Geography 291: Geospatial Analysis with Desktop GIS

Chapter 5

Notes:

You can use an area boundary to define the features inside. These can be created on top of features, can be used to select features inside the area/summarize selected features, and combine the area boundary and features in order to create summary data.

Single areas can be sectioned off to let you monitor activity or summarize information. For example, a stream buffer that is off-limits for logging. Then there are multiple areas, that can compare what’s within several different areas in a contiguous fashion. Examples of these contiguous areas are zip codes and watersheds.

You can change what you’re analyzing using different feature attributes (as discussed in previous chapters). Sometimes features will bleed out of the area; there are a couple different ways to deal with this. You can only include features fully contained, include features that partially extend outside (would use counts), or include only portions that are inside of the area (would use amounts). This decision all depends on what you’re mapping and the level of precision required.

Vectors are typically used with continuous data and can result in slivers, which can be smoothed out with the GIS. You need to keep in mind the extent of the data, the degree of accuracy you’re dealing with, and only have very small slivers removed automatically. Anything slightly bigger should be removed manually to ensure that important data isn’t lost.

Vector is more precise, but requires more time and processing power; it requires the summarization of category values in the final table. Raster is more efficient, but can be less accurate. The accuracy will depend on the cell size, and slivers can still be created using raster.

Definitions:

• Frequency– the number of features with a given value or within a range of values inside the area.
  • Represented with a bar chart or pie chart.
• Sum– overall total or total by category.

Chapter 6

Notes:

You can use GIS to find out what’s nearby and how that’s relevant to the data set and audience you’re creating a map for. When dealing with distance, you must define “closeness,” as it’s very subjective. You need to quantify what is “near” and what is “far.”.

Buffers can be used to give features more definition. They can be used to add a literal buffer along stream banks to forbid logging, or just to simplify complicated data sets. Network layers connect edges through the GIS to allow different usages of distance and cost, and can be used in conjunction with buffers.

Definitions:

• Travel costs– the effort or other detriment associated with one path/area over another.
• Planar method– calculating distance assuming the surface of the earth is flat.
  • Used for short distances or small areas (county, city).
• Geodesic method– taking into account the curvature of the earth.
  • Used for long distances (continent, earth as a whole).
• Inclusive rings– bands of data ranges used to see relative changes at varying scales.
• Distinct bands– for comparing distance with other characteristics.
• Straight-line Distance– specify the source feature and distance, then uthe GIS finds the area or surrounding features.
  • Primarily used to create boundaries.
• Distance or Cost Over Network– specify source locations and a distance or travel cost along each linear feature.
  • Used to find what’s within travel distance or cost over a fixed network.
• Cost Over a Surface– specify location of source features and travel cost, and creates a new layer showing the travel cost from each feature.
  • It calculates the overland travel cost.

Chapter 7

Notes:

Maps can also be made to change in order to document past conditions and/or predict future events. You can go date by date, or hop between a certain/set period of time in a pattern (every two days, every other month, every 3 hours). Make sure to keep note of how exactly time is changing and its relationship with the feature(s).

Time patterns can be used to track movements over time. You can use lines between points to better emphasize findings as well. The distance between points can represent various speeds. For example, two dots that are closer together show a slower amount of movement of a hurricane over a 3-hour period than dots that are further apart after the same amount of time has passed.

Coloration and shading to emphasize change with continuous features. Equal time intervals being used for each feature is critical to seeing an accurate rate of change. Events mapped over time typically use color grades that represent different (but equal in length) time periods. If there are several events reoccurring at the same locations, you can use pie chart markers in place of simple dots.

Definitions:

• Change in Location– see how features behave so you can predict where they’ll go.
  • Ex; bird migrations, hurricanes
• Change in Character or Magnitude– shows how conditions in a given location have changed.
  • Ex; land cover change in a watershed
• Travel– change between two or more dates or times.
• Before & After– conditions preceding and following an event.
• Cycle– change over a reoccurring time period.
  • Ex; day, month, year

Mitchell Chapter 5:

Key concepts and definitions:

Boundary: a polygon that is placed on top of features and is used to select features within in order to list or summarize features or in order to combine the boundary and features to create summary data. Boundaries can be shaded areas that go in front or behind the outer area (this emphasizes the area itself) or they can be thick lines (this emphasizes the inner area).

Drawing areas and features: a method that allows you to visually find out whether features are inside or outside the boundary. This method is for working with one area.

Selecting the features inside the area: a method that provides a list or summary of features inside the boundary. This method is for working with one area.

Overlaying the areas and features: a method that determines which features are inside which boundaries and also summarizes features by area. This method is for working with several areas or single areas. Using the raster model is more efficient than using the vector model.

Count: the total number of features inside an area or boundary.

Frequency: the number of features with a certain value inside an area or boundary displayed as a table or chart.

Slivers: very small areas where areas are slightly offset from overlaying. Slivers should be merged into adjacent larger areas according to minimum mapping unit and data accuracy guidelines.

Minimum mapping unit: the smallest area input in a dataset.

Notes and Questions:

• Finding what’s inside a single area lets you monitor activity or summarize information about the area and finding what’s inside numerous areas lets you compare the areas
• You want to include features that are partially within the boundary if you are gathering a list or count of features
• When looking to determine the amount of something within a boundary, you would only include the portion inside the area
• Am I correct in understanding that overlaying the areas and features is selecting the features inside the area just with an additional step and that selecting the features inside the area is drawing areas and features just with an additional step?

Mitchell Chapter 6:

Key concepts and definitions:

Traveling range: determines what’s within a set distance of a feature. Distance, time, or cost can be used.

Travel costs: often termed the impedance value. Time, distance, and money are very common.

Planar method: used for calculating distance on a flat earth and in a relatively small area.

Geodesic method: used for calculating distance taking into account the curvature of the earth and in a relatively large region.

Inclusive rings: useful for determining how the total amount of something increases as distance increases.

Distinct bands: useful for comparing different distances to other characteristics.

Spider diagram: formed when GIS draws a line between each location and its nearest source. They are useful for comparing patterns between two or more source points.

Junctions: points where edges meet.

Turns: used to determine the cost to travel through a junction.

Edges: street segments or lines.

Turntable: a data table that contains the junctions which you want to assign a cost to.

General boundary: it connects the farthest reaches of the selected segments (forms a blob).

Compact boundary: it outlines the selected segments.

Mask layer: used for blocking the assignment of cost values to cells. You would assign the cells a very high value or no value at all to do this.

Notes and Questions:

• Area of influence is typically measured using straight-line distance (putting a boundary of a certain radius, depending on the distance specified, around the chosen feature)
• Travel movement is measured over a geometric network (for example roads). Travel costs can also be applied to this
• Cost over a surface is used for overland travel and is useful for showing rate of change. It uses the raster model
• When finding features near several sources you need to create separate straight-line buffers otherwise you won’t know which source (or sources) the feature is near
• You should specify the maximum distance when finding what’s nearby
• Distance ranges are created using graduated colors
• If you need to be specific when calculating travel time (cost) include turns and stops
• The source should be a different, distinguishable symbol than other features
• To create a cost layer based on a single factor reclassify an existing layer for the attribute you want, and to create a cost layer based on numerous factors combine all the layers together after reclassifying each input layer
• I understand the theory of how to find what’s nearby but I don’t know the technical steps to take

Mitchell Chapter 7:

Key concepts and definitions:

Time patterns (trend, before and after, and cycle): a trend map represents change between two or more times, a before and after map represents change preceding and following an event, and a cycle map represents change over a recurring period of time.

Tracking map: shows the position of a feature or features at several times. This is useful for showing incremental movement and geographic phenomena.

Trendline chart: shows a relative value as well as that value’s growth over time.

Notes:

• You can map change by creating numerous maps showing the condition of features at each time or by calculating and mapping the difference in value for each feature
• When mapping trends you need to determine the time interval
• When mapping cycles you can map either snapshot or summarized data
• When mapping before and after you want to use snapshots as close as possible to the event
• When mapping discrete events you need to use summarized data and when mapping continuous data you can map summarized or snapshot data
• Time series maps are good for showing changes in boundaries, values, or surfaces. You create one map for each time; however, you shouldn’t have more than six maps
• A tracking map is good for showing movement in boundaries, lines, and discrete features
• When mapping change in magnitude use the same classification scheme for all the maps
• Quantile and equal interval schemes are useful for comparing values over time
• You can generalize categories if historical categories vary from existing categories
• To show movement in a trend map use different colors for each time period, to show movement in a before and after map use one color to represent the before and one color to represent the after, and to show movement in a cycle map use different colors for each time period
• To emphasize the “from” in “from → to” change, map those categories in shades of the same color, and vice versa if you want to emphasize the “to”

Chapter 1: Introducing GIS Analysis

Map uses:

• Where things are
• Most and least
• Density
• What’s inside
• What’s nearby
• Change

GIS can be simple, like a basic map, or a more complex figure with multiple layers (like an onion)

Geographic features can be discrete, continuous phenomena, or summarized by area

• It’s important to understand what you’re mapping!
• Discrete features can be pinpointed
  • Businesses represented by number of employees
• Continuous phenomena are always present, and we map the changes (e.g. temperature)
  • Interpolation– values that are assigned to areas in between points
  • Non continuous data can be continuous if showing variation across a given location
  • Centroid– center points
• Summarized data is used for density/ counts of individual points in a certain area
  • Would this mean the individual points would be discrete features, and the presence of a boundary is what makes it summarized data?

Geographic features can be represented using vector or raster

• Vector-
  • Feature is a row on the table
  • Shapes are defined by x and y
  • Lines= coordinate pairs
  • Shapes= closed polygons
• Raster-
  • Features are a matrix of cells in continuous space
  • 1 layer= 1 attribute
  • Sizing is important! Too large and info will be lost, too small and it takes longer to process

Geographic attributes:

• Categories
  • Groups of similar things 
  • Represented by numeric codes or text abbreviations
• Ranks
  • Order from highest to lowest
  • Used to compare features that are harder to quantify
• Counts
  • Number of features on a map
• Amounts
  • Measurable quantity associated with a feature
  • So a count would be the number of circles on a map, and an amount would be the number that each circle represents?
• Ratios
  • Shows relationship between two quantities
  • Dividing one quantity by another
    • Proportions- part of a total value
    • Density- distribution of feature per unit area

Chapter 2: Mapping Where Things Are

Maps show where action is needed. It is important to know what features need to be present and how to display them.

Creating a map:

• It needs to be relevant for your audience
  • Avoid unnecessary details
  • Smaller maps need to be concise and only show important aspects, while larger maps are able to provide more detail
• Features need to be geographically assigned!
• Categories help identify groups of features
  • Types and subtypes
  • If only one type, all features should be the same shape
• Subsets used for individual locations
• Context is important!

Categories can reveal patterns

• Can provide an understanding of how a place functions
• Most people can distinguish up to seven colors/ patterns on a map
  • I thought this was really interesting! Because of this, it is strongly encouraged that there are no more than seven categories displayed at once
  • The spacing of the categories also plays a role
    • I assumed that categories would be more easily distinguishable if they were spread out, but Mitchell says the opposite.
  • You may have to lose some information in the process of making it easy to read
    • How would you determine what information is needed the least? How often does this happen?

Grouping categories can change the viewer’s perception of the information

• Assign detailed and general category codes
• Create a table with detailed and general category codes side by side
• Assign the detailed categories a symbol that represents the general category

Symbols

• Size needs to be “just right”- large enough to be distinguishable, small enough as to not obscure
• Use different widths to distinguish lines
  • Patterns as well ( dashed lines, dotted lines, etc.)

Geographic Patterns

• Clustered distribution- Features more likely to be found near other features
• Uniform distribution- Features less likely to be found near other features
• Random distribution- Features likely to be found at any given location

Chapter 3: Mapping the Most and Least

Add a layer of information that can be useful instead of just a location on a map (What is the elephant population at this point, and why is it higher than the population at a different point?)

Can map all 3 types of quantities (discrete features, continuous phenomena, summarized area)

Continuous phenomena can be portrayed as a gradient of colors-

• More saturated= most
• Least saturated= least
• Red= =high
• Blue= low
• etc

As I am going through the chapters, I noticed that Mitchell will repeat the same definitions for words previously mentioned. Some people may find that annoying but I appreciate the repetitiveness as it helps me remember what certain words mean.

Creating Classes

Simplicity is key when it comes to comparing values

• Balance between portraying accurate data values and generalizing data enough to show a pattern
• Map example:
  • Shading each area with a unique shade based on its data value can muddle the image (too many colors!)
  • It is much easier to create classes that each contain a range of the values (less color good)

Mapping individual values can be overwhelming for viewers. The data may be more accurate but not necessarily better.

Standard Classification Pyramid Schemes

• Natural Breaks
  • Based on natural groupings of values
  • Use if data is unevenly distributed
• Quantile
  • Each class has same number of features
  • Use if data is evenly distributed and you want to show relative difference between features
• Equal Interval
  • Difference between high and low values is same for every class
  • Use if data is evenly distributed and you want to highlight the difference between features
• Standard Deviation
  • Placed based on how far they deviate from the mean
  • Use if data is evenly distributed and you want to highlight the difference between features

How does one decide between equal interval and standard deviation?

Map type is dependent on type of features-

• Discrete locations/ lines
  • Graduated symbols
  • Charts
  • 3D view
• Discrete areas
  • Graduated colors
  • Charts
  • 3D view
• Spatially continuous phenomena
  • Graduated colors
  • Contours
  • 3D view

In conclusion, you can’t go wrong with a 3D view

Chapter 4: Mapping Density

Shows you where the highest concentration of features are

More useful for mapping patterns

Looking at the images I can already tell that it is much easier to comprehend a gradient of density instead of a bunch of lines or dots

Two ways to map density-

• Density map
  • Dot maps
  • Calculate density value to create a shaded map
• Density surface
  • Raster layer
  • Requires more effort
• Trade offs
  • Use density map if you have data summarized by area, but beware if you want exact centers of density
  • Use density surface if you have individual features and are prepared for more data processing

When creating a shaded map, make sure to limit the amount of colors/ shades used

Dot maps

• Allows for more detail
• Dots represent values in each area
• Dots that are larger represent more values, and will therefore be more spread out
• Make sure the boundary is larger than the dotted area

Density Surface

• This is where I start to get lost
• Cell size determines coarseness of the pattern
  • Smoother= more data processing
• To calculate cell size:
  • Convert density units to cell units
  • Divide by the number of cells
  • Take the square root to get one side of the cell
• When the cell size is too big, it starts to resemble a shaded map
• Usually shades of a single color are used
  • Exception is standard deviation, where one color equals above mean, and another color equals below mean
• Contour lines connects points
  • Lines closer together= rapid change
  • Lines farther apart= slower change

Mitchel Ch. 1: This introductory chapter starts off with defining GIS analysis as the process of looking at geographic patterns in your data and at the relationship between features. It discussed how we start the process of analysis by figuring out what information you need and the question you have at hand. It is important to take into consideration how the information will be used and who will be using it. The order of events to start is to choose a method of gathering your information, processing the data, and looking and reviewing the results. After you’re done if the data is not useful you can rerun it with different parameters. Geographic features can be discrete,  continuous phenomena, or summarized by area. Discrete features are actual locations that can be pinpointed and the feature can be either present or not. An example of this is businesses – as individual locations, streams – as linear features, and parcels – as discrete areas. Continuous phenomena allows you to determine a value at any given location; for example precipitation or temperature. It starts with a series of sample points that are either regularly or irregularly spaced. GIS then uses these points to assign values to the area between the points, which is called interpolation. Summarized data represents the counts or density of individual features within area boundaries. We also learn that geographic features are represented by vectors and rasters. Vector models put each feature in a row in a table and the feature shapes are defined by x,y location in space. Continuous data and discrete features and data summarized uses vector models. Continuous numeric values and continuous data uses raster models. The chapter highlights that all data layers you use should be in the same map projection and coordinate system to ensure the accuracy of the results when combining layers. Then it moves into talking about attribute values: categories, ranks, counts, amounts, and ratios. 

Mitchel Ch. 2: This chapter discusses different aspects of mapping and how they affect the maps. It goes over ways to make maps more efficient and how to effectively display the information you are trying to show. By looking at the different distributions of features on a map can help you see different patterns. Sometimes overlaying a lot of features and points to show patterns are effective, while on the other hand, making separate maps for different categories can make it easier for comparison. This is because it needs to be appropriate for the audience and the issue being addressed with the maps. When preparing the data you need to check that the features have geographic coordinates assigned to them because each feature needs a location in the geographic coordinates. Also, when you map features by type they need their own code that identifies which type it is in each feature. To add categories you need to create a new attribute layer in the data; many categories are hierarchical with major types divided into subtypes. When making the map you need to inform GIS what features you want to be displayed and what symbols to use to draw them. To map features as a single type, you draw all features using the same symbols. GIS stores the location of each feature as a pair of geographic coordinates or as a set of coordinates to define its shape. For example linear features are drawn by connecting many points. You can map features in a data layer or subset based on a category value, using a subset you have selected based on a category value. You can map features by category by drawing features using different symbols for each category value, this is stored in the layer’s data table. The chapter also goes over map scale and ways to make the map easier for views to understand, and or the way you change categories and change the way the readers perceive the information, which is extremely important. 

Mitchel Ch. 3: This chapter titled “Mapping the Most and Least” informs you about how doing both can help you find places that meet criteria and actions to be taken or emphasize the relationship between places. Mapping features based on quantities adds an additional level of information. By mapping this way it can help you decide how to best present the quantities to see the patterns on your map. Then the chapter goes into different types of features being mapped. Discrete features usually represent locations and linear features with graduated symbols, while areas are often shaded. For continuous phenomena areas are displayed using graduated colors while surfaces are displayed using graduated colors, contours, or 3D perspective view. Data summarized by area is usually displayed by shading each area based on its value or using a chart to show the amount in each category. It is important to understand the quantities you will be mapping to better present your data. Using counts and amounts allow to see the value each feature is given and shows you the actual number. Ratios show you the relationship between two quantities and are created by dividing one quantity by another, like averages, proportions, and densities. Ranks put features in order and can show realtice values rather than measured values. Examples are a scale with different colors saying excellent, good, fair, and poor. Classes group features with similar values by assigning them the same symbol, you can create classes manually or use a standard classification scheme. The different classifications schemes are natural breaks, quantile, and standard deviation. This chapter also talks about the importance of looking at outliers and how it might skew your results. You can put each outlier in its own class and use a special symbol for them. When creating a map you can you use graduated symbols, graduated colors, charts, contrours and 3D perspective views to show the quantities. 

Mitchel Ch. 4: This chapter covers the idea of density. Mapping density can show you the highest and lowest concentration of a feature. This is helpful for identifying patterns. A density map allows you to measure features using a unoform areal unit, such as square miles, so you can see the distribution. This can be particularly helpful when mapping an area, such as census tracts or counties, which can vary in size. To show density you can shade different areas based on the density values. Also, you can map density features, like location of businesses or feature values, like the number of employees at each business. You can map density in a couple different ways: graphically, using a dot map, or calculate a density value for each area. When creating a density surface it is created in GIS as a raster layer. You should map density by area when you have data already summarized by area, or lines or points you can summarize by area. You should create a density surface when you have individual locations, sample points, or lines. When creating a dot density map you map each area based on a total count or amount and specify how much each dot represents. The larger the amount represented by each dot, the more spread out they will be. Also, you can change dot size to emohasize the patters. When a density surface is created in GIS it defines a neighborhood around each cell center, then it totals the number of features that fall within that neighborhood and divides that number by the area og the neighborhood; that value is assigned to the cell. When picking a cell size it needs to be appropriate for the graph, smaller cells will take more time process while larger cells can lose detail and make the pattern disappear. 

Chapter 1:

• I have been a geography student here at OWU for over 6 semesters now. I have taken roughly 10 courses that all involved GIS in some way and I have to be honest it is refreshing to go back to the basics of what really makes up GIS and GIS data. I thought that this chapter did a great job of explaining what makes up GIS and why it is important to understand the geographic features and types of features. “Geographic features are discrete, continuous phenomena, summarized by area.” (Mitchell, 2020). This quote puts into perspective the three different types of geographic features used in GIS. Discrete features like locations and lines can actually be pinpointed on a map. Continuous phenomena such as temperature can typically be found or measured anywhere. Summarized by area means that the summarized data represents the density or counts of individual features within the area’s boundaries. All three are essential in explaining the first chapter and what it means to begin to analyze GIS data. 
• The two ways geographic features can be represented in GIS are raster and vector. These are technically two different types of models within a GIS system. With the vector model each feature is a row in a table and this means that it is typically defined by x,y locations in space. This also includes the use of polygons that define an area based on the x,y location. They can also be defined by boundaries which can be legally defined or naturally occurring. 
• With the raster model features are represented as a matrix of cells in continuous space. Each layer represents one attribute and analysis occurs by combining the layers to create new layers with new cell values. This distinction between raster and vector models is important to note because analysis occurs differently with the two different models. 
• Cell size in a raster model affects the results of the analysis
• Accurate results start with using the same map projection and coordinate system. Map projections translate locations on the globe to the flat surface of your map. 
• A coordinate system specifies the units used to locate features in two-dimensional space and the origin point of those units. This is an important note because it shows what is happening behind the scenes when a certain coordinate system is used for the map and why there may be more than just one coordinate system
• Geographic features have one or more attributes that identify what the feature is or describe it. These attributes are represented as values and are extremely important when creating a map. 
• The different attribute values are categories, ranks, counts, amounts, and ratios. The main difference between some of these attribute values is if they are continuous values or if they are NOT continuous values.
• Tables that contain the attribute values are important to work with in GIS and the different ways to work with them are selecting, calculating, and summarizing.

Chapter 2:

• Mapping has become increasingly important and the best example of this provided by the chapter is that you can map where things are to show you where you need to take action. Police can do this by mapping where the most burglaries might take place in a city and wherever this area may be is where they can take action next.
• This type of mapping analysis includes looking for patterns that occur and where these patterns might be.
• Symbols are used to map features in a layer of a map to show these patterns.
• Multiple features can be mapped at once using GIS to see if these features/attributes are occurring in the same place. An example of this is if police map two features, which are theft and assault, at once and see the connection of these two features occurring in the same place and determine that there is a pattern that shows and this pattern is that theft and assault are occuring in the same place/area.
• The GIS map should be appropriate for the targeted audience and should properly address the issue at hand that the map is highlighting. 
• GIS maps should also include areas of reference for an audience that may not be familiar with the area being presented.
• Preparing your data for a GIS map includes creating a category attribute with a value for each feature and to also have geographic coordinates assigned.
• When mapping each feature mapped must have a code that identifies its type of feature. Many maps will display categories based on hierarchical features and there may be some features that are listed as sub-categories based on the hierarchy used.
• Mapping a subset feature in a data layer is helpful to determine patterns that are not easily seen when mapping all of the features.(this is commonly done for certain individual locations)
• Mapping features by category can help with understanding how a certain place functions.
• GIS helps with the above comment by storing a category value for each feature in the layer’s data table. 
• The way you group categories can change the way readers perceive the information, for example creating a map with more than seven categories makes it difficult for the reader to see the patterns associated with the map. 
• There are different methods of grouping categories that can alter the features that are being represented on the map.
• Patterns on the map will become recognizable when mapping categories, whether it be a single category or multiple categories. 

Chapter 3:

• Mapping the most and least in an area is important to see relationships between places, and to also see if a feature may meet a criteria or if action needs to be taken. 
• To map the most and least you need to map features based on quantity associated with each.
• Discrete features can be mapped using graduated symbols that are often shaded to show quantity. This is an important distinction between normally mapping discrete features and mapping them for quantity.
• Continuous phenomena can be mapped for quantity by displaying graduated colors
• Data summarized by area is usually displayed by shading each area based on its value or using charts to show the amount of each category in each area. 
• It is also important to note again that people can recognize up to 7 colors on a map but after that it becomes difficult and distorted for people to analyze the data
• An amount is different than a count because it is the total of a value associated with each feature, versus the count of the actual number of features on the map
• Ratios show you the relationship between two quantities and are created by dividing one quantity by another for each feature. Common ratios include averages, proportions, and densities
• Ranks put features in order from high to low and they show relative values rather than measured values. When direct measures are difficult this is when ranks come in to play.
• Once the quantities are determined the next important step is to determine how to represent the data and this is commonly done by grouping the values into classes.
• Classes group features with similar values by assigning them the same symbol.
• Standard classification schemes can be used to look for patterns in the data.
• There are various forms of classification schemes and each can be used for certain types of data and features that are represented in the map.
• Outliers are extremely low and high values that can obscure the data represented and the class ranges as well. This means that outliers should be looked at closely because they could show errors in the database or even anomalies with the data.
• This chapter mentions that the features and data values your are mapping must be consider when deciding what map type to use.

Chapter 4:

• Chapter 4 deals with mapping the density of features and the purpose of this is to see possible patterns and where things may be concentrated.
• Density maps are more useful for detecting patterns than for looking at the location of individual features.
• There is a slight difference in mapping features and feature values. This difference is due to feature values being located in features themselves.
• However you can also map individual locations. This is done by using a defined area and using a dot map to represent these locations. This is one way that density can be mapped
• Another way that density can be mapped is mapping density by surface. A density surface is created in the GIS as a raster layer. Each cell in the layer gets a density value based on the number of features within a radius of the cell. This is a more precise way than mapping density via defined area
• You can also map density for  a defined area by calculating a density value for each area and then shade each area based on this value
• With GIS and mapping density, the size of the cell used (specifically for calculating density values) will determine whether the map has a smooth or rougher type of surface. This is the difference between using small cells compared to bigger cells.
• Displaying density surfaces requires using graduated colors or contours. The reason for this is that each cell has a unique value and therefore requires classification which as stated before in previous chapters can be displayed graduated colors and contours.
• Contours can be useful because they connect points of equal density value on the surface. As shown in previous chapters as well as chapter 4, contour lines are typically created automatically by ArcGIS.
• The notes for this chapter above really show how detailed mapping density can be. I had never thought about the true detail and what all goes into mapping density so this chapter was a good insight to the detail of mapping density. 

• Some extra notes: I did think that the reading was a bit lengthy for this week, however I found it to be extremely informative and helpful. I think it should be noted that the maps shown with each chapter were also really helpful in providing examples.

Chapter 1

Notes:

Map projection will be dependent on the scale of data, level of precision required, and where the data is located.

Definitions:

• Discrete data– points or lines in space where a given feature is either there, or isn’t; there are ‘gaps’ in the map. Typically uses a vector model.

• ex; streams, parcels of land, businesses

• Continuous data– data covers the entire map, and you can determine the value for any given point. These are typically numeric values in raster, but can also be mapped using vector.
  • ex; temperature/heat maps, precipitation, soil type
• Summarized data–  a given value applies to an entire area, not a specific location. Typically uses a vector model.
  • ex; number of businesses in a zip code, total length of streams in a watershed.
• Vector model– features are shapes defined by “x, y” locations in space.
  • Can be discrete locations, events, lines, or areas.
  • Uses geographic coordinates (x, y).
  • Lines are a series of coordinate pairs.
  • Areas are closed polygons.
• Raster model– features are a matrix of cells in continuous space.
  • Consists of multiple layers (typically), with each layer representing one attribute.
  • Can use varying cell size (examples on page 11).
    • Small cell sizes result in a more defined map, but requires more storage space. Large cell sizes will show patterns, but they lose the level of detail achieved with smaller sizes.
• Attribute values– identify what the feature is, describe it, or represent some magnitude associated with the feature.
  • Types: categories, ranks, counts, amounts, ratios
• Categories– groups of similar things
  • ex; roads: freeways, highways, local roads
  • ex; crimes: burglaries, thefts, assaults
• Ranks– put features in order from high to low. Most often used when direct measurements are difficult, or if the quantity represents a combination of features.
  • ex; “scenic value” of rivers; area in mountain gorge ranks higher than area near a dairy farm
  • You can rank based on different attribute values
    • ex; soils of a certain type ranked the same in relation to suitability for growing a particular crop.
• Counts & Amounts– shows you total numbers. Count is the actual number of features. Amount can be any quantity associated with the feature.
  • ex; amount: number of employees at a given business
  • They let you see the actual value of each feature as well as its magnitude compared with other features.
• Ratios– shows the relationship between 2 quantities, created by dividing 1 quantity by another for each feature. They more accurately show the distribution of features.
  • ex; dividing (# of people in each tract)/(# of households)=(average # of people/household)
  • Proportions– show what part of a total each value is.
    • ex; number of 18-30 year olds/total population
    • They are often shown as percentages
  • Densities– show the distribution of features or values per unit area
    • ex; population of county/land area in miles squared= people/square mile
• Selecting– used to specify features to work with, or to assign new attribute values to specific features.
  • select ATTRIBUTE = VALUE
  • Can also use (>), (<), and unequal (<>)
• Calculating– used to assign NEW values to features in the data table.
  • select FIELD = VALUE → calculate ATTRIBUTE = VALUE
• Summarizing– [summarize] the values for specific attributes to get statistics.
  • ex; create a new table → list a value for each type → add count of features

Questions:

Would the census population data from GEOG 112 be considered summarized data?

Why would you use a rank based on an attribute rather than just using the secondary attribute?

Chapter 2

Notes:

The amount of information shown on a particular map depends on what the map will be used for. You need to know the intended audience for the map and its purpose before starting, and plan accordingly.

The category values discussed in the previous chapter may have subtypes that add varying levels of detail. The same base map can then be expanded upon, depending on its purpose and the intended audience at the moment.

Even if you’re intending on focusing on a certain set of data, having surrounded data can help to contextualize the information and resulting patterns. If the data is discrete, showing these data sets on separate maps may make information more digestible. If the data is continuous, displaying all or a couple of categories on the same map is favorable in many cases. When it comes to categories and how many should be displayed, 7 is a good rule of thumb for a maximum. However, the distribution of features and scale of the map can affect this. You can display more features if they’re scattered than if they’re clustered together.

So, it’s good to experiment with how many categories are being displayed. Getting another set of eyes that aren’t familiar with the data set is probably crucial to ensure the map is understandable. This is also a good way of figuring out how the data is being perceived by the reader. Depending on how categories are grouped, that perception can change dramatically.

Definitions: 

• Single type– when the same symbol is used for all features.
• Reference features– landmarks/locations that can be used to ground a map in a certain area, and convey more meaning to the reader.
  • ex; major roads, locations of cities/towns, stores
  • They should be mapped in light colors or greys to avoid dominating the map.

Chapter 3

Notes:

Mapping based on quantities can give additional context that can give a better picture of what’s being represented. Again, knowing the purpose of the map being created will tell you how to make it and whether quantities will be beneficial.

Discrete data uses graduated symbols or shaded areas, while continuous data uses graduated colors, contours, or 3D perspective views.

When mapping based on quantities, you will want to start off with the basic data set and figure out what patterns are present. Then, make a map that helps highlight these patterns. Each feature included in the data set should only be incorporated in a way that best represents the data.

Definitions:

• Quantities– a data set/set of points that have variation amongst the features.
  • These can be counts or amounts, ratios, or ranks
• Class– a grouping of a range of similar data, typically used when features all (or mostly) have different values, and the data range is large. Classes will make it easier to identify patterns.
  • Natural Breaks– natural groupings of data values present in the individual sets.
  • Quantile– each class contains an equal number of features.
  • Equal Interval– the difference between the high and low values of each class is the same.
  • Standard Deviation– features are broken into classes based on how much their values vary from the mean.

Chapter 4

Notes:

Density mapping is helpful in cases where there are many features. It will be easier to read in some cases than individual points representing each feature. You’ll have to decide two major things: 1) whether to shade defined areas, or create a continuous density surface and 2) decide if you’re focusing on features themselves or on values associated with features.

In general, summarizing data with map density can make patterns more general, but easier to look at and identify specific numbers for overall areas. Map density should be used for already summarized data with defined borders. Density surfaces provide the most detail, but require the most effort by far to put together. These are best for concentrated data.

The level of specificity in a data set/range of area can greatly affect what the resulting map looks like. In density surface mapping, areas between features are estimated through interpolation. Interpolation can cause extreme highs and lows to vanish. So, while patterns are easier to see, there should be another map that shows locations of features to provide context.

Definitions:

• Density– used to show where the highest concentration of features is.

Apologies in advance for the formatting of this blog post. Questions, definitions, etc are all written into the same sections. 

1. Chapter 1
   1. This chapter gives the very basics on making maps with ArcGIS and includes a step-by-step process on how to plan your map. First, it is mentioned that when getting your data,  you should create a specific question to guide your project. The more vague of a question, the more possible ways you could go about your research; therefore. The more specific your guiding question is, the more efficient your research will be. After that you need to understand your data, so you should identify features, attributes, and categories, and then, if needed, calculate different data based on what you already have. Then, based on your first question and guiding questions of what your map will be used for and who will see it, you will choose a method of map that works the best. Finally, you should process the actual data in GIS and analyze the results. 
   2. There are also many definitions that are important as well. Here are what I deemed the most confusing/most important. 
   3. Discrete vs. Continuous data: Discrete are data points of location that do not change. This point is an x,y coordinate, and it either exists or it doesn’t. However, continuous phenomena are measured anywhere and everywhere in an area, and they have no gaps. If there are gaps in the data, they use interpolation–the act of assigning values to these blank spaces in order to keep the map continuous. 
   4. Raster vs vector data: Raster data uses cells to represent locations, while Vector data uses points and lines to represent locations. 
      1. Question though: When is it the best time to use raster data vs vector data? What situations require each one? 
   5. Counts vs amounts: counts are the actual number of features on a map, while amounts are any measure of quantity associated with said features. One example would be how many trees there are in one section of a mapped national park. 
2. Chapter 2
   1. Chapter two is similar to chapter one in which it goes over the basics, except in this case, it goes deeper into each section about making the map. For generating questions, it describes how you should generate your questions based on what information you’re going to need from the final analysis and how you will be using the map. One example of the “how” would be determining if categories would be a good idea for the final project, or if they would just convolute the point. When preparing the data, you should start with assigning coordinates to places (either latitude/longitude or street address)  and giving them categories based on their features.  If available, it’s good to have a category attribute with a value for these.
   2. The next section is all about actually making the maps on ArcGIS. The first step here is to determine what features you want to display and what symbols you want to represent them. When mapping by category, you may have these different categories in a different map layer or subset, but the subset should mostly be used when mapping individual locations. You should make sure to check what you are making a subset out of because it may lead to confusion and incomplete data. An example of this confusion would be mapping certain roads, which then makes it look like infrastructure doesn’t connect. With categories, using multiple can reveal patterns about the data that may have been hidden before–just make sure to have individual maps for each category so that you have the ability to see simple data too. Yet, even with individual categories, you should avoid having more than seven, otherwise it gets confusing. If you end up needing more than seven, grouping the categories may be an option, but note that you may lose good information by doing this. 
   3. Put good thought into the shapes/color that you use to symbolize your data, as they may have underlying meanings, or they may be hard to distinguish from others. 
3. Chapter 3
   1. This chapter focuses primarily on mapping “the most” and “the least” information, ranking information with quantities. Mapping by these qualities introduces an additional level of information other than just the straight locations of each phenomena. In some maps, this information may be more valuable than other mapping goals. For example, if a city wanted to put in a daycare center and wanted to be the most centralized location for all workers, it would be best to map the places of business and by how many people work at each location. Additionally, you can map quantities with most data, meaning that discrete, continuous, and data summarized by area can be mapped with their associated quantities. However, they are mapped mostly in different ways. Discrete data is typically represented by graduated symbols or shaded areas; continuous phenomena  are represented with graduated colors or contours or maybe a 3-d view; finally, data summarized by area is usually displayed by shading each area based on its value. These representations may change with the objective of the map. Again, what is the purpose of the map–when creating a map for presentation, you’ll want to choose representations that make patterns easier to see, which might force you to sacrifice other parts of your data that you would keep if only using the map for pattern recognition. 
   2. Quantities can be counts, amounts, ratios, or ranks, and knowing which one your data is will help you decide which map you should be using. Counts and amounts can be used with both discrete and continuous data, but ratios are best for summarizing by data, as counts and amounts could potentially skew the data towards another conclusion. Typical ratio data are averages, proportions, and densities. Finally, ranks put features in order from highest to lowest.
   3. After determining your quantities, you’ll likely transfer into building classes. Classes are ranges of data that encapsulate several data entries, and they are typically used with counts, amounts, and ratios. You should make classes with group features that have similar values, and how you define each class (how you choose the range) depends on your data set.
      1. How do we choose which class type to do? I understand that if there is a wide range we should just make our own scheme, but when do I choose to use natural breaks vs quantile or equal intervals?
      2.  
4. Chapter four
   1. This chapter is all about map density, which is another useful subset of mapping. Although unlike other kinds, density maps are more useful with pattern recognition than mapping locations, as its way easier to see concentrations of data. You can map both the density of features or the density of feature values. Then, you can map these densities with graphs, dot maps, or simply the values of each area, and these are typically done with raster data. Dot maps are best for individual locations. 
   2. Since density maps are made with raster data, you will need to determine cell size. The bigger the cell size, the rougher the map will look, and the smaller the size the smoother the map will look. Yet, the smaller the cell size, the more storage you’ll need to store it, so there are advantages and disadvantages for each kind of cell. Next, when planning the search radius (the area which surrounds a point), you’ll need to decide if it’s a large or small radius that you need. Larger radii have more generalized patterns and consider more features, while smaller radii will show more variation and intricate patterns. 
   3. Getting the values in the cells can come from multiple ways. 
      1. Simple Calculation – only counting those features found within the search radius of each cell
      2. Weighted calculation – uses mathematical function to give more importance to features closer to the center of the cell. This type of calculation often results in smoother maps with patterns that are generally much easier to distinguish. 
   4. When displaying density, you should use either graduated colors or contours. With graduated colors, you should classify the data and then assign colors to each class. This should let you sense a pattern. With contours, GIS will automatically create the map from the surface without many other steps. 

Mitchell Chapter 1:

Key concept and definitions:

GIS analysis: The steps that are taken to find geographic patterns in a dataset and to find relationships between features.

Types of features (discrete, continuous, summarized by area): Discrete features can be pinpointed. Continuous features blanket the entire area and usually start off as a series of points which are then interpolated. Features summarized by area have a data value applied to the entire area which represents the sum or density of certain individual features within that area.

Interpolation: When GIS assigns values to areas in between points to create continuous phenomena.

Vector model: Every feature is a point, line, or polygon and a row of data in the attribute table. It uses coordinate data. Discrete features, continuous features, and features summarized by area are represented using the vector model.

Raster model: Every feature is a matrix of cells in continuous space; the size of the cells can be adjusted (too large and data is lost, too small and it takes a long time to process and doesn’t add additional precision to the map). Continuous features and numeric values are represented using the raster model. 

Map projections: They allow data to be viewed on a globe which is transformed to be a flat surface. Different map projections distort area, distance, and direction differently. 

Notes:

• Making maps is in effect analysis. Models (with many layers) also are analyses
• The steps to analysis are: frame the question (be specific!), understand your data (figure out what you have and might need so you can get the information you want), choose a method (there are faster, less precise ways and slower, more precise ways), process the data in GIS, and look at the results (which can be a map, a table, or a chart)
• The types of attribute values include categories, ranks, counts, amount, and ratios
• A purpose of GIS analysis is to find why things are where they are and how things are related
• I learned my first little bit about raster data and the raster model

Mitchell Chapter 2:

Key concept and definitions:

Subset: Only using certain attributes of a larger data set (for example, theft is a subset of crime).

Distributions: Features that are clustered are likely to be near other features, features that are uniform are less likely to be near other features, and features that are random have the same likelihood to be at any given location.

Notes:

• Many patterns can be determined just by mapping a phenomena
• It is important to consider your audience, medium, and purpose when mapping
• You can map more specifically or generally depending on your purpose; the goal is to make patterns easy to see
• Single codes can indicate both major type and subtype (for example, codes 500 to 599 are burglary and each number in between is a specific type of burglary)
• You shouldn’t display more than seven categories on a single map
• A general rule of thumb is to use less categories when zoomed out on an area, however when you are zoomed in on an area you can use more categories
• There are trade-offs in mapping; using fewer categories can make a map and patterns easier for the audience to understand but information is lost by reducing or condensing numerous categories into fewer categories
• Three methods of grouping categories are: assigning a general code to each more detailed record in the database, creating a linked table that matches detailed codes to general codes, and assigning the same symbology to certain detailed records to visually create a more general map. The first two involve using the Attribute Table and the last one is more artificial and involves using classification
• It’s harder to distinguish shapes than colors
• Since it can be different to distinguish narrow line colors, consider using different thicknesses or patterns (dotted, dashed, etc.) for lines
• Mapping reference features can be important as it gives people a visual bearing at what they are looking at. This should be done using non-dominant colors

Mitchell Chapter 3:

Key concept and definitions:

Counts and Amounts: Counts are the number of features on a map and amounts are the values attributed to each feature on a map. Both show total numbers and can be used with discrete or continuous phenomena.

Ratios: It is formed by dividing one quantity by another. They are useful when summarizing by area and will typically be averages, proportions, or densities.

Ranks: It is a relative ordering system rather than a measured one.

Classes: It is grouping values into groups so values that fall into a certain break are a part of one group and values that fall into a different break are part of another group . Counts, amounts, and ratios are usually grouped into classes.

Classification schemes (natural breaks, quantile, equal interval, and standard deviation): Natural breaks emphasize differences in values. Quantile schemes put an equal amount of values into each class. Equal interval schemes form classes with equal ranges. Standard deviation schemes form classes based on how values vary from the mean. 

Notes:

• Discrete phenomena can be represented using graduated symbols (points and lines), graduated colors (areas), or sometimes 3D perspectives (all)
• Continuous phenomena (areas) can be represented using graduated colors, contours, or 3D perspectives
• Features summarized by area can be represented using shading
• Features with similar values should be in the same class and there should be as great as a difference possible between classes
• Most people can determine up to seven colors on a map
• Reds and oranges attract the most attention and blues and greens the least
• Some ways of dealing with outliers include: putting each outlier in its own class, grouping outliers into one class, grouping outliers with the next closest class, or denoting them using a special symbol
• Circles are the most distinguishable graduated symbol
• You can use charts to show more information on a map, but don’t show more than five categories on a chart and don’t map more than thirty features
• Contour lines are used to show the rate of change for a spatially continuous phenomenon (like pressure lines)
• 3D perspectives have three parameters: viewer’s location, vertical exaggeration, and location of light source

Mitchell Chapter 4:

Key concept and definitions:

Cell size: It determines how fine (smaller cells) or coarse (larger cells) patterns will be. Cells are square and in general there should be between 10 and 100 cells per density unit.

Search radius: The larger the radius the more generalized the patterns. 

Calculation method (simple and weighted): The simple method only counts features within the search radius so that each cell has the potential to have a ring around it. The weighted method emphasizes features more near the center of a cell and results in a smoother, more generalized surface. 

Units: If areal units are different from cell units the values are extrapolated.

Centroids: Center points.

Notes:

• Density maps show you where the highest concentration of features are
• Density can be mapped using a dot map, by calculating the density for each area, or by using density surfaces
• Dots on density dot maps are distributed randomly throughout the area they correlate to
• Dot maps are good for giving a quick sense of a specific area’s density
• On dot maps, dots are often displayed based on smaller areas but the boundaries of larger areas are typically visually shown
• Density area maps should use a range of color values with one or two hues
• Density surfaces are usually created as a raster layer, are good at showing where points and lines are concentrated, and can be created using graduated colors (using shades of a single color) or contours
• Density surfaces are created by defining a search radius around each cell center and then GIS calculates how many features or values that cell radius contains and divides it by area or another value
• Just because there is a high density portrayed on the map does not mean there are actually any features in that cell; this is the result of a search radius that is picking up other features
• Density surface maps were the most confusing thing for me in these four chapters

Howdy! I’m Lisa Leonard (I prefer to go by Lee) and I’m a senior studying Zoology and Environmental sciences. I’m from Cambridge, OH. I’m taking this class because I realized I do not know much about GIS and wish to comprehend the program ArcGIS and other GIS-oriented things. I did an REU over the summer involving long term ecological research (Also drought legacies and how plant-soil feedback loops react when a stress variable is added in) and one thing my mentor recommended to me was learning how to work with GIS, so I’m here today. My interests in zoology and environmental sciences are biological indicators, specifically invertebrates, and lichen. I also like to study anthropogenic activity.

lover of annelids <3

I think one thing I heavily appreciated about the readings is the diversity in disciplines. It heavily emphasized that it wasn’t just used in geography, but rather spread across multiple fields. I personally never knew that those outside of the natural science bubble could have a use for GIS, so when I read that GIS was quite literally all around us (From getting your morning cup of Joe to organ donation), it blew my mind. I liked this chapter a lot because honestly I’ve stayed away from GIS because it seemed too complex, but now that I’m reading more about it I feel less intimidated? Stay tuned. It’s a nice dip your toes into the subject chapter in a way. I think it was more cooler seeing the figures than reading about it (i.e. Figure 1.4: Cholera in London in 1854) because that was before GIS was even computerized!

I looked into the different forms of GIS used at the place I did my REU at and found a lot of different images that I didn’t even know existed! Attached is a link to the W.K. Kellogg Biological Station’s Long Term Ecological Research site, where they have various scales of data, from soil to the roads in Kalamazoo county. (https://lter.kbs.msu.edu/data/gis-data/) Try not to click metadata because it has a more coding set up but please look at the images if possible! (The soil one looked so cool!)

For my research, I chose a paper called ‘A GIS-based method of lake eutrophication’, which was a fairly tough read honestly. While it isn’t 100% my preference, I felt it was significant to discuss eutrophication from a GIS sense because eutrophication is a form of anthropogenic activity caused by an overload of various nutrients leaking into waterways (this is usually caused by agricultural practices) and causing a decline in fairly sensitive organisms, such as amphibians. This paper doesn’t shine any light on our poor slimy amphibian friends, but rather discussing a variety of physical, chemical, and biological indicators. (Phytoplankton was the biological! I assume because some species do super swell under stressful conditions, while other species are extremely sensitive to eutrophic environments.) This study took place in a body of water, called ‘Lake Chao’, located in China with HIGH levels of eutrophication. These high levels have impacted the population around them socioeconomically, ecologically, and even caused the population to have some pretty intense health effects. The main GIS aspect these focused on in the results was a lot of spatial distribution, and what areas of the lake were heavily impacted and what parts were not. They actually said that the eutrophication levels and the genuine conditions of the lake were not too far off from each other. However, there is no distinct indicator or parameter that can be evaluated in a simple fashion when it comes to a body of water, but if we put multiple different indicators together to create a distinct evaluation of a lake assessment. I think this paper had a lot of complexity to it and frankly, the photo I’m attaching below from the paper seems intense to even explain

One fragment of figure 3 from the paper. It went from a-f and seemed to be explaining the trophic state index on a spatial scale? This was with the various indicators but holy moly I feel violently humbled.

Source:

Xu, F.-L., Tao, S., Dawson, R. W., & Li, B.-G. (2001, October 30). A GIS-based method of Lake Eutrophication Assessment. Ecological Modelling. Retrieved August 28, 2022, from https://www.sciencedirect.com/science/article/pii/S030438000100374X

This was from a journal called Ecological Modeling.

I also looked into the use of GIS with lichen and was not disappointed with what I found. Air in urban environments isn’t really good in terms of quality, and lichen is a great biological indicator to look at when understanding air quality. In this study they also used moss because lichen and moss both are great at absorbing things, making them great candidates for indicating toxicity in the air. In the article, there was a map (figure 4) showing agglomerations of lichen and moss (Used a high for higher concentrations in different areas) It was so cool to see it because I didn’t really expect GIS to include such microscale pieces of nature. I wonder if there is GIS data on every ant in Michigan. That’s so crazy to me. It also showed a wind rose in figure 2, which showed direction and speed of wind as well as the concentrations of toxins in the air. I think it’s awesome how GIS can have different ways of expressing data like how R can too (or scientists making graphs and different forms of data in general.)

Source:

Długosz-Lisiecka, M., & Wróbel, J. (2014, September 24). Use of moss and lichen species to identify 210po-contaminated regions. Environmental Science: Processes & Impacts. Retrieved August 28, 2022, from https://pubs.rsc.org/en/content/articlelanding/2014/em/c4em00366g/unauth

• Hello, my name is Nathan Sturgill but I prefer to be called Will (my middle name). I am a senior this year and I am a double major in Environmental Studies and Geography. I am from Portsmouth, Ohio, which is in Scioto County (roughly the southern tip of Ohio). My interests include skiing, golf, and wakeboarding. This will be my second class with Dr. Krygier, and I am looking forward to having a great semester! I plan on becoming the remote sensing lab student manager for the year. I also live in one of the fraternities here on campus with a great group of guys. My goal for this class is to become more skilled with GIS and to have a better understanding of the history behind GIS. 

• 2. Reading Schuurman chapter 1 was very helpful in understanding the origins of what makes up GIS. GIS consists of two specific uses which are making maps and analyzing data. I had no idea there was such a debate in the GIS community as to what really makes up GIS and which of the uses is the real identity of GIS. GIS is viewed by some to be just a tool or application that is used for creating visual representations of maps and mapping data. Many others it is viewed as being an analysis of spatial data that includes creating visual maps with many different layers and factors besides just a basic cartographic map. The debate between the two was also referred to as GIS as in Geographic Information Systems and GIS as in Geographic Information Science. I thought that the most interesting difference between the two meanings for GIS was how the chapter articulated the difference. The chapter states that the science in GIS asks the question of how versus the system in GIS is where the spatial entities are. I had never thought about the difference in GIS or had even known if there was a difference in what GIS meant to the Geography community. I also thought that how the chapter explained what visual representation can mean to people when analyzing data was very interesting in that many people do not realize how they interpret visual representation compared to numerical data and cartography.

3. I searched “GIS application urban expansion” and the first article that caught my attention was Characterizing and classifying urban watersheds with compositional and structural attributes. This article caught my attention because I have always been interested in urban expansion and how it affects the natural environment around us, and how we as humans contribute towards the degradation of natural environments via urbanization. This article explains the distribution of land cover, topography, infrastructure, and topography across a certain region in North Carolina. This article explains the rapid growth of urban expansion in the Southeastern region of the United States and how it affects the natural environment of the area using GIS to measure this effect. The article states that development threatens water resources in the region which in turn threatens water resources and further development of the area. This is fascinating to me because it explains how the region has suffered with poor water quality due to urbanization and the continued growth of urban communities in the area. 

• This figure represents the study area of the region and how the area is affected by urban expansion compared to the surface water resources shown in blue dots. 

https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.14339

• Another article I found under “GIS application urban expansion” mentioned streams in urban heat islands and the variability in temporal and spatial temperatures. This article examines how streams that drain urban heat islands are warmer due to urban air temperatures and ground temperatures, and paved impervious surfaces that the stream may run across. This article really interested me because it shows one of the main negative consequences (using GIS to do so) of urban expansion and what urban expansion can really do to the environment. The article also explains how urban heat islands are created and how they differ in temperature from rural, and forested areas. The main reason urban areas tend to have higher temperatures than rural and forested areas is because impervious surfaces used to create infrastructure as well as a lack of natural vegetation in the area which decreases the cooling effect for the area. 

• This figure represents a comparison between a rural area and an urban area in North Carolina.  Stony Creek being the rural area and Goose Creek being the urban area.  The biggest difference between these two is the obvious difference in temperature for the two regions. We can also see how the forested area directly correlates with a lower temperature of the area observed, and how the urban area directly correlates to a higher temperature recorded for the area. We are able to tell this correlation between areas and temperatures because of the GIS application that is used here to observe and take these measurements.
• https://www.journals.uchicago.edu/doi/full/10.1899/12-046.1