Month: August 2025

Chapter 1: 

In a broad sense, GIS lets you see patterns and relationships in your geographic data. This chapter helps to teach about the process for Performing a GIS Analysis. The first step is to frame the question by figuring out what information you need, often presented as a question. Specificity is important in deciding which methods to use and how you are going to present the results. The next step is understanding your data. You have to be aware of what information you already have and what information you will need to obtain. Next, you have to choose a method by completing the necessary steps in a GIS. Lastly, you have to look at the results and make a decision on what information needs to be displayed/included to best understand your data. 

Types of features: 

• Discrete features → the actual location can be pinpointed. A feature with a clear and distinct location
• Continuous phenomena → phenomena that can be found/measured anywhere with no gaps. With continuous phenomena, a value can be determined at any given location. Ex) precipitation (cm)
• Summarize data → the counts or density of individual features found within area boundaries. Ex) number of houses in each county

There are two ways to represent geographic features in GIS: vector models and raster models. In a vector model, each feature is a row in a table and the shapes are defined by x and y locations in space. Features include discrete locations, events, lines, and areas. In a raster model, features are represented as a matrix of cells in a continuous space. Each layer of the model represents one attribute and analysis usually occurs by combining layers. Continuous numeric values are represented with the raster model. A map projection is used to translate locations on the globe onto a flat surface such as a map. 

There are five attribute values including: categories, ranks, counts, amounts, and ratios. Categories are groups of similar things that help organize non continuous values. Ranks put features in order from high to low and are used when direct measures are difficult. Counts and amounts are used to show the total numbers, counts show the actual number of features on a map and amounts can be any measurable quantity that is associated with a feature. Ratios are used to show the relationships between two quantities. Categories and ranks use non continuous values while counts, amounts, and ratios all use continuous values.

The last portion of this chapter talks about working with data tables. Selecting features to work with, calculating attributes and summarizing values to get statistics are all important for GIS analysis. 

 

Chapter 2: 

Chapter 2 focuses on mapping where things are everything that has to do with understanding where things are mapped and how to map them properly. When looking at the distribution of features rather than a singular feature, you can better understand patterns of a given area. To look for patterns, you have to map the chosen  features in a layer by using different kinds of symbols. The map has to be understandable to your audience and the issue being addressed. The information will not effectively be shared if your target audience doesn’t understand what is being shown either with too much or too little information, confusing symbols, or overly complicated maps. 

Preparing your data:

You have to first assign each feature a location using geographic coordinates. Then they must be assigned a code that identifies its type.

Making your map:

In order to create your map, you have to tell the GIS what you want to be mapped and which symbols to use to draw them. To map features as a singular type, use the same symbol. The GIS stores the data you input and uses the given coordinates to draw single or linear features. It can also represent areas by drawing outlines or filling areas with a specific color or pattern.Features can be mapped by category to provide an understanding of how a place functions such as the major road systems and traffic patterns. It is possible for features to be a part of multiple categories which can help reveal different unseen patterns. When showing multiple categories in a map, be sure to include no more than seven as any more can be difficult to interpret. Density of the features is also important to pay attention to as denser features should have fewer categories. If there are more than seven categories, you can group them which makes representing/understanding larger sets of features easier but may hide key information that could be helpful for interpretation. A good understanding of how you are grouping your data is crucial. Choosing the correct symbols can help to reveal patterns in the data. In order to make your map easier to understand, you can include recognizable features and features that reference your data/ help to interpret the message. 

Analyzing Geographic Patterns

If mapped correctly, you may see some patterns emerge from your data such as clusters or random distributions. Patterns can be used to help explain why things are how they are. You can use statistics to find hidden patterns that cannot be easily seen or understood just by viewing the map. 

 

Chapter 3: 

Chapter three focuses on mapping the most and least so you can compare places to understand relationships. Mapping most and least map features rely on the quantity that is associated with each and leads to a deeper understanding. You’re able to map quantities that align with any of the three types of features that were discussed in chapter 1. This chapter highlights the importance of keeping a purpose for your map and ensuring you know your audience and their knowledge comprehension capabilities. With GIS you can explore data and how different patterns arise or you can present maps with patterns that tell a story or answer your question.

Quantities can be counts, amounts, ratios, or ranks. Knowing which type of quantities you have helps to determine the best type of map to present. The text talks about averages, proportions, and densities and how they relate to ratios. The next section discusses creating classes. Classes are grouped values that represent quantities on the map. Mapping individual values creates an accurate display of the data because no features are grouped together which ultimately allows you to search for patterns found in the raw data. Classes are used to group features with similar values together using the same symbol and these classes can be altered manually or by using a classification scheme. The text then goes over manual alteration and use of a classification scheme.

Comparing classification schemes:

• Natural breaks → finds groupings and patterns inherent in the data which means values in a class are most likely going to be similar. It’s good for mapping values that aren’t evenly distributed
• Quantile → each class has an equal number of features in it. 
• Equal interval → Each class has an equal range of values. Best for presenting data to a beginner audience
• Standard deviation → each class is defined by its distance from the mean value of all the features. 

As with any set of data, there is a chance for outliers. Using natural breaks can help isolate outliers. There are many different ways outliers can be caused so it is important to pay attention when they appear and double check your data.

Making a map:

This section discusses creating the map after data value classification. When creating a map with quantities, you can use graduated symbols, graduated colors, charts, contours, or 3D perspective views.

• Graduated symbols → map discrete locations, lines, or areas
• Graduated colors → map discrete areas, data summarized by area, or continuous phenomena
• Charts→ map data summarized by area, or discrete locations or areas. Show patterns of quantities and categories at the same time
• Contour lines → show the rate of change in values across an area for spatially continuous phenomena
• 3D perspective views → used with continuous phenomena to help visualize the surface

Chapter 1

This chapter introduces the fundamental concepts of Geographic Information Systems (GIS) and highlights the wide range of applications it supports. It serves as a solid foundation for understanding the analytical side of GIS by emphasizing the importance of beginning each analysis with a guiding question. This question shapes both the approach and interpretation of spatial data. Mitchell effectively outlines the essential steps involved in conducting a GIS-based investigation. A key component of this involves understanding how geographic features are represented, which can be done using either the vector or raster data models. In the vector model, each geographic feature is stored as a row in an attribute table, with its shape defined by x,y coordinates. Features such as roads, streams, and pipelines are typically modeled this way using a sequence of points. Conversely, the raster model displays features as a grid of cells, with each cell representing a specific area on the map. While raster data can be useful for representing surface features or continuous phenomena, adjusting cell size can affect both performance and storage efficiency. Regardless of the data model used, it is critical that all layers in a GIS project share the same coordinate system and map projection to ensure accuracy. Attribute data, which describes the characteristics of features, can take several forms—such as categories (groupings of similar items), counts and amounts (totals or quantities), ratios (comparative values), and ranks (ordered values).

When working with attribute tables, three key operations are often performed are selecting, calculating, and summarizing, all of which help users interpret and analyze the data effectively.

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Chapter 2

Chapter 2 focuses on how GIS can be used to analyze cause-and-effect relationships through spatial data. One of the most engaging aspects of this chapter is its explanation of how data is collected, prepared, and geocoded—either by entering street addresses or by using coordinate pairs. Whether you’re analyzing a single variable or multiple datasets, GIS can reveal meaningful insights by preserving the spatial location of each feature. However, when visualizing this data on a map, it’s important to consider how many categories you include. If more than seven categories are shown at once, the map can become difficult to interpret. Grouping categories thoughtfully can improve clarity and effectiveness. The text presents two comparative map examples: one with numerous distinct categories and another with fewer, more generalized groupings. The simpler map is notably easier to interpret. Still, careful attention must be given when grouping categories to avoid misrepresenting the data. Over-generalization can obscure patterns, while too much detail can overwhelm the viewer.

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Chapter 3

Chapter 3 explores the statistical dimensions of GIS, particularly how different types of data can be represented spatially. Three main types of mappable data are discussed: discrete features, continuous phenomena, and summarized area data.Discrete features represent specific locations, lines, or defined areas. Continuous phenomena refer to variables that change across space, such as elevation or temperature, and are often displayed using gradients, contour lines, or 3D visualizations. Summarized area data presents values aggregated over defined regions and is typically shown through shaded areas or charts. The method of visual representation—such as using points, lines, or shaded polygons—should align with the type of data and the goals of the analysis. Understanding your objective is crucial: whether you’re exploring patterns in the data or presenting findings to others, your mapping approach may differ significantly depending on the purpose.

Mitchell Chapter1

In this chapter, I realized in depth the fundamental information required for GIS analysis. First of all, I realized the importance of identifying the right information that is already available and creating new information as required to fill any gaps in the analysis. On that foundation, there is a requirement to make accurate decisions on what method of analysis is most appropriate based on purpose. There are usually two prevalent approaches to analysis: one that gives immediate results but is only an approximation, and one which requires more time, effort, and lots of data but yields more accuracy. I used to believe that “greater accuracy always is the best,” but found that there are conditions where rough approximation is better and more appropriate, and therefore came to appreciate the worth of analytical flexibility. Second, in order to identify, delineate, and quantify various geographic characteristics, one should know something about the nature of attributes describing them. Whether to employ an analytical approach or not depends on whether the attributes that one is dealing with are qualitative or quantitative. Types (categories) and orders (ranks) are qualitative values which are discrete, while counts, quantities, and ratios are quantitative values which are continuous, and this distinction strongly affects how data are classified and how patterns are perceived. By identifying these differences properly, it becomes possible to categorize data properly and make visualization and pattern detection more understandable. Besides, these fundamental principles are not only required to conduct the analysis per se, but also in order to be employed as guidelines for when and how the analysis results must be utilized and at what accuracy level at which stage. As an example, initial during the planning phase, quick estimates can be of invaluable worth, while in the final stages of decision making, high-precision data becomes absolutely essential. This way, I learned that adaptable selection of methodology is used in GIS analysis depending on the context and quality analysis is achieved by proper classification and visualization based on attributes.

Mitchell Chapter2

I learned in this chapter the value of plotting where things are. Maps can first be used to locate features one at a time and then to explore the patterns of occurrence of these features. Plotting where things are can illustrate where action needs to be taken or where areas meet particular criteria, which will make our activities more effective.
To visualize geographic trends in information, features in layers are symbolized by different kinds of symbols. Which elements to show and how to show them must be established based on the information being sought and the map’s purpose. Looking back on that, I discovered that as my OWU friends and I went to Niagara Falls, zooming out on the map specified road names and traffic status ahead while zooming in revealed nearby gas stations or McDonald’s restaurants. Based on what you need, the map provided the needed information at the moment. I also understood that not just is it important to categorize data, but also to think about how you’re going to make it easy for people to get at a glance. While you can pick up many patterns just by looking at a map, determining whether there are hidden patterns or whether the visible patterns are meaningful requires using statistics to quantify and measure relationships between characteristics. This caused me to think that if one is not careful in analysis, it is easy to be deceived, which surprised me.

Mitchell Chapter3

This chapter is with the identification of patterns on maps. Concentration of values within a place is called clustering, and even distribution of values throughout an area is known as values being spread evenly over a region. Clustering is employed to determine areas of concentration for marketing, city planning, or other purposes. The transition—the rate or form of change from low to high values—is also employed to observe how locations and social processes are related to one another.
Maps can also mark outliers, or elements whose values fall way out of line with the rest of the surrounding region. Outliers may be marked with unique symbols or placed in a special class so that the validity of the overall map pattern is preserved. Another consideration to keep in mind is the effect of aggregation. The mapping of small units highlights local variation, while large units can suppress subtle patterns. To get an even more comprehensive view of an area, supplementary maps displaying varying measures, such as percentages and densities, can be employed.
Applying these principles can prove to be effective in practice. A business owner, for example, can target areas where a particular customer cluster is concentrated, and sociologists can study the distribution of ethnic groups to monitor social trends. Compare places with rapid-changing changes in income, population, or other variables with those that have slow-changing changes to assist in policy or marketing decisions.
Overall, map pattern interpretation relies on correct selection of aggregation units, sound handling of outliers, and optimal utilization of complementary indicators. Correct interpretation of such patterns helps map readers make informed decisions and better perceive the distribution of social, economic, or environmental phenomena at a spatial scale. Detection of clustering, uniform distribution, and boundary transitions is crucial for map interpretation and spatial relations communication.

Chapter 4

Key concepts and definitions:

Map Density: Shows you where the highest focus of a feature is.

Dot map: Is one of the ways you can graphically map density.

Comments/Notes:

Map density is very important because you can not only see concentrations of the features, but you can also see those features within a uniform aerial unit like hectares or square miles. An example of how you can use map density is that a crime analyst might use it to see burglaries that occur over a year, per square mile, to compare different parts of that city.

When making a map, you should think about the features you are mapping and the information you need from this map; this will help you decide what method you should use.

You can also map density graphically, using something called a dot map, or calculate a density value for each area.

To be able to calculate a density value for each area, you divide the total number of features, or the total value of features, by the area of the polygon. Then each area is shaded based on its density value.

The areas you choose to map may affect the patterns. For example, the density displayed when mapped by census tract may appear quite different from the density shown when mapped by county.

When making a dot map, you often display the dots based on smaller areas but draw the boundaries of larger areas. That way, the boundaries would not obscure the dots.

When looking at the results, the patterns on your map partially rely on how you made the density surface.

 

Questions:

When did people first discover map density for GIS?

Chapter 5

Key concepts and definitions:

Single area: Finding what is inside a single area lets you monitor activity or summarize information about the area. Some examples of single areas are a service area around a central facility, such as a library district, or a fire response area.

Discrete features: They are unique, identifiable features. You can list them or count them or summarize a numeric attribute that relates with them.

Continuous features: They represent a seamless geographic phenomenon. With this you can find out how much of a category and how much is in that category or class occurs in each of the areas. Some examples of continuous features are the amount of each vegetation type in each of the watersheds.

Frequency: It is the number of features with a given value, or within a range of values, inside the area, displayed as a table.

Comments/Notes:

It is important to map what is inside because it is important to monitor what is happening inside, to be able to compare several areas based on what is inside each one. To find what is inside, you can draw an area boundary on top of the features, use an area boundary to select the features inside and list or summarize them, or you can combine the area boundary and features to create summary data.

If you need a list or count of features, you will want to include those that are mostly within the boundary.

Questions:

Are there limitations to how deep/far this map can go, and if so, what are they?

 

Chapter 6

Key concepts and definitions:

Distance: Is one of the ways of defining and measuring how close something is.

A summary statistic, such as an average, minimum, maximum, or standard deviation. An example is the mean square footage of buildings within three minutes of each fire station.

Straight line distance: This is when you specify the source feature and the distance, and the GIS finds the area or surrounding features within a certain distance.

 

Comments/Notes:

When you are using GIS, you are able to find out what is happening within a certain distance from a feature. To find out what is around you, you can measure straight-line distance, measure distance or cost over a network, or measure cost over a surface. Deciding on how to measure how nearby a feature is and what information you need from an analysis will help you make a decision on which method to use.

 

A summary statistic can be many things, some of which are: A total amount, such as the number of acres of land within a stream buffer. An amount by category, such as the number of acres of each land type. Examples are forests, meadows, etc.

To create a buffer, you need to specify the source feature and the buffer distance.

A geometric network is made up of edges, junctions, and turns.

There are two ways to make a boundary: you can manually draw a line around the selected segments, or you can have the GIS make the boundary.

Questions:

How long or wide can the buffer distance be?

Chapter 1:

The book introduces what GIS is and many of its various uses. It offers a good starting point when looking at the analysis side of GIS. It describes how when you are doing analysis, you always start out with a question that guides your process and how you understand the data. To begin this chapter, Mitchell did a great job at laying out all the important steps that go into a GIS inquiry. There are two ways that geographic features can be represented within GIS: vector and raster. In a vector model, each different feature is represented in a row within a table and features shapes that are defined by x,y locations in space. Streams, roads, and pipelines are all things that are commonly represented as a series of coordinates. In the raster model, different features are shown as a matrix of cells based on the original map. Changing the cell size in the model can result in analysis being slower and is not effective for storage. This model is good for showing the different types of features within the area. With both model systems, all data layers should be in the same map projection and coordinate system. When using these models, some attribute values are ratios, amounts, counts, ranks, and categories. Categories are groups of similar things. Counts and amounts both show the total numbers. Ratios show the relationship between 2 quantities and 2 things. Ranks put things in order, from highest to lowest. When working with a data table, three of the most common operations that are performed are selecting, calculating, and summarizing. These all allow the user to interpret the data that’s being analyzed properly. 

Chapter 2:

I found that chapter 2 was especially interesting as it highlights how GIS can be used to identify and evaluate cause and effect relationships. I liked learning about the process of collecting and preparing the data that is being analyzed. When assigning geographic coordinates you can use either input a street address or a pair of coordinates. No matter if you are tracking multiple types of data or just one, you can still learn helpful information. GIS is also very helpful as it stores the location of each geographic feature. If you are mapping and have more than seven categories then it is helpful to group some of these categories together to make it easier to effectively visualize the map. The reading offers two examples of maps where one has many categories while the other only has a few groupings. This change is very noticeable with the map with many less categories being much easier to read and observe. When combining categories, it’s important to group them properly so the data can’t be misinterpreted. It is also important to not combine the categories into too few groupings as it might make patterns harder to identify. Overall, this chapter was really helpful in shaping my view of GIS and how the mapping feature works in it. 

Chapter 3:

This chapter helped me to understand how GIS operates in terms of statistics. There are three kinds of data that can be mapped: discrete features, continuous phenomena, and summarized area data. Discrete features are individual locations such as linear features or areas. Continuous phenomena are areas or values that are represented as contours, graduated colors, or a 3D perspective. Data summarized by area is usually depicted by shading the area of interest or by using a chart that organizes the data in question. How this data is also important as there are multiple ways of doing so. Some of these options include using points and lines to display the information that is trying to be analyzed. When making a map, it is also very important to know what is your goal with said maps. depending on whether you are trying to explore the data or present the information, your means of doing each could change. 

Chapter 1- Mitchell

• • GIS grown enormously since 1999
    • New sources- lidar and drones
    • Shared more openly and widely
    • More people
  • ArcGIS Living Atlas of the World
  • GIS uses:
    • Mapping where things are
    • Mapping the most and least
    • Mapping density
    • Finding what’s inside
    • Finding what’s nearby
    • Mapping change

• GIS Analysis– process for looking at geographic patterns in your data and relationships between features

• Process For Analysis Steps:
  • Frame the Question
    • What info do you need
    • Question form- be specific
    • How will analysis be used, and by who?
  • Understand Your Data
    • Type of data and its features
    • Method you use could require you to obtain additional data (or change its format)
  • Choose a Method
    • Almost always multiple methods for obtaining needed information
    • Decide which method fits your question and research best
  • Process the Data
    • Once method is selected, use GIS to perform it
  • Look at the Results
    • Multiple ways of displaying results: maps, tables, charts
    • Decide what is new/valuable to add to your display
    • Decide whether your information is valid or useful; then adjust accordingly

Understanding Geographic Features

• • Types of Features:
    • Geographic features are discrete, continuous phenomena, or summarized by area
      • Discrete: location can be pinpointed
      • Continuous Phenomena: can be found or measured anywhere, blanket entire area being mapped

• Interpolation- GIS using sample points to assign values to the areas in between them

• • • • • Continuous data can also be areas enclosed by boundaries (everything within boundary must be same type)
      • Features Summarized by Area: counts or density of individual features within an area (ex: number of businesses in each zip code)
        • Data value applies to the whole area and not a specific place within it
        • Lots of data (especially demographic data) is sorted by area
        • GIS can be used to aggregate data that lies within similar areas
  • Ways of Representing Geographic Features
    • Vector
      • Each feature is a row in a table
      • Shapes defined by x,y locations
      • Features can be discrete locations or events, lines, or areas
        • Lines represented as series of coordinate pairs
        • Areas defined by borders and represented as polygons
      • Vector data analysis often times involves summarizing attributes in layer’s data table
    • Raster
      • Features represented as matrix of cells in continuous space
      • Each layer represents one attribute
      • Analysis is combining layers to create new layers
      • Cell size affects results of analysis/map quality
        • Cell size should be based on original map scale and minimum mapping unit
  • Discrete features and data summarized by area usually use vector model
  • Continuous categories either vector or raster
  • Continuous numeric values are raster

• Map projection– translate locations on the globe onto flat surface

• • Distort shape of features being displayed
• GIS systems usually already have their databases in the same coordinate system and projection

Understanding Geographic Attributes

• • Attribute Values
    • Categories
      • Groups of similar things that help you organize your data
      • Category values can be represented as numeric codes or as text (abbreviated)
    • Ranks
      • Put features in order from high to low
      • Used when direct measures are difficult or quantity represents multiple factors
    • Counts and Amounts
      • Total numbers
    • Ratios
      • Show relationship between two quantities
      • Created by dividing one quantity with another for each feature
      • Evens out differences between large and small areas

• Proportions- what part of a total each value is

• Density- distribution of features or values per unit area

Working With Data Tables

• Three common operations: selecting, calculating, summarizing
  • Selecting
    • Select features to work with subset or assign new attribute
    • In form of logical expression (code)
  • Calculating
    • Calculate attribute values to assign new values to features in the table
  • Summarizing
    • Could be mean, frequency, total, etc.

 

Chapter 2- Mitchell

Why Map Where Things Are?

• To see where/what an individual feature is
  • However looking at distribution of features allows patterns to form
• Can show you where to take action, or what areas meet your criteria

Deciding What To Map

• To look for geographic patterns, map features in a layer using different symbols
• Information Gained From Mapping
  • Where features are/aren’t
  • Locations of different features to look for patterns
• Make sure map is appropriate for audience

Preparing Your Data

• Make sure features being mapped have geographic coordinates
• GIS databases have coordinates assigned, incoming info must have coordinates (street address or latitude-longitude)
• When mapping by type, each feature must have an identifier for its type
• Many categories are hierarchical
• Sometimes a single code can indicate multiple types (major type and subtype)

Making Your Map

• • To create, tell GIS your features and their symbols

• Mapping a Single Type

• • • Draw all features using same symbol
    • Can use this to find differences in the feature to explore further

• What The GIS Does

• • • Stores location as pair of coordinates or set of coordinate pairs
    • Individual locations: GIS draws symbol for that specific point
    • GIS can also draw lines for line features, also draws area outlines

• Using a Subset

• • • Can map features in subset based on specific category values
    • Usually done for individual locations (linear features would often be incomplete)
    • For continuous data, you would be leaving out context of surrounding areas

• Mapping By Category

• • • Draw features by using different symbol for each category value

• What the GIS does

• • • • Stores category value for each feature in table
      • Separately stores specified symbols
  • Features might belong to multiple categories
  • Sometimes useful to make multiple maps with different categories to compare
  • Display no more than seven categories on one map (also affected by distribution of features and map scale)
  • Hard to distinguish categories on maps with small features
  • For large maps, multiple categories can distort trends and make it harder to find differences between categories
  • Conversely, important not to have too few categories on smaller maps (don’t leave out info)
  • If more than 7 categories, group the categories
  • The way categories are grouped can influence readers’ perception (categorize carefully!)
  • Ways to group categories: 
    • Assign each record two codes 
    • create table containing one record for each detailed code with its corresponding general code
    • Assign same symbol to various detailed categories that are apart of a general category
  • Symbols used to display data are important to display it properly
  • For areas/raster layers: display similar categories in different shades of same color
  • For linear features: use different widths/patterns (text labels could also be useful)
  • Maps more meaningful when they contain recognizable features, put reference features in light colors

• Reference feature– feature on map that makes the area identifiable

• Some GIS software provides base maps with reference features

Analyzing Geographic Patterns

• Play with map scale to try and find identifiable patterns in features
• Exceptions can reveal further causes/areas of further interest
• Use statistics to more scientifically find relationships between different features

 

Mitchell- Chapter 3

Why Map The Most And The Least

• Find places that meet criteria to take action or see relationships between places
• Mapping based on quantities adds additional info

What Do You Need To Map

• Discrete features
  • Locations, linear features usually represented by graduated symbols; area by shading
  • Continuous phenomena can be defined areas or surface of continuous values
    • Defined areas: graduated colors
    • Surfaces: graduated colors, contours, 3D perspective view
  • Data summarized by area displayed by shading each area
• Keep in mind whether or not you are exploring data or presenting your map

Understanding Quantities

• • Quantities: counts or amounts, ratios, ranks
    • Counts and Amounts

• Count- actual number of features on map

• Amount- total of value associated with each feature

• • • Can be mapped for discrete features or continuous phenomena
    • counts/amounts can skew data for areas with size variability
  • Ratios
    • Evens out differences for large/small areas (shows distribution of features)
    • Proportions often represented as percentages
    • Density good for showing distribution when large area size variability
    • Create ratios by adding new field to layer’s data
  • Ranks
    • Useful when direct measures are difficult
    • Assign ranks based on another feature attribute

Creating Classes

• • Counts, amounts, and ratios grouped into classes because each feature has a potentially different value
  • Valuable for maps used for public discussion
  • Mapping individual values = more accurate picture
  • If mapping ranks, assign one symbol to each rank
  • Can map ratios, counts, or amounts using individual values if:
    • No more than 11-12 unique values
    • <20 features

• Classes- group features with similar values by assigning same symbol

• • • Important to consciously define class ranges
  • Create classes manually if looking for features that fit specific criteria or comparing features to meaningful value
  • Use standard classification scheme if wanting to group similar values to find patterns
  • Four most common schemes

• Natural breaks- classes based on natural groupings of data values

• • • • GIS automatically determines high and low value for each class using math to test class breaks, maximizes difference between classes
      • Good for mapping not evenly distributed data values

• Quantile- each class contains equal number of features

• • • • GIS adds up number of features (plus puts them in order) then equally separates them into number of classes specified
      • Good for comparing areas of similar size, mapping evenly distributed data, emphasizing relative position of feature

• Equal interval- difference between high and low values is the same for every group

• • • • GIS subtracts lowest dataset from highest then divides that number by number of classes specified
      • Good for presenting info to nontechnical audience and mapping continuous data

• Standard deviation- placed in classes based on how they deviate from the mean

• • • • GIS finds mean, then calculates standard deviation and separates classes
      • Seeing what features are above/below mean value
      • Displaying data with lots of values around mean
  • Plot data values in chart to see their distribution
  • Easy to change class ranges in GIS so try different ones

• Outliers- few extremely high or low values

• To deal with them:
  • Put each in its own class
  • Group them together in a class
  • Group them with next closest class
  • Draw them using special symbol
• While exploring number of classes, start with more and work way down until desired clarity
• Rounding data when reasonable to make it palatable
• Give classes meaningful names

Making a Map

• • GIS options to show quantities
    • Graduated symbols
      • Which features: locations, lines, areas
      • Which values: counts/amounts, ratios, ranks
    • Graduated colors
      • Which features: areas, continuous phenomena
      • Which values: ratios, ranks
    • Charts
      • Which features: locations, areas
      • Which values: counts/amounts, ratios
    • Contours
      • Which features: continuous phenomena
      • Which values: amounts, ratios
    • 3D perspective views
      • Which features: continuous phenomena, locations, areas
      • Which values: counts/amounts, ratios

• Legend- displays subset of values and symbols to indicate relative value of individual features

• • Consciously choose colors to attract attention to what you want to
  • Charts on maps useful for quick study of patterns
    • Pie charts: show how much of total amount each category takes up
    • Bar charts: show relative amounts
  • Use contour lines to show rate of change in values across an area
    • Choose interval small enough to give surface definition but not too small that the lines are too close together
    • Label with value
    • Use bold line for every fifth interval
  • When using 3D perspective have viewer in proper spot so tall parts don’t block shorter ones

• Z-factor- value specified to increase the variation in the surface so differences are easier to see

Looking For Patterns

• Look at transition from highest to lowest values
• See distribution of values (clustered or spread out)
• Can summarize up but not down

Chapter 1:

This first chapter really goes over what GIS analysis is and the key points associated with it. GIS is really helpful when it comes to getting and creating data and mapping. Although it doesn’t just involve mapping, that is a big factor. First and foremost, GIS is a process that helps you look at geographic patterns in data and the relationships between certain features. This can involve both simple and complex methods.

There is a bit of a process in terms of analyses. Those include framing your question, understanding your data, choosing a method, processing all of your data, and finally looking back at all of the results to see how this may be of value to you. In the process of understanding geographic features, the chapter goes over the different types of features – discrete and continuous. There are also two ways of representing this which involves vector and raster models. Summarized data is also important to note in this chapter.

It’s important that every bit of data that you are collecting should be in the same map projection and coordinate system. Map projections are going to translate your locations onto a globe, while coordinate systems go into specifics to locate features within a two-dimensional space. Both of these work together to help enhance your data for the GIS analysis.

There are 5 different attribute values for geographic features – categories, ranks, counts, amounts, and ratios. Categories are similar things grouped together. Ranks are the features put into order, going high-low. Counts and amounts kind of go hand in hand with each other and they are going to help you find the total numbers. Lastly, ratios are going to show the relationships between two different quantities.

One last thing that was important in this chapter was the 3 different operations for features and values in tables which were selecting, calculating, and summarizing. Again, this chapter in general just kind of goes over what GIS is and the features that are connected with it/how you can use it.

Chapter 2: 

Chapter 2 goes over a lot about how you can use GIS for mapping and why mapping is useful. Mapping can be used for all sorts of things for different job areas such as police using it to map crime, a store using it to map out where they put their newest location, or even wildlife biologists using it to keep track of certain animal studies.

When creating a map using GIS, there are lots of steps that are important and you need to make sure they are done correctly so that your map comes out the way you want it to. The very first thing that you need to do is figure out what you want/need to map and what information you are going to need from the analysis. As said before, it can really help you find all sorts of different features and locations and that just depends on the type of information you are wanting to get out of it. The map should also cater to the proper audience. The amount of information and categories on your map is going to depend on who you are trying to target. Make sure you have all of your geographic information prepared before diving into the mapping itself. Some maps are going to be smaller with less categories which is fine and some are going to be larger. You want to make sure that it’s not too big or too small so find a good medium that will still be good for your specific research. This chapter also dives into assigning your geographic coordinates as well as category values. For the categories this means that each feature needs to have some sort of category and pattern. You can map either a single type or by category, and again, this just depends on what you are using it for. For a single type you would use the same symbol for all your features whereas for categories you would want to use different symbols for each category. Chapter 2 dives into more detail about both of those as well, but I won’t write out all of the details of both of those. A good rule of thumb when mapping categories is that you don’t want to display more than 7 different categories. This is because it becomes too confusing on one single map. If you do have more than 7, that’s when you would want to start grouping them together and the possibilities for different groups are endless.. It’s up to you what sections you put them in! When choosing your symbols for the map you can do colors, shapes, or both. Colors are easier to identify than shapes, especially on a smaller scale. You should be able to clearly see the different patterns and information on the maps if they are done correctly. This chapter overall went into a lot of detail on how you can start mapping and what it can be useful for.

Chapter 3:

In the third chapter, you learn a lot about the things you need to map and understand the quantities and patterns associated with creating maps. First off, mapping the most and least helps you find places that meet criteria, or to see the different relationships between places. It is important to note that you should map patterns of features with similar values. There are 3 different quantities you can map – discrete features, continuous phenomena, or data summarized by area (it dives into each of those in detail as well). Quantities can be counts or amounts, ratios or ranks. Knowing which one you are using is important. After deciding your quantities it talks about classes – you can assign each individual value a symbol or put them into classes. Individual mapping is a bit more detailed and accurate because you can look at each feature separately, but classes are better for when you have a bigger selection of features. When using classes, there are 4 common schemes: natural breaks, quantile, equal interval, and standard deviation. To figure out which is best, just look at your data distribution!

The chapter goes into a ton of detail about the 4 schemes including what they are and how to use them. In order to determine which scheme to use, it’s good to create a chart or spreadsheet of some sort to evaluate your data. If you have any outliers (which could happen) – there are a few different ways you can deal with it. One of which is grouping them together into their own class. After you decide everything with the schemes, it’s important to (with the help of GIS) decide on how many classes and make it easier to read them once they’re finished so you can easily interpret them.

Making maps is another big section. GIS gives you these options when creating maps – graduated symbols, graduated colors, charts, contours, and 3D perspective views. It goes into detail for all of those and when choosing your map it’s important to know those features and data values. You can look for all sorts of different patterns within your maps and each of the different options from above have different features/quantities that set them apart from the others. Overall this chapter I think went into the most detail regarding each section and it really helps the reader understand why we map things and all the little details that go into making them.

Chapter 1 

In the first chapter, it focused on what GIS is,  all the things it can do, and some basic GIS concepts. GIS analysis is looking for geographic patterns in data and at relationships between features. The first section describes the steps in the process of GIS analysis. It starts with framing a question to understand what type of data you need. This question will help frame the rest of the process. The next step is to understand your data, which this chapter really focuses on. The book explains that you have to know what kind of data you have in order to know what you need to create. The rest of the process is: choose a method, process the data, and look at the results. This little section made me realize the importance of understanding the basics and what you have in order to begin the process of GIS analysis. The chapter then focuses on understanding geographic features and attributes. It gives definitions of types of features, the ways they are represented, and information about map projections and coordinate systems. When it comes to map projections and coordinate systems, the book mentions that all the data layers should be in the same map projection and coordinate system to make sure results are accurate. The last section explains selecting, calculating, and summarizing when working with data tables.  

Definitions

discrete features–  actual location can be pinpointed

continuous phenomena– can be found anywhere and blanket the entire area that you’re mapping

features summarized by area-  represents counts or density of features in an area’s boundaries. 

Vector Model- feature is a row in a table, feature shapes are defined by x,y locations, points/lines, areas are defined by borders and are closed polygons

Raster Model- features are represented as a matrix of cells in continuous space, each layer represents an attribute 

Categories- groups similar features

Ranks- put features in order, from high to low, used when direct measures are difficult or the quantity represents a combo of factors

Counts and Amounts- count= actual number of features, amount= any measurable quantity associated with a feature

Ratios- relationship between two quantities, are created by dividing one quantity by another for each feature

 

Chapter 2

Chapter 2 starts off with explaining that there is a lot of helpful information that we gain from mapping. We can see patterns, areas that we need to take action in, and areas that meet our criteria. In order to look for patterns in your data, you have to decide what you are going to map. You also have to take into account the audience and issue that you are addressing when making a map. This is an aspect I hadn’t thought about when using GIS and showing the data. There is a lot of information and the correct presentation is important. The next section is about preparing your data. It states the importance of assigning geographic coordinates and assigning category values. The biggest section in this chapter is about actually making the map. To map a single type, you tell GIS to draw all features using the same symbol. It explains how GIS stores each feature as a coordinate pair to define its shape. You can also map a subset of features in order to reveal any other patterns you might have been missing. You can also map by category and subsets of categories, and use different symbols for each one. It is important to not show more than 7 categories and to have large bordering ones because it will become difficult to see the pattern. If you do have more than 7 categories, grouping them together can be helpful for the patterns to stay visible. It’s important to understand what the categories represent and to group them in a specific way depending on what you are trying to show. The book then explains 3 ways to categorize data. It also states the importance of picking symbols to display categories and mapping reference features. The final section is all about analyzing the patterns. What I’ve taken away from this chapter is how important it is to make sure the pattern can be visible while making your map. There are a lot of choices to be made in order to represent the data in a way that a pattern can be seen and understood. 

 

Definitions 

Single Type Map– all features use the same symbol

Grouping Categories– grouping similar categories together to make the pattern  easier to see

 

Chapter 3

Chapter 3 focuses on mapping the most and least. To do this, you map features based on a quantity associated with most and then with least. The book goes over some terms that we already learned in chapter 1 and applies them to mapping most and least. It talks about using classes to group features with similar values together. It goes over 4 common standard classification schemes to class data together which are: natural breaks, quantile, equal interval, and standard deviation. I enjoyed reading the section with how each scheme works, what they are good for, and their disadvantages. You first must choose a scheme, then decide on how many classes you will have, and then adjust to make the classes easiest to read. This chapter also includes a very large section on making the map. GIS gives you these options to show quantities: graduated symbols, graduated colors, charts, contours, and 3D perspective views. You have to consider your data and features and then pick an option accordingly. I was intrigued by the 3D perspective view and how you have to change the viewing position, pick a specific z-factor, and consider the light source. It seems a little difficult to me but I love that you can do all of that using GIS in order to show data in a better way. This chapter ends with looking for patterns in the highest and lowest values.  Overall I hadn’t thought about the mapping of most and least and all the relationships that the data could reveal, so I enjoyed learning all you can do with it. 

 

Definitions 

Classes- groups features with similar values together 

Natural Breaks- based on natural groupings of data values

Quantile- Each class contains an equal number of features

Equal Interval-The difference between the high and low values is the same for every class

Standard Deviation- based on how much their values vary from the mean

Graduated symbols- used to show the volumes or ranks for linear networks

Graduated colors- to map discrete areas, data summarized by area, or continuous phenomena

Charts- to map data summarized by area, or discrete locations or areas

Contour lines- to show the rate of change in values across an area for spatially continuous phenomena

3D perspective view- most often used with continuous phenomena to help people visualize the surface

Chapter 1
-GIS has grown greatly even offering new sources (Lidar and drones).
-Greatest change for better is larger amount of people doing spatial analysis and sharing results offering more data than ever
– GIS data easily accessed by sites such as ArcGIS Living Atlas of the World
– GIS analysis is a process for looking at geographic patterns in your data and at relationships between features
– To start gis analysis figure out what data you might need then think through a question (usually). Try to be as specific as possible. Also consider who will be using data and how they may be using it to flush out your question or data you’re seeking. Than understand what data you need and select the method of analysis accordingly. Finally analyze your results.
-Types of features are discrete, continuous phenomena, or summarized by area
–Discrete- the actual location can be pinpointed at any given spot the feature is either present or not
–Continuous Phenomena- such as precipitation or temperature can be found or measured anywhere. They blanket the entire area you’re mapping. Can determine a value at any location. (weather station)
–Features summarized by area- represents the counts or density of individual features within area boundaries. (for example number of businesses in a zip code). data value applies to the entire area but not a specific location within it.
-2 ways of representing geographic features vector and raster
— Vector model each feature is a row in a table and are defined by x,y locations (address of a business/location of a monument)
— Raster model, features are represented as a matrix of cells in a continuous space (i think weather radar maybe?)
–discrete features and data summarized by area are usually vector model
-Map projection and coordinate systems
–translates locations on the globe onto a flat map. All map projections are distorted. Which distortion can be negligible on small scale but can cause problems when mapping a larger area.
—If collecting data from multiple sources ensure that map projection and coordinate system is the same.
-Understanding Geographic attributes
— Every feature uses multiple attributes including- Categories, ranks, counts, amounts, ratios

—Categories are groups of similar things (help organize data roads can be classified as freeways, highways, or local roads). Ranks put features in order from high to low(nonspecific in value but tells you the order of features such as one may be ranked lower than another but you won’t know how much). Counts and Amounts show total numbers. Counts are number of features on a map. Amounts are any measurable quantity relating to a feature (amount of trees in a thicket of woods/area). Ratios show relationships between two quantities and are created by dividing one quantity by another (dividing amount of employees by number of business locations give avg number of employees per location).
-Working with data tables- common operations performed on features and values within tables are selecting, calculating and summarizing
–Selecting is to select features to work with a subset or to assign a new attribute value. Calculating is to calculate attribute values to features in the data table. Summarizing is for getting specific attributes to get statistics.

Chapter 2

• Deciding what to map
  • Map the features you are focused on such as a police department mapping crimes
• Preparing your data
  • make sure all features have geographic coordinates assigned before mapping
    • when bringing in data from another program or when entering by hand features will need specific locations such as the longitude and latitude coordinates.
  • Assigning category values
    • each feature requires code that identifies its type (sandwich: philly, blt, cbr, etc.)
    • to add a category you create a new layer in the layers data table and assign appropriate value to each feature
      • many categories are hierarchical with major types divided into subtypes
• Making your map
  • Features displayed and which symbols to use to draw them
  • mapping a single type- use same symbol for all features (may suggest differences in feature)
  • using subset of features- can map all features in data layer or subset based on a category value. (can reveal patterns that aren’t apparent when mapping features)(more commonly done for individual locations)
  • Mapping by category- map features by using different symbol to draw features for a different category value. (gain understanding of how a place functions)
  • Displaying features by type- Features can belong to multiple categories and using different categories can reveal different patterns.(can usually display all categories on same map but if features are to close or complex create multiple maps)
    • displaying a subset may show a relation between categories better(no more than seven categories/patterns)
  • Grouping categories- can group categories to limit patterns making it easier to see relationships between features. 
  • Choosing symbols- if mapping individual locations use single markers. use variety of shapes and colors or patterns to help distinguish features making relationships in categories and features easier to recognize
  • Also be sure to map noticeable reference points to make locations more recognizable for other people going over your data such as famous buildings and memorials  or roads such as highways.
  • Analyzing geographic patterns
    • clustered distribution- features more likely to be found near other features
    • uniform distribution- features less likely to be found near other features

Chapter 3

• mapping most and least- map features based on a quantitative value. Mapping features based on a quantity can reveal a more specific pattern such as instead of mapping cars in a town going into detail and mapping specific brands or types of cars reveals more in depth detail you may be looking for 
• What do you need to map?
  • by mapping features with similar values it allows you to gain a better understanding of your map 
  • you can map quantities  with discrete features , continuous phenomena or data summarized by area
    • discrete features can be individual locations, linear features or areas
      • individual locations and linear features usually represented by graduated symbols
      • areas shaded to show quantity
    • continuous phenomena can be defined areas or a surface of continuous values
      • defined areas- graduated colors
      • continuous values- graduated colors, contours, or 3d perspective view
    • Data summarized by area is usually shown by shading area based on its value or using charts to show value in an area
  • Creating classes
    • once you’ve determined your quantities determine best way to represent them by assigning each individual value its own symbol or by grouping values into classes (trade off between presenting the data values accurately or by trying to get the most accurate map)
      • counts and amounts and ratios are usually grouped into classes 
      • ranks  are to be mapped as individual values since they are not continuous 
    • Mapping individual values lets you find patterns easier 
    • using classes lets you see features with similar values
    • Use standard classification schemes if you want to group similar values for easier pattern recognition
      • natural breaks-data clusters are placed into a single class and breaks happen between clusters of value
      • quantile- each class has an equal number of features
      • equal interval- each class has an equal range of values(difference in high and low value is the same for each class)
      • standard deviation- each class is defined by its distance from the mean value of all features
      • when dealing with legit outliers put them in their own class, group them together in a class, group them with the adjacent class, or designate a special symbol for them
    • Making a map
      • GIS provides graduated symbols, graduated colors, charts, contours, or 3d perspective views

Chapter One:
This chapter was an introduction into GIS. The groundwork for GIS was provided by defining it as a process for examining spatial patterns and relationships of geographical features. GIS analysis starts with a question just like in a research hypothesis. From that question data is collected, modeled, and then presented. The process of GIS can be simply put as framing the question, understanding the data, choosing a method, processing the data, and then looking and interpreting the results. Three main types of geographical features are mentioned. These are discrete features which can be defined as a distinct point where the feature is either present or not like a stream or land value. The second is a continuous phenomena which can be measured anywhere and encompass an entire area such as temperature or rainfall. Lastly features summarized by area which is the amount of something in a specific area such as people per house in a county. All of these features can be shown with a vector model or raster model. Geographical attributes is another major concept which adds meaning to features such as categories- qualitative types, ranks- ordering, counts and amounts- numeric totals, and ratios- numerical relationships. The utility of tables in GIS for selecting, calculating, and summarizing information is a key component of analysis.
What I noticed is how GIS blends different data together to create one source of information. It is also about more than looking at maps but also finding the answers to spatial questions. Accuracy and perception have a huge role on one another. The way it is presented can change perception of the data.
Is there a way to minimize distortions for large areas or do we simply just choose where it has the least effect?


Chapter Two:
This chapter focuses on the practical side of GIS mapping. It explains that the maps help to reveal geographical patterns not just showing the locations. It helps to explain or show why features are there. Each map has multiple layers with different features assigned and symbols for their features. Mapping can be done in a few different ways. These are showing all the features as a single type- having the same symbol for each feature, displaying categories- different symbols/ colors per type, or mapping subsets- features filtered to highlight a theme. It is also warned against overloading a map as we as humans can only really work with seven categories at once. This is due to it getting too hard to tell different categories apart. Due to this, grouping categories together can be helpful and sometimes necessary. Scale and audience are also important. Those who are map experts can distinguish more details than the average person as they have a more trained eye for the smaller details. Geographic coordinates- addresses, latitude, and longitude are a requirement that one must be very careful with. This ensures that placement is accurate.
The crossover between science and communication are shown throughout this chapter. The choice of colors, symbols, and reference features are very important for the design of a GIS analysis and also correlates with psychology and design.
When it comes to grouping categories what guidelines should one be thinking of to avoid oversimplification or messing with the data.


Chapter Three:
This chapter focuses on comparing places based on quantities and where things do and don’t occur. This requires using quantities, can be in a few different forms, ratios, counts, amounts, etc. This chapter has classification schemes to group data values into classes, natural breaks (jenks)- groupings and patterns inherent in your data, so values within a class are likely to be similar and values between classes different, quantile-an equal number of features in it, equal interval-equal range of values, the difference between the high and low value is the same for each class, and standard deviation-distance from the mean value of all the features. Graduated symbols-symbols of different size to represent a variation can be used for mapping as well as graduated colors- colors that get darker or lighter depending on the data in that area, charts, contours- lines around to show variation, and 3D perspective views- looking at something from the side to help show the elevation. These all help to show the continuous phenomena. Ratios are important to normalize data values and allow for fairer comparisons across an area. There is a balance between precision and readability where too many classes or decimals can obscure the meaning while too few can hide something.
This chapter helped me to realize how much power analysts hold when interpreting something. The same data sets can produce different results depending on how it is interpreted. There is a strong link to map-making and statistical choices. Outliers can also affect the data and how they are taken within a data set.
Should consistency be prioritized by analysts while using classification schemes or flexibility?