Category: Course Student Work

Chapter 1: Introducing GIS Analysis 

 

> GIS analysis is the process of finding geographic patterns in data and at the relationships between features.

 

Understanding Geographic Features  

 

Discrete Features / Data: The actual location can be pinpointed

 

Continuous Phenomena / Data: Can be found or measured anywhere (precipitation, temperature, etc.)

 

• The phenomena cover the entire area you are mapping, and there are no gaps.
• You can determine a value at any given location (precipitation in inches or temp. in degrees). 
• Usually starts as a series of sample points that are then used to assign values to the area between the points (interpolation). This can be used to show how a quantity, such as annual precipitation, varies from place to place. 
• Continuous data can also be represented by areas enclosed by boundaries (if everything inside the boundaries are the same type of something, such as the type of soil).

 

Features Summarized by Area: 

 

> Shows the density / counts of features within a boundary 

Examples: Number of businesses in a zip code, total length of streams in each watershed, number of households in each country

 

• The data value applies to the whole area, not a specific location in the area.

 

Two Ways of Representing Geographic Features

 

Vector Models: 

 

• Each feature is a row in a table 
• Feature shapes defined by x,y locations 
• Can be discrete locations, events, lines, areas

 

> Locations are represented as points with geographic coordinates

> Lines, such as streams, are represented by a series of coordinate pairs.

> Areas are represented by borders that are closed polygons.

 

Raster Models: 

 

• Features represented as a “matrix of cells in continuous space”
• Each layer represents an attribute 
• Analysis occurs by combining layers to create new layers with new cell values

 

> Cell size affects how the map looks as well as the results of the analysis, and should be based on the original map scale and minimum mapping unit 

 

• Using too large of a cell size can cause info. to be lost 
• Using a cell size that’s too small takes up a lot of storage space and takes longer to process without adding precision to the map.

 

> Continuous categories can be represented by either the vector or raster models, but continuous numeric values are represented using the raster model.

 

Understanding Geographic Attributes: 

 

Attribute values include:

 

• Categories
• Ranks
• Counts: Actual number of features on a map 
• Amounts: Any measurable quantity associated with a feature, ex: number of employees at a business 
• Ratios

 

> Categories and ranks are non-continuous values. 

• There is a set number of values in the data, and multiple features may have the same value.

> Counts, amounts, and ratios are continuous values.

• Each feature may have a unique value anywhere in the range (between the highest and lowest values).

 

Chapter 2: Mapping Where Things Are

 

Preparing Data

 

> Before you begin mapping, you need to make sure that you have geographic coordinates assigned. If the data is already in a GIS database, coordinates will already be assigned. If not, you will have to manually enter them.

> If you are mapping features by type, you must assign each feature to a category. 

 

Making Your Map

 

Mapping a Single Type: 

 

> Draw all features using the same symbol to map features as a single type. This can suggest differences in the feature that may need to be explored further. 

 

> You can also map features in a data layer or subset based on a category value that you create. For example, instead of mapping all crimes, you could map only burglaries. 

 

Mapping by Category: 

 

> Using categories can help to understand how a place functions.

 

> Use different categories to reveal different patterns.

 

> If you are displaying several categories on the same map, use no more than seven categories at a time. Most people can distinguish up to seven patterns on a map, so using more can become confusing or difficult to see.

 

Grouping Categories:

 

> Using fewer categories can make it easier for a broader audience to understand your map, but there will be less detailed information shown. 

 

> Patterns may be easier to see if you group many, similar categories together. 

 

> You must be explicit with what is included in each category to help others understand what your map is showing. 

 

There are multiple ways to group categories:

 

Option 1: 

 

– Assign each record in the database two codes. One for its detailed category and the other for its general category. 

 

Option 2: 

 

– Create a table that contains one record for each detailed code, with the corresponding general code. 

– Join the feature table with the new table, and use the general code to display features.

 

Option 3:

 

– When you make the map, assign the same symbol to the detailed categories that make up each general category.

 

Mapping Reference Features: 

 

> You may want to add recognizable landmarks to your map to make it more meaningful, especially to those who may not be familiar with the area they are observing. 

 

> You may also want to reference features that are specific to your analysis so that you can observe geographic relationships. 

 

Chapter 3: Mapping the Most and Least 

 

Counts and Amounts:

 

• Use to map discrete features or continuous phenomena 

 

Ratios:

 

• The most common ratios are averages, proportions, and densities.
• Ratios are good for summarizing by area

 

> Create ratios by making a new field and adding it to the layer’s data table, and dividing the two fields containing the counts or amounts. 

 

Class Schemes:

 

> The most common schemes are natural breaks, quantile, equal interval, and standard deviation. 

 

Natural breaks: 

 

• Classes are based on natural groupings of data values
• Class breaks are set where there is a jump in values

> Finds patterns inherent in the data 

> Good for mapping data not evenly distributed 

 

Quantile: 

 

• Each class contains an equal number of features.

> Good for comparing areas that are similar in size, and for data that is evenly distributed

 

Equal interval:

 

• The difference between high and low values is the same for every class

> Easier to interpret since the range for each class is equal 

> Good for mapping continuous data 

Standard deviation:

 

• Features are placed in classes based on how much their values vary from the mean

> Good for seeing which features are above or below the average and for displaying data that has a normal distribution

 

Choosing a Map Type:

 

Graduated symbols:

 

• Use to map discrete locations, lines, or areas. 
• Used to show volumes or ranks for linear networks

 

Graduated colors:

 

• Use to map discrete areas, continuous phenomena, or data summarized by area

Example: percentage of population aged 18-29 (darker colors with higher values)

 

Charts:

 

• Use to map data summarized by area, or discrete locations or areas. 
• You can show patterns of categories and quantities at the same time 
• Can use pie charts or bar charts 

 

Contour lines:

 

• Use to show the rate of change in values in an area for spatially continuous phenomena 

 

3D perspective views:

• Use with continuous phenomena to help visualize the surface 

 

Chapter 4: Mapping Density 

 

> You can create a density map based on features summarized by defined area or by creating a density surface. 

Defined Area:

 

• You can map density graphically, using a dot map. You can also calculate a density value for each area. 
• Creates a shaded fill map or dot density map 
• Easier, but doesn’t pinpoint exact centers of density 

> Use if you already have data summarized by area or if you want to compare natural / administrative areas with defined borders

 

Density Surface:

 

• Usually created as a raster layer 
• Each cell in the layer gets a density value
• Creates a shaded density surface or contour map
• Requires more data processing, but gives a more precise view of centers of density

> Use if you want to see the concentration of point or line features

 

Mapping Density for Defined Areas: 

 

> You can map density for defined areas by graphically using a dot map or by calculating a density value for each area and shading each area based on this value.

 

Calculating a density value for defined areas:

 

• Calculate density based on the areal extent of each polygon 

> Add a new field to the feature data table to hold the density value. Then, assign density values by dividing the value you’re mapping by the area of the polygon. 

 

Calculating Density Values

 

Cell Size:

 

• Cell size determines how coarse or fine the patterns will appear 
• Cell size is the length of one of its sides 

> To calculate cell size: convert the density units from square kilometers to cell units (meters), then divide by the number of cells per density unit. This will give you the area of each cell. Then, take the square root of the cell area. 

 

Displaying a Density Surface:

 

> You can display a density surface with either graduated colors or contours

 

Graduated colors:

 

• Density surfaces are usually displayed using the shades of a single color
• Areas with higher density are typically shown with darker colors, since people tend to equate darker colors with “more.” 

 

Contours:

 

• Connect points of equal density value on the surface 
• Good for showing the rate of change across a surface (the closer the contours, the quicker the change).

 

1. Chapter five 
   1. This chapter focuses on locating different aspects and patterns within the features of your map and how to analyze them. One important aspect to search through is your data, and there, you should start with the areas that you are mapping. If it’s a single area, you can figure out what information or patterns are specific to that area, but if you have multiple areas, you can compare them for your information. Additionally, you should recognize what types of data you have (continuous? discrete?) and if you need a count or a summary of an area. These can help you focus on certain types of information that are specific to your guiding question. Also within the area, you could analyze how features of your map interact with areas–do certain features only take place in certain areas, do they cross into multiple areas, etc. 
   2. There are three ways of finding information from inside your map. First, drawing your areas and features can provide you with very direct, visual ways of displaying and locating patterns. This type is also good for seeing patterns in or outside of a single area. Next, you can select certain features inside an area in order to find information. This is much better for finding lists or summaries of information. Finally, overlaying the areas and features with different layers requires more processing, but it can be very useful for determining which features are in several of your areas or how prevalent some feature is. 
   3. Frequency – the number of features with a given value or within a range of value, inside the area, and displayed at a table. 
   4. The most common summary of numeric attributes:
      1. Sum. – the overall total number of something (like the total number of workers at businesses within a neighborhood)
      2. Average/mean – the total of a numeric attribute divided by the number of features
      3. Media – the middle value in the of a range of values
      4. Standard deviation – the average amount of values away from the mean. This gives insight into how tight or loose the values are grouped. 
2. Chapter Six
   1. This chapter is all about maps that focus on places that are located close to the map’s subject, audience, or creator. It covers how to define what you need and how to actually find it before discussing how to add realities to what you are mapping, such as cost or time. 
   2. You are able to map categorical data such as cost or time, but most of the time, you’ll likely just need distance. It all depends on the information that you end up needing. 
   3. There are three ways of finding what you need–straight line distance, distance over a network, and cost over a surface. Straight line distance is the calculation of area within a features of your choosing, and it’s great for the creation of boundaries. Distance or cost over a network connects a source location to an aspect of the network within a chosen distance or cost. This is best for finding a location that matches distance or cost parameters (like, cannot travel for more than 20 minutes). Finally, cost over surface is where you take both aspects and specify the location as well as the travel cost. 
   4. If you want to find actual locations from your chosen feature’s source, you need GIS to calculate the actual distance between each location and the closest source. 
   5. When working with distance, it’s often recommended that you set a maximum distance, as without it, you can end up with extraneous data that does not realistically apply to your reason for mapping.  
   6. When measuring distance over a network, you should set travel parameters. This could include specifying cost for particular segments, turns, or junctions. 
   7.  When finally getting information in your mapping that supports your question, you can further identify the area within a specific distance or summarize your data that is within the chosen distance parameter. 
3. Chapter Seven
   1. This chapter is all about celebrating the fluidity of society by mapping how certain phenomena change and grow over time, and using these changes to design a better future. 
   2. Change is important–it shows the trends of a time period, or what society deems to be relevant at the time. Change can come in several different forms, such as changes in location, in magnitude, or in character. 
   3. Your chosen features for your map is the best way to determine which area of change you should focus on. Yet, these features can also be categorized: features that move include discrete features and events, and features that change in character or magnitude include discrete features, data summarized by area, continuous categories, and continuous values. 
   4. When measuring change, you should also focus on the time period that you are using. What type of pattern are you using? Before and afters, trends over time (multiple events) or cyclical patterns are all good choices. Intervals are also important, as these can skew your data and/or presentation of data towards a different conclusion. 
   5. Another aspect to focus on is how much change actually occurs. Percent change is a common way to display how much occurred. How fast it changed is also good information to know. 
   6. There are three ways of mapping change: creating a time series, creating a tracking map, or measuring change. A time series is equivalent to mapping where the most or least are, but this time you are replacing it with certain dates. You will need to consider how many maps you’ll create. Tracking maps shows a certain feature at various points in time, and they are pretty useful for tracking discrete features. When measuring and mapping change, which is when you calculate the difference in value of a feature between two dates, you can calculate the change for discrete features, data summarized by area, continuous categories or continuous numeric values. 

 

 

Hello, my name is Cailee Plunkett and I am a junior Environmental Science major from Cincinnati, Ohio. I am also a transfer student, so this is actually my first semester here at Ohio Wesleyan. I am very excited for these next two years and can’t wait to become more involved on campus. I love to hike and do anything that gets me outside, I love animals, and I am a runner.

Chapter 1:

What I found interesting about this chapter was just how many different uses GIS has, and how many people in different jobs and fields use it. For example, it can be used for farming and municipal management, but GIS can also be used to map complex networks that provide power, fuel, and water to a town or city. Waste collection routes are mapped using GIS. Even Starbucks has reportedly used GIS. I also thought it was interesting how the acronym “GIS” can be split into GISystems and GIScience, and that GIScience is almost the theory that underlies GISystems.

 

The Application of Remote Sensing and Geographic Information System (GIS) for Monitoring Deforestation in South-West Nigeria

In this article, GIS was used to detect deforestation in Southwest Nigeria between 1978 and 1995 and detect land use and land cover change in Southwest Nigeria as well as to assess the stability of the land. The results of the study show that in 1978, forest vegetation covered 88.25% of the surveyed area, and that this had decreased to 63.13% by 1995. With forest cover change, between 1990 to 2000, Nigeria had lost an average of 409,700 hectares of forest per year.

Urban Sprawl Development Around Aligarh City: A Study Aided by Satellite Remote Sensing and GIS

In this article, GIS was used to rapidly assess the developments of sprawl in Aligarh City. The results of this study show that the urban area of this city has increased around three times since 1971, and that around 1990, there was a sharp increase in land consumption as compared to population growth. As the city does not have a sewage treatment plant, with a growing urban area, there is less area for the water to drain into soils, and there will also be more flooding in low lying areas. By studying and watching for urban sprawl in an area over time, residential development can be better monitored.

References:

Peter, Yohanna, Innocent Reuben, and Emmanuel Bulus. “The application of remote sensing and geographic information system (GIS) for monitoring deforestation in south-west Nigeria.” Journal of Environmental Issues and Agriculture in Developing Countries Vol 4.1 (2012): 6.

Farooq, S., and S. Ahmad. “Urban sprawl development around Aligarh city: a study aided by satellite remote sensing and GIS.” Journal of the Indian Society of Remote Sensing 36.1 (2008): 77-88.

Mitchell ch. 5: Finding What’s Inside

Overall: Finding what is inside can let you see activities that occur in a certain area or summarize information for several areas for comparison.

• To find what is inide you can draw boundary lines on top of a feature, use area boundary to select the feature inside and list or summarize them, or combine the area boundary and features to create summary

• Finding what’s inside a single area

  • Service area around a central facility

  • A buffer that debines a distance around some feature

  • An administrative or natural boundary

  • Area you draw manually

  • The results of a model, such as boundaries of floodplain modeled in GIS

• Finding what’s inside multiple areas

  • Contiguous – zip code/watersheds

  • Disjunct – state parks

  • Nested – 50 year old floodplains/area 1 or 2 miles within store

• Discrete features: identifiable features, can list or count them or summarize a numeric attribute associated with them. 

• Continuous features: seamless geographic phenomena, can summarize features

  • Spatially continuous categories – vegetation type/elevation

  • Continuous values – temperture, elevation, precipitation

• Drawing areas and features: good for finding out whether features are inside or outside an area 

• Selecting the features inside the area: good for getting a list or summary of features inside an area 

• Overlaying the areas and features: good for finding out which features are inside which areas, and summarizing how many or how much by area

• Statistical summaries include:

  • Count – total number of features inside area

  • Frequency – number of features with a given value, or within range of values, inside area, displayed as a table 

  • Sum – overall total

  • Average – mean

  • Median – middle of range of values

  • Standard deviation – average amount of values are from the mean

• Vector or a raster method to overlay areas with continuous categories or classes

• Vector – GIS splits category or class noundaires where they cross areas and creates a new dataset with the areas that result, then use data table for new layer to summarize the amont of each category in area

• Raster – GIS compares each cell on the area layer to the corresponding cell of the layer containing the categories

Mitchell ch. 6: Finding What’s Nearby

Overall: Finding what is near lets you see what is within a set distance or tavel range of  a feature. Can monitor events in an area or find an area served by a facility or the features affected by an activity

• Travel range is measured using distance, time, or cost. 

• To find whats near you can measure straight-line distance, measure cost/distance over a network, or measure cost over a surface

• Can calculate distance assuming the earth is flat (planar method) or curved (geodesic method)

  • Planar – good when area of interest is small like city, county, state

  • Geodesic – good for large areas such as large region, continent, or whole Earth

• Once you have identified which feature are near source you can get a list of the features, a count, or a summary statistic 

  • Summary statistics can be: a total number, an amount by category, a statistical summary (mean, maximum, minimum, or standard deviation)

• If you are specifying more than one range you can create either inclusive rings or distinct bands

  • Inclusive rings are useful for finding out how the total amount increase as the distance increases

  • Distinct bands are useful if you want to compare distance to other characteristics

• Ways of finding what is nearby

  • Single-line distance: defining an area of influence around a feature, and creating a boundary or selecting features within the distance

  • Distance or cost over a network: measuring travel over a fixed infrastructure

  • Cost over a surface: measuring overland travel and calculating how much area is within a travel range

• To create buffer you specify the source feature and the buffer distance, GIS draws line around feature, can have several source features with buffers around them

• If you are finding individual locations near a source feature and you can GIS calculate the distance between each location and the closest source

Mitchell ch. 7: Mapping Change

Overall: GIS lets you map where thing move and knowing what’s changed can help you understand how thing behave over time, anticipate future conditions, or evaluate the results of an action or policy

• Mapping change in character or magnitude shows you how condition in a place have changed. Change can be a feature or can be associated with a quantity with each feature

• Can map discrete features that physically move or events that represent geographic phenomena that change location

  • Discrete features – change in character or in the quantity of an attribute associated with them

  • Data summatized by area – totals, percentages, or other quantities are associated with features within defined areas

  • Continuous categories – show the type of feature in a place, such as each land cover type

  • Continous values – these quantities are continuous, such as air pollution levels

• Can map time 3 differnet ways:

  • A trend – change between two or more dates or timess

  • Before and after – conditions preceding and following an event

  • A cycle – change over a recurring time period, such as a day, month, or year

• Snapshots show the condition at any given moment and are used to map phenomena that are continuous in time 

• Summarizing is used for mapping discrete events in a particular place that are not continuous in time

• Duration divided by the number of dates yields the interval

• Number of dates you use depends on the consistency of the change

• When calculating change in magnitude you subtract the numeric values associated with each feature

  • Can also calculate percentage to show which feature changed the most relative to their original value 

• To measure change in type or category you sum the land area of each category and calculate the the actual or percentage difference between the dates

• 3 Ways to map change:

  • Time series – Strong visual impact if change is substantial; shows conditions at each date/time

  • Tracking map – Easier to see movement and rate of change that with time series, especially if change is subtle

Measuring change – Shows actual difference in amounts or values

Chapter 5: Finding What’s Inside

You can map what’s going on inside an area if you want to compare different areas or if you want to show what locations need action

• What’s inside a single area vs multiple areas
• Features either discrete or continuous

GIS can be used to:

• Figure out whether a feature is inside of an area
• Find all features in an area
• Find number of features in an area

Some features may be partially in an area

• Only use portion actually in the area

Three (3) ways to find what’s inside:

1. Draw features/ areas
   1. Effective visual 
   2. Need dataset of area boundaries and dataset with features
2. Selecting features within an area
   1. You determine the area/ layer, and GIS selects all features 
   2. Good summary of features in an area
   3. Cannot use for surfaces
3. Overlaying areas and features
   1. Good for finding which features are in a number of areas
   2. Requires more processing

Based on the information provided in the book, it seems like the second method is not particularly effective. It cannot be used for surface features and overlaying features (method 3) allows you to see the same information. 

More details on each method:

Drawing areas/ features

• Make sure it is easy to see what features are in the area
• Locations and lines require different symbols/ thicknesses in order to differentiate them from each other
• To map discrete areas:
  • Lightly shade the area
  • Make the area translucent or shade the area with a pattern
  • Draw only the boundary of the area
• To map continuous data:
  • Same as discrete
  • Place a screen on the outside area to emphasize what’s in the area

Selecting features in an area

• You determine the features and area, and GIS will let you know what features are within the boundaries of an area
• GIS does not distinguish which areas the features are in (L)
• You can use this method to generate a report of the results, which can be used to relay information to the masses

Overlaying areas with continuous categories/ classes 

• GIS uses vector/ raster method to overlay info
• Overlaying areas on areas requires slivers
  • Slivers are small areas that are slightly offset
  • An area with an areal extent less than the smallest dataset
• Raster vs vector
  • Vector- more precise but more processing
  • Raster- more efficient, prevents slivers, but sometimes less accurate

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Chapter 6: Finding What’s Nearby

What’s occurring around a feature?

• Important for projects that need to be conscious of the surrounding area (development, demolition, etc.)
• Measure line distance, distance/ cost over a network, or cost over a surface

Taking the curvature of the earth into account

• Planar method is used for smaller areas such as cities, states, or countries 
• Geodesic method good for regions and continents
  • Will be displayed with the curvature of the globe

Information from analysis 

• List
• Count 
  • By total 
  • By category
• Summary statistic
  • Total amount
  • Amount by category
• Statistical summary
  • Average
  • Minimum/ maximum
  • Standard deviation

Number of ranges

• Inclusive rings- how total amount increases as distance decreases
• Distinct bands- compare distance to other characteristics

Straight line distance:

• Defines area of influence around an area
• Quick ‘n easy
• Only gives an approximation
• Create buffer to define a boundary
• Select features in order to find features in a given distance 
• Calculate feature-feature distance to assign distance to locations
• Create distance surface to find continuous distance from source

Distance/ cost over a network:

• Measures travel over a fixed infrastructure
• More precise 
• Needs an accurate network layer
• GIS identifies all lines in a network
• Source locations in networks are centers
• Street neworks 
  • Street segments (edges)
  • Intersections (junctions)
  • Tagged with cost to travel from center to surrounding locations (impedance value)
  • Set travel parameters
    • Can specify cost for turns from one segment to another
• More than one center
  • GIS assigns segment to each concurrently

Cost over a surface:

• How much area is within an overland travel range
• Allows you to combine multiple layers
• Needs data preparation
• Creating a cost layer
  • The book uses an example of the cost between deer traveling through open forest vs thick underbrush, and how it would be easier for deer to travel through open forest (yay animals). Therefore, open forest= lower cost
  • Reclassify layer based on existing attribute

For me, this chapter really showed how specific you can get with GIS, especially when it came to determining traveling costs over a network or surface!

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Chapter 7: Mapping Change

Mapping change allows us to make predictions on what the future could look like (big emphasis on meteorology) 

Types of change:

• Location
  • How features will move
  • Can map features that physically move or geographic phenomena that change locations
  • Discrete features can be tracked as they move through space (organisms or meteorological events)
  • Events happen at different locations (earthquakes, deaths)
  • Showing patterns of movement for individual features or
  • Number of large yet distinct features 

• Character/ magnitude
  • How conditions in a given place have changed
  • Discrete features are changes throughout a period of time
  • Data summarized by area are presented as percentages or totals
  • Continuous categories show the type of features in a place
  • Continuous values are quantities that fluctuate
  • Magnitude- similar issues as mapping most and least (ch. 3)
  • Character- the way categories are defined may differ between dates

Measuring time:

• Three types of patterns
  • Trend- change between two periods
    • Increasing or decreasing?
  • Before/ after- self explanatory
    • Impact?
  • Cycle- change during recurring time period
    • Patterns?
• Choosing time interval
  • Need to choose interval if given a range 
  • Should be long enough to show change, but include all info
• Three ways to map change
  • Time series- change in boundaries, discrete areas, surfaces
    • Good visual impact, easy to understand 
    • Need comparisons
  • Tracking map
    • Showing movement of discrete locations, linear features, area boundaries
    • Easier to see subtle movement compared to time series
    • The more features, the harder to read
  • Measuring change
    • Show amount, percentage, rate of change
    • Shows difference in values
    • Omits actual conditions

Number of maps to show

• Fewer maps spaced longer apart shows more drastic change
• More maps account for possible patterns that may have been missed
  • More maps are more overwhelming for the viewer

Results

• Showing tables and graphs can supplement what you are trying to show thru maps

Mapping linear features

• Differentiate between each point with labels, colors, or symbols

Mapping contiguous features 

• Draw boundaries for areas at each date/ time

Mapping events

• Use different colors for each time period

 

Week 3:

Chapter 5:

Key concepts=

• It is important to map what is inside an area to monitor what is happening in that area and it is important to map several areas to compare these areas and what is taking place inside of the areas. 

• It is important to define you analysis when mapping what’s happening inside of an area. It is equally important to evaluate and consider your data/ areas and what types of features are inside the areas.

• The features inside of an area can either be discrete features or continuous features. Discrete features are unique and identifiable features, you can count them or list them. You can also summarize a numeric attribute associated with them as well. Discrete features are either locations, linear features such as streams, or discrete areas such as parcels.

• Continuous features represent geographic phenomena and you can summarize the features for each area. A type of continuous features is spatially continuous categories or classes. You can measure this continuous feature by finding out how much of each category or class occurs inside the area you are mapping

• Another type of continuous feature is a continuous value. These are numeric values that vary continuously across a surface and can include elevation and precipitation.

• The three ways of finding what is inside the area of a map are drawing areas and features, selecting the features inside the area, and overlaying the areas and features.

• All three methods mentioned above are good for individual reasons when it comes to finding what is inside the area of a map.

• Drawing areas and features can be done using different methods that are required for different areas such as discrete areas or continuous features

• Selecting features inside an area lets you use the results as a tool for analysis such as the frequency, count, and a summary of a numeric attribute.

• Overlaying areas and features lets you find which discrete features are inside certain areas and summarize them. It also allows you to calculate the amount of each continuous category or class inside areas, and summarize continuous values inside areas.

• When overlaying areas with continuous categories or classes the GIS uses either a vector or raster method. The vector method is more accurate but requires more processing and the raster method is more efficient since it automatically calculates the areal extent for you but is still less accurate. 

Definitions:

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

Chapter 6:

Key concepts=

• This chapter is all about finding what is nearby and the purpose behind this is to find out what’s occurring within a set distance of a feature and to also find out what is within traveling range.

• There are different ways of finding what’s nearby and this can be done by measuring straight-line distance, measuring distance or cost over a network, or measuring cost over a surface.

• Nearby can be based on a set distance you specify, or on travel to or from a feature. Typically if travel is involved you would measure nearness by distance or travel cost

• Travel costs can include things like time, money, and effort expended. These are considered travel costs because of the costs associated with each.

• Taking the curvature of the earth into account is important for geodesic method and ignoring the curvature of the earth and measuring across a flat plane is called the planar method

• The information needed from analysis can be summed up as a list, count, and summary. Each have their own purpose for the features that are mapped

• Straight line distance is a good and simple way of finding what’s nearby, but measuring distance or cost over a network, or cost over a surface can give you more accurate measurements as to what is nearby.

• Creating a buffer is important because you can use the line created by the buffer as a permanent boundary or use it temporarily to find out how much of something is inside the area. Creating a buffer is done by specifying the source feature and the buffer distance

• GIS will also allow you to create buffers around multiple source features at once, and can also buffer each source differently depending on an attribute of each.

• Finding individual locations near a source feature is useful if you need to know exactly how far each location is from the source instead of just figuring out if it falls within a certain distance from the source

Definitions:

Distance or cost over network= specify source locations and a distance or travel cost along each linear feature.

Cost Over a Surface= specify location of source features and travel cost, and creates a new layer showing the travel cost from each feature.

Chapter 7:

Key concepts=

• This chapter was all about mapping change. Knowing what has changed can help with the analysis of the way things interact, predict future conditions, and evaluate the results of an action.

• Mapping change can include showing the location and condition of features at each date, or calculate and map the difference in a value for each feature between two or more dates.

• Mapping change in location can help to predict where features may move in the future

• Mapping change in character or magnitude can show how the conditions in a given place have changed over time 

• It is important to not that change in location and change in character are not mutually exclusive 

• A trend is when there is a change between two or more dates and times, this typically occurs when measuring time

• There are three ways to map change and these are, time series, measuring change, and tracking maps

• A time series is particularly useful for showing change in character or magnitude for discrete areas and surfaces.

• Measuring change is to show the amount, percentage, or rate of change in a place

• A tracking map is another key concept and basically shows the position of a feature  or features at several dates or times (this is good for showing incremental movement). What examples could be used with a tracking map?

• The last important key concept for the chapter is measuring and mapping change. Calculating the difference in value between two dates and then mapping the value of this is how mapping/measuring change takes place. There are various data/features you can measure and map change for including discrete features, data summarized by area, continuous numeric values, and continuous categories. 

Definitions:

Change in character or magnitude= how conditions in a given location change over time

Cycle= change over recurring time period

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.