing geographical features: vector and raster. With vector, each feature is a row in a table, and feature shapes are defined by x,y locations in space, and features can be events, locations, lines, or areas. With the raster model, features are represented as a group of cells in the same space continuously, with each layer representing one attribute. This chapter does offer a warning when we are using coordinates when mapping. This chapter warns us to check all of our data to make sure all of the data is in the same coordinate system and map projection. The rest of the chapter talks about the understanding of different geographic attributes, and provides examples of when to use each, each with its own visual.

Chapter 2: This chapter begins by reinforcing the importance of geographic coordinates and why they are important for GIS. Along with reminding us that if our data already has geographical coordinates, we do not need to add any, but if our data does not, we will need to have location information, such as a street address or latitude/longitude values. The GIS will then read these and assign geographic coordinates. We also learn that even if we map our data as a single type, all data represented with the same symbol, there is still the possibility that we can further explore our map. We also learn how GIS works when creating maps; for individual locations GIS draws a symbol at the point defined by the coordinates for each address, but for linear features GIS draws lines to connect the points that define the shape of each street, and for areas such as parcels of land the GIS draws their outlines or fills them in with a color or pattern. Mapping features by category also allows us to better read our map and be able to differentiate between different subsets of categories (i.e., which roads are city/federal/highways). When mapping, we must also make sure we are keeping things to scale. To make our maps easier to read, for ourselves and others, it’s helpful to include familiar landmarks/geographical things. The patterns we see on our map can be either totally random or planned and have some correlation to one another. In order to find the pattern, if we suspect there is one, we must complete a statistical analysis with our data.

Chapter 3: This chapter talks about mapping most and least quantities and how to determine how to present the quantities. Mapping most and least is important, not only because it tells us where the most and least of a certain criteria is, but it also adds additional information that can be useful for businesses and cities. When making and presenting our map, we must always keep our audience in mind to make sure we’re providing the information needed, no more and no less. With mapping quantities, we need to know first if we are mapping counts and amounts or ratios, because depending on that, we will choose how to present our data. However, when our data cannot be represented to the best of our ability with numbers, we can use ranks to sort and represent our data. To make our maps, we should present our data within different classes. Classes should be assigned by similar values within our data, and the values between our ranges should be as large as we can make them without removing any of our data. We must create these ranges manually. When picking a classification scheme to use, first, we need to know how our data is distributed across the range. To do this, we can make a bar graph of our data, and then, based on these graphs, decide how to classify them. Whenever we create a map, we want to make sure that those who are not skilled in GIS know what they are reading and what our map is trying to represent. This chapter also provides a list of map types to use based on our data. It also tells us when to use. If our map presents the information clearly, we can compare different parts of the map to see where the highest and lowest values are. And by looking at the transition between where the least and most are, it can give you further insight into relationships between places.

Key Words: Discrete Features (actual location can be pinpointed), Continuous Phenomena (can be found or measured anywhere), Clustered Distribution (features are more likely to be found near other features), Uniform Distribution (features are less likely to be found near other features), Random Distribution (features are equally likely to be found at any given location)

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.